Why Spreadsheets Still Rule Finance and Why That’s a Problem

By Martha Ryan

In an informal poll of 20 former colleagues, now working at 20 different Silicon Valley firms, every finance department used spreadsheets to produce their monthly and quarterly reports. All their companies are at least $500M in sales and they have a host of expensive ERP and FP&A applications that keep track of their operations and finance activities. So how do they close the books? Analyze pricing? Explore merger opportunities? Plan for expansion? Collect budget inputs? Generate forecasts? Integrate newly acquired entities? Spreadsheets.

Survey results cited by The Saas CFO on LinkedIn cites that spreadsheets have a 73% market share for financial planning and analysis. Finance Director Arnaud Lemaire summed up the situation nicely in a comment: “[Spreadsheets] are winning because the data feeding the forecast is messy, the process changes every quarter, the person building the model needs to tweak things nobody anticipated when the platform was configured. FP&A tools work great when inputs are clean and stable. Most companies aren’t there yet.”

Spreadsheets are used and re-used for critical decision making and reporting. They are almost all extremely complex with 10s of thousands of formulas. In this post, I will detail the problems with spreadsheets, how they come to be so complex, why companies depend on them, and what might be done to address the multiple challenges they pose.

Why Spreadsheets Become So Complex

Complex spreadsheets are built by talented finance professionals who take pride in their ability to tap into their deep knowledge of obscure formula types and layer on a bit of code to produce amazing tables and charts that point their leaders to a decision. And then, in previous job markets, this talent moves on to another position, another company. In today’s job market, talent is laid off with executives expecting that AI will fill the gap. The smaller remaining team has two options: decipher the thousands of 10-line formulas throughout 30 worksheets, tapping into Claude or Chat GPT, and still spend dozens of hours figuring out how the workbook actually works. Or these teams use the analyses as is without really understanding them. They make changes and pray to not see the dreaded #error. Or worse, they get no #errors and assume they are correct, only to discover months later that a key column has been excluding key data because of a formula error.

The Fragility Problem

CFOs know that their teams depend too much on spreadsheets. They queue up for IT resources to add code to the ERP systems to meet their needs. It’s a never-ending queue because business is dynamic and the needs constantly evolve. Other crucial projects take up IT resources and CFOs continue to rely on spreadsheets. CFOs expect that AI will help. They are not exactly sure how, and still have a deep-seated confidence that AI is the solution that will ensure the integrity of the quarterly results, the new direction the forecast is pointing, or the proposed acquisition really is a slam dunk. In a survey two years ago cited in The Wall Street Journal, 40% of public company CFOs were not confident in reported results. AI has been evolving at a screaming rate since then. Will it help?

Have you thrown a complex formula into Chat GPT to understand what it is trying to do? It kicks out a line-by-line definition of how the parentheses are impacting the “if, then” aspects of the formula. Claude takes it a step further and reflects the column/row header names, so you don’t have to hunt these down yourself. If you are good at keeping track of 10 different moving pieces and how they interdependent, you can then move on to the next formula in a complex workbook of 50+ formula types. What neither solution will do is decode the institutional memory buried in the workbook.

Let’s say that AI plus “elbow-grease” helps you decode the spreadsheet function, its dependencies, and its weaknesses. The institutional memory aspects often address the “why is this so complicated?” and is assumed common knowledge by the creator. You back into this awareness when you do the decoding. Often, it takes a whole other level of questioning to understand the history, data weirdness, and rationale for why “it’s always been done that way.”

No matter the case, spreadsheet are still the norm for critical analyses. A team dependent on spreadsheets is dependent on tools out of its control, understood in detail by one or two experts. The team that relies on these analyses and experts for repeated, critical path analysis is teetering on a fragile base.

How Complexity Creeps In: A Familiar Story

Complex workbooks often start with a modest request. “Please provide an analysis of the current MSRPs and determine the characteristics of lost customers as a result of the price increase last month.” Assigned the project, an enterprising analyst collects the relevant raw data from company ERP sources, uploads them into a new spreadsheet, and then starts sum-if-ing, xlookup-ing, match-ing, and tallying the results in a summary sheet. They look funny. He notices there are some outliers skewing the results. He figures out how to identify and exclude the outliers. To keep the workbook ‘robust’ so he can re-import the same dataset from a different period, he excludes the outliers with a formula. To not overwhelm the workbook, he nests that formula inside an xlookup.

The summary reflects new totals and still something is off. Research reveals there are some duplicates – not sure how those got in there. Make a note to follow up with the IT team to fix the data. He nests another exclusionary formula. The summary starts to look reasonable, so he shares it with a manager. She points out that it would be great to see if the channel had any impact on the pricing. His data doesn’t include channels. He learns that channel data comes directly from the distributor via a PDF file. After scanning it, he realizes the distributor PDF uses the marketing part number instead of the manufacturing part number. The marketing number has a one-to-many relationship with the manufacturing part number so matching becomes tough. The analyst figures out a nifty way to pivot table the results. The pivot adds three more tabs to the workbook including a crucial step of copying values into the third sheet – he makes a mental note to flag the hard copy sheet.

The evolution continues with the manager’s manager adding a few suggestions. Within a few days, the analyst has created a 30-tab workbook with table functions, pivot tables, over 10,000 formulas, hard coded entries, all parsing raw data from multiple sources to provide the analysis. The next day, the analyst starts to cleanup, labeling the tabs, and column headers. He is interrupted to work on another critical deliverable and doesn’t pick up the analysis again for a few days. He spends 30 minutes refreshing his memory of what he did on Monday and proceeds to make the workbook more robust with those cool somewhat obscure formulas he learned online that are just right for this analysis.

The meeting with the CMO is next week and the analyst has time to put together some clever charts which makes the workbook even larger and requires a bit more manipulation – a second and third summary tab so the data will reflect in the chart in a way that can be best appreciated by the CMO. Unfortunately, the CMO was called out of town on an emergency and the meeting has been pushed out two weeks. The analyst moves on to other things and as the rescheduled meeting approaches, tries to remember where exactly he put that analysis. After a fifteen-minute search, he finds the most recent version in a weird folder on his laptop where he saved it because the WiFi was broken at his apartment and he wanted to do some additional work at home. He refreshes the data (since a whole month has now passed) and fortunately everything looks good.

At the meeting, the CMO is very impressed with the analysis. She asks if it can be the basis of a forecast template? Of course! The analyst returns to his cube and makes a bunch more changes. Pricing updates every six months, and the new proposed pricing won’t be available for another two months. He saves the analysis on the network, labeled with a naming convention that is sure to be understood.

A month later the analyst gets an incredible offer from another firm and leaves the company. Another month after the analyst leaves leave, his now-previous manager has an outstanding commitment to the CMO for a new forecast deliverable and no idea which version on the network is the right one. All the data needs to be refreshed, and the next analyst doesn’t realize that the downstream summaries depend on hard coding the results of that pivot table on tab 25 (who would do that?) The next meeting with the CMO doesn’t go very well.

The scenario where key decisions depend on spreadsheets affects more than the finance department. The planning department and marketing department and reliability team and product development folks all rely on manipulating data from multiple systems which don’t quite capture their current needs. The industries affected are similarly widespread – manufacturing, SAAS, services, consultants, and government. It affects large, medium, and small companies. The smaller and younger the company, the more likely they depend on spreadsheets. A small concern I know with $20M in sales, had a plan to transition its planning from spreadsheets to a demand planning tool until “the downturn”. They are still using those same complex spreadsheets to plan their now $10M company with only one person who understands the plan.

Why Companies Stay Dependent

To summarize: spreadsheets are relied upon because large systems are incapable of adapting to the rapidly changing environment. They are complex because the data are imperfect and analysts must create huge workbooks and nest data cleansing and merging with the analysis. The spreadsheet skills of analysts are self-taught and different analysts use different formulas and approaches that can be a mystery to fellow analysts. The logic and sequence are difficult to visualize because they are buried in cells and sheets without an overall map. Once created, spreadsheets are saved in a range of shared and individual folders. Finding them requires diligence in naming the file, naming the folder, and managing versions. Limited time and resources mean that critical documentation details are frequently an afterthought.

Spreadsheets certainly have their place. What else gives you the flexibility to build on the fly, manipulate data, and summarize in tables and charts? However, as soon as you start using them repeatedly for critical decision making or reporting, you encounter myriad pitfalls of their fragility. When Arnaud Lemaire says that “most companies are not there yet” with clean data and stable inputs, I wonder what company is so staid and methodical? What analyst works in a predictable environment with such long sightlines that IT can be called upon to re-configure the ERP and finance systems to meet unanticipated needs nine to twelve months in the future? Dynamic describes all markets and the companies that address them. Even the government is rapidly evolving. Layer on budget and resource constraints in IT and among the data analysis teams, and spreadsheets soon become the analysts’ go-to for close, forecasting, pricing analysis, budgeting, integration, etc. These uses are constantly evolving and underpin critical decision making. Hence, the inherent fragility becomes a significant corporate risk.

This begs the question, really? After all these years, that’s what we have? Yes, that is the current situation. How does a CFO or finance manager deal with this reality of fragile dependency?

What Can Be Done: Practical Steps Forward

Getting your company’s data in order is a critical, if unglamorous, first step. AI can help find data anomalies and be enabled on a continuous search provided it is well trained on the typical examples of errors. A key source of institutional memory can be replaced with systematic corrections and overhauls. By eliminating the formulas in a spreadsheet that address data corrections, their complexity could be reduced by maybe 20%.

Reengineer with Structure (SIPOC)

Reengineering the spreadsheets periodically is another important step. With your AI assisted clean data, arrange your worksheets leveraging the concept of SIPOC:

• Supplier,
• Input,
• Process,
• Output,
• Customer.

Identify the data sources and what fields you need from each. That takes care of the Supplier and Input.

The Process can be a bear. Often first built with random improvements, on short timelines, workbook formulas get longer and longer.

1. Take a step back and understand the overall objective.
2. Figure out the simplest way most of the analysis can be accomplished.
3. Keep a list of the corner cases and don’t embed them in the general analysis. Rather, segregate them in well-labeled sheets with simple and, if necessary, numerous distinct formulas instead of long complex ones.
4. Consider simplifying by using models to address the occasional situations. For example, buffer the results with fixed percent adjustment, or group small outliers together as a single entity.
5. These techniques work well in future looking analyses where there are many unknowns anyway. For both general and corner case treatments, be sure to document the processes.

Outputs are your results, they go hand in hand with your Customer.

• What is your team looking for in the results?
• What questions are they trying to answer?
• What insights do they seek?

In defining your results, KISS. As the workbook creator, you are deeply invested in the analysis. You know all the corner cases and all the pitfalls. Chances are this knowledge will NOT clarify the results. Frame the output as primary objectives (no more than 3) and secondary objectives (your long list of details and exceptions). Restrict the graphs and tables to the primary objectives. If someone wants to deep dive, design the results of the secondary objectives for those who know spreadsheets well and can navigate the data leveraging filters and pivot tables.

Spreadsheets – The Strategic Risk and Opportunity

None of the ideas I have outlined here are new or rocket science. They reflect decades of experience and observation learned from the value of discipline. The fundamental concept is to think of workbooks as a process. By cutting out wasted steps and complexity from the spreadsheet process, the streamlined workbook will help leverage AI. It will also help transition to ERPs and analytical software. The most essential motivator, however, is the risk a team incurs by NOT being disciplined, by NOT simplifying, and by NOT harnessing the institutional memory. In the short term, the results can be wrong and/or lead to bad decisions. In the long term, it will guarantee missing the benefits of the AI train.

About Martha Ryan

Martha Ryan is a co-founder of zValuate, a startup that provides analysts a tool to bend data and answer complex queries. Visual, fast, accurate, and collaborative are key objectives. Previously, she spent 25 years in finance and accounting at two Fortune 500 technology companies, deep in spreadsheets. Always striving to make things better, she earned a Lean Six Sigma Black Belt and led 100+ cross-functional
process improvement initiatives. She and a few colleagues invented zValuate to address the challenges of spreadsheets that frustrated them as analysts.

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Mark Stiving walks through the Six Laws of Value as a practical map of how buyers actually make decisions. Not how we wish they did. Not how sales decks assume they do.

Each law explains a specific point where buyers hesitate, defer, or quietly disengage. When those breakdowns happen, price becomes the scapegoat.

Mark explains that pricing success depends on understanding buyer confidence, not just features or cost. Buyers make decisions by predicting future outcomes and choosing the option they feel most confident about. Confidence is driven by three factors: payoff (value), probability (likelihood of success), and anticipated regret (risk of failure).

Value comes from solving real customer problems, yet buyers often express solutions instead of underlying issues. Sellers who clearly articulate customer problems—better than the customer can—build trust, increase confidence, and raise willingness to pay. Most deals fail due to lack of commitment, not competition.

Mark Stiving on Pricing Problems

About Mark Stiving

Mark Stiving is a pricing expert, speaker, and author who helps companies make more money by figuring out how buyers actually decide what to pay, then aligning products, packaging, and pricing with that reality. He pushes hard on value-based and context-driven pricing and spends very little time on academic theory and a lot of time on what moves revenue and profit in the real world.

He’s best known for simplifying complex pricing concepts into frameworks that executives and teams can actually use. His work shows up in books, bootcamps, boardrooms, and consulting rooms, often with companies that have strong products but weak clarity on how to justify and capture their value.

The unsugarcoated summary: Mark helps organizations stop guessing at prices and start getting paid for the value they already deliver.

Few voices resonate with the clarity, insight, and expertise of Mark Stiving. His profound understanding of buyer behavior and his unique ability to distill pricing strategies into actionable insights have cemented his reputation as a sought-after advisor and instructor. His firm, Impact Pricing, works with private equity firms and their portfolio companies to focus on customer value, optimize pricing, and maximize valuation.

He hosts the popular Impact Pricing Podcast, has a Ph.D. in pricing from U.C. Berkeley, and is the author of four books:

• Impact Pricing – Your Blueprint for Driving Profits (2011)
• Win Keep Grow – How to Price and Package to Accelerate Your Subscription Business (2021)
• Selling Value – How to Win More Deals at Higher Prices (2022)
• Instant Profits – How to Raise Prices Without Losing Customers (2024)

The AI Gold Rush: It Still Takes Teams

Teaser quotes

Sean: We’re in the age of gentleman science. They’ve deployed these models, but they don’t really have a use case. It’s kind of like they’ve deployed new instruments and now we’re learning how to play them.
Jeff: It’s not going to happen overnight. And how is that change going to be proliferated into your organization? How are you going to leverage this new technology? It’s teams that develop products, not just individuals.
Sean: Westinghouse would pick a hard problem to solve that was the first example of a new kind of use case. The cost reduction drives more use and then more use leads to novel structures.
Jeff: Okay, time out here. This is how this thing works. This is how we want, this is how we need to implement it going forward.
Sean: NVIDIA is not talking about laying people off. Right? NVIDIA’s probably embraced this and they’re doing some amazing stuff and they’re not saying yes. And what we hope next year is to lay off 20% of our staff.

Edited Transcript

Norbert: This a conversation with Sean Murphy and Jeff Allison, both of whom have spent decades in Silicon Valley high tech. We break down what AI is, what it’s not. And how organizations can adopt it without losing the craft of engineering. I’m Norbert Korny, let’s jump right in.

Sean: I’m really excited to be here, to be in a conversation with some other guys that actually know what they’re talking about, so that I can, by association, at least appear as if I belong in the room. I’ve worked on introducing various forms of automation, computerization, and digitalization to teams and organizations since 1980.

I think that AI provides new affordances and new capabilities. But there are a number of useful rules of thumb for innovation that will still hold. We are in the process of discovering what these new possibilities can do for us. But my focus has always been, going back to Doug Engelbart, the augmentation of human intellect, not the replacement. The trick is to augment people who are good at their jobs to help them develop and extend their expertise.

This idea of technology change, managing the introduction of new capabilities into organizations, is something that I’ve worked on for small firms and large firms for a while. In 2003, I started SKMurphy, and I now work with early-stage technology companies, helping them navigate the introduction of new products and methods to business customers.

Jeff: My name is Jeff Allison. I’m so glad that you’re pulling this together, Norbert. There are so many conversations happening around AI, and so many that need to happen.

Every day, I open my newsfeed and read that AI is going to do this, AI is going to do that, and wonderful things are going to happen. And I think they will, but the conversations need to get into more detail about the realities of AI and how it can help.

I have spent more than 30 years in the Valley. The Valley is just an innovative hotbed. It’s full of very creative people who have created fantastic technology. There are so many unsung heroes. You walk down the street and may meet someone who was involved in developing IBM’s ATM infrastructure years ago. They were able to do that because they were supported in the challenges they faced. Their organizations invested in tools and methodologies to enable what they developed individually and in teams.

I think AI is going to help push innovation to much higher levels, but it’s not because they’re using AI; it’s because of how they’re using it and how we create infrastructure to enable that. It’s not just about saving cost; that’s not the best way to look at it. It’s about how to help people do their best in a shorter period of time in a way that can be absorbed by the overall team.

Individuals come up with great ideas, but teams make products. Companies invest in teams. I have not mastered the new AI tools, but I do know how to introduce new technologies into organizations and what that entails. And it’s not as easy as, well, we’ll just bring this thing in, and people will start using it, and things will go great. There are all sorts of issues that need to be figured out. And I think being able to share some of my experiences with how difficult that has been over the years will help introduce this new technology into the development process.

“The future is already here–it’s just not evenly distributed.”

Norbert: I know you like the William Gibson quote, “The future is already here–it’s just not evenly distributed.” What do you think that quote gets right about AI in 2026?

Sean: It’s spot on. The new path forward only becomes clear in hindsight.

There’s an article going around that we’re in the age of gentleman science, where individual kind of amateurs are creating breakthroughs using chatbots and AI tools. And I think the major AI companies, they’ve deployed these models, but they don’t really have a use case. It’s kind of like they’ve deployed new instruments and now we’re learning how to play them. And so a lot of people are exploring the landscape, And I don’t think we’ve quite figured out the implications of what it means. And so I think what it gets right is that we’re back in an age of kind of individual exploration, individual tinkering. And we’ll look back and realize that something that happened six months ago or nine months ago, or maybe a year and a half ago, actually was the branch of the tree that we’re following. But right now we’re in this forest and it’s hard to tell.

It’s spot on. There’s an article going around that we’re in the age of gentleman science, where individual amateurs are creating breakthroughs using chatbots and AI tools. And I think the major AI companies have deployed these models, but they don’t really have a use case. It’s like they’ve developed new instruments and now we’re learning how to play them.

A lot of people are exploring this new landscape, but I don’t think we’ve quite figured out the implications or what it means. We’re in an age of individual exploration, individual tinkering. At some point, we’ll look back and realize that something that happened six months ago or nine months ago, or maybe a year and a half ago, actually was the branch of the tree that we’re now following. But right now we’re in this forest, and it’s hard to tell.

What’s Driving the AI Gold Rush?

Jeff: People who are doing new things or just starting out in their endeavor to create a new product or deliver a useful innovation, pick up new methods naturally. But 90% of us continue to stick with “the old way” because it’s proven, predictable, and we understand it. We need to talk about how those folks can get on board with this new product development paradigm. And I don’t think there’s enough being talked about that.

Also, the rate of change in AI tooling is still so high that new methods are evolving faster than they can be documented and debugged. So this transition from explorers and early adopters won’t happen overnight.  A lot of what’s being described is experiments, pilot projects, or proof-of-concept. There is much less about how a successful pilot is proliferating as a proven methodology in an organization. Much less about how to use AI technology to improve your methods and provide better products for customers. I think that’s where we’re at.

Norbert: Is the Gold Rush really about efficiency, innovation, or a competitive arms race?

Sean: I think our sense of a Gold Rush is driven by competition and a fear of obsolescence. I think there’s been a lot of messaging that “Everything you know is wrong” or “Everything is going to change.”

Lately, I’ve been fascinated by the introduction of electricity into both industry and daily life. In some ways, it was a struggle between Edison and Westinghouse. Edison was a tinkerer who would try many things in parallel. One reason I think Westinghouse ultimately set the direction toward an alternating current architecture was that he chose hard problems and solved them for industrial use. This was formed by his early sales to the railroad of an air braking system that had to work reliably.

When he wanted to test his AC dynamo paired with an AC motor, he went to a remote mine in Colorado and ran it all winter, debugging problems as they came up. His approach was to solve a real problem with a satisfactory level of performance. That’s missing from much of today’s embrace of AI. Many announce tiny incremental breakthroughs, typically based on contrived benchmarks, not real-world outcomes. Everyone wants to be five minutes ahead, driven by a massive fear of missing out.

How do we Define and Adopt a New Paradigm?

Jeff: New tools, whatever they may be, can help us come up with very creative ways of developing products. We have been doing this for decades in the Valley; it’s great. The more you can automate and enable individuals to be more productive, the more wonderful it is.

The challenge is that teams develop products, not just individuals. Whenever you’re bringing in something new, you have to consider how it will work within the team.
It’s great to become more efficient in one area, but that may account for only 10% of the overall product development cycle. It’s an improvement, but if it slows down 90% of it, then it’s not really a win. It takes a team to develop today’s complex products that deliver value for customers.

Part of what I worry about, and what I want more discussion on: how do we leverage this technology so that the team gets more efficient, the team gets more competitive and the team is able to innovate beyond current constraints and not just accept them.

When we would develop algorithms or integrated circuits to perform critical tasks, we would then review results and say, “Okay, this is good, but we need to make it better.” So, if this technology allows us to make things better, I think it’s a great thing. But how do we do that at a team level, not just at an individual level?

I think the true Gold Rush is about the teams that really embrace this technology and are able to use it at the team level to collaborate and to develop product, they’re going to win. The guys that just take it and say, okay, this makes me more efficient, and they’re in a little box doing it, They’re not going to win here. They’re just going to get more efficient at what they do and it’s not going to affect the overall product.

Norbert: Yeah, and it seems like the adoption is driven by the individuals, unlike previously for enterprises, even better yet, the army before then. And it acts like a force multiplier. To be best, you need to be good at the beginning.

Jeff: Yes, you do. And we observed this when we were putting automation into the hardware development process for developed systems on chips. We got really good at doing specifications, but then we realized that we were really good at creating millions of gates, but now we have to get really good at testing them. So then we had to bring more people on and develop methodologies around that.

So I think this is what’s going to happen with this AI. AI is going to allow us to move very quickly in certain areas. But this will uncover blind spots that we will need to address. We’ve moved really quickly here and were really innovative there. But what does that mean to the overall development process? What else do we need to do to get this thing to actually realize everything that is promised?

Sean: Couple of quick thoughts. In the beginning, most technologies get adopted because of a cost reduction of some sort, or they come in as toys or kind of an artisanal tool.

I think a lot of AI has been sold to the CFO, by promising to cut costs by allowing you to lay people off. The most recent example is Block just laid off 40% of their people. I think that’s a smokescreen for years of bad decisions on their part. When you actually look at who’s getting laid off, it’s a lot of people with expertise. So I don’t buy that story.

The next step after cost reduction is more use, and then or use drives the creation of novel structures. Right now we see time savings at the individual level and thus more use.

Norbert: I agree.The story Jack Dorsey is running with is that they built smaller teams to make them more effective. Is this is the best thing we can do right now, because an individual wouldn’t make that big a difference in an enterprise? Small teams, maybe yes, but if you got 10,000 people, is it enough?

Individual Insight Can Enable Breakthroughs

Sean: I think an individual can have a significant insight that leads to an architectural or a novel combination breakthrough. The breakthrough always comes first in one person’s head, or maybe two people working in an intense collaboration. But then, to actually ship a product or deliver an industrial-strength or highly reliable processor, normally requires a cross-functional team of four to twelve to look at it from all the right angles.

If we go back to the Gold Rush, you can trace an evolution of individuals panning for gold—which is an artisanal model—to industrial-scale mining and extraction methods. Today, I think we’re still in the panning-for-gold phase. I don’t think we’ve even figured out how to facilitate a team of six to twelve.

Jeff: Well, this technology was initially developed for consumer or individual use, not industrial. I don’t see much of enterprise or industrial focus on what teams of engineers or scientists need.

Sean: It’s hard to track everything that’s going, but the Perplexity guys seem to be passing a test of giving answers with substantiation with links. As opposed to providing an answer that you have to treat as if it was delivered by an oracle. So Perplexity is at least a half-step towards deploying something that could be part of a reliable business process.

Jeff: That’s a big step from a consumer-based product to company-base. We’ve all done that. If anyone’s trying to bring technology into their organization and it works well for an individual, there is still a long process to get it adopted by one more more teams.

It requires a higher level of reliability, support, and well-defined infrastructure to enable all of that: standards, databases, defined development processes, revision control, methodologies, etc. These support infrastructure elements all take time to figure out and develop, regardless of whether they are for an outside vendor or an internally developed tool. There are many steps from a proof of concept to getting something widely deployed, making many people more productive.

Different parts of the organization may have different architectures and different cultures. An established business unit can have a big centralized architecture, but newer smaller business units may be more decentralized with small teams, some scattered all over the world. And then Different organizations have different architectures, right? They work differently and may benefit from different support and infrastructure models.

I think there’s a lot to be discussed here. And I’m happy, Norbert, that you’re doing this and trying to shine some light on some of these issues so that we can have these kinds of discussions and kind of work through it. Because I think some of these things are just not getting talked about enough.

Norbert: Is the software something where we’ve seen the biggest impact? Because from my point of view, it seems to me the software seems to be reinventing itself. We introduced the concept of shared libraries to reuse logic. AI recently introduced skills. Now you need a skill repository, and there are  “malicious skills” you need to scan for security threats. And this is a whole software development lifecycle you need to revisit. And we’ve been through this already.

Adoption Takes Months to Years

Jeff: We could draw lessons from earlier disruptive technologies for software development. How long did they take to go from individual adoption mainstream use. It wasn’t days or weeks. It was months and years.

I don’t know how long AI will take to become a reliable mainstream development environment for even small teams. But I think to Sean’s point where you’ve got people that are making decisions on this technology, they really don’t understand the technology, but they are just looking at cost savings. I can pay for an agent that can develop a product that costs X vs. 5-10X for a human being.

That’s a metric, but to me, it’s very short-sighted. It’s no way to run the business.
I think we are at a point where the business guys are running the show, but I’m hoping that the engineering and development organizations step in here and to say, “Hey, okay, time out here. This is how this thing works. This is how we need to implement it going forward, and this is what’s going to make sense for us rather than just counting the dollars.”

Sean: The business guys remind me of the early rapid early evolution of HTML. Netscape on a whim decides to add the blink tag or to implement cookies in a certain way. I think we’re going to look back on MCP and on some of these other things constructs and conclude that they should have never left the lab.

MCP is an interesting first effort, but it’s not a reliable, scalable protocol, and it throws away 90% of what we know about security and protocol design. I’m okay with the individual experimentation and allowing individuals to scout and report on what they were able to do. But because of this consumer focus and this fixation on replacing professionals, knowledge workers, or white collar workers, they really haven’t engaged the people who define processes, the methodologists who look at this in a structured way. And it doesn’t seem like those people are on staff at the major AI companies; they seem to hire people with different expertise.

Another interesting thing for me is that Jensen Huang at NVIDIA is not talking about laying people off. NVIDIA has deeply embraced AI and is doing some amazing stuff, but they are not saying, “Next year we hope to lay off 20% of our staff.” They realize they are in a Red Queen Race, running faster and faster just to hold on to their position in the market. They understand that the bar for acceptable performance will be continually raised.

Jeff: I think NVIDIA is airing this technology out internally and they’re probably developing methods, processes, maybe developing tool chains around this to enable them to develop their product. I think those tool chains and those methods will become more available to people over time.

This reminds me of design automation in the early days of hardware. The first Design Automation Conference was held in 1964, but it wasn’t until 1982 that they formally recognized independent vendors with an associated trade show. EDA was incubated by larger organizations for almost 20 years. IBM and other big companies developed technology internally, so they understood it.

And then, those technologies became more commercially available, creating a whole market. So I think that has to happen here too. And I think NVIDIA is probably taking the lead on it. And there may be more companies out there that are doing stuff behind the curtain, right? That, you know, they’re not talking about right now, but they’re maybe saying, okay, we’re going to develop this next generation of product or this product, you know, we’re going to use an AI model on this product and we’re going to air it out. And once we figured out how it all works, then we’re going to make it more mainstream in our organization. Maybe that’s going on. That’d be great. It’d be great to talk to people that are doing that. The AI automation gurus of the future, right? Where are they and what are they doing?

Sean: It seems like there’s two tracks running here. So, I agree it’s very possible that there are organizations that are getting value and deploying this to make internal processes or design processes more efficient, and they’re getting big advantages that they’re not disclosing. Because most of the stories that are being told seem to be told from the perspective of, “Don’t worry, we’re not falling behind. Here’s some examples of how we’re using AI.” But when you dig into most of those stories, it puffery.

We are still in the early stages. The Design Automation Conference was a community of practice that allowed competitors to compare notes and learn. People would not give away everything they were working on, but there was still value in solving common problems.

I remember watching a YCombinator LightCone podcast where they essentially concluded, “We think we can solve electronic design kick the currently EDA industry in the ass in a year from now.” Not so much. The semiconductor industry and the EDA guys have been running a Red Queen Race ever since they passed Moore’s Law. They are incorporating this technology. We haven’t so far seen anyone come out who’s made current practices obsolete. I think the current vendors are obsoleting their current tools, but that’s different from some outsider using this to destabilize the industry. All the majors now have a VP of AI. Everyone on the semiconductor side, the systems houses, and the design tools are all looking at this. I think it will be like electricity: It’s going to raise the level for everyone.

Jeff: VP of AI? Good.

Sean: You asked where we’re seeing the biggest impact. I think right now it seems like it’s amongst experienced developers working on greenfield designs. That to me is kind of a splinter or a scouting activity. There’s some changeover point where you’ve got a working proof of concept and now you actually have to harden it and deploy it and support it. And that’s not going to be normally just one person. I don’t know that we’ve got the models for that yet.

Norbert: Yeah, but there’s the rush for AI to replace humans, especially the juniors. Do you see the impact on the job market in Silicon Valley?

Sean: That’s absolutely going on.

Related Blog Posts

• Mark Bennett on Using Claude Code for Application Development (Q&A)
• AI in Action: Practical Automation by Alex Panait (webinar)
• Matt Trifiro on Lessons Learned using AI for Marketing (webinar)
• Andrew Shindyapin: AI’s Impact on Software Development (Q&A)
• Alex Panait on Current Trends and Possible Futures for AI (Q&A)
• Time to Market S01 E03 – How Will AI like ChatGPT Impact B2B Founders? 

References

Gentleman Science

• Sean Goedecke “We are in the “gentleman scientist” era of AI research” (Oct-18-2025) “Many scientific discoveries used to be made by amateurs.”
• Garry Tan “AI is in its Gentleman Science Era”

YCombinator LightCone Podcast: Why The Next AI Breakthroughs Will Be In Reasoning Not Scaling

“One thing that really stood out about o1 in particular—if you read one of the papers talking about it, so capabilities and potential for the future—it talks about how it does really well in chip design.[…]  At some point the AI will get good enough to just like design chips better than like humans can, and then it will just like eliminate one of its bottlenecks for like getting greater intelligence. And so it feels like that’s already kind of like we’re on the pathway to that in a way that we just weren’t before.

SKMurphy Take: AI may have a strong impact on electronic system design but it’s unlikely to enable a startup to obsolete the established players. See for example

• “How to Plan Agentic AI Deployment for Chip Design.”
• AI Growing Impact on Chip Design

Image Credit: 3 views of Antelope Canyon (c) Kevin Murphy, used with Permission

This post was republished on LinkedIn at https://www.linkedin.com/pulse/ai-gold-rush-still-takes-teams-sean-murphy-lua4c/

Hope for the best, prepare for the worst

Some thoughts on the value of preparing for a range of outcomes from your current course of action.  I think the most famous version of this advice comes from a song by Mel Brooks.

Hope for the best, expect the worst
The world’s a stage, we’re unrehearsed

Mel Brooks lyrics to The Twelve Chairs, “Hope for the Best, Expect the Worst” (1970)

I think the best version comes from Zig Ziglar, urging a flexible response to a range of outcomes.

“Expect the best.
Prepare for the worst.
Capitalize on what comes.”
Zig Ziglar

The oldest versions I could find come from Benjamin Disraeli and Mary M. Bell, but this may have been a proverb in circulation in the 19th century.

“Hope for the best,
prepare for the worst,
and take what God sends.”
Mary M. Bell in “Seven to Seventeen; or, Veronica Gordon” (1873)

“I am prepared for the worst, but hope for the best.”
Benjamin Disraeli in “The Wondrous Tale of Alroy” (1845)

Tony Hsieh offers a more tactical prescription in his book “Delivering Happiness” that also applies to cash management for bootstrappers:

• Always be prepared for the worst possible scenario.
• Play only with what you can afford to lose.
• Make sure your bankroll is large enough for the game you’re playing and the risks you’re taking.
• Remember it’s a long term game. You will win or lose individual sessions, but it’s what happens in the long term that matters.

Tony Hsieh in his book “Delivering Happiness” in the chapter on “Poker”

I used this in Texas Hold’Em as a Model for Technology Startups.

“What you prepare for, what you hope for, and what you expect are often three different things.”
John D. Cook (@JohnDCook)

Make affordable loss bets

“Look at the Underside First: Legions of people are paid large sums to promote the positive aspects of commercially available products. Very few people earn their daily bread by pointing out malfunctions, bugs, screw-ups, design failures, side-effects and the whole sad galaxy of trade-offs and failings that are inherent in any technological artifact. To counteract this gross social imbalance, a wise designer and a wise critic will make it a matter of principle to look at the underside first.”  Bruce Sterling in Viridian Design Principles (1998)

There is much more at the Viridian Design Site. In “Constructive Pessimism,” I observed that you have to be willing to acknowledge the possibility of problems and look for them to be able to prevent or at least manage them. Many entrepreneurs who are naturally optimistic make a serious mistake in discouraging pessimistic thinking instead of putting it to good use. The clever utilization of constructive pessimism is one of the keys to success.

Postscript: Optimism vs. Confidence

Axiom 9: Optimism is expecting the best, confidence is knowing how you will handle the worst. Never make a move if you are merely optimistic. Know how you will handle the worst.”
Max Gunther in Zurich Axioms

Related Blog Posts

• Constructive Pessimism
• SKMurphy Perspective Counterbalances Excess Pessimism or Optimism
• Managing Recurring Problems In Your Startup
• Startup Uncertainty At The Very Beginning
• Making Business Decisions in Uncertain Times
• Q: How Do I Plan for Pivots or Even Shutting Down My Startup?

Quotes for Entrepreneurs Curated in March 2026

I curate these quotes for entrepreneurs from a variety of sources and tweet them on @skmurphy about once a day where you can get them hot off the mojo wire. At the end of each month I curate them in a blog post that adds commentary and may contain a longer passage from the same source for context.

My theme for this month’s “Quotes for Entrepreneurs” is data, stories, and evidence.

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“Research means that you don’t know, but are willing to find out.”
Charles Kettering

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“ABCD – Always Be Collecting Data”

I originally used “always be collecting data” on the SKMurphy Blog on Nov-29-2010 in “Keeping Your Customer’s Trust” Under Law 6 in Weinberg’s Laws of Trust. “Always trust your client–and cut the cards.” My comment was “Always be collecting data. Always be collecting multiple perspectives.” I then used it as the title for a blog post.

I recall seeing this phrase in a presentation as “ABCD – Always Be Collecting Data,” in the late 1980s or early 1990s. It may been a riff by the presenter on the Glengarry Glen Ross movie speech by Alec Baldwin where he writes “ABC – Always Be Closing” on the chalkboard during a briefing for the sales team.

I traced the phrase to an answer to a 1925 letter to the editor of Printers’ Ink; but I suspect the phrase is older.

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“A wise man proportions his belief to the evidence.”
David Hume

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“The first principle is that you must not fool yourself – and you are the easiest person to fool.”
Richard P. Feynman

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“No research without action, no action without research.”
Kurt Lewin

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“Without data, you’re just another person with an opinion.”
W. Edwards Deming

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People Manage People, Tools Manage Data

This was a principle for systems design suggested in a talk I heard in the late 80s or early 90s. I can no longer remember the speaker’s name but I remember that he was in the disk drive business. Google has proven unavailing in sourcing it so it was probably an original insight with this engineer that hasn’t gained wider currency. Used as the title for a blog post.

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“Do something today that reflects who you are, what you are capable of, what you care about. Whether it is at work, at home, for pay or for free, do something that shows you. We need to see evidence of our abilities; we need to see evidence of our relevance. Give yourself plenty of evidence of what you can do, and you will not doubt your abilities to do anything.”

David Niven, PhD in “The Simple Secrets for Becoming Healthy, Wealthy, and Wise“

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“Founders have infinite time horizons and concentrated ownership; professional managers have quarterly time horizons and zero ownership.”

“A founder treats the company as a tool for solving a problem; a CEO treats the company as the thing to be preserved.”
Mark Atwood (@_Mark_Atwood)

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“No data yet. It is a capital mistake to theorize before you have all the evidence. It biases the judgment.”
“There is nothing like first hand evidence, as a matter of fact, my mind is entirely made up upon the case, but still we may as well learn all that is to be learned.”
Two quotes by Sherlock Holmes in “A Study in Scarlet”  by Arthur Conan Doyle

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“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
Jim Barksdale

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“Do not seek for information of which you cannot make use.”
Anna C. Brackett in “The Technique of Rest” (1892).

Especially true for entrepreneurs when interviewing prospects. There is a corollary: calculate the expected value of perfect information; don’t spend more on gathering data than the impact it will have on your plans. A reasonable probability is normally the best you can achieve: you can only be certain of missed opportunities, not the ones that are available to you. More context:

“If the train stops, don’t ask a hundred questions, which don’t concern you, as to the cause of the delay. Do not seek for information of which you can make no use. When the steamer goes slowly because of fog, do not attack the captain every time he appears on deck with your inquiries as to whether he thinks he will run into an iceberg or another vessel, or whether there is always fog in that part of the ocean, and a hundred others, so various as to leave no doubt in the mind of anyone who listens to them of the great power of invention of their propounder.”
Anna C. Brackett in “The Technique of Rest” (1892).

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“When action grows unprofitable, gather information; when information grows unprofitable, sleep.”

Ursula K. Le Guin  in “The Left Hand of Darkness” (1969).

Originally curated in March 2012

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“There is much evidence that the proper study of programming is done at the level of the programming social unit. Not that the individual level is unimportant, but we might start by asking why, if the average programmer spends two-thirds of his time working with other people rather than working alone (yes, it’s true!), that 99 percent of the studies have been on individual programmers.

One answer, of course, is that if studying programmers is expensive, studying groups of programmers is extravagantly so. Moreover, not just any groups of programmers will do–not, for example, a collection of trainees put into a “team.” Putting a bunch of people to work on the same problem doesn’t make them a team. […] Even studying teams as they are constituted today may not be sufficient, for these are teams that have grown up in an environment pervaded by the myth that programming is the last bastion of individuality.”

Gerald Weinberg in “The Psychology of Computer Programming” (Silver anniversary edition) | 1988

This also applies to gaining an understanding of other kinds of knowledge work.

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“The warning signs are flashing bright red that the venture market has never been more consensus-driven. We believe that the consequences of continued concentration will be catastrophic for venture capital and the broader innovation economy. ”
Omar El-Ayat and Nic Poulos in  “We Have Met the Enemy an He is Us“

This impacts bootstrappers indirectly as a widespread consensus builds on the “right way” to build a startups and filters into our niches in the entrepreneurial ecosystem.

“Venture capital has long celebrated itself as the business of contrarianism. The best investors prided themselves on spotting what others missed: the non-consensus founder, market, or product. The venture model was designed to harness outlier outcomes — but it relied on individual conviction, not herd behavior, to find them.”
Omar El-Ayat and Nic Poulos in “We Have Met the Enemy an He is Us

I think this still applies for bootstrappers: leverage your unique experience, perspective, and skills to find a niche you can serve exceptionally well.

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“When I look at my grandchildren or I hold them, I can feel that it’s only my individual strength that is subsiding. The strength in the family, in the species, and in the whole beating heart of the universe hasn’t subsided at all.”
David Milch reflecting on his Alzheimer’s and his spirituality in his memoir “Life’s Work“

I am a huge fan of David Milch’s writing in “Hill Street Blues”, “NYPD Blue”, “Deadwood”, “John from Cincinnati”, and “Luck.” I read his “True Blue: The Real Stories Behind NYPD Blue” he co-wrote with Bill Clark and enjoyed it immensely. I thought this might be similar to Somerset Maugham’s “The Summing Up” or Sid Meier’s “Memoir” where creative people offered assessments of what they learned and some insights into their process. This is a very bleak book that recounts some terrible childhood experiences that shapes his life as well as his gambling addiction, drug addiction, and alcoholism. Part of the problem is that by the time he started on the memoir his Alzheimer’s had progressed to a point that he is relying on transcripts of writing sessions and older notes but he has very little memory of events except those that are incredibly painful emotionally.

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“There’s a fundamental connection between seeming and being. We all become what we pretend to be. Everyone tells a story about themselves inside their own head. Always. All the time. That story makes you what you are. We build ourselves out of that story.”
Patrick Rothfuss in The Name of the Wind (2007)

This works at a founding team-level as well. This reminds me of two older quotes:

“We are what we pretend to be, so we must be careful about what we pretend to be.”
Kurt Vonnegut in “Mother Night” (1962)

I think our beliefs, dreams, and visions allow us to act our way into expertise. This is not “fake it till you make it,” it’s “acting as if” to allow you to become what you aspire to be.

“No man for any considerable period can wear one face to himself, and another to the multitude, without finally getting bewildered as to which may be the true.”
Nathaniel Hawthorne, The Scarlet Letter, 1850.

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“Everything you read in the newspapers is absolutely true except for the rare story of which you happen to have first-hand knowledge.”
Erwin Knoll

Known as “Knoll’s Law of Media Accuracy” in the 80s. Two decades later Michael Crichton labelled it “Murray Gell-Mann Amnesia effect.”

“Media carries with it a credibility that is totally undeserved. You have all experienced this, in what I call the Murray Gell-Mann Amnesia effect. (I call it by this name because I once discussed it with Murray Gell-Mann, and by dropping a famous name I imply greater importance to myself, and to the effect, than it would otherwise have.)

Briefly stated, the Gell-Mann Amnesia effect works as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward-reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story-and then turn the page to national or international affairs, and read with renewed interest as if the rest of the newspaper was somehow more accurate about far-off Palestine than it was about the story you just read. You turn the page, and forget what you know.

That is the Gell-Mann Amnesia effect. I’d point out it does not operate in other arenas of life. In ordinary life, if somebody consistently exaggerates or lies to you, you soon discount everything they say. In court, there is the legal doctrine of falsus in uno, falsus in omnibus, which means untruthful in one part, untruthful in all.

But when it comes to the media, we believe against evidence that it is probably worth our time to read other parts of the paper. When, in fact, it almost certainly isn’t. The only possible explanation for our behavior is amnesia.”

Michael Crichton in “Why Speculate” a talk at International Leadership Forum, La Jolla (26 April 2002)

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“Honor and shame from no condition rise;
Act well your part, there all the honor lies.
Fortune in men has some small difference made,
[…]
Worth makes the man, and want of it, the fellow.”

Alexander Pope in Epistle V in his “An Essay on Man” (1891)

English has drifted a little since Pope wrote this almost 140 years ago so I will offer my interpretation of these few lines: what matters is how well you play the hand you’ve been dealt. Circumstances, wealth, and social status make little difference, it’s how you manage the situations you find yourself in that allow you to distinguish yourself. This is the mindset that helps entrepreneurs to thrive.

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“A story has no beginning or end: arbitrarily one chooses that moment of experience from which to look back or from which to look ahead.”
Graham Greene “The End of the Affair”

The story of your entrepreneurial journey begins much earlier than you may realize; if you inquire and research, you can find antecedents in the lives of your grandparents and, likely, your great-grandparents. There are lessons in the lives of your ancestors that apply to and can inform your journey. For better and for worse, strengths, tendencies, flows, and choices, good and bad, echo in your character and capabilities. And the story doesn’t end with the failure of any one business you start or any one success.

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“Sit down before fact as a little child, be prepared to give up every preconceived notion, follow humbly wherever and to whatever abysses nature leads, or you shall learn nothing. I have only begun to learn content and peace of mind since I have resolved at all risks to do this.”
Thomas Huxley

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“In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
Herbert Simon in “Designing Organizations for an Information-Rich World”  collected in “Computers, Communications and the Public Interest”

On of the risks that imaginative entrepreneurs face is apophenia: seeing patterns and connections that are not there.

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“Science, my boy, is made up of mistakes, but they are mistakes which it is useful to make, because they lead little by little to the truth.”
Jules Verne in “Journey to the Center of the Earth”

This reminds me of

The Road to Wisdom
The road to wisdom? Well, it’s plain
And simple to express:
Err
and err
and err again,
but less
and less
and less.”

Piet Hein

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“Pure data. You don’t believe data–you test data.” He grimaced. “If I could put my finger on the moment we genuinely fucked ourselves, it was the moment we decided that data was something you could use words like believe or disbelieve around.”
Paolo Bacigalupi in “The Water Knife“

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image source: 123rf.com/profile_alrika 186083413

“We built something people really liked, but they didn’t need it.” – Suchita Kaundin

Discovery and Customer Validation Lessons

Suchita Kaundin concluded that better discovery could have revealed the problem earlier. Important questions she would now ask include:

• What happens if this problem is not solved?
• How do customers solve it today?
• Who owns the budget?
• Is there urgency to solve it this quarter?

Testing enthusiasm alone is not enough; founders must test urgency and willingness to pay.

In the full video, we unpack the realities of shutting down an early-stage AI company: unclear documentation, unpaid pilots, weak product-market fit, and painful pivots. Then we explore how those experiences strengthen the next startup: sharper customer discovery, disciplined validation, better legal and equity foundations, and clearer go-to-market focus. Suchita Kaundin offers practical insights for founders navigating transitions, turning hard-won lessons into strategic advantages for her next AI venture.

Key Takeaways

• We built something people really liked, but they didn’t need it.
• During the discovery phase it is important not just to test enthusiasm but also urgency.
• If they do not pay for a pilot, they probably will not pay later
• Building the product is actually one of the easiest parts… getting people to buy it is the hard part

About Suchita Kaundin

Suchita Kaundin is a Product Manager at Guestrix with deep expertise in embedded systems, hardware–software integration, and platform technologies. She has worked across startups and multinational companies, building and scaling products in IoT, data storage, firmware, and software platforms. With a strong foundation in engineering, she transitioned into product management and excels at the intersection of technology, business, and customer needs. Suchita specializes in embedded software, ensuring seamless hardware–software interactions. She enjoys solving complex technical challenges, collaborating closely with firmware and hardware teams, and defining scalable, high-performance platforms that maximize efficiency and long-term product value.

Related Content on Customer Validation

• Where Do Lean Startup Methods Help Most?
• Customer Development: Scouting A New Market
• Steve Blank on Customer Development Process for Startups

Scaling Requires Building a Repeatable Process

Scale = turning “what I do” into “what we do.” The hard transition is extracting your tacit know-how into a repeatable method other people can deliver.

Lead with Results

Sell outcomes with proof, not self-description. Parents (and most buyers) care about results: testimonials, concrete examples, timelines, and a clear process—not “I have X degrees / Y years.”

Biggest Mistake

Ask for testimonials early—within two to three months—once you’ve delivered results, instead of waiting years. Timely requests dramatically increase volume and growth. If clients are satisfied, capture proof quickly; strong testimonials build trust, credibility, and accelerate business traction.

Growth Requires Investment and Experimentation

You need a “play budget” for marketing tests (PPC, ads, website tweaks). Most experiments won’t work; a few winners can drive disproportionate growth.

Too Much on Your Plate, What to Delegate?

When tasks become inefficient and draining, like invoicing, recognize it quickly and delegate, freeing time to focus on higher-value work and growth.

Full video

https://vimeo.com/manage/videos/1170101132/player

About Amadeus Ciok

Amadeus Ciok is founder of Learn Vibrant Math Tutoring, a high-performance tutoring company focused on delivering measurable academic results for students in competitive environments. Starting as a solo tutor in the Bay Area, he has grown the business 7× into a multi-tutor team serving dozens of students locally and nationwide.

With over 15 years of experience in competitive mathematics and tutoring, Amadeus developed a structured, conversational teaching methodology that emphasizes deep understanding, confidence-building, and rapid grade improvement. His team specializes in helping students achieve top academic outcomes, including admission to leading universities such as Stanford, UC Berkeley, and Dartmouth.

Known for his disciplined approach to hiring, training, and quality control, Amadeus has built a scalable services business rooted in clear processes, continuous coaching, and strong client relationships. His work reflects a results-driven philosophy: focus on what clients value, deliver consistently, and use testimonials and outcomes to fuel growth.