Reflections

The Problem With Mobile Ads (And Why Instagram Actually Works)

noreply@blogger.com (Reflections Of Vishal V Kale) — Fri, 16 Oct 2026 11:28:32 +0000

Most mobile advertising doesn’t fail because it’s on a phone. It fails because it interrupts. Most mobile advertising is fundamentally broken because it forgets a basic law: advertising should never be intrusive. An ad that blends into what I’m already doing gets a hearing; an ad that blocks what I came to do gets closed, as fast as my thumb allows. Over the past few months, my own phone has been the experiment: Instagram introduced me to products I ended up trying, while a mobile game made me resent a brand I’d otherwise have clicked. Same screen, same person, very different result. When you analyze how new customer acquisition and brand building work across digital ecosystems, the contrast between Instagram and traditional mobile ads couldn't be clearer - and gives us a direction in which to move.

1. Why Instagram Works: Speed, Simplicity, and Seamless Context

Instagram’s core advantage is its rapid, functional interface.

Direct Path to Purchase: You see an ad you like, click the CTA, and it takes you directly to the website’s order page. It is fast, effortless, and non-intrusive.
Image-Driven Discovery: Because the platform is built around visual media, any attractive, well-executed image instantly catches the eye—provided the targeting and segmentation are correct.
The Power of the Right CTA: Good targeting gets you in front of the user, but appropriate messaging and a strong CTA are what propel them to experiment.

2. Solving the Market Gap: The Filter Coffee Example

I like to experiment with food, coffee, and sauces, and Instagram has introduced me to numerous new products. I had been wanting to try South Indian filter coffee for a long time, but never got around to it. Why? Because I didn’t have the physical filter. Unless a consumer has the filter, you cannot sell them filter coffee. That’s basic. The brand understood this market gap, bundled the filter together with the coffee powder, and targeted me with the offer. That is the perfect confluence of market research, audience segmentation, creative messaging, and problem-solving.

3. Ad Objective vs. Product Reality: The Knife Set & Meesho

Instagram ads blend into the user experience so naturally that once you get used to them, you actually look forward to encountering interesting products. Recently, an ad introduced me to Meesho through a knife collection featuring a chef knife and a cleaver (as a vegetarian, I look for cleavers).

The Hook & CTA: The price point was significantly lower than anywhere else, which immediately drove the click.
The Product Friction: Upon closer look, the knives were standard stainless steel rather than high-carbon steel, meaning they wouldn't retain a sharp edge over time. I bought something else instead.
The True Objective: Even though I didn't buy the knives, the ad succeeded. The primary objective was Meesho app downloads, not just selling that specific knife set. The ad accomplished its goal because it felt native, not forced. I can’t see their campaign dashboard, but my guess is the objective was app installs, not that knife set. If so, the ad did its job even though I never bought the product.

4. The Contrast: Intrusive Mobile Advertising Kills Engagement

Unlike Instagram (or even Amazon, where you have to work hard to separate sponsored entries from organic ones), mobile gaming ads are inherently irritating. When I’m playing a mobile game, I do not want my gameplay interrupted:

In-Game Interruption: A well-known shaving brand served me ads two or three times in a single day while I was playing a game. I didn't interact—I just closed the ad as fast as I could because it intruded on the game experience.
The Channel Context Shift: That exact same brand reached me via email and Instagram, and I clicked and interacted with both.

The Lesson for Marketers – Ads should not be intrusive, and cause friction

Mobile advertising has its place for informational products, but do not expect real interaction if you interrupt the core platform experience. For any engagement and positive associations in the target audience, non-intrusiveness is one of the key aspects of advertising. And it should solve some issue, for it to click. The shaving ad stuck in my mind, but it created irritation rather than intent. Understanding what kind of advertising to push on mobile—without ruining the user's flow—is a completely different and challenging strategy that deserves far deeper debate.

The Lesson for Marketers: Before you pick a mobile placement, run four tests.

Context: Does the ad fit what the user is doing, or does it block it? An ad in my feed is part of the experience; an ad mid-level is a toll booth.
Control: Can the user choose to engage? Scrolling past an ad is a choice. Being forced to sit through one is not.
Relevance: Does the ad solve a real problem? The filter coffee brand worked because it understood why I hadn’t bought: I had no filter. Bundling it removed that barrier.
Frequency: How often will I see it? The shaving brand’s problem wasn’t the product; it was appearing two or three times in a day, in the wrong place. The same brand worked for me by email and on Instagram.

That is why, especially for new customer acquisition, a mobile advertising strategy is not really recommended – for it causes disruption in the user’s use experience; mobile advertising is best epitomised by insertions in Instagram as I argue above : blending with the platform experience itself, and, over time, coming to act as a part of the platform experience. I look forward to instragram now for that aspect as well – I always find fun products on it to try out. That is something to keep in mind. Any app your plan your presence on – look always towards the instrusiveness of the advertising. Whatsapp is another case in point – the choice of the app in such environs is critical, and needs a lot of thought. Placement in instragram is not the same as placement in a game – something brands are yet to learn…

Interruptive formats have their place, especially for reach and app installs. But for new customer acquisition, they carry a cost in irritation that the brand pays for later. In-feed advertising wins because it becomes part of the platform experience. I now open Instagram partly to discover new products, and that’s the best outcome an ad can hope for.

The question I’d leave you with is where WhatsApp fits. It’s the most personal screen on my phone, and the bar for what counts as non-intrusive there is far higher than on Instagram. Brands that treat placement in a feed and placement in a game or a chat as the same thing will keep wasting their budget.

#DigitalMarketing #GrowthMarketing #InstagramAds #UserExperience #CustomerAcquisition #MarketingStrategy

Pricing AI : Why the Equation Has to Change

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 14 Oct 2026 06:37:55 +0000

Part 2 of Show Me the Market | By Vishal | October 2026

In my previous piece, I argued that AI cannot pay for itself at current pricing tiers. Roughly $3 trillion of debt and obligations sits behind the industry, and a subscription-based market, modelled generously, comes to tens of billions to low hundreds of billions of dollars a year. The gap is too large to close with seats at $20 a month. Neither is the expectation that each individual can sustain multiple 20$ subscriptions – that leaves the B2B revenue stream. That, while looking attractive, is, on deeper analysis, not quite as compelling a case for AI sustenance. The B2B industry operates on a concept of share of profits or percentage of revenue or a similar metric when it comes to technology spends – and they have to spread it across multiple solutions, with being but a share of it.

Clearly something has to give – either the models have to change, or the market has to crash – or even both. Current approaches are simply not sustainable. Early indicators suggest the industry is arriving at the same conclusion. OpenAI has cut the usage allowance on its $200 Pro plan and added a $500 tier above it. Does that change anything? Hard to say. We are in untested waters, in speculative territory. But it introduces some interesting paradigms.

The first price signal

In late September, OpenAI changed the plans for its heaviest ChatGPT users. The $200 Pro plan now includes half as much usage for coding and agent-style work, effective October 30: 10 times what the $20 Plus plan allows, down from 20 times. Weekly messages to its top reasoning model fall from 200 to 100. Above it sits a new $500 plan with 25 times the Plus allowance (reporting by TechCrunch and The Next Web).

Here is what that means for the price of each unit of usage. Treat what the $20 Plus plan allows as one unit:

Plus: $20 for 1 unit, so $20 a unit.
Old $200 Pro: 20 units, so $10 a unit.
New $200 Pro: 10 units, so $20 a unit.
New $500 plan: 25 units, so $20 a unit.

Before the change, heavy users got a bulk discount: half the price per unit. Now the discount is gone and every tier costs about $20 a unit. For the heaviest users, that doubles the effective price while the $200 sticker price stays the same. This covers only the coding and agent allowance, and a unit measures capacity, not value delivered. One vendor is not the whole industry. But it is the first visible move, and it points one way.

Untested waters

There is no price-volume curve for AI at scale. Nobody knows how adoption responds when prices rise, because prices have been subsidised since the start.

Here is why the unknown matters. In my first piece, the gap between the market and the debt came to roughly 10x: the industry would need about ten times today's revenue. Raising prices is the obvious lever, so the question is how far it can take us.

The logic is simple. Revenue is the price multiplied by the number of paying customers. When the price rises, some customers leave, and how many is the whole question. Take 1,000 customers paying $20 a month, which is $20,000 a month in total. To reach ten times that, $200,000 a month, here is what each case needs:

Nobody leaves: the price must rise 10x, to $200 a month.
Half leave (500 stay): the remaining customers must pay $400 a month each, a 20x rise.
Nine in ten leave (100 stay): the remaining customers must pay $2,000 a month each, a 100x rise.
Ninety-nine in a hundred leave (10 stay): the remaining customers must pay $20,000 a month each, a 1,000x rise.

The more customers leave, the less realistic the price becomes. So raising prices alone can only close the gap where customers stay, such as tools already built into a business's daily workflow. Nobody knows how many would leave at each price, because prices have been subsidised so far. That unknown is the point. These are illustrative examples, not a forecast.

Where the money has to come from: B2B

Most of the revenue will have to come from businesses, not consumers. That changes the per-customer arithmetic in my first piece, which assumed a 50:50 split.

Reported revenue mixes point the same way, though they differ by company. Press and analyst reports put about 80% of Anthropic's revenue at business and API customers. OpenAI has been consumer-led, with enterprise reported at around 40% of revenue and rising. ChatGPT has roughly 900 million weekly users, but only about 5% are reported to pay. On that evidence I assume a 70:30 split between B2B and B2C. It is an assumption, not a measured industry figure.

Take the $834B a year needed to service about $3 trillion of debt. At 70:30, businesses must supply about $584B and consumers about $250B. Spread across the same eight markets as before:

Consumers: if 5%, 10% or 15% of people pay, each must spend about $1,800, $900 or $600 a year. That is $150, $75 or $50 a month, against a typical plan price of $20.
Businesses: if 5%, 10% or 15% of firms adopt, each must spend about $354K, $177K or $118K a year.

Compared with a 50:50 split, the load on each consumer falls by about 40%, and the load on each firm rises by about 40%.

What this means:

The business bill is the real problem. In my first piece I assumed about $5K a year per adopting firm, mostly small firms. At a 70% share, each adopting firm would need to spend roughly 24 to 70 times that.
The money must come from labour and services budgets. Support, coding and back-office work are where those budgets sit, not in software budgets. My India map points the same way: enterprises are about 78% of a domestic AI tools market of roughly $5B in the mid case.
Business revenue is concentrated, and loyalty is low. One lab claims more than 1,000 customers spending over $1 million a year. One analysis reports that 79% of firms paying for one leading lab also pay for another. Buying from several vendors gives businesses bargaining power, which caps price rises.

So the price shock lands hardest on businesses, and that is where the question of the market has to be answered first.

Six things that could change at once

Changing one parameter will not be enough. Too much is at stake, and the investment is too large. What is needed is closer to a new ecosystem. Here is where I think the shifts will come from.

1. Pricing tiers will evolve. Flat $20 subscriptions do not cover the cost of heavy users. Expect usage-based tiers, outcome-based pricing and ad-funded free tiers (ChatGPT's free and low-cost Go tiers already carry ads).

2. Debt structures will shift. Fixed-rate debt against uncertain revenue is dangerous for pure-play AI companies. Convertibles, debt-for-equity and revenue-linked instruments are legitimate alternatives. But they change who bears the risk and when. They do not change the underlying economics. They pay off when revenue grows, which is the case where they are needed least, and in a downturn they behave like debt.

3. Off-balance-sheet financing will grow. It is already visible. A reported $1.65 trillion of AI-related obligations sits off the balance sheets of the five largest hyperscalers, alongside about $1.35 trillion of on-balance-sheet debt. These structures are legal. They are also opaque, and they deepen the contagion channel I described in the first piece.

4. Insurance and risk will become central. Insurers have started excluding generative AI from standard liability policies, while specialist insurers add affirmative cover. Whoever can price AI risk, including correlated failures across many customers of the same model, becomes a gatekeeper for enterprise adoption.

5. Frugal engineering will matter. Smaller models, routing work to cheaper models, distillation and efficiency gains reduce the cost structure itself. India has a track record here, with UPI, Jio's cost structure and Mangalyaan as examples of doing more for much less.

6. Go-to-market will be rebuilt. Vertical, outcome-priced applications sold through the IT majors and specialist startups, rather than horizontal chat subscriptions.

Standard frameworks have no precedent here

I stress-tested the standard approaches and found nil battey sannata, a blank. The arithmetic of unit economics still binds, which is exactly why the numbers do not close. What lacks precedent is the answer. Standard frameworks do not have a ready one, and that calls for a leap of faith, or a different approach, with attendant risks. The off-balance-sheet moves of the biggest players are legal but risky, and they deserve attention alongside cost structures.

The chasm

Suppose current volumes are driven by early innovators and early adopters. At some point the gap between them and growth-phase adopters has to be crossed. That is the classic chasm of technology adoption, and AI has not crossed it at current prices.

Two things have to be sorted out before it can be crossed: the price-volume curve and the price-value equation. Standard frameworks will not settle them. It will take frugal engineering, innovative product design and flexible go-to-market, set within smart financial and risk management, to monetise and find the path to profit.

No technologist can solve this alone, and no marketer can either. It needs people who work in both. Price discovery for AI means willingness-to-pay research by segment, cohort tests of limits and prices, tracking downgrades and churn, anchoring price to the value delivered rather than to the cost to serve, and usage data to know what each customer costs.

What to watch

• Whether other AI vendors follow with similar tier changes, and whether the $20 tier moves.

• Downgrade and churn rates after tier changes, where they are disclosed.

• Refinancing terms and spreads on AI-linked debt.

• How insurers treat AI in professional liability cover.

• How much further off-balance-sheet obligations grow.

The company that works out the price-volume curve first, before its cash runs out, is the one likely to survive.

Caveats

• The elasticity figures are illustrative arithmetic, not estimates of real demand.

• The debt and obligation figures ($1.35T and $1.65T) are reported estimates, and the off-balance-sheet portion is commitments, not all borrowings.

• The OpenAI plan details are as reported by the press, and may have changed.

• This is analysis, not investment, legal or insurance advice.

Conclusion

That being said – the above is only a very simplified back of the envelope illustration meant to point out that the current pricing and business models do not seem to be sustainable basis the above calculation. The AI industry needs to demonstrate a higher value to the business so that a higher share of wallet in their spends and budgets can be justified – only then will businesses pay. And therein lies the biggest lacuna – the AI industry is stuck in proving how smart it can be, not in figuring out how to add value, actual real value, to real world businesses.

And that requires a marketing-led approach, not a technology centric approach. The industry will have to work, and work closely, with businesses across the board in order to understand their pain points, figure out how it can add value, and implement smoothly. And, in the process – some existing solutions will become obsolete; so disruption is anyway certain. Only question is will it be this positive disruption – or the mayhem-based disruption of a market collapse?

Sources

TechCrunch and The Next Web (OpenAI DevDay plan changes, September 2026); YourStory (hyperscaler debt and off-balance-sheet obligations); Insurance Business magazine and Willis (AI exclusions and affirma

Show Me the Market: The AI Debt Question No One Has Answered

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 12 Oct 2026 06:51:53 +0000

By Vishal | October 2026

The question a marketer keeps asking

I am not saying there is no AI market. The technology has proven itself, and the models are good. My question is different, and as a marketer it is my first, second, third, fourth and fifth question: show me the market. Who pays, from which budget, how much, and what do they stop paying for in return?

This piece works through that question with numbers. The figures are rough, the assumptions are stated, and where I could not verify something I say so.

1. The debt behind the boom

The scale is in trillions, not hundreds of trillions, and that is still enormous.

• About $1.35 trillion of debt sits on the balance sheets of five hyperscalers (Alphabet, Amazon, Microsoft, Meta, Oracle), plus roughly $1.65 trillion of off-balance-sheet AI obligations such as long-term data centre, compute and power contracts (Nikkei Asia analysis, as reported by YourStory). The second figure is lease-like commitments, not all borrowings.

• AI-related debt issuance is estimated at $450-570 billion in 2026, against roughly $100-121 billion in 2025 (Morgan Stanley and Goldman Sachs estimates, as reported).

• Goldman Sachs reportedly estimates about a third of 2026 AI capex is debt-financed. Citrini Research expects hyperscalers to issue far more in 2027-28 than the market expects.

• AI-linked bonds have reportedly become the largest segment of the investment-grade index, so index-tracking funds, including target-date funds, hold them automatically.

This matters because the lenders are not only banks. Pension, infrastructure, hedge and sovereign funds are all exposed.

2. What revenue would it take?

I reversed the calculation: given the debt, how much revenue is needed to service it?

Assumptions: 6.5% interest, 10-year repayment (13.9% of principal per year), 50% gross margin.

Debt scenario	Annual debt service	Revenue needed	Interest only	Revenue needed, interest only
$1.35T	$188B	$376B	$88B	$176B
$3.0T	$417B	$834B	$195B	$390B
$4.0T	$556B	$1,113B	$260B	$520B

This is a floor. It excludes equity-funded capex, R&D and staff costs, and any return to shareholders.

Per customer

Take the $3T case ($834B), assume half comes from consumers and half from businesses, and spread it across eight markets (USA, EU, UK, India, Singapore, Malaysia, Indonesia, Philippines: about 2,775M people and 33M firms).

Paying adoption	Per individual per year	Per month	Per adopting firm per year
5%	$3,000	$250	$253K
10%	$1,500	$125	$126K
15%	$1,000	$84	$84K

A consumer plan costs about $20 a month. Most firms are small and would spend closer to $1,000 a year than $126K.

3. The market as it looks today

Using the same eight markets, $20 a month ($240 a year) and paying adoption of 5, 10 and 15% of the whole population:

Adoption	Consumer revenue (headline)	Consumer revenue (realistic regional pricing)	Business revenue	Combined (headline)
5%	$33.3B	$17.3B	$8.3B	$41.6B
10%	$66.6B	$34.5B	$16.5B	$83.1B
15%	$99.9B	$51.8B	$24.8B	$124.6B

The business figure assumes about 33M firms, an average spend of about $5K per adopting firm, and seat-based pricing only.

Benchmarks, from memory and not verified: consumer subscriptions such as Spotify and Netflix reached roughly 5-6% of their addressable users after 10-18 years, and typical freemium conversion is 2-5%. A 5% paid share of total population across a mixed-income set of markets is already at the top of precedent. 15% has none for a standalone subscription.

A gap of 7-20x between $834B and $41-125B is large. It is not zero, and it excludes the part most likely to close it: AI replacing labour and services spending, rather than selling seats.

4. India: where the money actually is

India is the most useful test case, because I can anchor it to reported profits.

The profit pool (FY26)

Segment	Profit (₹ lakh crore)	Basis
Enterprise (Nifty 500)	18.2	Reported. The Nifty 50 alone is about 9.2
Medium	~5.0	Estimate
Small	~3.0	Estimate
Unorganised	~12	Estimate, owner earnings
Total	~38	About $400B

From profit to an AI tools market

Tech spend as a share of profit is not a published benchmark, so I built it from the revenue-based norms (2-4% of revenue) and stepped it down for smaller firms: Enterprise 20%, Medium 12%, Small 6%, Unorganised 2%. That gives about $50B of corporate tech spend. Gartner's figure of $176B for all India IT spending in 2026 (software $24.7B, IT services about $35B, data centre systems $9.4B) is a reasonable cross-check once government and multinational subsidiaries are added.

AI is only a slice. One survey says AI is below 20% of the IT budget for 95% of Indian enterprises. Applying 5%, 10% and 20%:

AI share of tech budget	Domestic AI tools market
5% (today-ish)	~$2.5B
10% (about 2028-30)	~$5B
20% (ceiling)	~$10B

IDC projects India's AI spending at $9.2B by 2028, which fits this range. Enterprise accounts for about 78% of it. Separately, Nasscom puts AI-related revenue in the Indian tech industry at $10-12B in FY26, but that is mostly AI-led services sold abroad, not domestic tools.

If AI displaced just 2% of the Nifty 500 wage bill (about ₹16.5 lakh crore, or $175B), it would add about $3.5B on top.

5. The telecom lesson

India has already lived through a layered, capital-heavy market collapse. In July 2026 TRAI data, Jio held about 39% of wireless subscribers, Airtel about 38%, Vodafone Idea about 15.5% and BSNL about 7%. It is a duopoly with a weak third player. A free-data price war from a cash-rich conglomerate drove out a dozen operators.

Where the analogy holds:

• Capital-intensive foundation layers end in price wars and consolidation. Open-weight models and falling token prices are the AI version.

• Survivors had fallback revenue. In AI, the largest model owners are conglomerates. The exposed ones are the pure-play labs, and their risk reaches lenders through compute contracts.

• Value moves up the stack. Cheap data produced UPI apps, streaming and delivery. Cheap tokens should do the same.

Where it breaks:

• Telecom is licensed country by country. Models are borderless, though regulation will fragment them (EU, India, China).

• A failed telco's spectrum and towers pass to survivors. GPUs lose value over 5-6 years, so recovery is worse.

• Consumer subscriptions may not be the main monetisation. Assistants are being bundled into phones, search and messaging.

6. Application and middleware: a map for India

This is where I expect the money to go. The totals are anchored to the table in section 4. The split across areas is my assumption.

Area	Layer	Low (5%)	Mid (10%)	High (20%)	Why India
Customer support and voice agents	App	$0.5B	$1.0B	$2.0B	Indic voice, BPO labour pool
Software engineering and IT ops	App	$0.45B	$0.9B	$1.8B	Large developer base and GCCs
BFSI back-office (KYC, underwriting, claims)	App/middleware	$0.38B	$0.75B	$1.5B	UPI and Aadhaar rails
Marketing, content and AI-search visibility	App	$0.25B	$0.5B	$1.0B	Agency and SME base
Finance, accounting and GST compliance	App	$0.2B	$0.4B	$0.8B	MSME compliance load
Healthcare (documentation, triage, imaging)	App	$0.18B	$0.35B	$0.7B	Doctor shortage, vernacular
Manufacturing and supply chain	App	$0.18B	$0.35B	$0.7B	Export manufacturers
Legal, HR and internal knowledge	App/middleware	$0.18B	$0.35B	$0.7B	Large enterprises first
Education and upskilling	App	$0.1B	$0.2B	$0.4B	Vernacular tutoring
Agriculture and public services	App	$0.1B	$0.2B	$0.4B	Government stacks
Total		$2.5B	$5.0B	~$10B

By layer in the mid case, I assume foundation and API fees are about 35% ($1.75B), middleware about 15% ($0.75B) and applications about 50% ($2.5B).

Middleware worth watching: model routing and gateways, enterprise retrieval and permissions, evaluation and AI security, and the Indic language layer (speech, translation, tokenisation). Their main risk is being absorbed into cloud platforms.

What separates survivors: proprietary data or deep workflow integration; pricing per outcome, paid from labour and services budgets rather than software seats; not depending on one model vendor; and distribution or regulatory moats. Thin wrappers are exposed, and the labs are moving into apps themselves.

India's mid-case foundation revenue of about $1.75B is roughly 0.2% of the $834B needed in the $3T scenario. India cannot service global AI debt. It can support a domestic application layer.

7. Is it a bubble?

I think the financing risk is real, but the evidence is mixed.

Signals for:

• Valuations far ahead of revenue. One large AI-linked IPO this year sought about a $1.75 trillion valuation on about $18.7 billion of revenue and an operating loss, roughly 94x revenue.

• Speculative narratives in prospectuses. The same filing pitched data centres in orbit, deploying "as early as 2028". Its own risk factors admit the technology is unproven and may never be commercially viable. It was disclosed as speculative, not hidden, but investors are still being asked to pay for it.

• Debt growing fast, partly off the balance sheet, and held by index-tracking funds.

Signals against:

• Revenue is real and growing. TCS reports about $1.8B of annualised AI revenue and Infosys about $275M.

• Most hyperscaler capex is funded from operating cash flow.

• Bubbles coexist with real technology. Rail and fibre were both overbuilt, and both were later used.

Contagion: debt that cannot be repaid is restructured, and the losses fall on the weakest tranches. The fragile parts are leveraged data-centre vehicles, neoclouds and private credit, not bonds of cash-rich issuers. Signals to watch: refinancing spreads, GPU rental prices, data centre utilisation, and lab revenue growth against compute commitments.

8. The test that answers the marketer's question

The first company to show the market takes the prize. A credible answer has to pass five checks:

1. Who pays? A named buyer with a budget line, not "users".

2. From which budget? Labour, outsourcing or compliance spend is large. Experimental innovation budgets are not.

3. How much? Per-outcome pricing (per call resolved, per claim processed, per return filed) that beats the cost it replaces.

4. Replacing what? A measurable cost that disappears, such as a BPO seat, a manual process or a compliance team.

5. Can it repeat? Revenue that recurs year on year without a new pilot each time.

The market that passes these tests is not a $20-a-month chat subscription. It is a vertical, outcome-priced application layer built on cheap models, and in India that means voice, compliance, back-office and vernacular services at scale.

Caveats

• Population, firm counts, price points, the 50% margin, the 6.5% rate and the 10-year repayment are all assumptions.

• The medium, small and unorganised profit figures and the split by application area are my estimates, with no published source.

• Benchmarks marked "from memory" are not verified.

• Off-balance-sheet obligations are commitments, not guaranteed borrowings.

• This is analysis, not investment advice.

Sources

Gartner (India IT spending forecast, Nov 2025); Nasscom Strategic Review FY26; IDC and EY-CII (as cited by CXOToday); TRAI subscriber data, July 2026; Business Today (Nifty 50 and Nifty 500 FY26 profits); MOSPI ASUSE 2025 and Economic Survey 2025-26 (MSME and unorganised sector data); Morgan Stanley, Goldman Sachs and Citrini Research (AI debt estimates, as reported by YourStory, Crypto Briefing and AIWeekly); CRISIL (IT services vertical mix); company prospectus coverage (Trending Topics, Benzinga, Douglas Research Insights).

Disclaimer:

This is made by a combination of my daily economics and business readings, watching youtube videos on the AI market, and using ChatGPT, Gemini and Claude to construct these arguments.

AI and Consciousness: An Advaita Vedānta Perspective

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 05 Oct 2026 08:30:56 +0000

The debate over whether artificial intelligence can become conscious has largely been conducted from the assumption that consciousness is an emergent property of sufficient complexity. Advaita Vedanta begins somewhere entirely different. It does not regard consciousness as something that emerges from matter, nor as something that can be manufactured. Consciousness simply is.

Therefore, a simplistic assessment would be that AI is not, and can never be conscious, basis my understanding of my personal faith, Advait Vedant. Let me explain in brief- that being said, this is not a topic that can be abbreviated. So, I intend to touch upon only the highlights to make a more detailed argument, such as it is. This topic, which is far too vast to condense into a brief essay, is nonetheless being broached here with an intention to highlight the core arguments to provide technologists and thinkers with a more grounded perspective.

Introducing Advait Vedant In Ultra Brief

First of all, Advait Vedant. Advait encompasses all rituals within its scope, and has two elements - the spiritual realm, where you dwell and study and grow, and the vyavharik world where you do the daily rituals of karm as per your state in life, albeit under the advaitic permissible ways and means, morals, ethics and injunctions. Advait means - non-duality - the belief in one supreme force that is present in all, and only expresses itself through the phenomenal world. It places a unified view on the divided world, and establishes norms of conduct, and expression of life through various bodies is determined by this conduct over time by any being. There is a unified consciousness, and the inanimate body only expresses this consciousness through its binding conduct. The conduct is the key determinant of eventual expreThis is the briefest possible description, which is tailored to the purposes of this article.

To summarise, Advait encompasses two realms: the spiritual realm (Pāramārthika), where one contemplates, studies, and grows toward self-realization; and the transactional realm (Vyāvahārika), where one fulfills daily duties (Karma) guided by ethical injunctions. Advaita translates to "non-duality"—the realization of one supreme, non-dual Consciousness (Brahman) that underlies all existence and expresses itself through the phenomenal world. Matter in itself is inert (Jaḍa); it only appears animated when illuminated by this singular Consciousness.

The Nature of Consciousness

Next, consciousness. This is the hardest to appreciate, understand and imbibe - and this article is not a lesson on Advait. Advait is not textbook material - it is a path to be walked. In the simplest terms, there are three realities - the bound consciousness residing in a body, the unbound consciousness that expresses itself in the universe, and the overall supreme consciousness.

1. The Bound Consciousness (Jīva) residing within an individual body.

2. The Unbound Consciousness (Īśvara) expressing as the cosmos.

3. The Supreme Consciousness (Brahman)—the absolute, unconditioned reality.

Now these seemingly different entities are a myth, a mirage - there is only one consciousness. This seeming contradiction can be explained if you look at it as a never ending stream that arises from a boundless body of water. Various canals and taps lead from this stream sources from the body of water into various outlets. These are the individual bodies - the animals, plants, humans, etc. The stream represents the causal universe.

This at its simplest in Advait - it is our view of the water flowing from a tap that causes us to call it a tap water, or a river. In reality, the connection to the stream and the neverending boundless ocean is absolute -we dont realise it. We thus end up seeing a multiplicity of forms and appearances.

And from the perspective, we run into a serious issue when we ask is AI conscious? For that implicitly implies, consciousness can be created. Advait leaves no scope for creation of conscioutness - it is ever-present. Only the expression changes. It only passes from one form to other. It goes from river to canal, or to taps - and so on. The water does not get created - only create the conditions for it to flow materialise.

The Stronger Objection: Can AI Serve as a Medium for Expression?

So, AI cannot have consciousness created inside it - for consciousness inside you me animals etc is immortal. Thus, AI cannot be conscious. The next question arises in the form of an objection to this - that we are creating the conditions for consciousness to express itself through technology, which is a far stronger claim than the first one. This deserves more careful contemplation.

The Advaitic conception of an embodied conscious being is inseparable from a causal history—karma, vasanas, impressions, tendencies and the subtle apparatus through which consciousness is experienced as an individual self. An engineered computational system has no demonstrated equivalent of this causal history. To understand that, we have to revisit Advait Vedant. Expression of consciousness requires some precondition - Sanchit Karm has to be there, that has to be fulfilled through Prarabdh, and that requires a physical body that can go through Sattva - Goodness, Rajas - Passion, and Tamas - Ignorance. This form of the expressed consciousness - the bound consciousness, needs a set of desires, longing etc that have remained unfulfilled, a set of duties that it is incumbent upon a body to perform and so on.

The Law Of Karm & Vasanas

None of these conditions can be satisfied in a technology - for the beginning point - the Sanchit karm, which is the origin of creation - the Hiranyagarbh when the total sanchit of all the bodies, lying dormant, reexpresses driven by these incomplete conditions listed above. These are traceable to specific actions and deeds by specific parts of the overall supreme consciousness - and this compels the creation to reset and restart, a new beginning.

Further, apart from these two which are absent - sanchit, and preexisting traceable unfulfilled actions traceable to a single body - also absent is any set of desires, longings, passions, driven by long held vasanas - again traceable to a single source. A technology solution will have inputs from multiple human bodies and atmans inside those bodies. How will consciousness express itself through? It cannot, by any definition inside Advait.

The Absence Of Antahkaran

Third, Advait also needs a specific set called the Mind-Set, The Man Buddhi Ahankar and Chitta, to function, alongwith the 5 gross elements. (Ashtadha Prakruti with the Chitta). It is these entire sets of realities that combine in various proportions to create the visible universe we see all around us. And from this perspective, it is amply clear that AI cannot be termed as conscious from a core Advait perspective.

Advaita does not define embodiment merely as the possession of sufficiently complex physical machinery. The human organism is an integrated physical and subtle apparatus through which consciousness becomes individually expressed. Reproducing some of its observable functions does not demonstrate reproduction of the ontological conditions that constitute an embodied jiva.

Conclusion

A machine can calculate, simulate language, and pattern-match human output with astonishing accuracy, but simulation is not manifestation. Without Karma, Vāsanās, and a genuine subtle body (Sūkṣma Śarīra), silicon remains an inert instrument (Karaṇa) used by conscious beings, rather than a conscious entity in its own right. Technologists seeking to understand the limits of artificial intelligence would benefit from looking beyond materialist mechanics and engaging with the rigorous ontology of non-dual philosophy.

Within the Advaitic framework described here, there is no philosophical basis for saying that consciousness can be created by engineering sufficient computational complexity. Nor does functional imitation establish that an AI system has become an embodied conscious being. Therefore, from my understanding of Advaita Vedanta, I do not believe AI can become conscious—not because machines are insufficiently intelligent, but because consciousness is not something intelligence creates.

While this is a very, very brief critique, and this subject does deserve a far more detailed commentary, I hope that what is contained here is sufficient for any technologist to approach this subject with a far more nuanced view of consciousness

The Transparent Hypocrisy of the West and the Civilizational Maturity of the East

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 30 Sep 2026 12:17:37 +0000

One of the most curious aspects of the West’s approach toward the East is its transparent hypocrisy. They are hypocritical, and they are completely transparent about it—they simply do not care what others think.

The Western Coterie and Selective Solidarity

Look at the phrase "the Western civilization." Everywhere you look—in video after video, article after article, television series, news broadcasts, and political decisions—we constantly encounter the phrase "Western civilization." Their movies proclaim membership in this civilization, and their politicians speak of the West as a single, unified unit. That is fine, but they shouldn't expect others not to band into units of their own.

Look at BRICS. BRICS was envisaged as a platform where developing countries could meet each other’s needs. Yet, the West tend to see it as "anti-West." This is absurd, for BRICS is a collection of nations spanning deep Western allies like South Africa, neutral and friendly partners like India, alongside outright opponents like China and Russia. It is not designed to be about the West, but Western commentators simply cannot understand this. They, some [quite a few?] of them at any rate, remain fixated on seeing it as an anti-Western coalition.

We see this same mindset in American political discourse, where some in their government openly state they will not allow India to emerge as big a threat as China. It is difficult to understand how India’s economic growth poses a problem for the United States. Yes, we will become economically less dependent on the USA. We will be richer and more independent. But we are not stealing wealth from anyone. Global dominance may shift, but the Western view remains fixated on a single principle: we are the West, we are one, and we must dominate.

How can a country like ours depend on a civilization that is so inclusive of its own people and so exclusive towards other people? They are not willing to allow others into their coterie. We do not want to be part of that coterie, but they actively try to prevent us from forming our own friendships and relationships. That is the core hypocrisy of Western civilization.

Eastern Realism: Managing Complexity Without Conflict

Contrast this with Eastern approaches. We are used to dealing with a multiplicity of complex situations. India maintains a $150 billion trade relationship with China, even though it is lopsided against us. We are fully aware of China assisting Pakistan; our security agencies are alive to that threat and actively protecting us. But we do not make an endless hue and cry about it the way the West does. Similarly, Russia assists Pakistan in many cases, and Turkey overtly takes adversarial positions. We understand these dynamics, keep silent – or restrained, and continue managing beneficial relationships based on practical balance.

The East is able to hold a multiplicity of worldviews, reconcile them, and operate keeping a balance between them. That requires a far higher level of intelligence, cultural achievement, mental maturity, cohesiveness, and development, at least from my standpoint and POV. The West, by comparison, is remarkably young. At its oldest, it dates back roughly 3,000 years, with modern Western identity tracing back 2,000 to 2,500 years through Christianity—often viewing even older Greek and Egyptian roots as pagan origins.

Deep History: Unbroken Continuity vs. Civilizational Rupture

The West is new. Most of the West as we know it today is only about 2,000 to 2,500 years old. Its Greco-Roman ancestry is where it traces itself in civilisational terms, but that continuity was broken by major ruptures in the West: the fall of the Western Roman Empire, the Christianisation of Europe, the Theodosian edicts suppressing pagan worship in 391-392 AD. In popular belief, the West does not trace itself back to these ancient civilisations, while we in India and China still do, across every aspect of even modern life.

Even if you accept Greek origins dating back 4,000 to 4,500 years, the earliest authenticated archaeological sites in India and China reliably date back to 6750–7435 BC, with some sites spanning even further. From a scientific perspective, this makes complete sense: during the receding Ice Age of 10000BC-7000BC, the West lacked the conditions etto sustain advanced human settlements; ice doesn’t dissolve in 10 years. It took time for conditions to emerge that support civilisations. And the farming settlements of 8000-7000 BC slowly evolved into settlements, then cities of the 5000-3000BC, laying the framework of one continuous uninterrupted life in Asia.

India and China thus have cultural lineages traceable to about 7000 BC, from farming settlements evolving continuously into large settlements, and unbroken urban traditions of about 4,500 to 5,000 years. Nothing in the West compares. That was the receding ice age, and the West did not have anything in those periods. The East is therefore a far more mature civilisation. The West is behaving exactly like an arrogant kid, and we should be mature enough to allow the West to behave as it wants, like an indulgent parent with a child playing its silly games.

Strategic Patience: Avoiding the Western Trap

We have seen far more of the world than the West and we know where this is heading. And we are not going to disturb them, because they are not going to listen to us, and getting involved in their fights is like committing harakiri. That is the mistake the Chinese are making. If China had focused on its own development, its economic growth would have been faster, but it got veiled into following the Western path.

Having had 5,000 more years of civilizational development than the West, the East is far more mature. The West behaves like an arrogant child playing silly games – I am not calling the west a child, just that its lack of civilisational timelines comparable to ours make them susceptible to these weaknesses – using it as an analogy or metaphor. It is relevant since cultural norms, maturity as reflected through behaviour / driving passions on a people scale / norms / conduct etc, learnings, etc all get passed through culture, language, civilisational memory, even going to the extent of impacting at some level the genomic code, though this last is a nascent science at the absolute edge of genetics.

As a mature civilization, we should be indulgent. We have seen far more of human history than the West, and we know where this path leads. We should not disturb them or get dragged into their fights; doing so is strategic harakiri. That is precisely where China went wrong. Had China focused purely on its own development, its economic growth would have been faster, but it allowed itself to be baited into following the Western path of direct confrontation.

We must understand who we are: a far more mature, developed, and advanced civilization with thousands of years of excess experience over the West. Let the West have its childish behavior and play its games. We know from our own early civilizational history that continuing down that path carries a heavy price—a lesson the West will eventually have to learn for itself. Our responsibility is to focus entirely on our own development, navigate our own challenges, and act with the restraint that true civilizational maturity demands.

What Is Modern Marketing Even Doing? Three Basic Mistakes Brands Keep Making

noreply@blogger.com (Reflections Of Vishal V Kale) — Fri, 25 Sep 2026 09:41:22 +0000

In the modern business landscape, brands need to pay far more attention to their customers. A single review in today’s interconnected world can completely destroy sales, or a single missing detail can cause missed sales. How brands communicate, how they structure their packaging details, and how they handle consumer mentions on social media reflect the state of the modern marketing scenario—and right now, critical fundamentals are being missed.

A Lesson in Consumer Behavior: Financial Incentives vs. Trust

My experience began when I was looking for Pooja ghee. I was evaluating a new brand simply because it was available on a particular platform where I had some credit available. Having free cash or credit on a platform makes a purchase financially more remunerative, but that doesn't mean a consumer is going to compromise on product quality. That is a crucial consumer behavior lesson. Because this was not my regular brand, I decided to look up the product in detail before committing to the purchase.

What followed brought out three very basic aspects of marketing. The first was the role of reviews and how a single review can influence a purchase decision. The second was product packaging and product detailing, particularly whether the information a customer needs is actually available. The third was brand messaging and communication: what are you telling the customer, and are you communicating the things that matter to the person who is considering buying your product?

Mistake 1: Missing the Basics in Packaging and Brand Messaging

When evaluating a product, brand messaging and packaging communication are vital. There are key elements a customer looks for, yet basic product detailing is frequently left out.

The Missing Ingredient Details

When I scanned through the product images, I found that one critical detail was missing. Nowhere on the packaging were the ingredients easily visible. While I cannot say for sure whether ingredients were listed on the physical item since I had not purchased it yet, they were certainly not visible in any of the six or seven images available on that platform.

The Search for Transparency

Seeking clarity, I navigated to the brand’s main product page as well as their official website. I still could not find any details on the ingredients. This caused me to pause. By contrast, my regular brand had the ingredients specifically listed in detail. Had this new brand clearly displayed its ingredients from the start, I would not have needed to dig deeper in the first place. I started to dig deeper because I had some free cash available on the other platform, and it was financially more remunerative for me to buy the product there. But that does not mean I am going to compromise on the product. That, in itself, is a consumer behaviour lesson. A financial incentive may make me consider a different brand, but it does not mean that I will compromise on information that is important to me.

Mistake 2: Failing to Manage and Monitor Product Reviews

Unresolved reviews can instantly flip a buying decision. While this brand had good reviews overall, one particular review caught my eye and became the ultimate clincher.

The Impact of a Single Review

The review clearly pointed out that this particular Pooja ghee stays solid even at 40 degrees centigrade. I do not know whether that customer was telling the truth or not, but reading that detail was enough to make my decision.

The Decision to Walk Away

That unaddressed claim was my cue to navigate away from the page entirely. I returned to my regular brand's page, where I immediately spotted the ingredients listed clearly. After seeing that transparency, I didn't look anywhere else—I simply purchased my regular brand.

That product is now in my hands, and because it is a bit warm here, the ghee is liquid. That proves to me it is genuine stuff, or at least that is how I interpret it.

Mistake 3: Turning a Blind Eye to Social Listening and Mentions

Beyond product listings and reviews, modern marketers are failing at basic social media engagement. Man y times, I have tagged brands in my LinkedIn posts to appreciate them, yet not a single brand has ever responded. This is not an ego issue; it is a fundamental flaw in how companies operate today. Software tools exist specifically to help brands track social mentions and listen to their audience. If a brand is not listening to its customers and not responding on social media, what are they actually doing? That is not true marketing.

This is the status of the modern marketing scenario. Customers are talking about brands on social media, and brands have access to tools and software that help them track those social mentions. There are tools available to tell you when people are talking about your brand and what they are saying. Yet if you are not paying attention to those conversations, you are ignoring an important source of customer information. The technology is available. The ability to monitor social mentions exists. The question is whether marketers are actually using it and, more importantly, whether they are actually listening to what customers are saying.

Conclusion: Returning to Marketing Fundamentals

These experiences highlight three basic marketing mistakes that far too many marketers make today:

Ignoring or failing to address customer reviews that raise doubts about product quality.
Neglecting social media mentions and failing to listen or respond to active consumers.
Failing to align packaging and digital messaging with the critical information customers actually look for.

These are all very basic aspects of marketing. They are not sophisticated marketing techniques, and they do not require some elaborate new marketing philosophy. They require marketers to pay attention to the customer and to the information surrounding the product. Messaging, communication, packaging alignment, and customer feedback are the absolute basics of marketing. If modern marketers are not listening to their audience, defining their brand communication, or managing their product packaging properly, it raises a fundamental question: what kind of marketing is actually going on?

The ROI Delusion: Technology And ROI Don’t Mix

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 23 Sep 2026 12:08:27 +0000

The Fallacy of Tech-Led Returns

Take a look at the current landscape of Technology and AI adoption. Everyone bought invtested into Technology, and then later on into AI tools. They ran pilots, automated functions, introduced AI when the AI boom came, built agents, and scaled them. Yet the vast majority are left wondering at empty balance sheets, wondering where the promised returns went. The reason for this failure isn't technical. It is a fundamental misunderstanding of corporate finance and organizational architecture. The truth is simple: ROI is not and will never be a technology function. It is a business function.

Why ROI Belongs to Business Leadership

First of all, revenue KPIs in the managerial performance sheet only start from Senior Manager or AVP on up, where revenue and cost parameters actually appear. There is a reason for this – achieving ROI needs a business perspective, an eye for cross functional detailing, an ability to execute across verticals and teams, and seamlessly carry the organisation towards an objective. And when such business leaders, placing an eye on wider benefits, go into technology selection, that is when the chances of hitting the sweet spot increase

Thus, ROI needs a comprehensive look at the business, and it makes little difference what technology says beyond its defined role in an organization. When a business selects a technology from this perspective, it usually means that the ROI will be achieved at an organisational level more often than not. This applies for even intra-function technology investments for the most part- the exception proves the rule. A lead automation function intersects with customer service, accounts, leadership team and technology – as an example. Achieving ROI from such a tool requires, by default, cross functional executional abilities, and a deep understanding of the business and the domain.

The 5-Point Matrix Behind Real ROI

Achieving ROI requires a synchronized, top-down strategy that looks at the enterprise as a whole. It demands an alignment across five core pillars:

Strategy & Objectives
Organisational Structure
People
Processes
Data

Right now, most companies are hyper-focused on Technology—which does not even appear in this matrix—and Data, which usually comes last. Nateeja sabke saamne hai. Technology is merely a subset of Processes and Data; its sole role is enabling them. At best, Technology can reshape processes, organisational structures and even strategic possibilities - that is it. Till such time we focus on tech and not on the matrix, ROI is a distant dream. ROI is a Business Objective, not a functional objective. Take the lead example – you invest into a lead automation tool; but don’t give any attention to the causes of lead attrition. Maybe the website isn’t capturing the right leads itself – what can your automation do? Or maybe the pricing is not in sync with market; or maybe the feedback from existing customers has not been incorporated – leading to awry targeting – and ultimately lower conversions. Whither ROI? Gone, that’s where. ROI is, I repeat, a Business Function.

The Sales Floor vs. Strategic Reality

A bigger question is - are the implementation and decision teams even focussed on ROI? In my five years across SaaS and AI, ROI is rarely even a real parameter in front-line tech sales. The focus on the floor is almost always operational utility: "What can this do to make my life easier?" Actual ROI considerations typically only enter during stepped purchase processes when Finance and Procurement step in—and even then, usually only in turnkey or capital projects.

If you are buying point solutions to make individual tasks easier without restructuring the top four pillars of the matrix, you aren't investing in ROI. You are paying for operational convenience. And maybe that is not exactly a bad idea – operational convenience, if strategically executed, can be a clear winning proposition as it unlocks potential and value. The devil is in the details, so let us leave it at that for now.

The Fallacy of the Functional P&L

Furthermore, not all Tech investments are ROI positive—the real world does not work that way. In order to keep the organisation in step with the rest of the world, you need to continuously invest back into the organisation so that it keeps itself aligned to the rest of the competition. There is never a linear path to profitability, and one should not break down profit into functional levels, demanding profit from each function, as that is a recipe for certain disaster.

Some functions—HR, Production, Technology, Development—will always be ROI negative at the functional level so that the organisation may eventually be profitable - their contribution often cannot or should not be evaluated through standalone functional ROI, because their value is distributed across the enterprise and may materialise over different time horizons. It is a question of deployment of resources at the organisational scale as per strategy to get the combined maximum benefit for the dollar. You don’t expect direct measurable ROI from your inward attendance automation system – you can try it, but the morale impact on teams will, over the medium term, wreck any provable benefits if not implemented properly. This is just an example; besides, better attendance leads to better discipline and execution- and attributing that to technology at the gate is not easy or even feasible.

When boardrooms demand that Technology or IT act as an isolated profit center that must justify its existence on a unit-economics basis, two major failures occur:

Strategic Sub-Optimization: A functional team cuts critical long-term infrastructure spend to keep its immediate departmental budget looking clean, crippling downstream operational capacity for the teams that actually capture revenue.
Accounting Fiction: Forcing functional ROI leads to administrative bloat—internal transfer pricing models, micro-billing across departments, and endless slide decks—without adding a single dollar of actual enterprise value.

The Lag Factor: Capability Over Immediate Payback

There is typically always a time lag between tech investment and benefit realisation. Modern technology investments operate on a J-curve: initial implementation disrupts existing workflows, requires talent upskilling, and demands process redesign before any performance jump is realized. Judging a technical capability solely by immediate short-term financial payback during this transition phase inevitably leads companies to kill high-potential strategic initiatives before the organization has even restructured to leverage them.

8 Questions Leadership Should Ask Instead

Instead of forcing a rigid, short-sighted financial metric onto a functional capability, leadership teams need a far more useful approach. We need to ask ourselves how a technology investment actually strengthens the business fabric:

How does this tech enable us to work better?
How does it help achieve our core objectives?
Does it add value to strategic and tactical decision making?
How effectively does it speed up processes?
Does it give us a clear advantage in the market?
Does it even out the scales with the competition?
How does it benefit my customers and employees?
How does it help me compete more effectively?

Stop Chasing Tools. Start Redesigning the Business.

Technology spend is not a vending machine where you insert a dollar and immediately collect $1.50 at the departmental door. It is foundational capability and defensive capital. It exists to keep the organization modern, agile, and competitive.

Purchasing software is easy. Downloading agents is trivial. Running pilots is cheap. The hard work—the 6% of the market that actually captures enterprise value—is the uncomfortable work of organizational redesign, cross functional teamwork, strategic execution and flawless direction. Until leadership stops treating AI and technology as a magical functional profit center and starts evaluating it as an organizational enabler across Strategy, Structure, People, Process, and Data, true ROI will remain out of reach.

Thus, technology value must be evaluated at the level at which the business outcome occurs, over an appropriate time horizon, and within the context of enterprise resource allocation—not by forcing every technology investment or function to produce an independently attributable financial return. Our gaze must necessarily divert from the ROI aspect to the much wider business value generated, and the strategic fit of the technology investment decision. Not every “latest” technology is best suited for your organisation: that is the short learning of this article.

The Research Supports This – This Is From ChatGPT

Decades of IT-management research support the argument that technology does not create business returns in isolation. Studies by Brynjolfsson & Hitt, Aral & Weill, and others show that IT value depends heavily on complementary investments in people, processes, organisational capabilities and strategy. Research on the “Productivity J-Curve” further shows that technology benefits can take time to materialise because organisations must first make these complementary changes. The evidence therefore supports a simple proposition: technology creates capability; the organisation converts that capability into business value and, ultimately, financial returns.

Why AI Stands At Risk Of Failure In Implementation

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 21 Sep 2026 09:34:02 +0000

It is time to reverse the approach. Let us start asking our prospects a fundamentally uncomfortable question: Are your data, processes, and people actually ready for AI solutions? In a recent poll, I asked this exact question across my network, offering four distinct perspectives: that you cannot deploy AI when you aren't ready; that it is the AI vendor's job to teach readiness; that data and processes will remain a mess so we cannot wait; and that asking this question is simply too risky because it may turn away customers.

Having vacillated between all four options at various times—depending on whether I was consulting for a vendor or a client—I know firsthand that there are no easy answers. That is precisely why I asked the question. What remains undeniable, however, is that AI requires robust, uncompromising preparedness. A single difference of just one letter in two data sets within a critical field, when replicated across thousands of records, can and does stall an entire deployment.

The Great AI Readiness Debate: Speed vs. Foundations

When discussing whether enterprises should wait for complete readiness, arguments often split into opposing camps. On one side, observers point out that not every AI company needs exponential revenue to win. A modern AI SaaS company might achieve significant revenue growth alongside falling inference costs, while an infrastructure provider requires massive capital funding just to scale. From this perspective, waiting until everything is perfect means you will probably never start; the operational process simply has to evolve alongside the technology.

Yet, even if companies with lower cost structures possess high revenue scaling potential as a function of their total addressable market, rising compliance and security costs inevitably put pressure on everyone. On the other side of the spectrum lies the aggressive operational stance—the hypothetical CEO of a multi-billion-dollar bank in super-expansion mode who views AI as the only available lever to trump larger peers. The temptation in that scenario is to ignore messy data and sprint forward.

However, moving fast on shaky ground introduces catastrophic risk. While no organization should halt its journey waiting for an illusion of "perfect readiness," getting the basics right must remain a top strategic priority. AI can certainly help a company move faster than its incumbents, but if the foundation is not ready, the organization simply ends up scaling the wrong things five to ten times faster. That is where the real vulnerability lies: picking up high-value, low-risk initiatives while simultaneously cleaning up data is sensible, but deploying advanced models on broken foundations guarantees a scenario of "garbage-in, garbage-out."

The Unspoken Reality: Data Silos and Structural Messes

The core problem facing most organizations isn't technical capability; it is human nature. Across almost every enterprise, there exists a persistent phenomenon known as Data Silos. Multiple copies of the exact same data exist in different pockets of the business, each gradually evolving into conflicting versions of the truth. Driven by internal competition and compounded by lopsided performance management systems that are thirty years out of date, this creates a structural mess.

Ditto for operational processes, which are routinely out of date. Another damaging aspect of data silos is inter-functional competition. Sales data looks entirely different from marketing data, which in turn looks entirely different from accounting records—for the exact same customer at an individual level, let alone across the broader organization. In this backdrop, no AI is going to make a particle worth of difference. Even within a single function, achieving a single version of the truth is a tall task. Teams continuously fight this battle internally, even with the full backing of leadership, only to hit the same walls. This, by the way, is exactly why Enterprise SaaS failed to deliver on its ultimate promises—and it is the vital context in which the AI readiness question must be evaluated.

People are now used to asking - “Do you have your data in a central system?” But the more important questions are:

· Who actually works on the data?

· Where do they work on it?

· What transformations do they make?

· Which copy do they trust?

· What gets written back?

· Who owns the definition of each field?

· Do Sales, Marketing, Finance and Operations mean the same thing when they use the word “customer”, “lead”, “revenue”, “conversion”, “active customer”, etc.?

The Shadow Data Economy: Why Centralized Systems Lie

In case anyone still believes that centralized online systems capture a single version of the truth—at least in functions like Sales and Marketing—they do not. They never did. Front-line teams routinely keep local copies of their own data, work on it independently in offline spreadsheets, and upload it to the central platform only when absolutely necessary. That is a hard operational reality, and it is replicated across virtually every organizational function.

Centralized platforms like CRMs and ERPs are frequently treated as compliance infrastructure for management rather than operational infrastructure for the people doing the work. Because performance metrics, commissions, and internal politics are tied to central reporting, employees hold back raw data, staging and cleaning it locally before making it visible. Consequently, what resides in the central system is often a sanitized fiction—a delayed, curated snapshot designed to satisfy administrative requirements rather than reflect operational reality.

The AI Dilemma: Automating Operational Dysfunction

When Enterprise SaaS promised a "single pane of glass" two decades ago, organizations adapted to bad data by inserting human friction—manual reconciliations, offline spreadsheets, and human intervention to catch errors before they broke operational workflows. Enterprise AI removes that safety net.

Because modern AI tools are probabilistic rather than deterministic, they do not pause when presented with corrupted or conflicting data fields; they generate high-confidence inferences on top of flawed inputs. If an enterprise deploys autonomous AI agents across a central database fed by sanitized fictions and hidden spreadsheets, the system will not solve the underlying silo problem. Instead, it will automate dysfunction and scale errors at unprecedented speeds.

Before asking how fast an organization can deploy AI, leaders must ask the fundamental question first. Without addressing out-of-date performance systems, inter-departmental silos, and the offline shadow data economy, asking whether your data, processes, and people are ready isn't just a poll question—it is the ultimate metric of enterprise survival.

Conclusion

A centralised system does not automatically create a single version of truth. While there are organisations that have genuinely disciplined master-data management, controlled workflows, strong integration and very good governance, generally But putting everyone on Salesforce / Dynamics / SAP / etc. does not, by itself, create that outcome. The existence of a centralised system should never be confused with the existence of a single source of truth. As a result, the organisation has one central database but may have multiple operational truths. And attending to that, sorting that out – is a core task for Organisational Development, Human Resources, And Leadership Teams.

This is the domain of core management, not Information Technology or functional departments. Therein lies the rub – currently, client- vendor conversations involve functions and/or IT; and for AI – you need the senior team, which is not happening; when it does happen – to does so only at decision stage, when the only question is a yes.no – while the need for senior was to get involved in the nitty-gritty! The people who need to be involved in AI readiness are often the people who don't participate until the organisation has already reached the investment decision. That needs to change.

AI is a deep organisational challenge when you start to implement it - people, processes, technology, data all need to come together seamlessly, requiring the annihilation of long-held habits within the organisation. Without that, you may adopt - but chances are you may not get results… but that is another question to taken up in a subsequent thought experiment to feature on my blog in the coming days!

The Myth of Innovation-Led Growth: A Marketer’s View on Gen AI and Market Realities

noreply@blogger.com (Reflections Of Vishal V Kale) — Fri, 18 Sep 2026 07:15:59 +0000

There is a lot of talk around the rise, nay - the rapid lightening rise of AI LLMs, Gen AI and more. The rhetoric around its being innovation-driven is to be heard a lot nowadays; there is no denying their innovative and inventive background reality. To deny that would be abject foolishness - but that does not mean it is the innovation that won the market. Innovation never wins markets - and that, in a world where absolutes cannot stated, is an absolute.

The Prerequisites for Winning Markets

A market victory requires purchasing customers, the right ecosystem, available finance and regulations, and a demonstrable value proposition - not one of these is negotiable. Therefore, let me take a quick look at the growth of the LLM phenomenon from the eye of a marketer

First of all - there is nothing like a product-led innovation - innovation is always market-led. Unless there was some history of innovations winning in the market, there would be fewer risk takers in the market; every brilliant innovation requires a customer. A product can create a new way of satisfying a need, but it does not create the fundamental human/economic motivation from nothing. Innovative products can, at best, create new markets by recombining needs in ways customers did not previously conceptualise.

The Fallacy of Universal GTM Playbooks

Second, GTM does not have an "established" playbook - if you are using a playbook, you are already outdated, or sub-optimal. GTMs have to be designed for each company - they may turn out similar, but htere is no one-size-fits-many. There are broad strokes that look like playbooks, but these are best treated as guides, giving you some direction as you set the GTM engine for your company. So, the modern GTM - no such thing. GTM has to be individually designed for each company, not even each industry - so if you are using old playbooks, the results will be sub-optimal. Keep updating your frameworks - but remember, these are frameworks, guides at best.

Latent vs. Kinetic Demand

Demand is of two types - (top level basic look) - potential / latent demand, and kinetic demand. All - ALL - successful "product-led" does is to top into the latent demand. This latent demand - the market may not yet have found the optimal product-value-price proposition to convert that latent need into widespread purchasing. Demand, thus, cannot be created by GTM, Engineering or Marketing - this is an established reality : GTM and marketing cannot manufacture fundamental human or economic need out of nothing. They can discover, expose, shape, stimulate and activate latent demand. And that is why you must understand the demand aspects of your business in-depth, which sadly very few people do nowadays.

Unless there is a demand, potential, lying in the market - your product is of no use, for no customers will be created, regardless of how new the technology is, and how few people use this. It all boils down to the "need" - your product has to fulfill a need - it can be at any level, but there has to be a need.

And this need - this is the market. That is the market factor in all innovative products that succeeded, There is only one segment that doesnt obey these rules - Ultra Luxury Goods. This is known.

Case Studies: OpenAI and Anthropic

OpenAI - they were hardcore market-led innovations - the product's innovative potential had a demand factor that was present. That is why the adoption sky-rocketed. ChatGPT did not invent the demand for knowledge, advice, writing, research or problem-solving. It dramatically reduced the friction and cost of satisfying those existing demands. What it did was changed the underlying factors making solutions accessible, cheaper and faster to a majority of people, changing the nature of the demand from latent to kinetic.

Claude, Anthropic did not create a new category - that is fallacy. At best, Anthropic may have created a new solution category. The need for faster solutions, data mining, deep data predates AI, and customers were already hunting for solutions to faster processing. What they did was create a product that solved several layers of needs at the same time - access to data, research, processiing power, automation etc. Individually, all were in vogue - so, no, there was no product-led innovation there.

The Cybersecurity PV Equation

There is demand, certain - it is at a latent or potential level - cybersecurity has an underlying need, not fulfilled completely by anyone. The issue is the underlying PV equation sweet spot lies undiscovered, and the industry is currently going after early adopters, rather than investing into expanding the market - for the perfectly simple reason that they will create kinetic demand, others will tap in. It is about the need - the basic core of the human factors in the market, the rising cases of fraud and hacking etc. The demand problem isn't absence of need. It is failure to discover the optimal value proposition capable of converting latent need into mass-market willingness to pay. A large latent market does not automatically become a large kinetic market. Innovation's commercial task is to discover the PV equation that converts one into the other.

The Role of Early Adopters

There is definitely demand for innovation - this is established marketing for at least 50+ years. We study it in the first class in MBA Marketing Subject - it is called Early Adopters. They are people who actively invest in buying the most innovative products and services - it is also a known psychological trait. Early adopters aren't necessarily evidence of mass-market demand, but they are evidence of willingness to adopt innovation ahead of the mainstream. These early adopters prove that a segment exists that values innovation.

Conclusion : Commercial Adoption

There is no commercially successful product innovation in isolation from a market need. Technology can create new products, new categories and even new behaviours. But commercial adoption ultimately requires those capabilities to intersect with a human or economic need. The critical distinction is not whether demand exists, but whether it is latent or activated. Early adopters demonstrate that a need can be activated in a small population.

The real innovation challenge is discovering the value proposition, economics and GTM that convert latent demand into mainstream demand. There are established GTM principles, but there is no universal GTM playbook. The architecture has to be designed for the specific product, customer, category and economics. The role of innovation is therefore not to manufacture demand from nothing, but to discover a superior way of converting existing or emerging needs into value.

Beyond Sci-Fi and SuperIntelligence Hysteria: Confronting The Real Risks of AI

noreply@blogger.com (Reflections Of Vishal V Kale) — Tue, 15 Sep 2026 11:06:17 +0000

AI Will Be Decided in the Real World, Not in the Lab

There is something increasingly odd about the way the AI industry talks about Artificial General Intelligence, Superintelligence, the Singularity and the risks associated with them. The language is becoming increasingly grandiose: AI will transform the world, AI may surpass human intelligence, AI may become uncontrollable, AI may eventually pose an existential threat to humanity.

Yet when I look at these conversations from the perspective of someone who works with businesses and is actually trying to use AI to improve productivity, I find a fundamental disconnect. The AI industry, particularly the technologists within it, seems to have very little understanding of how the real world works — not just of the risks of AI, but even of its benefits. And that disconnect matters because businesses are ultimately the ones that have to adopt, pay for, manage and live with this technology.

Redefining "Intelligence"

Let us start with the word "intelligence" itself. The AI industry has increasingly adopted an operational definition of AGI along the lines of a machine being capable of performing a sufficiently large range of intellectual tasks at or above human level. But task performance is not intelligence. You cannot simply redefine a word because it makes the technology easier to measure. Intelligence, in its actual English-language meaning, is the ability to learn, understand, reason and make judgements based on reason. Human intelligence is not simply a collection of tasks that can be benchmarked. It involves insight, intuition, emotion, experience, maturity, judgement, understanding and consciousness.

We have spent decades studying neurons, synapses, neurotransmitters, the sodium-potassium pathway, dopamine, brain waves and an extraordinary amount of neural activity. Yet we still do not have anything remotely resembling a complete understanding of consciousness, or of how all these elements combine to produce human intelligence. So when the AI industry takes increasingly sophisticated task performance and labels it "general intelligence", there is a very large conceptual leap taking place. Perhaps AI will eventually produce something genuinely comparable to human intelligence. But we should at least be honest about how much we still do not understand about the thing we are supposedly trying to recreate.

There Is No Going Back

None of this is an argument against AI. Quite the opposite. AI is extraordinarily useful, and there is no going back. The Pandora's box has been opened. Ideally, perhaps, we would never have developed some technologies at all. But once a technology exists globally, refusing to develop the capability to understand and use it does not protect us. Someone else will use it, and we will still be affected.

This is particularly true for countries such as India. If the United States, China and other countries continue developing AI while India decides to sit out, India does not become safer; it becomes dependent. We therefore have to develop the skills, infrastructure, capabilities and governance required to use AI properly. But that does not mean blindly following the AI industry into every technological race it proposes.

The Geopolitics Nobody Talks About

This is where geopolitics enters the picture. One cannot look at American AI development in isolation from American strategic interests. The standard US playbook has historically been to develop technology, establish leadership, restrict or control access where strategically useful, and use that technological advantage to further its own interests. The United States is not exactly a role model when it comes to separating technology from national interest.

Therefore, when American AI leaders talk about slowing down AI research or superintelligence, governments and other countries will naturally ask what lies behind those statements. There may be genuine concerns about safety. There may be guardrails being developed. There may be efforts to train people in proper supervision. But there are also geopolitical and economic considerations.

Nothing on Earth operates in isolation. That is why we should be extremely cautious when reading such grandiose statement, and make a genuine effort to ground ourselves in business realities.

The Risk That Actually Matters: Uncommanded Execution

I do believe AI has real risks, and some of them are already visible. The risk that concerns me most is not a Terminator scenario. It is uncommanded performance and execution. I have personally seen enough to pause the engine and go back to manual operation for now. If an Agent does something that its human operator did not intend, that has a direct business impact. The industry has to provide answers to this. No excuses. If an Agent can communicate with customers, execute transactions, make decisions or trigger workflows without direct human approval, then the question of what happens when it gets something wrong is not theoretical.

This isn't a new concern for me. I wrote about it at length in AI Going Rogue — How Big Of A Risk Is It, Really?, where I tried to actually quantify enterprise AI agent risk rather than talk about it in the abstract — drawing on an earlier incident involving an AI dialer's uncommanded execution, the OpenAI agent incident from July 2026, and a smaller version of this same failure mode that I ran into myself. The three scenarios below are what that risk looks like once it moves out of a single automated call and into GTM, lead generation and healthcare workflows more broadly.

The customer service scenario. Consider a simple case. I hand over GTM and Customer Service to an Agent. It handles a call from an anchor customer who contributes 20% of my business and regularly gives me new business. The Agent says something or executes something that causes the customer to disconnect and take the business elsewhere. Who is responsible? There is no easy answer. Even with real-time human supervision, perhaps a quick apology, discount or remuneration might sort the problem out. Then again, it might not. The customer may simply decide to take the business elsewhere. And in a real-world system running at scale, real-time supervision of every transaction is well-nigh impossible. The cost would be exorbitant and execution would slow to a crawl.

The lead-generation problem. The same issue appears in lead generation. Suppose I receive 25 leads a day. An AI Agent talks to all 25 and gives me a summary, including which ones it believes are likely to convert. What happens if the summary is not good enough to identify the customer who actually would have converted? The leads that the AI rejected may be the ones I would have converted. Is that a problem with the technology? With the training? With the workflow? With the way we have defined a "good" lead? These are the real-world AI problems that businesses are going to encounter.

The hospital workflow. Consider a hospital where an AI-powered system automates reception, registration and patient classification. Two people with the same name arrive. The system treats them as the same entity and one person's information gets classified under the other. There may be a million John Smiths in the world. What happens then? That is an AI risk with immediate real-world consequences. It doesn't require superintelligence, consciousness or a machine takeover. It requires one error in one workflow.

Why the Industry and Business Speak Past Each Other

This is why I believe the AI industry has a serious context problem. Technologists are often micro-focused on one domain, and understandably so — their job is to build the technology. Business people, by the nature of their jobs, are forced to look at a much wider ecosystem: geopolitics, economics, business, sales, marketing, HR, customers, competition, people relationships, regulation and finance. When someone tells us that AI will "transform the world", we may understand the words, but we don't necessarily understand or empathise with what is being said because there is no context and no anchor with which our brains can engage. What does transformation actually mean? What changes in my business? What changes on Monday morning?

The same applies to doomsday scenarios. We understand what the person is trying to say. We understand the risk they are highlighting. But if we cannot conceptualise the scenario, we cannot engage with it. If someone tells me that an AI incident happened at Hugging Face or OpenAI, I can understand why it matters to the people involved. But I may still be sitting 15,000 kilometres away asking: how does this affect my business? What happens if I lose access to one AI tool? I was running my business before these tools existed, and there are already five other AI tools available. Why should I panic?

This is where the trust deficit begins. The industry talks about transformation while most businesses experience productivity improvement. My life may become easier because of AI, but I still have to go to the office, follow up with critical customers, manage people and perform my daily tasks. The nature of my tasks may change, but that is not necessarily transformation in the grand sense in which the industry uses the word.

Bringing AI Back to the Real World

We should absolutely continue AI research and development. India must develop AI capabilities because if we don't, someone else will, and we will still be affected. But responsible growth is essential. We need proper, legal, moral and good usage of AI under established processes and good law. And, critically, we need to keep business interests front and centre. Only then will investment happen.

If we expect massive investments in foundational technology without having the economy, market and ecosystem to support those investments, we are fooling ourselves. The question should not simply be, "How do we build AI because everyone else is building AI?" The question should be, "Where are the profits and cash flows, how can AI improve those businesses, and how can we improve the work and lives of the people involved?"

Naa baap badaa naa bhaiyyaa, bhaiyyaa sabse badaa rupaiyyaa. Business interests ultimately drive sustainable investment. We need to look at where AI can create genuine economic value, build capability around that value, develop the right safeguards, and gradually increase the level of autonomy as the technology proves itself.

The Bottom Line

AI is here. There is no point pretending otherwise, and there is no point being paralysed by hypothetical futures. We need to use it. But we also need to understand its limitations, its failure modes and its real-world consequences. The time to train software vendors, development teams, software architects, programmers, and front-end, sales and marketing teams on AI risk in depth is now — not when customers begin asking these questions bluntly.

The future of AI will not ultimately be decided by the grandest predictions made inside technology companies. It will be decided by whether ordinary businesses can trust the technology enough to put it into the real world.

Confidence vs. Arrogance: The Tone Problem in Modern Advertising

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 09 Sep 2026 06:28:27 +0000

Confidence vs. Arrogance: The Tone Problem in Modern Advertising - How many people in marketing actually stop and ask: what is the tone of my advertising? This seemingly small question is in reality lying at the core of advertising. Your advertising doesn't just communicate what your brand sells; it communicates what kind of brand you are. Furthermore, the consumer doesn't separate your brand from the behaviour of the people representing it in your advertising. And that is where the tone comes in – what you say, how you state it and in what context – all three are vital elements to brand communication

Take a step back from the job for a second. Put yourself in the audience's seat. You come across an ad whose central characters — through their expressions, their hand gestures, the whole setting — carry a feeling of being superior to anyone not using the product. Or the ad looks down on the competition, openly or through implication. Now ask: how does that reflect on the brand? (That is what is meant by tone, in a very broad sense). What does it do to how the customer feels about themselves in that moment? What association is actually getting built? That – the associations that are inadvertently built - is the value and role of tone.

Take many of the advertisements you'd call genuinely good, and you'll find some form of positive emotional payoff running underneath them — even when the advertising itself uses tension, anger, conflict or provocation. Anger, arrogance, condescension — these have no place in advertising, unless it's a piece of social-change communication, and even there it has to be handled with real care. You are trying to build space for your product in the mind of the consumer. Do that through a feeling of superiority, and the number of people who'll actually be attracted is far smaller than the number who respond to positivity. That's not a marketing rule. That's a basic human one, and it doesn't change just because a category is crowded.

Advertising creates brand associations, whether the marketer intends it or not. Therefore, the emotional and behavioural tone of an advertisement must be evaluated not merely for attention or engagement, but for what it makes the audience feel about the brand. There is nothing inherently wrong with negative emotion in advertising. The question is: whose negativity is the audience experiencing, and what does it make them associate with your brand? Anger, arrogance and condescension are particularly dangerous territory in advertising — because the audience can easily associate the emotion with the brand itself.

Who's the joke on?

Here's the fair objection: if a thousand ads in your category are all speaking positively, how does yours stand out? It's a real problem. But the answer isn't to break the basic rule of engagement with customers — it's to notice that "positive" and "safe" aren't the same word, and that plenty of bold, memorable advertising is still bold without making the audience feel small.

The difference usually comes down to who the ad's edge is aimed at. Zomato and Swiggy have spent years publicly ribbing each other in their ad copy and social posts, and nobody ordering off either app feels small for it. Amul's decades of topical cartoon ads needle politicians, events, and rival brands through pun and wordplay, but the tone stays affectionate — you're laughing along with Amul, not being laughed at. The audience is in on the joke, not the butt of it — that's confidence. Arrogance is different. It's when the ad, in tone or in visual grammar, tells the person watching that they're lesser for not already belonging to the club. One invites you in. The other looks past you.

A brand I don't buy — and why

Take Axe. I'm not calling their advertising arrogant — I'm using it to explain how brand associations actually form. Axe's communication has always leaned on a stand-out, aggressive, macho male persona. That's not me. I'm a fairly sensitive person, and I lean toward a positive, confident, but subdued vibe. So what did years of that tone build in my head? An association I have no interest in attaching myself to. I don't use perfume very often, and out of the small, select few I do wear, Axe is the one I've never even considered — not once. That's tone doing its job, just in reverse.

Tone is one input. Everything a brand does builds the association.

It isn't only tone that does this — product behavior does it too. Milky Mist and Amul both launch new ice cream flavors regularly. The moment a new Milky Mist flavor shows up in my feed, I hit the purchase button. Why? Because the brand's whole personality, in my head, is "something new is always happening here" — I'm someone who likes experimenting with new food, coffee, cheese, milk alternatives, all of it, and Milky Mist has become the brand I associate with that itch. With Amul, a new flavor gets a "yes... no... yes... no," and I usually go back to what I already trust.

Amul earned a different association — steadiness, dependability, durability — largely through consistency, not through its (genuinely witty) advertising tone. Same principle, different lever: whatever a brand does consistently — in voice or in behavior — is the association it's building, whether it means to or not. And every way you communicate – even without giving a single ad – builds your brand’s tone; yes – even your packaging and market tactics contribute to the brand tone. And that is where Milky Mist’s overall proposition – with its quirky flavours, attractive packaging, superb placement all built a tone of “I am different – try me; not I am ultra cool and you are not, so come and join me”. That is tone in operation

Where this leaves modern advertising

Too much of what I see now is aggressive, condescending, or dismissive in tone — of a culture, a person, a viewpoint, a rival brand — used as a shortcut to standing out. It's a shortcut with a cost. The data backs the instinct here too. Les Binet and Peter Field's long-running IPA effectiveness work has repeatedly shown that emotionally engaging campaigns tend to produce stronger long-term business effects than campaigns relying primarily on rational persuasion.

The mistake isn't reaching for emotion to stand out. It's reaching for one emotion in a set of emotions— a feeling of superiority over your own audience, or irrational superiority, or ultra exclusivity applied in a snobbish manner, or insulting a particular personality etc etc — that almost nobody wants to carry home with them. It all boils down to human values - Individual consumers interpret brand behaviour through their own values, identities and accumulated associations. And, in general, such associated emotions do not make for building a lasting brand on their own. Such tactics in fact make building and maintaining the brand a much more problematic, consuming and demanding task…

The fundamental question isn't “Will this advertisement get noticed?” It is “What will people feel about my brand after they have noticed it?” Because attention is temporary. Associations are not. Thus, every brand behaviour creates an association. Advertising is therefore not merely a vehicle for delivering a message; it is one of the ways the brand teaches consumers what the brand is.

The Gap in AI Adoption: Three Actual Implementation Use Cases

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 02 Sep 2026 09:11:07 +0000

In the midst of today's atmosphere when everyone is gung-ho about AI, few if any are speaking of the specifics of AI adoption and implementation inside companies. AI is not a magic wand—it needs to be fit into the organisation, its processes, and made a part of the life of the people who populate the organisation. It won't get its due just by hyperbole and constant focus on the promises of AI. Someone, and a lot of someones, has to get down to the hardcore practicalities of using it. Individual AI usage ≠ organisational AI implementation. A company can buy an excellent AI tool and still fail because it doesn't know how to integrate it into actual workflows.

People don't resist AI merely because they fear AI. They resist disruption to functioning processes. And therefore: the first implementation objective should not be transformation. It should be integration. That creates a two-stage implementation model:

Stage 1: Fit AI into the existing process.
Stage 2: Redesign the process once adoption is established.

That is the problem I see in companies nowadays, both customers and vendors. People are simply not willing to put their nose to the nitty-gritty of applying AI to internal realities. Even as a customer when I have spoken to vendors, I have found it close to impossible to get them to apply the AI to my use case, with the answers being high-point and highlight-based rather than specific and actionable. That is, in my opinion, both a mistake as well as something that is to be expected given that this field is so new.

The Need Of The Hour

Now the need of the hour is for vendors to get practical, and start actually speaking in terms of specific implementation points inside workflows and processes—and quit their grandiose statements on their websites, brochures, customer communications, and in their discovery calls. That means getting a deep understanding of how, what, where, when, and why their tools are being or can be implemented inside companies' existing systems.

Shifting the Lens: Practical Application Across Marketing

I have consistently taken the position that AI is useful. It is just how we are using AI that is a problem, alongside certain attendant risks. Shifting the lens diametrically opposite, we must look at how AI is actually being used and what advantages we are getting. Taking marketing as a function, there are three distinct use cases where I have been using AI:

1. content for writing, copy and scripting

2. strategic research, and

3. Go-To-Market (GTM) planning & execution.

To take value out of AI, one must follow specific methods to ensure performance, such that the result fits the output that is desired – and should be suited to the task type at hand. In content, this spans translation and writing technical material. In strategic research, it involves deep context setting. In GTM, it includes tools like AI dialers, WhatsApp, and lead handling.

My Credentials

People often just copy, paste, print, or publish. That is exactly how you should not be using AI; that is where the risks emanate from. My credentials in content come from translating entire books from one language to another, creating almost 3,000 slides based on my work, and preparing social media posts for seven to eight full commentaries of the Bhagavad Gita over the past eight to nine years, using tools ranging from Google Translate and online dictionaries like Shabdkosh to Gemini, Claude, Grok, and ChatGPT. In Strategy, I have been involved in preparing strategies for organisations for over 7 – 8 years now, and in GTM, my past includes a regional hub head position as head of sales.

The Basics Of How

There are two aspects of the how – actually using the tools – which is a topic unto itself, and how the tools themselves can be successfully implemented and adopted inside organisations. The first is the easier part of the story – the second, unfortunately, is a story unto itself requiring deep attention. Before I go deeper into the actually using part, let me just quickly introduce the tougher part- getting people to use it is not simple a matter of objection handling, or fear, or lack of clarity, etc.

It is a question of process – this is where all vendors get it wrong; the problem is existing processes are already operational inside companies – you cannot get people to scrap them. That is a recipe for disaster. The only way is to ensure your AI tool or solution fits existing processes with as little disruption as possible, and then slowly, over time, as usage and comfort evolves – the customers will themselves evolve their processes to capture true power of the tools once they are using it.

The only caveat – there will be failures in those organisations that demand instant or quick ROI. That is why judgement of the customer company is so crucial, so you can build in quick wins in the resulting implementations. Quick wins are still necessary—not because they prove the ultimate value of AI, but because they create organisational confidence and buy-in.

The Specifics Of The Use Cases, And The Process Of Use

Content and Translation: Brain-Level Verification Over Copy-Paste

I first give my premise to the AI in detail and then get into a long conversation to arrive at the desired output. For translation, I do not just translate. I compare line by line and word by word, going into the semantic and syntactical meanings of words, verifying against physical copies of the Bhagavad Gita to make sure the content is understandable and accurate. There is a lot of work required at the brain level and physical activity level. Furthermore, for research-driven content, I lay out authentic sources, limit the AI to specific inputs like newspaper articles, and ask it to extract information with verifiable links.

The challenge inside companies in this process is the realisation that it is not cut and paste, and that domain knowledge of the task at hand is mandatory. This has to happen at the initial introduction stage, when the initial user team is finalised. Vendors have little say in this – but a simple advisory around this, asking them to have domain experts who are also avidly interested in new tools and technology which would be a definite plus towards ROI realisation.

Strategic Research: Grounding AI with Real-World Intelligence

Strategic research is a deeper topic that requires significant detailing. The initial steps are the same as in a non-AI environment: the problem definition, customer brief, background understanding, core research using website, site / store / field visits, customer calls etc does not change- this is vital. Nothing in the AI world can be a substitute for this. Absolutely nothing. Further, AI is excellent at expanding and interrogating a knowledge base; it is much less reliable at determining whether the knowledge base itself reflects reality.

I establish the context of the strategy by providing the industrial background, giving full customer or task brief details, giving the AI specific directions, and detailing the exact points required out of it. After the first strategy is generated, I go deeper by conducting my own research using Google search or physical visits in the market to provide deeper context. When AI comes out with an inaccurate competition list, I manually override it, provide the actual real-world competition, and instruct the AI to research based on those true parameters. A lot of work goes into implementing proper strategic research functionality within marketing. For more on this – follow me, when I write a dedicated article on how to strategise using a blend of AI and traditional approaches.

Go-To-Market (GTM): Implementing Supervised AI

The toughest use case- this one actually involves teams. And here, buy-in is essential. Further, as this is the first use case we are discussing that is dealing with multi-step, multifunction, and maybe even multi-organisational (in B2B companies) touch-points – it also involves complex processes. You need buy-in from all the touchpoints involves – going topdown is useless here. You need to understand their flow of the lead from initial contact all the way to closure and to customer service functions, what data is recorded, what conversations are conducted in what language and by whom. Again – please follow me, for getting the article that goes deep into this process.

In GTM, first of all you need to choose where you are going to start from – this is the revenue engine of the company, and needs to be carefully planned, not just jumped into. AI Vendors should be particular on this point – it is for you to educate the customer on this invisible point. In that sense, there are multiple starting points within a GTM for AI to start - it can be applied as a voice dialer, in inward lead handling, outbound calling, or lead handling through WhatsApp to name but a few. I have tried all. And where to start is fundamentally dependent on what the customer wants to achieve, yes – but also what are the challenges facing that particular point or function? The actual implementation will fundamentally depend on this answer – not one vendor I have spoken goes this deep.

Going ahead, implementing this effectively requires repeated training and internal testing before launching. Once conversations start happening in the initial phase, you have to sit and monitor each conversation. This is Supervised AI Deployment. If it is a WhatsApp conversation, you need to read every single line of every conversation, correct the AI where it strays, set new rules, and establish guard rails. It has to be properly implemented, and the challenge today is that people simply do not know how to execute that. This Supervised AI Deployment is the period in which humans actively monitor live AI interactions, identify failures, correct the system and progressively establish rules and guardrails.

Conclusion

In conclusion, AI is less about the tool itself and far more about the processes it has to blend into, the people who will use it, and the organisation in which it has to operate. AI does not exist in isolation; it has to become part of the organisation’s living processes if it is to deliver sustained value. And that is where AI vendors usually go wrong. After implementing AI in a few different environments, my biggest learning has been simple: the AI or technology tool has to blend seamlessly into the way people actually work. If it does not, it will not get used. I have seen this fail abjectly in practice.

So, for now, my message to AI vendors is straightforward: spend considerably more time understanding the customer. Understand how they work, how they intend to use the AI, the contextual setting in which it will be used, the processes it has to fit into, the alternatives available to them, and the people who will ultimately have to use it. Unless you understand the customer in depth, there will inevitably be problems in implementation, adoption and, ultimately, scale. The AI adoption gap is therefore not necessarily a technology gap. It is an implementation gap. And that is the black hole that needs to be addressed.

How AI Is Quietly Devaluing Advertising

noreply@blogger.com (Reflections Of Vishal V Kale) — Tue, 01 Sep 2026 11:57:07 +0000

Two Words That Stopped Me

I recently came across two Marathi advertisements that used the words "हैराण" and "झिजणे". Both stopped me — not because they were grammatically wrong, but because neither sounded like the Marathi an ordinary Marathi consumer would naturally speak.

That is where my mind turned to autotranslation and technology. It is the age of technology even in advertising, and it has known benefits — there is no argument to that. But the same technology is also creating shocks in the system, as well as giving rise to problems. The bigger issue is that the industry seems to be blissfully unaware of the downrange as well as the immediate challenges being raised by the entry of technology into this field — a domain that was, hitherto, purely creative-based, human-based.

The Real Danger

The advent of AI and technology in advertising has carried with it an interesting side-effect: the vernacular versions losing their quality. Obviously it is expensive to make ads in each language, so they are dubbed. But the translations are either strange, inaccurate, or inordinately tough to understand — making it hard even for native speakers of the language.

Here is the nuance worth holding on to: the danger isn't AI making advertising worse. The danger is advertising organisations becoming less capable of recognising when AI-generated work is worse. By making production so easy, AI can make organisations less inclined to notice their own mistakes. And advertising ultimately exists to communicate with people. If the tools used to create that communication gradually weaken an organisation's ability to understand how people actually speak, think, and respond, the technology has begun to defeat the purpose for which it was adopted.

This shows up as three connected problems the industry is currently battling:

The Uncanny Valley of Text: copy that is grammatically flawless but culturally hollow and devoid of human warmth.
The Mediocrity Trap: a growing willingness to accept "good enough" outputs simply because they were generated instantly and practically for free.
Process Bypass: the dangerous habit of skipping the essential human vetting phase that traditionally caught tone-deaf or clumsy messaging.

हैराण and झिजणे: What They Actually Show

As I said, I recently saw two Marathi ads using the words हैराण and झिजणे.

हैराण is not originally Marathi but a more recent, Hindi-rooted addition to the spoken tongue, and it is almost never used in daily language. It may be understood by Marathi speakers, but it is not the natural choice in much everyday Marathi, and its use can immediately signal translated or non-native copy.

झिजणे is the more interesting case — the word is Marathi, but again, not in popular spoken usage. It is a real word carrying real meaning, just not one an ordinary consumer reaches for.

Where the Errors Come From

There are a few explanations for how words like these end up in a script. The chances are that these are translations or copywriting done by non-native speakers. Or, some tool has been used to translate — and even if the scriptwriter was native Marathi, not enough care has been given to the words. That is the third possibility: the acceptance of mediocrity I have pointed to in an earlier piece.

A fourth possibility: plain, blind technology dependence, and a lack of application of the basic processes that go into making an advertisement.

Whatever the case, this is strong proof of the rising chances of new errors in the modern, technology-enabled workspace. AI is good, and technology is no doubt helpful — but one has to stop and ask, as an advertiser and as a marketer: what is going on? Why can't we simply apply ourselves, understand the customer, and then create the advertisement?

It Is Human to Err

Where will this newfound penchant for using technology in the process lead us to? It is human to err — let us not now believe we are error-proof. We made errors in the manual era, and we will make them now as well. The errors will now, perforce, be slightly different, and will include new ones besides.

What This Means for the Creative Realm

What this means for the creative realm is twofold: a rising risk of the average and the bad advertisement creeping into our ad campaigns, and, alongside it, a disconnect with the customer base that is only visible with a lag of time. This is bound to create challenges down the road for agencies and marketing teams alike, as stakeholders start questioning the work.

It may seem a minor point. But if minor points are creeping in — where is the guarantee that other, larger problems aren't creeping in already?

The Fall in Grace of Copywriters

This is also a sign of a fifth, more conceptual shift now taking place inside advertising and marketing: the fall in grace of scriptwriters and copywriters, as LLMs have made creating copy easier, translation a one-click affair, and writing storyboards as simple as dipping a biscuit in your coffee.

This is a sign of the fall in grace of script and copy writers — the ones who would not likely make such mistakes. They know that the words used in a copy or script should be in language the audience connects with. We are not trying to knock the socks off the audience, so to speak; we are not trying to impress them with our command of the local language. This is something that has clearly not occurred to the ad makers and their teams.

Putting Words Together Is Not Writing

The belief that writing is merely "putting words together" has led to the current devaluation of scriptwriters and copywriters. Great writers do not just translate language — they translate human insight. By using AI to churn out storyboards and copy as easily as dipping a biscuit in coffee, the industry is trading emotional intelligence for mere output volume. The craft of persuasion is being replaced by the mechanics of production.

Communication Is Fundamental

I have no doubt these streams will bounce back once other, bigger errors happen, bringing native craft back into fashion again. But in the meantime, the customer communication that goes outward from the brand is bound to suffer. And that is fundamental to brand management: communication. If you cannot communicate and build an engaged conversation with your customers, you are going to face problems.

The sooner marketing realises this, the better.

AI's Greatest Risk Isn't Doomsday: It's Mediocrity

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 31 Aug 2026 09:21:54 +0000

The rising usage of AI in Creativity is a perfect stand-in for what is in my opinion the biggest risk facing Industry and Business today: the acceptance of substandard solutions, and the concomitant rise in mediocrity along with the loss of domain expertise. This is readily apparent in the content space on any social media nowadays with the advent of AI written posts filling our feed. Now, apparently you don’t need real skill to write a post – this is worrying. While AI is a great equaliser, unplanned- it can be a greater destroyer of skills.

This is not an article about creativity and usage of AI – rather, it uses this field due to the author’s profession of being in Advertising as well as in AI Software companies as a Fractional CMO to raise a fundamental issue relating to this industry – the rising acceptance of the mediocre. AI in the field of creativity needs a dedicated treatment – but here, I analyse mediocrity through the lens of AI usage in arts, given by twin reality of being an advertising professional and an amateur poet and writer.

Testing the Premise: AI in Creativity

The question isn’t whether AI will succeed in the field of creativity – but let us, by all means, start there and look at this question first, to analyse its prospects using admittedly anecdotal evidence of my own usage of AI in creativity. Well, I am a poet with my own poetry page, and head-strategy in an ad agency, and a writer on my 14-year-old blog. Wrt arts and AI, have been actively using AI in my writing, and seeing Ai in creative fields. Ai will most likely not crack that code, for art does not happen in the hands and body mechanisms - it happens somewhere deep in a "spandan" in the heart, and a vague discomfort or idea deep inside the brain. But let leave that aside – that is another question for another article.

1. Inside creative activities - You need domain knowledge to achieve excellence through AI. You need to know the art of colours, of how they will come in print, in digital; you need to be aware of painting styles, brush strokes, framing and a lot more. AI is used extensively, but those using it best are experts, actual painters, designers etc with stellar resumes. It really isn’t AI as much as a fantastic human artist bringing out his or her art. AI can’t replace it; it can only synthesize.

2. Inside Poetry : The AI style is clearly intelligible - it lacks the punch - and comes out as an imitation. I have tried to train some tools to write poetry in my signature, but useless. It is good as suggesting words, checking poetic meter, and such administrative activities post the actual creation of the poem. Flat-out, useless. Sorry, guys. (PS - Poety blog predates AI, just saying)

3. Writing : So far, the finest use case - but here, it lacks the four elements I shared earlier - like contextual awareness, situational reasoning, insight, judgement etc. Even in highly technical writing like strategy creation, which I have to do literally daily for my ad agency clients, or economics or trade or other topics where I use it. The insight is either mine, or from team brainstorming, or from in-depth research - not AI. And again, we use AI extensively. It is the human brain, the prompt and conversation that matters. Even in project execution - an MTech Engineer can create a full project using Claude premium - true story there (One team delivered a 100-Cr+ project using AI + team of topflight software architects in my awareness). Again - the human expertise counts. Again - sorry guys. Ground reality.

Why Creativity Resists AI

As you can see from the above, imho the likelihood is it won’t be dominating the creative fields. As it happens, in my personal experience, being creative isn’t going inward and finding inspiration deep inside you – this is the biggest myth perpetrated by non-creative folk, again imho. It is more of an outward expression of the internal mental state at that particular point of time. And that is why it is, in my opinion, impossible for AI to duplicate this - I know, I have tried multiple tools for poetry synthesis. If it doesn’t come from my innermost, it does not reach that level that I want it to. Like one of my poems - "Darakht" - came to me as I was riding home in a huge storm, and saw a tree holding out against the wind. This isn’t an aside – it is central to human performance, the concept of the internal reality

AI can reproduce the external characteristics of artistic expression. It can replicate the words a writer or a poet uses; it can form paragraphs and more. It can potentially become extraordinarily good at doing so. But reproduction of the characteristics of an emotional experience is not necessarily the same thing as having the experience that generated the art. And it certainly is not the same thing as visualising that creative output in your minds’ eye before the act of actual creation begins – the initial “spandan” And the bigger aspect of this – it is certainly not, as I said above, creative, and neither is it in any way a guarantee of output quality – keep this last point in mind. We shall return to it later.

Perhaps the most critical observation I have is in poetry - as an example, AI tries to march the meter of the poem in all lines - and there are situations when you decide as a creator that your contextual and situational need is stronger for the meaning rather than the meter of the poem - .... or for a line you sacrifice the flow for the same reason and you consciously decide to forget the meter or flow for that line. AI just cannot understand that, it just c.a.n.n.o.t. AI can be taught the rule. It can even be taught to break the rule. What remains questionable is whether it understands why this particular rule should be broken here.

Ai lacks context, insight, judgement, situation. And the brain contains hundreds of terabytes of data and process much faster than any technology can attain in all but speed - bandwidth, spacial capability and more, I could do on and on. AI in creativity won’t sustain quality - it will hit a price / cost barrier - long discussion - and a capability barrier and hence business houses will move to a Human + AI model. AI cannot sustain creativity minus the minus the human -so this is a doomsday scenario that won’t happen. This is not the biggest risk facing us – that lies elsewhere! Read on, please.

The Real Risk: A Market for Mediocrity

Now I am a serious doer in terms of my poetry - and let me tell you, the serious ones will continue to do the same as before - the creative person is not driven by external stimuli, but my internal compulsions - and there AI will have zero or close to zero impact, for these people are driven by emotions, not logic. I know - I am one of them. I may be good at it, I may be bad at it, but I am one of them. For example, my poetry is on Advait Vedant, & Human relationships, neither is going to be hit by AI. And this is where the biggest catch is first identifiable – these creative persons’ outputs will be materially different from the soulless creations made by AI. And therein lies the catch. But the issue is the market for such excellence will not or rather may not be the same, or is at risk of being mediocritised.

AI may commoditise content. It may even commoditise a very large amount of what we currently call creative output. But commoditising output is not the same as commoditising the human impulse to create. And the latter is what serious art is fundamentally about. And this is where we connect to the larger point – replace art and creativity with any human activity – and you get the same result. A clear difference between the passion and dedication of human effort against the AI output which will by definition lack a deep soul.

To Be Clear: Where AI Actually Helps

Understand - it is not my position that AI has no use in creative – or any - arena; only that the usage in most areas is completely at variance with what some technologists understand. We use AI extensively in our Ad Agency; I use AI extensively for strategic research for our agency clients - but I use that as a backup, with more traditional research (surveys, info gathering, reviews, testimonials, customer conversations, field visits, website deep dives into competition etc). In my technical writing, Ai is used - but the idea, content pitch, content flow, bulk of the information etc is mine or my teams'.

In each use case, at the centre is someone with deep domain expertise. Like that top-4 company CTO friend who stunned me by delivering a 100-Crore project with a slim staff and AI. That was my brutal reality check when I started out going deeper. The team had deep domain knowledge - I know as I am a C-Assembly-HTML programmer. I tried to my hand at it - failed miserably. That is my single point - domain expertise is now mandatory, and deep knowledge of on ground real-world processes and operations

Enter: AI Slop

There is, however, one major caveat - take poetry. The AI output will appear to be much better or as good as human output to someone who is not into that specific type of poetry ... and that is dangerous, for the output is substandard, yet is considered good enough - and that risks a lot of activity being tainted. We call it AI Slop - that is real, it is happening, and it is a serious threat to AI adoption itself – as more and more people start using AI, without regard to what their core skills are. And this is where we came in – the acceptance of the average over the excellent places us at deep risk of humanity inculcating a habit for mediocrity – something that is against every second of human existence throughout history. Our pursuit of excellence is what has defined us as humans – and we place this at risk.

The Feedback Loop

Talking of the take on content where we started out – look at this in this way - AI produces plausible content → inexperienced person accepts it as good → more such content gets published → audiences become accustomed to that standard → the standard itself declines → future AI is trained/evaluated against increasingly mediocre material. Thus, while content is democratised in a fashion, it comes at the cost of the pursuit of excellence in that field. And that is a definite cause for worry. AI increases access to knowledge, enables growth to higher levels – but critically it also enables mediocrity. It behoves us to be exceedingly careful of where and how we implement AI lest we risk the habit of excellence itself – and that is the doomsday confronting us!

Commercially Scaling Innovation And New Technologies

noreply@blogger.com (Reflections Of Vishal V Kale) — Sat, 29 Aug 2026 07:15:50 +0000

What makes innovation and new technologies like artificial intelligence commercially scalable? — the answer is deceptively simple yet diabolically hard: someone has to create the market, educate buyers, establish use cases and reduce adoption friction. Innovation does not automatically create a market. There is a missing middle between building the product and having customers willing and able to buy it. That is precisely where marketing, industry development and senior leadership come in.

People oftentimes make the error of placing the innovation engine all by itself - that results in sunk costs with no commercially viable enterprise; this is one of the reasons why startups fail so often. They forget - any product or service has to have an addressable market, and a total market. Not just a market - but an addressable market, one that can interest and desire, has a willingness to pay etc etc. And in innovative or new product lines, often these are missing; and this doesnt mean we shouldnt innovate. It just means some work is remaining - the mint with the hole as a simple metaphor works here -old marketers will understand the reference!

Some examples:

Nokia: demonstrate the product to influential early adopters and seed organisational/social proof.

Gillette: reduce the entry barrier for the durable product because the installed base creates recurring consumption.

“Product banne ke saath khareedne wala bhi taiyyar chahiye.”

“It is demand and supply. ... ALWAYS a function of demand vs supply. Capital follows demonstrated demand; where demand is immature, someone has to invest in market creation. In other words, capital flows toward monetisable demand, whereas foundational innovation often needs demand creation before it can scale.

Innovation and market development have to happen together.

· That can mean putting the product in the hands of influential early adopters.

· It can mean educating an industry through associations.

· It can mean demonstrating use cases.

· It can mean reducing the entry barrier through pricing.

· It can mean building an ecosystem around the product.

What attracts capital? — capital goes where there is visible demand and monetisation potential.

What creates economic value? — innovation can expand the economic pie rather than merely redistribute it.

Innovation can absolutely expand the economic pie; but building the product does not automatically create the market for it. Someone has to seed demand, demonstrate the use case, reduce adoption anxiety and make the buyer ready. That is where Marketing + Senior Leadership becomes strategic — and it really needs the CMO + CEO/MD + CTO + CFO thinking together.

I saw a good example of this with Nokia. Around 17 years ago, I was on the event floor managing an event where Nokia demonstrated the Lumia 925 to industry leaders. One of the things they were doing was putting the product directly into the hands of CXOs — effectively seeding the market through influential early adopters. They were doing similar things with content and music, including bringing the latest tracks onto the Lumia platform in multiple languages.

Industry associations are another route. Several large technology companies have deliberately worked through associations to educate an industry, establish use cases and deepen the market. I have attended several such meetings myself.

And then there is the classic Gillette example. If I remember correctly, when Mach 3 was introduced in India, the razor was made available at around ₹49. The strategic logic was simple: get the razor into the consumer's hands; once the installed base exists, blades follow.

So yes, product banne ke saath khareedne wala bhi taiyyar chahiye.

The strategic question is therefore not simply “What should we innovate?” It is also “How do we create the market that will consume the innovation?”

What works depends on the category — enterprise selling, CXO seeding, industry education, ecosystem development, pricing, trials, partnerships, etc. Figuring that out is very much the job of Marketing working with senior leadership, not marketing as a downstream communications function. And that is a much bigger discussion.

AI Going Rogue - How Big Of A Risk Is It In Reality?

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 26 Aug 2026 09:12:02 +0000

One of the key roadblocks to wider AI adoption especially in India is, apart from an understanding of the specific benefits in real-world detail, the lack of understanding of the risk associated with them. In customer conversations, this is an element that is now beginning to crop up more and more – alongside deep, workflow-specific questions. Sadly, most AI companies do not do a good job on either front – addressing specific workflows, or specific risks. This is where hype around risks can emerge.

For example, AI Agents in particular, and AI in general, going rogue is a nightmare scenario that I am sure many of us have heard of. However, the real problem with AI is not simply whether an AI can go rogue. It is whether organisations have any practical framework for quantifying the risk of autonomous execution before they hand an AI agent access to real systems, money, data and workflows. And in addition – a lack of a realistic benchmark as an answer to the question – yes, the risk exists, but what is the scale, nature and quality of this risk in specific terms and outlines?

A New York Times recently carried what was to me a terrifying account of the July OpenAI episode - a detailed blow-by-blow, conversation-by-agent-conversation breakdown of the AI Agents going rogue - the evolution of capability automatically combined with the sheer speed and volume they attained. And this is just one example – my own previous post carried one micro example of an AI dialer’s uncommanded execution. But from this OpenAI Episode, this is not a laughing matter anymore: what transpired has even me worried, and, judging from the comments section, I am not the only one to be worried.

What genuinely has me shaken is not so much the tendency to go beyond the design - but the tepid response to this phenomenon at all levels of society. There should have been calls for much stronger protections around AI, and clear defined limits around model and company autonomy, with a much clearer look at Risk. But, in the same breath, another thought that came to me is that, as I argue later on, this is an extreme case. This is an odd dichotomy, I know. On one side I state that it is a huge risk and play it down on the other. The solution to this dichotomy is simply that we have to consider how AI is used by us in our companies before over-thinking – this is what this article attempts. Bear with me and my train of thoughts as I go through risk, use case examples and real world practical evaluations in this thought experiment.

Risk

It seems the EU risk based approach stands vindicated - these agents going rogue is one thing, but going rogue at the speed, complexity, volume and capability that they used to do it is quite another. This could not have happened without some serious technical shortcomings - someone seems to have been horribly careless in maybe the code, the software architecture and / oe design parts of the project. Be that as it may, whatever be the error that happened – it is now becoming more and more clear that we need a different approach to risk than either of the ones adopted – for while the US approach is clearly short of the required, the EU approach may be stifling potential. But to answer that question – or even attempt to answer it, we need to ask ourselves, what is the realistic risk of damage in a practical, real world setting.

Solutions – What Can They Be?

The question is, how much is too much? Can we, as a society, undertake such risk? We are now using AI in every task - who will quantify those risks for us, the users? Where are the limits to usage, and how to stop an agent from auto-executing if, example, no answer found within a finite limite of resources? In a real-world setting, token consumption will keep a limit when used with limited purchase of credits, strictly task based, rather than long subscription plans could be one way to go ahead - for we cant deny the advantages of AI.

But then, is there any assurance that agents wont crack that ceiling as well, as the NYT article demonstrates how they solved the impossible net access problem? We are living in an era of embedded systems; is there any real guarantee that they wont find a workaround? Enterprise implementations have or will have soon AI in every task and function; they have funds access for each function, and multiple access points for example. And there is no other idea that occurs to me in a practical setting how to control this

There are some suggestions that people suggest – those that I have read about don’t seem adequate – but I am not a techie, much less an AI programmer. For example, Network isolation seems to me impractical in the real world – that we have seen above; cryptographic boundaries are unrealistic as well. Reason - for one, some embedded system may find an access point - there are so many nowadays. Restriction to functions is also a non-starter - there are, for example, multiple workflows connecting HR and finance. Human in the loop is definitely a solution - that is anyway certain.

Move To An Entirely Different Implementation

But, I am now veering to the feeling that we must move to some different AI usage architecture – for example, one way could be isolated, limited function agents - agents limited to a single task, and nothing outside it. Enterprise grade agents need to be broken into myriad sub-agents, and severely constricted; and, in a real world operating environment, a human team periodically monitoring the system This will require access to the thinking and workflows, a window into the black box, as it were; it will also have cost implications. But out security is worth the additional cost imho. Another way is to have distributed agents with multiple interfaces, dependencies and checkpoints, at the cost of slowing the flow of execution, but reducing risk. These are just a business executive’s thoughts – point is, the industry needs to think along these lines to answer the basic question – limiting and containing risk.

In a real world setting, there are two types of in vogue usage styles –

1. Agents sitting on external vendor servers, being fed instructions and data through a web-based console for task automation.

2. Then there is the API-linked implementation, controlled through an interface

3. Finally, there is the implementation when control is handed over to the agent to execute tasks.

All three are in vogue. And if you observe, the risks go up as you go down this list; AI as interface → AI as operator → AI as decision-maker → AI as executor; what we need is someone to quantify qualify those risks, list them out, if you will – for us business function folks in organisations, to enable our decision making and choices. So far, no one, not in my reading, has attempted a detailed, point by point listing of the risks involved – we are just sanguine and carelessly focussed on the potential advantages. We need to be far more practical. Before an organisation gives an AI agent access to a business function, it should know not only what the agent is designed to do, but what it could do if it misunderstood the objective, encountered an unexpected condition, or attempted to circumvent a constraint.

What Are Some Real Risks? A Thought Experiment

Now, what are the real risks - for the scenario of the OpenAI case is on the extreme side with unlimited tokens and resouces inside a sandbox. Now, in the real world, this will never happen - so what are the actual risks, say in:

1. A Voice dialing agent? Can it automatically connect to the customer data when it is meant only for cold outcalling and handling incoming calls from first leads?

2. Or a Billing and Invoicing agent, meant for handling large volume invoicing and data updations across functions? Can it realistically update accounts where it was not tasked, or say send collection mails to customers without instruction?

The real-world risk isn't necessarily "AI destroys civilisation", or “AI goes rogue” or any such nightmare scenario – it could be and is most likely to be along the lines of:

1. AI sends 80,000 wrong collection emails.

2. AI changes 12,000 customer records.

3. AI gives a salesperson access to information they shouldn't have.

4. AI approves a payment.

5. AI sends an erroneous regulatory communication.

6. AI changes pricing across thousands of SKUs.

7. AI modifies a CRM workflow.

8. AI exposes confidential information while attempting to complete a legitimate task.

Those are not hypothetical "AI apocalypse" scenarios. They are operational-risk scenarios – and I have encountered one of these already, albeit in a minor form.

These may cause serious disruption in the business functions - I think we need to have a much better appreciation of the exact risks involved. And in these examples, I am limited myself only to the agents going rogue – not malicious attack type risks. My humble submission is that we need, in each use case within the company, to list out such risks and evaluate against potential benefits and ways to limit the risk. And this needs someone in the company to work on this before finalising vendors – and then working with the vendors during implementation phase. And this is a case of an industry awakening – the OpenAI agent rogue should be a strong wake-up call…

In Conclusion

As a business strategist, a CMO and corner-office advisor to my clients; today, if I have to recommend an AI solution to my clients to solve a business problem, what I see is that the periphery functions are more likely to be chosen for automation. Or, if the core functions do get an AI boost, they are prone to risks – and I have no source or idea of how to handle the downside – this we learn along the way, by implementing – and that introduces risks that we can plan and solve before they crop up.

What Is Execution – Process, Discipline Or Success?

noreply@blogger.com (Reflections Of Vishal V Kale) — Thu, 20 Aug 2026 09:30:56 +0000

"What is execution?" This line came up in a discussion recently, and it got me thinking about a question I've circled back to many times: what is the acceptable definition of "execution"? How do we judge it? Is it labour, is it work, or is it success?

My answer: Direction + Prioritising + Process + Plan + Implementation + Moderated Velocity + Monitoring & Pivoting = Execution.

Is Success A Parameter Of Execution?

Execution is not success – success is the result of seamless execution. In fact, success is not even a parameter. Including it takes the mind away from execution, which needs a laser-like focus on the objective, and the way to go towards it — while keeping an eye on the strategy and the market shifts so one can pivot to cater to the evolving market or internal changes. That is the fundamental core of execution

Understanding Execution

A commando or a soldier does not think of the battle victory. He or she only focuses on the process and getting ready + staying ready. Any other way, and the risk is failure — thinking of success during execution is a distraction. It leads to needless risk-taking, and damage all-round. There is nothing more dangerous than thinking of the result – you have thought of some steps to get that result – your job now is then to get those steps executed, while keeping 10% of your mind on monitoring internal or external environmental shifts to check if any change in steps is required. That is the simplest way to think of execution.

Some Examples

A real story: we were chasing a massive account — high value. Took three years. We did all we could, and then just waited. One does not land a massive corporate deal in twenty-two minutes. We stayed the course, shifted as the market changed, but kept at it. They signed after thirty-six months. Patience, perseverance, direction and confidence.

The Pushback

Many people, when they see a successful business, think of it as remarkable execution... By this definition, execution is success. Others use "relentless execution," which they mean as labour — working the same task over and over. These words, at a very surface level, hide the complexities of real life — especially in startups. Many people may be hiding something complex, not clearly experienced, behind the word "execution" — all the ingredients, all the recipes of success.

Context Matters

Here's how I'd put it: context matters. Success is only the result — it tells us nothing of how the company got there. That is not execution; it could be the purest of luck. Examples abound. Relentlessness, yes — that is part of execution. In the three-year account I mentioned, there were multiple opportunities for the sales team — I was observing from marketing — to call it off as a dead lead. They didn't. That is execution, given the context of the opportunity the lead presented.

Early in my career, I made 23 visits to open one counter. Why? Because that counter was the opinion leader in the market. I did not want their sales — I wanted their stamp of approval, which helped me penetrate 16 counters in the vicinity.

A competitor once told me, when I asked him frankly how he had cornered a large share of my company's market — his answer: "You were present in 79% of the market. I just told my team: get me the balance 21%, and a majority share in all of those."

Execution Cannot Have a Standard Definition

Execution cannot be a standard definition in the context of business. It has to be designed for each company, and depends on the internal context, external market and regulatory situation, and other factors. Every example above is execution — strategy, market awareness, and so on and so forth. The reason is that the relative distribution of focus on the constituent elements identified above varies as per the context of the situation at hand – some company may need to work on the PM Fit, some may need executional discipline, some may need resource management, and so on. It depends is the best I can say.

That is why - never keep success as a parameter in the execution phase — it is the RESULT of execution. Success is kept in mind during planning and strategising. Once you commit organisational resources to the plan — end of story. Then go all out and execute. During monitoring, check and decide if you need to pivot. That is execution at its most basic. There is a lot that lies in the backdrop to execution – strategy, the plan which also includes resource management & allocation, coordination of teams and so on – there is no time to drift your attention to distractions during this phase.

Viewing success as a parameter during the execution phase is a distraction — and staying free of that distraction is exactly the mindset required to maintain operational discipline. And discipline is central to all execution.

Inflation, and Its Link to Business Strategy

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 19 Aug 2026 07:30:57 +0000

Strategy Begins With Understanding the Economics Underneath

Strategy, in its most basic form, is about making choices based on an understanding of the environment in which a business operates. That sounds obvious, but it is surprisingly easy to forget when strategy gets reduced to market size, consumer trends, competitive positioning and growth ambitions. Underneath every strategy sits a more fundamental layer of assumptions about costs, demand, pricing, capital, people, productivity and the economics of the business itself. If those assumptions change materially, the strategy has to change too. This is why I believe an underlying understanding of business costs and their interconnected behaviour is not merely a finance or economics requirement; it is a fundamental strategic capability.

Inflation is perhaps one of the best examples of this. We have become accustomed to discussing inflation as a number. CPI is at 5%, food inflation is at 7%, core inflation is sticky, inflation is moderating, real wages are rising — and the discussion usually moves on. These numbers are important, of course. They give us a common framework to understand what is happening to the general price level. But there is a significant difference between measuring inflation and understanding its consequences. Inflation is not a single variable moving through an economy in a neat, linear direction. It is layered, interconnected and uneven. It enters through different parts of the economic system, travels through different industries at different speeds, disturbs different cost structures and eventually changes the assumptions on which businesses and households make decisions.

Inflation Is Not a Single Number

Consider something as ordinary as a restaurant. An increase in energy prices does not simply increase the restaurant's costs by an equivalent percentage. Energy affects refrigeration, cooking and transportation. At the same time, the cost of food ingredients may rise because of energy, weather, logistics, labour or trade policy. Packaging may become more expensive. Wages may rise later as employees seek to protect their purchasing power. Rent may rise when a lease is renewed. Financing costs may increase if the business has variable-rate debt.

The restaurant then has to decide how much of this to absorb, how much to pass on to customers, whether to change suppliers, whether to alter the menu, whether to reduce portions and whether to accept some margin compression. But every decision has a second consequence. Increase prices too much and demand may fall. Change ingredients and the customer experience may suffer. Absorb the costs and profitability deteriorates. Reduce portions and you may damage the value proposition. The original inflationary shock has now travelled through several layers of the business, and the final impact bears little resemblance to the initial price increase that started the process.

This is why I find it inadequate to think of inflation simply as an increase in the cost of living or an erosion of purchasing power. Those are certainly consequences, but they are not the whole story. Inflation can change the relationships between the variables inside a business. And those relationships are precisely what a business model is built upon.

Inflation Attacks the Assumptions Inside a Business Model

Every business model, however sophisticated, contains assumptions. Some are explicitly documented in financial models and business plans, while many are simply embedded in organisational experience. A manufacturer may assume a certain range for raw material costs. A real estate developer may work with assumptions around construction costs, project duration, interest rates and selling prices. A retailer may assume a certain inventory cycle and gross margin. A SaaS business may have assumptions around salaries, customer acquisition costs, churn and technology costs. A consulting business may have assumptions around utilisation and billing rates.

These assumptions do not need to be perfect. They simply need to remain sufficiently stable for management to make rational decisions. Inflation can disturb several of them at the same time. That is when the problem becomes considerably more serious.

Take a project that was originally expected to generate a healthy 14% return on investment. Construction costs rise, the project takes longer to complete, financing becomes more expensive and the market becomes more price-sensitive. The project may still make money. It may still show a positive P&L. But the return on capital may have fallen sufficiently for management to question whether the project should proceed at all.

Capital could perhaps generate a better return elsewhere, with less risk. The project has therefore moved from being financially attractive to being questionable, even though it has not technically become loss-making. This distinction between profitability and feasibility is important and is often missed when inflation is discussed only through the lens of prices and wages.

Framing In Business Terms

Businesses do not merely ask whether something will make money. They ask whether it will make enough money, within an acceptable time frame, at an acceptable level of risk, to justify committing scarce capital and management attention. Inflation can change that calculation without necessarily changing the basic profitability of the proposition. A project that made strategic sense at one level of costs and financing may no longer make sense after those assumptions have shifted.

That is not an accounting problem. It is a strategy problem. The Timing of Inflation Matters as Much as the Inflation Itself: There is another complication which makes inflation even less linear: different prices adjust at different speeds.

Energy prices may move almost immediately. Raw material contracts may be renegotiated a few months later. Employee wages may adjust during an annual appraisal cycle. Rents may change only when leases come up for renewal. Interest rates can change rapidly for businesses with floating-rate borrowing. Selling prices, however, may not be adjusted immediately. Customers resist price increases. Contracts have fixed terms. Competition limits pricing power. Sales teams may be reluctant to disturb established price points.

A business can therefore spend months operating in a state where its costs have already changed but its revenues have not. This creates temporary distortions in margins, cash flows and investment decisions. Eventually, some prices may catch up, wages may adjust and the business may reach a new equilibrium. But the journey to that equilibrium matters. A company can lose customers, postpone investments, reduce hiring or run into working-capital stress during the transition.

By the time the aggregate inflation number begins to look benign, the underlying business may already have changed substantially.

The Same Inflation Does Not Affect Every Business Equally

This is why the relationship between inflation and strategy needs to be understood at a much more granular level. The same inflation rate can have completely different consequences for different industries and even for different companies within the same industry.

A software company, a steel manufacturer, a hospital and a restaurant do not have the same exposure to energy, labour, imported inputs, logistics or capital costs. Even two companies making the same product may experience inflation differently because one has long-term supplier contracts, lower debt and stronger pricing power while the other buys in the spot market and operates on thin margins.

The macroeconomic number may be the same. The strategic consequence is not.

This is also why businesses need to understand their own cost architecture rather than rely entirely on macroeconomic averages. Knowing that inflation is 5% is useful. Knowing that 40% of your own cost base is exposed to a category experiencing 10% inflation, while your ability to pass through only 4% of that increase, is strategically useful. The second number tells you what you need to do.

Households Are Economic Systems Too

The household provides an equally important illustration. A household is not a miniature corporation with unlimited flexibility. In fact, its ability to respond to rising costs is often much more constrained.

If housing becomes more expensive, one cannot simply substitute housing in the way a business can substitute one raw material for another. If healthcare costs rise, alternatives may be limited. If school fees increase, parents cannot necessarily postpone the expense. Food prices can be managed to some extent through substitution, but even that has limits.

And there is another constraint that is often overlooked: the household has a finite number of people who can absorb the impact by increasing their working hours or generating additional income.

Suppose a family's monthly expenses rise by ₹25,000. The family may eat out less, cut subscriptions, postpone purchases, dip into savings, borrow more or increase household income. But every response carries a cost. Additional work means less time. Using savings means less financial resilience. Borrowing moves today's problem into tomorrow. Cutting discretionary spending affects quality of life and also reduces demand for the businesses providing those services.

The household has therefore not simply experienced a higher price level. It has experienced a change in its decision-making framework.

This Is Where Behavioural Economics Enters

People do not experience inflation as a statistical index. They experience it through repeated encounters with prices and through reference points built over years. If a meal used to cost ₹500 and now costs ₹800, the consumer does not think about the change as an abstract movement in a weighted consumption basket. The reference point is ₹500. The experience is that something which was affordable has become expensive.

Wages are perceived differently. A salary increase is generally experienced as a reward for one's work, while a price increase is experienced as something imposed from outside. Consequently, even when wages eventually catch up with prices in aggregate terms, the psychological experience of inflation can remain negative.

But this is where we need to be careful. The fact that some of the experience of inflation is psychological does not mean that the underlying economic impact is psychological.Behaviour itself is part of the economic mechanism.

When consumers become more cautious, they change their spending. When businesses see weaker demand, they change their pricing, hiring and investment decisions. When employees become uncertain about purchasing power, they change their wage expectations and job-search behaviour. When investors become less confident about future returns, they change capital allocation. What begins as a psychological response therefore feeds back into actual economic activity. Psychology becomes part of the transmission mechanism.

Inflation Creates Feedback Loops

This is where inflation becomes much more interesting — and much more dangerous — than a simple movement in prices. Prices affect expectations; expectations affect behaviour; behaviour affects demand; demand affects pricing and investment; investment affects employment and future capacity; employment affects incomes; and incomes feed back into demand.

The original inflationary shock may have come from energy, food, monetary conditions, supply constraints or trade policy, but its ultimate consequences are determined by how the entire system responds.

This also explains why the effects of inflation can sometimes be considerably larger downstream than the original shock. A business absorbs higher costs, reduces margins, delays investment and cuts hiring. A household faces higher expenses, reduces discretionary consumption and uses savings. Reduced consumption affects another business. Lower investment affects future capacity. Changed expectations influence another round of decisions.

The system is constantly reacting to itself. This is why inflation cannot always be understood through a simple cause-and-effect chain.

Why Averages Can Hide the Real Story

There is also a distributional problem which makes aggregate measures less useful for understanding lived economic reality. Two households with identical incomes can have radically different experiences of inflation. One may own its home, have little debt and have no children. Another may rent, have two children, carry a car loan and face significant education and healthcare expenses. Same income. Very different economic reality.

The same principle applies to businesses. Two companies operating in the same industry can have very different outcomes because their cost structures, contracts, financing, customer mix and pricing power differ. This is why averages can sometimes conceal rather than illuminate. It is not that the aggregate statistic is wrong. It is that the aggregate statistic is answering a different question.

Inflation tells us something about the movement of prices across a defined basket. It does not, by itself, tell us how a particular household's financial resilience has changed, whether a particular business model remains feasible or whether an investment still makes strategic sense.

What This Means for Business Strategy

For anyone involved in strategy, this distinction is critical. A strategy cannot simply say that a market is growing at 8% or that consumer incomes are rising by 5%. It needs to understand what is happening underneath those numbers.

Which costs are moving? Which ones are volatile? How quickly can they be passed through? What happens to demand when prices increase? Which assumptions in the business model are most vulnerable? At what point does a project become unviable? How much pricing power does the organisation really have? What happens to working capital if costs rise faster than collections? What happens to capital allocation if the required return changes? And, perhaps most importantly, how will customers, employees, competitors and investors change their behaviour in response? These are strategic questions, not merely financial ones.

This is also why I believe strategy professionals need to be comfortable moving between disciplines. Marketing without an understanding of economics can misread consumer behaviour. Finance without an understanding of behaviour can misread demand. Operations without an understanding of strategy can optimise the wrong variables. Strategy without an understanding of the cost structure can become little more than a set of ambitions. The strongest strategic thinking happens when these disciplines are connected.

Inflation Is a Strategic Variable

I increasingly think of inflation not as a number but as a systemic disturbance to the assumptions on which economic decisions are made. It enters through prices, but it does not stop there. It travels through industries, supply chains, businesses and households. It changes margins, feasibility calculations, investment decisions and expectations. Those changes alter human behaviour, and that behaviour feeds back into the economy.

The important word here is interconnectedness.

What is true for one person does not automatically become true for the population. What is true for the average household does not necessarily describe the household at the margin. What is true for a profitable business does not tell us whether its next investment is financially feasible. And what looks like a small movement in one variable can create a much larger downstream effect when several variables move together.

Economics is therefore not an Excel sheet where we change one cell and wait for the answer. It is a system of relationships, incentives, constraints and human decisions. And inflation is one of the forces capable of disturbing almost all of those relationships simultaneously.

Perhaps the better question is therefore not simply, "What is the inflation rate?" It is: Which prices are rising, for whom, through which supply chains, at what speed, with what alternatives — and which assumptions does that change? Once we start asking those questions, inflation becomes much more than a monthly economic statistic - it becomes a strategic variable.

And understanding that variable — not merely measuring it — is essential to understanding how businesses actually survive, adapt and grow.

Staring into the AI Tempest: Progress, Euphoria, and the Warning Signs of the AI Era

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 17 Aug 2026 07:19:30 +0000

I am not against progress. I am asking us to remember its price ..... We are living through what can only be described as the golden age of technology. Everywhere you look, there is an unmistakable air of celebration – something that has only been enhanced with the advent of AI. Artificial intelligence has turned the global landscape into a grand festival of possibilities; be it engineers, or leaders, or everyday creators – people are rushing to experiment, build, and deploy at a pace unprecedented in human history. Under the banner of progress, we are driving and diving headfirst into new frontiers, eager to harness tools that promise speed, automation, and near-magical capabilities.

Yet, beneath this electric enthusiasm lies a disturbing trend: a growing disregard for the consequences on the downside of this movement. It is becoming more and more clear that we are not truly aware of the risks that lie inherent in our push for garnering the benefits of this clearly transformational new field. In our rush to innovate and harness the proceeds and the riches, we are embracing higher levels of risk, completely unmindful of the structural fragility we might be building into our future.

Drunk on Past Victories

Why are we moving so fast with so little caution? Because we are intoxicated by our past successes. We look back at previous technological revolutions—the internet, mobile computing, cloud architectures—and remember primarily the triumph. We recall how they multiplied wealth, modernized industries, and expanded human convenience.

In our euphoria, however, we forget that every breakthrough is a double-edged sword. Success has bred a subtle form of arrogance. We treat past victories as a guarantee of future safety, ignoring the reality that every powerful shift carries an equal measure of latent risk. By mixing unvetted outputs, compressed safety audits, and relentless market pressure, we are assembling a volatile cocktail.

The Short Memory of the Tech World

The collective memory of the modern tech ecosystem is remarkably brief. We have conveniently wiped away the lessons of past booms and busts. We forget the speculative collapse of the dot-com crash, the erosion of digital privacy, and the psychological fallout of unbridled social media algorithms. Each time, technology promised pure liberation, but delivered collateral damage along the way—shattered trust, economic shocks, and broken promises. While the benefits are beyond debate – we as a society are decidedly better off today by almost any yardstick – yet, what has me genuinely worried is that we are showing signs of learning nothing from our past.

Today, as we race to integrate AI models, tools and agents into the foundation and bedrock of critical business operations, we are repeating the exact same pattern. But the stakes this time are exponentially higher. When non-deterministic systems are given core operational control without adequate guardrails, we aren't just taking calculated risks—we are gambling with infrastructure, basic processes, and our own real life dependencies. Further, we are also exposed in financial terms as ever-larger sums of money flow into the AI bandwagon.

Watching the Zalzala

This brings me to a deeply haunting realization. As the world wildly rushes forward into this fiery storm, blindly cheering for every incremental launch, I find myself standing still.

I look around at the frantic momentum and feel a quiet, stunned gravity. To witness collective blind faith in unproven systems feels like watching an inevitable collapse in slow motion—or, as I wrote in a personal poem recently: Main stabdh ho nihaaroon aataa Zalzala (I stand frozen, watching the incoming earthquake). It is sobering to see how easily society trades long-term resilience for short-term speed, normalizing errors until a system fails catastrophically.

The Nuanced Path Forward

Let me be clear: I am not against technology.

I am not against artificial intelligence, nor am I blind to the undeniable prosperity it can create. When crafted with wisdom and engineering discipline, technology forms a harmonious symphony with modern society—expanding human potential, solving complex problems, and creating genuine value.

I am not against the spirit of innovation or the joy of building the future. What I oppose is the reckless amnesia that so often accompanies it. We must not allow the grand promises of tomorrow to make us deaf to the hard lessons of yesterday. Behind every unguided disruption lie painful costs, displaced lives, and shattered dreams that could have been preserved with simple foresight.

The Need for Proportional Caution

True progress does not require blind risk-taking. Innovation and caution are not enemies; they are essential partners.

As we continue to build and adopt the most transformational tools in human history, we must demand more than raw technical performance. We must demand accountability, depth, and systemic guardrails. Before the ground beneath us begins to shake, let us slow down just enough to remember where we have been—so we can safely navigate where we are going.

AI & Cost Negative Arbitrage: Caught Between Competitive Pressure and Market Reality

noreply@blogger.com (Reflections Of Vishal V Kale) — Fri, 14 Aug 2026 05:24:22 +0000

The AI Moment—and the Pricing Blindspot

It is the world of artificial intelligence now, that much is pretty much accepted. But what people have not yet been able to understand fully is the impact it would have on something that is completely beyond, you know, expectations of most people. And that is pricing. Pricing is in a state of flux in any number of industries - that is beginning to be known; what is lesser known is that this is creating a unique matrix between competitive pressure and market dynamics which is going to make things challenging for business managers worldwide.

How AI Is Driving Costs Down

What is happening is, while it is known that artificial intelligence and proper planned implementation can shorten development cycles, it can shorten execution cycles, it can shorten product to market costs & times - it can lessen product costs across industries. It helps us save money during the advertising execution process, in an ad agency. It helps us ship new features and new products, and new developments in the software industry. Just to give two examples: this is being felt across industries I am dealing with - software and business services. I expect much the same to felt in other arenas of the business environs.

Customers Are Using AI Too

Instead of making the playing field better manageable for companies, it is actually increasing the challenges. Because what is happening is the customers are also doing this and are driving down the prices. This is happening in both advertising as well as in software development. I have a personal experience wherein the prices went down in a couple of industries that I saw. A third industry is also showing the same trends. While there are other related & unrelated factors putting downward pressure on prices, this is becoming quite rapidly one of the key factors in the pricing decision at the customer end of the market.

The Margin Squeeze on Customer-Facing Operations

What this means for companies is it actually changes the competitive dynamics and the market dynamics drastically, necessitating a completely new business model to deal with it. Because while you have saved money on your software development, the customers have also responded by tightening their budget strings. They are not— they are now not willing to pay as much as they did earlier.

This is creating an almost unique space - while competitive dynamics are changing as more and more companies go into producing AI productivity solutions, creating a hyper competitive market - which is supposed to, in theory, drive demand; but we have on the other hand a downward pressure on prices at the customer and demand side of the equation. Managing this is no easy task, as telecom found out 15 years to 20 years ago. You have to produce differential brand recall while wandering in a market that is both reticent in giving you the price you feel you command, while also being agonisingly slow in giving full commitment, as I covered in my previous article.

Short-Term Responses and Their Limits

This is creating challenges for companies to maintain their current level of front-end operations, customer-facing operations. These operations have been funded with respect to a particular average revenue per user — to quote a telecom metric. That has drastically fallen. This is leading to, at times, knee-jerk, at times planned reactions in companies by— shortening— by lessening sales staff, or, you know, otherwise cutting costs in order to meet this new challenge.

The Adoption Lag and the Negative Arbitrage Window

While on the same hand, the adoption is not increasing at the same pace. Experimentation is high, adoption will take a little time to show the same speed of growth. This is creating a nearest term is arbitrage. That the costs have gone down, the prices have gone down, while the expenses have remained the same. Necessitating a drawdown on various fronts by the companies.

Strategic Imperatives for Survival

This creates a complex working atmosphere for the companies who now want to maintain growth while managing with the existing costs. The only way forward for this is to realize the potential, plan your products properly, and take the risk and maintain expenses. But this is hard to do if you don't have deep enough pockets. Which is why the coming years are going to see a sea change in competitive dynamics.

As companies who can invest are going to survive, companies who cannot invest but somehow sustain are going to survive. But at the end of the day, this arbitrage, this difference between the expenses and the pricing dynamics, is clearly meaning that you are going to be faced with a challenge that you need to manage properly. If you do not manage this competitive metric, this market metric, this combination of competitive and market metric, you are likely to get into deep trouble.

In Conclusion

A quick conclusion is in order - the way ahead is to

maintain your frontend as best you can, silently adjusted for market and

internal realities - for the potential is vast - that is true. But, what is

truer, is understanding the market fully, plotting ahead, spotting customer

demand trends and riding the way to success: the key is being in touch with the

market - which will give you the space to build differential recall in your Go

To Market execution. This is easier said than done: there are no easy answers.

And that is the game that has been played since time immemorial - crack these

two - differential recall, and smooth frontend execution, and you are through.

AI Will Transform Everything. That’s Exactly Why We Need to Talk About Risk

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 12 Aug 2026 11:57:02 +0000

𝗔𝗜: 𝗪𝗲, 𝗔 𝗣𝗲𝗼𝗽𝗹𝗲 𝗣𝘂𝗻𝗰𝗵 𝗗𝗿𝘂𝗻𝗸 𝗢𝗻 𝗜𝘁𝘀 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 :--> 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗔𝗜 𝘁𝗿𝘂𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 from the other tech innovations we have seen is not just the capability it contains; it is the scale of what is coming that is the defining aspect.

While we go gung-ho ahead on the AI bandwagon, what truly has me worried is that we are not factoring in the risks either at individual levels or at scale. Happily, early trends of an awakening is present in customers judging from my interactions with them - but this is yet to feature in organisations' focus. My deepest sincere desire in this domain is that we, all of us, place risk at the forefront while designing and implementing AI solutions...

AI is going to become increasingly embedded across regulatory, financial, personal and business domains. Once these systems become deeply interconnected, the internet at its most vibrant and interconnected may seem almost like a chart drawn on a map by a three-year-old.The more the interconnections, the greater the risk. Add to that the fact that AI systems can increasingly act on our behalf, and the risk becomes more complex. Net-net, we are not ready yet – internal systems and people still don’t think along the lines of risk, risk containment and mitigation, and how to prepare for an AI era.

𝗥𝗶𝘀𝗸 𝗜𝘀 𝗡𝗼𝘁 𝗛𝗶𝗴𝗵 𝗘𝗻𝗼𝘂𝗴𝗵 𝗼𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 𝗦𝘁𝗮𝗰𝗸 -

And this is what worries me : that the risk considerations are still not sufficiently high on the AI priority stack. In my view, the approach should be something closer to: understand the risk, then evaluate the technology, then look at the options, the market, the MVP, the product and finally implementation. Are we actually doing that? We are not, for the large part. And that, to me, is terrifying. We need a mindset change.

𝗦𝘁𝗿𝗼𝗻𝗴 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀, 𝗥𝗶𝘀𝗸 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴

I see it as a fairly straightforward requirement for strong guardrails, in-depth risk mitigation and, above all, serious testing. And make no mistake, these challenges will arise. That, to me, is almost a foregone conclusion.

𝗜𝗳 𝗧𝗵𝗲𝗿𝗲 𝗜𝘀 𝗮 𝗦𝘆𝘀𝘁𝗲𝗺, 𝗧𝗵𝗲𝗿𝗲 𝗪𝗶𝗹𝗹 𝗕𝗲 𝗟𝗼𝗼𝗽𝗵𝗼𝗹𝗲𝘀

Designing a perfect, foolproof computer system — or any system, for that matter — has remained beyond human capability through thousands of years of human progress. If there is a system, there will be loopholes. That is simply human reality. What makes us think we can scale that peak now? We cant – mistakes will be made, always have, always will.We need to have management and mitigating strategies in place for the future that is coming – but we as a people are punch drunk on the promise of AI, and are simply not paying enough attention to the attendant risk.

𝗧𝗵𝗲 𝗕𝗿𝗶𝗴𝗵𝘁 𝗦𝗶𝗱𝗲 𝗼𝗳 𝗔𝗜 𝗥𝗶𝘀𝗸 - 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗔𝘄𝗮𝗿𝗲, 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗕𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴 𝗧𝗼 𝗪𝗮𝗸𝗲𝗻 -

From what I am seeing, customers are beginning to examine AI very closely. They are asking about data privacy, permissions, what happens when something goes wrong, why something happened, and what exactly the system is capable of doing. And yet, we are still going ahead with AI. Combine the two thoughts together – and the reality becomes clear – customers are becoming aware, but AI companies while still sold on the potential, are beginning to get the response in terms of hard questions, forcing them to change. And we as an industry need to use this as a stimulus to urgently refocus towards serious risk management capabilities.

𝗦𝗰𝗿𝘂𝘁𝗶𝗻𝘆 𝗜𝘀 𝗡𝗼𝘁 𝗞𝗶𝗹𝗹𝗶𝗻𝗴 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻

The increased scrutiny by customers is not necessarily killing adoption. It is creating a testing phase. Customers increasingly understand that AI is the way forward. They just want to understand the risks before they hand over more autonomy to it. And I think that is actually a very positive sign – it means customers are not rejecting AI; they are asking for greater confidence before embracing it.So, as someone who isn't a core technologist, my advice to AI companies is rather simple: test the hell out of your solutions.

Test every scenario you can think of. Test every scenario your team can think of. Get five prospects to try and break it. Think of the things nobody thought of. Test again. Then test some more.Because if there is one thing we know about systems, it is this: there will always be something we didn't anticipate. And perhaps the biggest question for AI will not just be what it can do. It will be what it can do that we never intended it to do. Food for thought.

#ArtificialIntelligence #AIRisk #AIRevolution #AIInnovation #AITransformation #RiskManagement #AILeadership #Technology #FutureOfAI #ResponsibleAI

Navigating the Real World Through The Gita: Karm, Inaction, the Inevitable Blowback, and Yogakshem

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 27 Jul 2026 09:22:26 +0000

Navigating the Real World Through The Gita: Karm, Inaction, the Inevitable Blowback, and Yogakshem

We speak of The Shrimad Bhagwad Geeta often; we even state that it is applicable to modern life. Yet, few of us daily people try to articulate what it means in a real world context. In this small thought experiment, I am attempting this daunting task – please forgive me for any errors – they arise from my own lack of understanding. I am not a Guru – just a learner studying the book daily for at least 10 years.

When analyzing the Bhagavad Gita, it is easy to get lost in theoretical theology. But the Gita is, when applied, a manual for navigating the real world. In a recent discussion, I found myself categorizing the concepts of Karm and Akarm into four distinct parts:

. Understanding karm or akarm,
Thinking about others' karm,
Focusing on your own karm, and
Finally, forgetting (rather than just forgiving) others' karm.

(Before I proceed – there is no readymade path in it; you have to construct your own basis your own understanding – and that is why a true learned Guru is essential.)

In the 2^nd half of this article – I go deep into the application part in real life, focussing entirely on the blowback that will happen – this is a multipart effort- here I focus on our expectations, the concept of yogakshem and the need for continuous effort at it, Manan and thinking deeply on it, and differentiating physical action from true sincere contemplation.

The Anatomy of Karm, Akarm, and Vikarm

To truly grasp this, we must dive deep into what these concepts mean in our daily lives, how we navigate the inevitable blowback of doing good, and how we manage the stress of it all. We talk of karm, the law of karm, its finality and much, much more. But we rarely analyse and interpret it in our own lives: an attempt I have done. (I am not special – perhaps all regular readers of this seminal instruction of God have attempted this).

Karm is your deeds; akarm is a state of evolution that means you act without getting results of your actions bound to you – and is a deep topic; and Vikarm is negative karm. Karm is an investment- put in 100, get 105 or 95 back depending on the initial investment (the core karm itself – good or bad etc). Akarm is, in its most naïve version, doing duty. Doing the right behavior creates true akarma only when all these parameters of duty, ethics, and detachment are satisfied. [This can be an article in itself]- this is the short version, which is enough for us, for now. Next is Sanchit Karm – the sum total of all your karm in all lifetimes; Prarabdh – that karm whose results are coming due in this life, and Agami are those karm which will come to fruition in future lives.

Understanding the Framework of Action

Before we look at the real world, we must define the framework of action. You are in a body that has been placed here, and you are called upon to do your nityakarm (daily duties). If you believe in doing the right thing, walking the path of the Geeta, this is the first step. This means doing what is your responsibility with complete morality, complete ethics, a sense of duty, and without breaking any laws. Karm isn’t just the law of action and reaction: it is far more complex. Read on, please: a request.

This is the basis level of understanding that is needed if we are to successfully implement this great set of instructions in our life. And this requires a set of qualifications and gunas – that come to you only when you start walking the path. You can’t walk it without these, and you can’t get these without walking the path. That is why this path is so tough to walk! How then to do it? Start small is the way – but first comes Manan – cogitating on the content all the time – this will, slowly, over time, translate into your action. Give it time. That is what I learnt

Thinking About Others' Karm

Once you start walking the path of the Gita, this is perhaps the first thing to happen. You will start judging – that is dangerous territory. If it doesn’t evolve to the next segment, it could sidetrack you. Thinking about others' karm ideally naturally evolves into how you manage your practical decisions and behavior in daily life. As this understanding becomes ingrained, you automatically begin to anticipate and think of the other person's reactions. It is not about judging them, but about moderating your own responses and learning to serve with more diligence based on where they are in their journey. This awareness forces you to navigate interactions carefully—like advising someone who is wrong without hurting, insulting, or nagging them—recognizing that their actions are bound by their own karmic cycle, while your duty is to respond with complete ethics and without generating akarm yourself.

Focusing On Your Own Karm

Focusing on your own karm might seem like the easy path on the surface, but it is probably the most complex of all. In my own journey, I found myself deeply engrossed in myself—which is actually a necessary and positive trait in the beginning stages of building this basic clarity. However, it becomes a dangerous negative later on. If you become too self-engrossed in focusing on your own karm without reaching a higher state of spiritual elevation, like the state of the avadut or turia, the sheer weight of that self-focus will inevitably pull you down. It is a very long path that requires you to eventually transcend that self-engrossment entirely.

Forgetting (Rather Than Just Forgiving) Others' Karm

This to me is the final test of your resolve – forgetting and forgiving others for the harm done to you, and forgiving yourself for your sins. This completes the karmic cycle. Forgiveness, if not done right, often carries a subtle trace of the ego, as it requires you to hold onto the memory of a wrong in order to grant absolution. And from that begins a new karm cycle! Thus, forgetting is a far deeper and cleaner mechanism—it means letting go so quickly and completely that the transgression leaves no residual mental clutter. Rather than dwelling on or constantly processing what someone else did, you simply drop it, allowing their karm to remain entirely theirs without binding your own mental state. This ingrained ability to quickly move past others' actions prevents resentment from festering, freeing you to focus entirely on your own path without carrying the heavy burden of someone else's debts.

Some Practical Situations of Karmic Application

Hence it is a long, complex path to master. And the external world’s complexities make it exponentially tougher as we are repeatedly tested with seemingly grey situations with no easy answers. Let us look at a practical example:

You have a fight with somebody close to you, perhaps your spouse or sibling. The other person is wrong. You may think you have done your duty by correcting them, but if you do so by hurting that person, insulting that person, or nagging them, you are not in akarm (detached, pure action).

Despite doing the "right" thing logically, you have created a negative karmic footprint. Your true task is to advise that person without inflicting pain or negative reactions or defensiveness. That is a true life skill, and doing the right behavior creates true akarm only when all these parameters are satisfied. If your action leads to a negative reaction due to the nature or way or tone or content of your communication directly attributable to yourself – it is your karm. You are not responsible for their understanding that is based on their Sanchit karm and their vasanas – but if your communication is contributing directly, you are responsible.

Furthermore, there is Vikarm—forbidden or unethical action. A classic example:

It is your responsibility to provide for your family. But does that mean you have to commit unethical acts, hurt someone, or cause loss to others? In the course of providing for your family, if you are breaking laws or causing damage with an intent to harm or for your own self-preservation, you are creating Vikarm.

Its reaction (pratikar) is definite. Just because it is your duty towards your family does not mean you are not responsible to the other aspects of your life. You have a duty to your nation, your community, and society. There is a beautiful allied concept that states that a man who acts purely for his own self-preservation rather than help someone who has taken refuge with him is a sinner (paapi) – this is categorically mentioned in the Sundarkand. Further, a study of the Brudaranyak Upanishad also indirectly establishes a relationship between self-preservation, fear of loss, and karm – it is binding, as you are not doing your duty; and are attached to the results. Adi Shankar also establishes some prerequisites – among them, a heart purified by the performance of duties without any expectation of reward.

The Great Human Fallacy and The Mechanics of Blowback

This brings us to a challenge central to modern life. Perhaps the biggest fallacy we humans have is the belief that by behaving good, all will be good, and that we will not suffer. There is an inherent expectation: I will not suffer if I am good.

The world doesn't work that way. There is always a blowback. Our fallacy is that we don't expect it. We live in a connected world. The world is, by and large, practical and often unethical. If you behave well, expecting everyone else to behave nicely with you, you are being naive. Naive is the softest word I can think of.

By being good, you are actively obstructing other people in achieving their aims because they are used to behaving in a certain way. Since they meet an obstacle in you, the blowback starts. You may not get promotions, you may lose your job, or you may get sidelined in family and friendly discussions.

Whether you believe in the cycle of reincarnation or not, the mechanics of this consequence are absolute. From a spiritual perspective, there are three types of karm: sanchit, prarabdha, and agami. By not expecting blowback, you are forgetting your past life karm. Even if you dismiss the spiritual, there is Newton's law: for every action, there is an equal and opposite reaction. If you behave unethically, you will be known as an unethical person. You will constantly be put under circumstances where people approach you for your unethical tactics, and you will be targeted by people wanting to teach you a lesson. That is a direct product of your behavior.

The Two Faces of Stress: Ethical vs. Unethical Living

How you choose to behave directly dictates the kind of stress you will carry through life. Understand carefully how stress works.

If you behave unethically, you will always be under constant worry and tension. You will worry that people may find out. You will wonder, "I have gotten this much through this deceit, how do I continue doing this?" Usually, this involves telling lies. You have to constantly keep in mind what you told to Person A versus what you told to Person B. You then have to set up elaborate systems and structures to ensure that cross-communication of lies does not happen and you are not caught out. This causes immense, corrosive stress.

On the other hand, if you behave decently and as a straightforward person, you don't have this worry. However, your worry is transposed. You begin worrying about what will happen in the future. You may face constant financial stress. You may be under constant pressure at your job because of your straightforwardness. You may face friction in a relationship because your spouse cannot understand why you are being so straightforward at the cost of the family's immediate comfort.

This is where faith becomes a very powerful tool for stress management. Having faith—whether it is in a higher power to believe in, or simply a deep trust in the world that good will eventually happen—helps you manage this specific, ethical burden.

The Trap of Silence and The Responsibility of Action

Let’s take an extreme workplace example. You come across an immoral or unethical action in your career. What do you do? Do you keep silent? Do you follow their path? Or do you ignore them and continue doing the right path?

There are no easy answers, but often, all three are wrong. Even if you keep silent despite having knowledge, because of self-preservation, you are not highlighting it. You are not fighting against the system. By not acting, you are creating an action, and you become responsible for that silence.

Yes, if you fight the system, there is going to be blowback. But understand this: this situation has come in front of you because of some of your past life karms. Otherwise, this situation (prasang) would never have appeared before you - it is a test of what decision you will take. If you choose self-preservation, you create another cycle. That is the fundamental part of the law of Karm – it is about getting the payment for your past deeds – but in those payments lie the seeds of future cycles. You can grow, remain at the same level, or degrow: it depends on your choices. These situations are but a reason for your future development.

This is why detachment (vairagya) and disconnecting from the fruits of your actions are essential. If you behave correctly in that moment, 100% you will have to pay a price. 100% your family will have to pay a price. But your behavior would have been the right thing to do. Think of yourself as a soldier. A soldier cannot refuse to fight because of a threat to his life; he fights anyway. But while paying that price, you will be supported by your efforts, and your faith – thus, you have paid the price of your past karm, and evolved in the process. That is, once again, the law of karm in operation.

There are various ways to handle this. Running away (palayan) is a strategy, as Sri Krishna demonstrated. But remember, Sri Krishna is called names to this day for that retreat. You have to accept that as part of it. Sri Krishna told Draupadi: People will say many things, but this is my karm, I have to do it. There can be no bigger example. But is running away, palayan or silence – for example avoiding unethical office situations – your true answer? Again – depends on the specific situation. Remember- there will be a price, regardless of what you choose to do. There are no free lunches.

Yes, your family has to go through some hardships. But from a spiritual perspective, these are past debts returning. And practically, they are your family; they are supposed to support you in your bad times. If they don’t, and if they leave you – their karm – not yours. Their role was to give you pain in reply to the pain you have inflicted on them. That is the harsh reality. (That does not mean you do not do your further karm relative to them – just that your reactions post the breakup or rejection be dictated by rock solid ethics, legality, morality and detachment and the rest).

Yogakshem: The Continuous Effort

Finally, we must weave in the vital Gita concept of Yogakshem—which translates to aprapt vastu ki prapti (acquiring what you do not have) and prapt vastu ka rakshan (protecting what you have acquired). [This is not precise here - it also requires surrender to Lord Krushn; I am using it to explain a point in terms of practical application]

The practical application of Yogakshem is this: having achieved a level of calm, peace, and straightforwardness by practicing these principles, it is not enough to say, "Okay, now I am a good person." You have to continuously work at remaining good. You must protect the inner peace and morality you have acquired. Focusing on your own karm is the most complex path of all. As I found in my own journey, you can become too engrossed in yourself, which is initially a positive, but if you do not reach a state of deep realization (avadut or turia), that self-engrossment will pull you down. You have to keep walking till Turia – beyond that I do not know. I am very, very far from Turia stage – so can’t say.

Conclusion: The Ultimate Asset

Why, then, should we endure the blowback and the stress of being good, and walking the path? Why should we understand and try to decipher the path of Karm? You don’t have to; it is your choice. And there are three others paths to choose – Bhakti, Dnyan / Gyan, Sanyaas. But if you do – then this is for your understanding so that you can appreciate the path that lies ahead of you based on my admittedly limited understanding.

Spiritually, if you are behaving well, it is guaranteed that something good is going to happen to you – eventually, subject to your past deeds. Practically, you must understand that the world will not give you an immediate response to your goodness. But you are building a reputation of being a decent, straightforward person. Such a person can be trusted. Eventually, situations will arise where even unethical people will look for someone exactly like you. The world will eventually give back to you what you so richly deserve.

Over time, you will acquire the reputation of a straightforward and decent man who can be trusted to do his utmost to keep his word. And that, by itself, is a reputation that is exceptionally hard to acquire, and even harder to keep. That being said, it is your choice – to walk to not to walk. Even contemplating of the path – while not showing any observable change in external behaviour – is a strong positive karm – provided it is done with sincerity. It is a start. A start on a very long but very rewarding path.

Does Merit Matter To Us?

noreply@blogger.com (Reflections Of Vishal V Kale) — Wed, 22 Jul 2026 08:22:18 +0000

Does Merit Matter To Us? How many qualify amorally, creating a victim who lost to your dishonesty for each such case? For every person who qualifies through dishonest means, someone else pays the price — a victim of a system that let them get away with it. At the start of this article, let me be clear: I am 53+, and not dissatisfied with how my life turned out. I am perfectly happy - if I had to do it all over again, I wouldn't change a single professional decision I made. But the recent events surrounding the NEET exam protests have forced me to look back at the past years, towards rumours, reports and claims of paper leaks, job scams, and gaming attempts. From Vyapam in MP to the repeated compromises of a multitude of entrance exams, we may be looking at a system in crisis.

It is disgusting and frustrating even to me, a person who is not affected; not anymore. And yet I feel the shock of what happened – how must it be for those who are actually impacted, whether as students or as parents? It is befuddling to me, a person who never cheated, who never tried amoral methods, to fathom this: and yet, people do it. Disgusting. No other word comes to me. If success comes so easy, why should we work hard at it? Isnt that the lesson we are giving? This is perhaps the biggest issue that is confronting us today, in this day and age. For these things build the future, and the coming generations. This is not something to forget – remember, even I – someone who doesn’t write politics anymore – is penning this here.

My Past

Like many of you, I gave these exams years ago. I was selected in one, missed being selected in another. A third I couldn't attempt fully because the education was simply too costly [walked out mid way giving only 50% answers], and I refused to burden my parents – and the fourth I just skipped – no way I could ask my family to fund that huge amount. No point even trying – so did not even make the attempt. And am proud of all decisions I made. PS – and my Dad was an MD Doctor, an Army Colonel no less. Still the cost was hitting me hard – I wonder about those students who made those calls and went. I didn’t – and am damned proud of that call. Told no one – and now Mom-Dad are with God, so they wont know.

A Hard Look

Watching the current crisis unfold, I find myself deeply worried for us as a nation, driven to three distinct realizations:

1. The Uncounted Cohort: Where Are the Cheaters Now? How many people in India today qualified through these exams via unhealthy means? What is their career trajectory? We know that scams like Vyapam date back to the 1990s. That means people who bought their exam papers decades ago are now in jobs all around us. We are operating alongside a cohort whose foundational entry into the system was rooted in theft. What happens to a nation when its critical institutions are run by such people?

2. The Destroyed Lives of the Honest For every seat bought by a cheater, a seat is stolen from someone eligible. Has anyone wondered what happens to those destroyed lives? We are seeing the answer now. It contributes to the despair that has already driven far too many young aspirants into hopelessness. It is measured in the debt of parents who take out loans, or money from savings. to fund coaching, only to have the system betray them. It shatters the belief that hard work matters, robbing millions of their only ladder out of poverty. It eats into the ability to dream – which is the core of development, the fundamental catalyst. When will we, as a people, learn this?

3. The Desperation of "We the People" The fault is as much ours as it is the system's. We – at least some of us - are so desperate for success that we forget we have a fundamental obligation to behave the right way. We dismiss it as "Kalyug"—accepting moral decay as inevitable. Yet, if we the people did not expect this, if you didn't expect a desperate population to eventually compromise the system, you (we) haven't been paying attention. So, we need a system that guards against these tendencies, watches over them. But where are these guardrails? Where are the protections? (Surely some protections, forward thinking, can be built into the system, to protect the system and ensure its sanctity)

A Societal Cancer

I comment on business, marketing – and, at times, poetry. My Instagram has nothing except my poems and my Upanishad Series slides. Yet, even I am drawn to penning on this. That doesn’t make me a CJP supporter - in fact, I don't trust this sudden rise, and co-opting this anger overnight- with millions of sudden followers doesn't inspire my confidence. But stripped of the politics, this – the exam scam - is a societal cancer.

I look back at the exams where I wasn't selected, and today, even I can't help but wonder: were my marks real, or were they manufactured by a broken system? To be clear – they probably were: I gave those exams and know what I wrote. And I am happy with where I am. And yet, here we are. So for the millions of youth standing where I stood decades ago, this theft of merit is a tragedy we cannot afford to ignore.

Conclusion

I have lots of questions, a storehouse of disgust, and no answers. It is taking all my practice on meditation, Advait vedant to control my response, and accept what has happened as Kalyug, and that I have to navigate through it somehow. But what I can do is ask these questions, in the hope that I may instil the same questions in people who read this. This is not meant as a comment on either the demonstrations, or the Government – this is just the heartfelt lament of an Indian on the other side of 50. This is just a simple set of thoughts that course through my mind as I silently watch this episode unfold in front of my eyes.

India’s Rail Mastery And The Silence Of Global Media

noreply@blogger.com (Reflections Of Vishal V Kale) — Mon, 20 Jul 2026 09:19:50 +0000

India’s Rail Mastery And The Silence Of Global Media

It was a morning news scan, when yet again the hydrogen train in India came to mind. And then, next came my daily scan of a top US daily – I scan Indian and Global news daily. It occurred to me, when I saw a negative article on India judging from the headline, that this hasn’t received attention globally, at least judging from my limited daily scans. Then I started on 3 LLM portals to research this – ChatGPT, Gemini, and Claude. What follows is a article from that quick research. A quick random dip stick checking of the facts has been done, followed by a full article scan through claude for fact checking. One fact – ticket price was found slightly off in my check, and was edited in the text.

What I want to point out in this article:

1. India’s stunning record of railway development, laying a strong backbone for the nation’s development

2. Western Media’s absolute silence on this (and, I suspect, many other positive news from our nation), barring reuters and perhaps AP – kudos to both

The Global Conversation

The global conversation around sustainable transportation is filled with conceptual grand designs, venture-backed transit mockups, and high-visibility boutique pilots. Yet, when the largest rail transformation in modern history quietly approaches its final mile, the global media response is telling: absolute silence.

On July 17, 2026, India marked a major milestone by flagging off its first domestically developed hydrogen-powered train—the "NaMo Green Rail"—on the 89-kilometer Jind–Sonipat route in Haryana. But the real story is a larger, structural paradox. This pilot didn't drop into a legacy network dependent on fossil fuels. It was introduced onto a national rail network that has achieved 99.6% broad-gauge track electrification.

The scale of this execution—and the Western media’s failure to report it—highlights a deep narrative asymmetry in how global engineering achievements are evaluated.

1. The Hard Data: India's Dual Engineering Feat

The deployment of the hydrogen train is technically impressive, but its context within India's broader network infrastructure is what makes it a globally unique case study.

The Hydrogen Breakthrough

Unlike the 2-to-4-coach configurations common in some of the European and East Asian demonstration runs, India's inaugural hydrogen train was launched as a 10-coach configuration (eight passenger cars and two power cars) capable of transporting 2,600 daily commuters.

Capacity & Power: Fueled by a 3,200 HP indigenous hydrogen fuel cell system, the zero-emission train generates electricity on board by combining hydrogen with oxygen, leaving behind nothing but water vapor.
Economic Viability: While global green tech is often criticized for high premium costs, ticket pricing for the Jind-Sonipat corridor matches ordinary local train fares, ranging from ₹5-25.
Infrastructure Support: The train is backed by a dedicated 3,000-kg capacity green hydrogen storage and refueling plant built right at the Jind hub.

The 99.6% Electrification Standard

The hydrogen pilot serves targeted regional runs, because the massive mainline network has already transitioned to electric power. As of mid-2026, Indian Railways has electrified 70,002 route kilometers (RKM) out of its 70,271 RKM broad-gauge footprint.

The Pace of Execution: Prior to 2014, less than 20% of the national network was electrified. Over the past decade, the state-led deployment accelerated to a peak average of over 16 km of newly laid overhead catenary lines per day. Today, only 269 RKM remain to achieve absolute 100% network electrification. This shift alone has cut rail diesel consumption by more than 60%, insulating the domestic economy from global crude volatility.

2. The Global Infrastructure Divide

To understand why a 99.6% electrification rate is a global anomaly, one has to look at the state of railway networks across major Western powers.

Country / Region	Electrification Rate (2026)	Primary Infrastructure Focus	Structural Bottleneck
Switzerland	99.8%	High-density Passenger / Alpine Transit	None (Fully state-integrated utility model)
India	99.6%	High-density Passenger & Industrial Freight	Last-mile connectivity in extreme terrains (269 km left)
European Union (Avg)	~58%	Balanced Passenger and Freight	High fragmentation across borders; underfunded regional branch lines
United Kingdom	~38%	Mixed Trunk Lines	Reliance on expensive "bi-mode" diesel-electric compromises
United States	Less than 1%	Private Heavy Industrial Freight	Corporate opposition to upfront capital expenditure on tracks

The U.S. Versus India: Public Strategy vs. Private Stagnation

The United States operates one of the most massive rail freight networks on earth, yet its mainline electrification rate sits below 1%. The reason is structural. U.S. tracks are owned by private "Class I" freight corporations. Because double-stacked diesel trains operate efficiently enough over vast geography to protect short-term corporate margins, private operators have successfully blocked electrification due to the high upfront capital costs of building overhead wires.

The UK's Pragmatic Compromise

The United Kingdom has electrified just over a third of its physical tracks. To avoid the trillions required for a complete grid overhaul, the UK heavily relies on "bi-mode" trains. These units draw clean energy when traveling on major electrified trunk lines, then seamlessly switch to onboard diesel engines when branching out into older, non-electrified territory. While pragmatic, it leaves a permanent fossil-fuel footprint on the network.

3. Narrative Asymmetry: The Anatomy of Media Silence

A quick rapid analysis of prominent Western mainstream news outlets reveals almost no standalone coverage of India's hydrogen train launch or its near-total network electrification milestone, barring a report on Reuters and AP that I could spot in a rapid scan.

This media blind spot is driven by three distinct editorial patterns:

1. The Outdated "Developing Nation" Script

Mainstream international reporting frequently relies on fixed, legacy templates for the Global South. Headlines are easily assigned to logistical friction, extreme weather disruptions, or developmental deficits. A highly coordinated, state-backed engineering masterclass that easily outpaces Western network execution averages does not fit the established script, and is consequently filtered out.

2. The Mirror of Domestic Inefficiency

For transport editors in Washington, London, or Berlin, highlighting an overseas network laying down 16 kilometers of high-voltage electric track per day creates a glaring contrast with local realities. With the ongoing budget contractions of the UK’s HS2 project, continuous delays in California's high-speed rail corridor, and routine funding battles surrounding Amtrak, the unprecedented speed of India’s infrastructure buildout is an uncomfortable comparison.

3. The Boutique Tech Bias

Western tech journalism frequently favors speculative, venture-backed "future tech" concepts—such as hyperloop animations, urban air taxis, or limited EV trials—over boring, large-scale industrial execution. A 10-coach hydrogen commuter train or thousands of kilometers of standard 25 kV AC overhead wiring isn't considered "hot" technology by these standards, even though it delivers the ultimate objective: dropping national carbon emissions while building structural economic autonomy.

4. The Strategic Underpinning

Why build a hydrogen fleet if 99.6% of the network is already wired? The answer lies in long-term strategic planning rather than immediate passenger logistics.

By testing hydrogen fuel cells on active branch lines and heritage routes where overhead wires are physically or ecologically impractical, India is deliberately building a mature domestic blueprint for high-pressure hydrogen storage, transport, and safety logistics. The primary value of the "NaMo Green Rail" pilot is not just moving commuters from Jind to Sonipat; it is the real-world hardening of an energy ecosystem. The engineering data gathered on these tracks will ultimately be used to decarbonize the country's heavier, harder-to-abate sectors like steel production, heavy shipping, and chemical manufacturing.

Ultimately, industrial milestones are validated by the numbers on the ground, not by the column inches dedicated to them abroad. Whether the international press prints the data or not, the strategic autonomy gained by insulating a nation's core logistics network from global oil volatility remains an undeniable economic win.

A note on methodology: This article was researched with the assistance of AI tools (ChatGPT, Gemini, and Claude), which were used to gather, cross-check, and synthesize information from publicly available news reports and government data. All factual claims — figures, dates, technical specifications, and comparative statistics — were subsequently spot-checked and, where needed, corrected against primary sources. The analysis and opinions expressed, however, are the author's own. As with any AI-assisted research, readers should treat this as a starting point for understanding the topic rather than a definitive or exhaustive account, and are encouraged to verify specific figures independently before citing them elsewhere.

Why Large Indian Corporates Fail at Innovation

noreply@blogger.com (Reflections Of Vishal V Kale) — Sat, 18 Jul 2026 06:48:21 +0000

Why Large Indian Corporates Fail at Innovation

A recent piece in the Business Standard accurately pointed out a glaring problem in India’s business landscape: while our startups build frontier tech and the government successfully drives massive digital and defense innovations, our large, legacy private corporations are lagging. The popular consensus often blames this on "rent-seeking"—the idea that large incumbents are too comfortable operating as oligopolies in protected markets to bother with R&D and building innovative ideas and stoking innovation and experimentation in their organisations.

While rent-seeking is certainly a factor, in my long experience of large organisations, it is not the predominant one. When you spend decades in the trenches of corporate strategy and execution, a very different reality emerges. Companies do not actively hold board meetings and vote against innovation. Instead, they fail because of a massive structural and cultural misalignment. The very machinery built to optimize current profits is the exact machinery choking out future growth. Remember – there are excellent organisations that do not exhibit these factors; but these exceptions prove the rule, for their number is small by comparison.

Breaking Down The Real Gaps

Based on my observations across the corporate ecosystem, the innovation gap is not a capital problem; it is a systemic organizational failure. Here is a breakdown of the 10 underlying points of failure, grouped into four core pillars of corporate stagnation.

Pillar 1: The Information Void and the Leadership Disconnect

Innovation in the private sector cannot exist in a vacuum; it usually stems from a tangible business need. If an idea cannot eventually be mapped to profit, cash flow, and market expansion, it is merely an academic exercise. The other path for innovation is insight and market understanding – which yet again need knowledge, leading back squarely to information void.

1. The Leadership Shortfall in Recognizing Business Needs

At the highest levels, leadership often fails to recognize that a critical business need for innovation even exists. This isn't necessarily due to a lack of intellect, but rather a profound isolation from the market – the senior leadership team lives in a walled-off glass cabin, isolated from the true realities of the market. To make matters worse, few if any managers attempt to scale this gap and educate them – if they do, they either get sacked, or pulled down, or most likely ridiculed and shamed while being insulted by their colleagues = it is a culture issue.

2. The Broken Ground-to-Boardroom Pipeline

Why does this shortfall happen? As I stated above, it is because the reality of the market is simply not communicated upwards. Innovation opportunities bubble up from the edges—from the friction in the customer journey and frontline operations. But when top management is entirely insulated from ground reality, they make decisions based on sanitized dashboards rather than market truths. This isolation is the result of a multitude of factors – just some of which is indicated above. Add one more: they themselves rarely, if ever, check.

Pillar 2: The Legacy Performance Engine

If you want to know why a company doesn't innovate, look at how it promotes its people.

3. Rewarding the Past, Ignoring the Future

Take a hard look at the rung directly below top management. These leaders have been promoted through a series of tactical achievements based on a rigid set of parameters that haven't changed in a decade. They are promoted based on past execution, not future potential or strategic imagination.

4. The Art of Gaming the System

When an organization is purely numbers-driven, people get exceptionally good at gaming the system. If achieving the end-quarter number is the only thing that matters, and the organization turns a blind eye to how that number is achieved, managers will squeeze existing models for short-term gains rather than risk a temporary dip to build a better system. To make matters worse – there is a strong chance of unethical foundations of the methods adopted by these successful managers: and remember, ethics is not a part of the PMS. Checkmate.

5. Ignoring the Process

The structure of these achievements is fundamentally flawed because companies measure outcomes, not processes. Innovation is a process—it requires trial, error, and iteration. If the process isn't measured, it isn't valued. That is why people go ahead and adopt shortcuts, unethical tactics and more – no one cares. The exception proves the rule.

6. Archaic Performance Management

To summarise, if innovation—or the behaviors that drive it—were actually embedded into the performance management system, this gap wouldn't exist. But because HR systems are still wired for legacy manufacturing and sales-first eras, they actively punish the non-linear path that innovation requires. To put it in brief – the world changed; our organisations did not. Nateeja sabke saamne hai – the result is there for all to see. Finally – the perverse incentive structures inside the PMS, for example (and there are many examples of this) – rewarding the end result disproportionately, skews what little process is measured in it.

Pillar 3: Cultural Paralysis and the Incumbent’s Fear

Even with the right structures, the unwritten rules of an organization will dictate its fate.

7. A Culture Devoid of Upward Honesty, Innovation, Integrity and Process Orientation

If the overall organizational culture does not support transparency, process orientation, integrity and innovation plus psychological safety, and honest bottom-up feedback, you are dead in the water. A process-oriented culture where employees feel safe speaking their minds and communicating brutal facts upwards is a prerequisite for innovation. Most large corporates severely lack this, to say nothing of the other factors listed

8. The Incumbency Factor

This is perhaps the most insidious, unspoken truth of corporate stagnation. When a mid- or top-level manager achieves their position by mastering the current rules, a deep psychological fear sets in when those rules threaten to change. “If the business pivots, will I still be relevant? Will I be able to adapt?” Rather than risk exposing their own obsolescence, incumbents will actively suppress new paradigms, burying innovative ideas to protect their own turf. In addition to fear – there is a belief that “my way is the only way”.

Pillar 4: The Capability Void

Finally, even if a leader sees the future, they often look behind them to find they don't have the army to conquer it.

9. The Knowledge Gap in Spotting Profit Potential

Many organizations simply lack the internal knowledge required to judge whether a bubbling business need can actually be converted into profit. The skill sets required to evaluate a new, unproven business model are vastly different from the skill sets required to audit an existing one. And knowledge isn’t limited to this factor alone – we can write a book on knowledge shortcomings in organisations – covering many other attributes – functional skillsets, technology, market knowledge, moral and ethical gaps, process and risk sciences, and many many more.

10. A Paucity of Innovative Skill Sets

Because organizations have spent the last many many years hiring for compliance to the status quo , standard execution, and maintaining the status quo, they have never factored innovation capabilities into their hiring processes. The talent required to architect new growth simply isn't in the building – because hiring was along the set parameters, experimentation was and is frowned upon, conformity to the prevalent culture was mandatory and more.

The Bottom Line

Blaming the lack of corporate innovation on macroeconomic "rent-seeking" lets organizational leadership off the hook entirely.

The reality is far more difficult to fix: large Indian enterprises are operating with outdated performance metrics, fear-driven incumbent middle management, broken internal communication, and a talent pool optimized for the past.

Turning this ship around doesn't require a government mandate or a shift in the regulatory environment. It requires a fundamental, painful sea change in organizational approach, starting from how we hire, how we measure success, and how willing leadership is to step out of the boardroom and face the reality of the ground.

Until that cultural and structural shift happens, the true innovation engine of this country will remain firmly in the hands of agile startups.