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My co-founder, Marek Paris, came from Rossum, the AI document automation pioneer. He led AI Automation at Rohlik. Duvo’s first deployments went into production at Rohlik through full enterprise procurement. Both sides of the table: buyer and builder. We are not learning the enterprise.

At Rohlik, the hard part was never knowing that an action had to happen. The hard part was knowing which action mattered. When. Why. With what context. Under which control.

Here is what I saw.

Most enterprises do not just have work being done manually.

They have high-leverage work that is not done at all.

The supplier follow-up nobody has time for. The fraud alert that ages out before someone reviews it. The denied healthcare claim that never gets appealed. The long-tail invoice dispute that is not worth a human’s time. The process improvement everyone knows should happen but never gets staffed.

That is where the future is.

Duvo maps how operations actually run, identifies the work that should run, redesigns the process, and then runs the improved version with AI agents. Under the control of the people who own the business outcome.

Most AI companies are building agents. Agents execute tasks.

Duvo runs the loop those agents live in.

At the beginning, Duvo captures the real process: screen walkthroughs, interviews, SAP data, documents, handoffs, exceptions, workarounds, costs, and controls. The documented process is usually fiction. The real process lives in inboxes, Excel, old portals, Slack threads, and the operator who says: “We don’t do it that way anymore.”

Then Duvo turns that reality into an executable operating model.

Detect. Decide. Act. Document. Learn.

A supplier misses confirmation. Duvo sees it, gets the context, follows the policy, asks for approval when judgment matters, acts when the decision is low-risk, writes back to the system, and leaves an audit trail.

The operator does not disappear. The operator becomes the controller of the system.

They set boundaries. They review exceptions. They approve changes. They stop carrying the process in their head and start operating the loop.

This is the part I am most excited about. This is what we are building towards.

Agents should not only run processes. They should understand whether the process is working.

If an agent runs the same workflow every day, sees the same exception every week, and knows the business outcome it is meant to improve, then it should not just execute. It should suggest a better process.

This supplier always replies late. Move the chase two days earlier.

These deductions below €500 are almost never worth fighting. Auto-close them.

This price movement creates margin leakage before the weekly review. Trigger a negotiation the same day.

This handoff creates 40% of the delay. Remove it.

The agent proposes. The company’s process owner decides. Duvo turns that decision into the next run. The process improves the next day.

That is a different way to run an enterprise.

Today, transformation happens in projects. Big diagnostic, steering committee, deck, implementation plan, partial adoption, drift.

In the future, transformation becomes continuous. Every run teaches the system. Every exception improves the map. Every operator decision sharpens the agent. Every process can get better tomorrow, not in next year’s transformation program.

That is also why Duvo has to be a different kind of company.

The default AI playbook now is forward-deployed engineers. Palantir invented it. Anthropic just stood up a $1.5B FDE-led venture with Blackstone and Goldman Sachs. OpenAI built a $4B Deployment Company on the same model. Send engineers into the customer, wire agents into messy systems, learn, repeat. It is powerful. But operational transformation is not only an engineering problem.

The bottleneck is knowing which work should exist.

So we forward-deploy consultants, not just engineers. Ex-MBB or Big-4. They give us credibility with enterprise buyers, accelerate go-to-market, and help customers think through what to automate and how to transform. The runtime is their leverage. Together with our FDEs, one transformation manager does what a partner-led engagement at Big-4 does, at a fraction of the cost.

At Duvo, the transformation memo becomes the agent spec. The change plan becomes the run plan. The steering committee becomes a control loop. The output is not a deck. It is work running every day.

Customer support already has its winner. So does engineering. So does legal. Operations do not yet. The biggest of these markets is still up for grabs. We have about two years.

This is what makes Duvo hard to copy. Not the phrase “system of action.” Everyone will use that phrase. Not agents. Everyone will have agents.

The moat is the operating loop.

Operators inside the customer. Transformation managers inside Duvo. Engineers building the runtime. Clarity discovering the real process. Agents running the work. Business outcomes telling the system what better means. Human owners deciding what changes.

We collapse consulting, integration, and runtime into one accountable operating model. The handoffs that kill traditional transformation are no longer the customer’s problem.

All of this is real today. We started selling in January and we’re already past $1M in revenue.

Five playbooks already run in production: supplier onboarding, out-of-stock exception triage, chargeback resolution, commodity renegotiation, promo setup quality. Each one was built through a real deployment and captured in Clarity.

The consulting channel is moving with us. Deloitte signed as a sales and delivery partner. MBB engagements in motion. We are ISO 42001 certified, one of around 50 companies globally. Backed by Index Ventures and Northzone.

Operations work is universal. Every industry has it. The runtime travels.

This is not enterprise software as a place to record work.

It is enterprise software designed to improve work. All-the-time.

$240 billion in operations consulting and operational-process BPO globally. Trillions more in the operations labor enterprises run in-house. That is what we capture when transformation stops being a project and starts being a system.

So what the hell does Duvo do?

Duvo turns operations into a living system: real work discovered, better work designed, agents running it, humans controlling it, and the process improving tomorrow because it ran today.

Are we there yet? No. But that’s what building a company is about.

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Something is happening to the management profession that has not happened in fifty years. The job is being rebuilt under our feet, and most managers can feel it before they can describe it.

Consider the numbers.

In 2013, the average American manager had 8.2 people reporting to them. By 2024 that was 10.9. By 2025 it was 12.1. Thirteen percent of managers now run twenty-five or more direct reports, a span that any management textbook from the last century would have called impossible. Ninety-seven percent of managers are now doing individual contributor work alongside leading their teams.

The middle has been hollowed out at the same time. Job openings for middle-management roles have fallen by more than 40% since 2022. Forty-one percent of employees say their company has cut middle management in the past year. Gartner projects that by the end of this year, one in five organisations will use AI to remove at least half of their management layers.

The named cases are loud. Block has cut 40% of its staff, about 4,000 people. Amazon has eliminated roughly 30,000 corporate roles since October 2025. Meta is planning a 20% reduction this spring. Klarna has gone from 7,000 employees to under 3,000, with the CEO publicly targeting under 2,000 by 2030.

None of this is being framed as a downturn. It is being framed as an upgrade.

What management was actually for

To make sense of what is being upgraded, it helps to remember what the manager job was originally for.

Peter Drucker, who invented modern management as a discipline, coined the term “knowledge worker” in 1959 and spent the next forty years arguing that knowledge work would replace industrial work as the defining activity of the developed economy. He was right about that. He was also right about something subtler: managing knowledge workers was fundamentally different from supervising factory workers, because knowledge workers, by definition, knew more about their job than their manager did.

That paradox forced a redefinition. If you cannot supervise the work, what is the manager for?

The honest answer, for half a century, was: routing. The manager existed because information could not flow efficiently across a large organisation by itself. Someone had to gather context from below, push priorities from above, align teams sideways, translate between specialist languages, and resolve the small daily conflicts that came from people not having each other’s full picture. The manager was the human protocol layer of the company.

That worked. It also created the modern white-collar career: the ladder where you started doing the work, then stopped doing the work, and then spent the rest of your career routing other people’s work.

That is the job that is now being rebuilt. For the first time, the routing layer can be replaced by software.

What AI now does

The manager job decomposes into roughly five things. Route information up the hierarchy. Route information down. Align teams sideways. Supervise the work being done. Develop the people doing it.

AI now does the first three better than any human, at any company, at any scale. This is no longer a frontier claim.

In finance, Hewlett Packard Enterprise built an internal agent called Alfred with Deloitte that has cut its financial reporting cycle by about 40%. More than half of surveyed CFOs (54%) say integrating AI agents will be a digital transformation priority this year. The 2026 finance department is described in the trade press as a “Human + Agent collaborative”: agents handle reconciliation, invoice processing, variance detection; humans interpret anomalies and partner with the business.

In HR, organisations using agents for screening, scheduling, onboarding workflows and routine employee questions are reporting 50% faster time-to-hire. ADP’s new Payroll Variance agent is saving HR operations teams roughly thirty minutes per payroll cycle just by answering plain-language questions about discrepancies.

In customer support, Klarna’s AI handles work equivalent to more than 850 human agents. Revenue per employee at Klarna has gone from about $340,000 in 2022 to roughly $1.2 million today.

These are not pilots. These are line items in the operating plan.

What AI does not do, and shows no sign of doing soon, is the last two of the five. It does not own outcomes. It does not develop people. Those are the two pieces that still require a human.

That leaves every existing management role with a stark question. If three-fifths of what you used to do is now being done by software, what is left of your job? And the answer that is emerging, across industry after industry, is that what is left is to own the outcome and to build the system that produces it.

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The Shopify line

The CEO who said this earliest and most clearly was Tobi Lütke at Shopify. In April 2025 he sent a memo, later posted publicly, that became a quiet inflection point for how serious companies are now operating.

Two lines from the memo travelled. The first: “Reflexive AI usage is now a baseline expectation at Shopify.” The second, harder one: before any manager could request additional headcount, they had to prove the work could not be done by AI.

The memo was not a productivity exhortation. It was a filter. It told everyone in the building that the floor of the job had moved. Within eight months, AI fluency in job postings had nearly doubled, from 5% to 9% of all listings. The number of workers in occupations requiring AI fluency had grown from one million to seven million in the same window.

Shopify did not invent this. It said it out loud first.

Founder mode, properly understood

In 2024, Paul Graham wrote an essay about something he called “founder mode.” He had noticed that the best founders he knew systematically refused to do what management consultants told them: namely, to “hire good people and give them room to do their jobs.” Those founders ran skip-level meetings. They got involved in decisions five layers down. They broke the org chart whenever the org chart got in their way.

Graham’s framing was that founders were a special case, with a special licence to operate this way.

He was half right. The founders he was describing were special, but not because they had a special licence. They were special because they were already doing what the rest of the management profession will be forced to do in the next decade. They were treating the routing layer as overhead, and going around it.

The reason that workaround now generalises is that the routing layer no longer needs to be staffed at all. It can be built. And once a thing can be built rather than staffed, the question for every manager, at every level, in every industry, becomes: are you the builder, or are you the routing layer?

There is no third option. The old role of the manager-as-router is being eliminated, by AI, on the order of tens of thousands of positions per quarter. The new role of the manager-as-builder is being created, by AI, faster than the supply of people who can do it.

This is what I mean when I say the old founder mode is dead and the new founder mode is everyone. Founder mode was never about being a founder. It was about being the kind of person who builds the system rather than supervising it. That kind of person used to be a luxury. They are now the standard.

What the new job looks like in practice

The new job is not theoretical. It can be described concretely, role by role.

The head of HR no longer routes CVs to hiring managers and chases status updates. She builds the recruitment system: which agent screens, what the screening criteria are, where the human steps in, what the interview workflow produces, what the onboarding sequence looks like, what numbers come out the other end. She owns time-to-hire, quality-of-hire, and retention. The system is the job.

The CFO does not “oversee” the month-end close. He builds it. He decides which reconciliation runs as an agent, which exception handling is escalated to which human, which review is automated, which is preserved for judgement. He owns the close calendar in days, the audit trail, and the variance reporting cycle. The system is the job.

The COO of a manufacturing plant does not “supervise” the floor. She builds the optimisation loop: which sensors feed which model, which dispatch decisions are automated, which require a human, what the throughput targets are and how they are revised when reality changes. She is fluent in her own factory the way a software engineer is fluent in her own codebase.

The head of sales no longer “manages the pipeline.” He builds the system that produces qualified pipeline: the targeting model, the outreach sequencing, the qualification logic, the handoff to humans, the closing playbook. He owns conversion, ACV and sales cycle.

The CEO is in the engine room with everyone else. She uses the same tools as her engineers and operators, because she needs to feel the constraints and the leverage in her own hands. She is not on the press tour. She is closing tickets and looking at dashboards she built herself.

None of this is a startup story. The function-level rebuild is happening at HPE, ADP, Klarna, Shopify, Amazon and Meta, none of which are startups. It is happening at banks, insurers, hospitals and law firms, more quietly but on the same schedule.

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What we still get wrong

Two arguments are commonly made against this view. Both are wrong, but they are wrong in interesting ways.

The first is that AI is just a productivity tool, like the spreadsheet or email, and managers will absorb it without changing the shape of the job. This is the position you hear most often in established companies, usually from people whose job depends on it being true. The empirical record is now strongly against it. The shape of the job has already changed: spans of control have nearly doubled in twelve years, middle-management roles have collapsed by 40% in three, and entire functions are being rebuilt around agents at named companies. This is not the spreadsheet. The spreadsheet did not eliminate accountants; AI is eliminating the routing function that the entire middle of the org chart was built around.

The second is the opposite extreme: that AI will simply replace the job entirely, and there is nothing for the manager to learn. This is the Klarna-coded view, and it has the advantage of being half-true in the short term. But it ignores the most interesting voice in the debate, which belongs to Patrick Collison at Stripe. Collison’s view is that AI is “the GLP-1 for efficiency”. It does not just help you cut. It lets you do dramatically more of what creates value. Stripe is hiring more in 2026 because of AI, not less. The question Collison asks is not “what can we cut?” but “what would we elect to do much more of, if we suddenly had the leverage?”

Both arguments fail for the same underlying reason. They both treat AI as something that happens to the manager. The reality is that AI is something the manager has to do. It is the new tool of the trade. If you are not the person building with it inside your domain, you are the person being built around.

How I hire now

This changes what good looks like in a candidate at every level. The signals are the same whether I am hiring an analyst or a CFO. The artifact gets bigger. The shape of the person stays the same.

What I look for:

• A candidate who has shipped something this quarter that exists outside their head and that someone is using. Software, a workflow, a process, a product. They can describe it, defend it, and tell me what is wrong with it.

• A candidate who uses AI tools (Claude Code, Codex, Cursor, ChatGPT, the underlying agent platforms, whatever) daily, by reflex. They can show me their actual usage. They have opinions on which tool is better for which job.

• A candidate who can describe a workflow they own as if it were a system: inputs, transforms, outputs, exceptions, escalation paths. They do not say “the team handles it.”

• A candidate who owns a number (a P&L line, a conversion rate, an SLA, a defect rate) and recites it from memory.

• A candidate who, when asked what is broken in the system they own, has the list ready.

• A candidate who has built or rebuilt something that an organisation three times their size still does manually.

What I now treat as a red flag:

• A candidate who cannot describe their job without the word “stakeholders.”

• A candidate who optimises for the meeting rather than the work it is meant to produce.

• A candidate who has more direct reports than shipped systems.

• A candidate who talks about “the team” doing things they could not do themselves.

• A candidate who treats AI tools as something the engineers use.

• A candidate who has a brilliant strategy and no Tuesday.

• A candidate whose deck is better than the work behind it.

None of this requires the person to be a programmer. It requires them to be a builder. The HR director who has built the screening agent passes. The MBA who has shipped a working compliance workflow with an agent passes. The forty-year-old “experienced operator” who has been a director of operations at four companies and cannot show me a single thing he has built does not.

This is uncomfortable. Particularly for senior people who built their careers in the previous game and who, in many cases, are exceptional at the previous game. I have had to look at my own bench and make hard calls. So has every honest CEO I know.

Why Europe should care most

I want to be careful with this part, because it is the most likely to be misread.

The Draghi report, published in 2024, laid out the European productivity problem with a clarity nothing before it had managed. Thirty years ago, EU productivity was about 95% of the US level. Today it is between 75 and 80%. GDP per capita in Europe has fallen to 77% of the US. The gap grew by 12% in 2023 alone, of which 72% was attributable to productivity.

A year and a half after Draghi, only about 11% of his 383 recommendations have been fully implemented, and another 20% are partially in motion.

There are many causes for the productivity gap, and I am not claiming to have the full picture. But one piece of it is now structural and is about to become decisive.

Europe has a long-standing overweight in operator talent and an underweight in builder talent. We are very good at running systems we did not build. We are less good at building systems no one has run yet. Our most prestigious talent funnels (the global consultancies, the top business schools, the corporate ladders of legacy industry) are excellent at producing routers. They produce, with great consistency, the kind of person whose career was perfectly suited to the manager-as-protocol-layer job that is now being eliminated.

This was a manageable disadvantage when the routing job still existed. It is becoming a structural one as the routing job goes away. Every quarter that we keep hiring routers when we should be hiring builders, the gap with the US, and increasingly with China, widens. The bottleneck is not capital. Europe has capital. The bottleneck is the talent stack.

The fix is not to import a different culture wholesale. It is to reset the hiring funnel inside European companies, at every level, including the C-suite, so that the candidates with the most prestigious CVs are not given automatic priority over the candidates who can actually show you something they built.

This is not a plea for less rigour. It is a plea for different rigour. The rigour of building, not the rigour of supervising.

The kicker

There is a simple test now. What did you build this year? Not what your team built. What you built. If the answer is nothing, the job is going away.

That is true for the founder. It is true for the individual contributor. It is true for the manager in the middle who used to be safe.

For the routers, this is the end of an era.

For the people who can build, the ceiling just disappeared.

The real work still happened in the messy middle:
copying data between tools, chasing people, updating spreadsheets, logging into portals, following up again.

Copilots were the first wave of AI at work. They help you think and write faster.

But the bigger shift is starting now:

AI that finishes the work.

Today, we’re opening Duvo to everyone.

No waitlist. No sales call.
Go to duvo.ai, describe what needs to happen, and Duvo executes it end‑to‑end across your tools: email, spreadsheets, CRMs, portals… even systems with no API.

You can schedule it or trigger via API, and it runs in the cloud - so work keeps moving even when your computer is off.

This isn’t “more automation”. It’s a new way of operating:

• You describe the outcome.

• Duvo runs the steps across systems.

• It asks before doing anything sensitive.

• It keeps a clear history of what it did.

And when the fastest path is a real conversation, Duvo can even make outbound calls as part of the workflow.

Why we’re opening it up

We built Duvo with enterprise teams first, because if AI is going to do real work, it has to be safe by default.

But this shift is too important to keep behind the walls of massive companies.

SMBs deserve the same leverage.
Enterprise teams who live in spreadsheets deserve the same relief.
Solopreneurs deserve a way to scale without hiring an army.

“Isn’t this just another agent tool?”

We get this question a lot. Here’s the plain-English difference.

Duvo vs copilots
Copilots help inside one app (write a doc, summarize an email, draft a reply).
Duvo is built to complete multi‑step work across many tools - and come back with “done”.

Duvo vs agent builders/workflow platforms
Many tools are powerful, but you pay a “builder tax”: setup, connectors, flows, maintenance.
Duvo is designed so non‑technical teams can start out of the box.

Duvo vs Claude Cowork

Cowork is a big step forward - it brings agent-style execution into Claude Desktop, with access to files on your computer.
Duvo is that + it is built for running business workflows across company systems, in the cloud, on a schedule - with controls teams need.

One more thing that matters when you try it

There’s a hidden barrier with a lot of “AI automation” products: you’re asked to bring your own model key and manage usage.

With Duvo, you don’t need an OpenAI or Anthropic API key. And you don’t get surprised by token bills while you’re still figuring out if it’s useful.

Just try it.

The video is attached.
Free trial → duvo.ai

The point wasn’t to improve the vehicle. It was to slow a new kind of movement down until it fit the old world.

It was fear of the unknown. A new capability entered public space, and the first response was supervised movement.

AI is entering operational space the same way. Most organisations are deploying AI with its own red flag: useful, supervised step by step, and constrained to advising humans rather than executing work.

Autonomous execution does not mean completing a sentence. It means completing a piece of work end to end - across systems - to a finished outcome with accountability.

If you remember one line, make it this:

Autonomous execution is not a feature. It is the new operating model.

It matters because execution across messy systems is where cycle time, cost-to-serve, and operational risk quietly accumulate.

The pattern behind big shifts

The decisive moment is not the invention. It is the redesign that follows.

Consider electricity in manufacturing. Early factories electrified by swapping steam for motors, but kept the old architecture of belts, shafts, and layouts organised around power transmission. The productivity leap came later, when factories redesigned around what electricity enabled: unit-drive machines, flexible layouts, and work that flowed instead of queued. It took thirty years!

Railroads and container shipping tell the same story: scale depends on coordination standards and predictable units.

Rail networks needed shared time standards to coordinate schedules across cities and reduce confusion. In the U.S., railroads implemented standardized time zones in the 1880s, and public timekeeping followed.

Containerization wasn’t just faster ships. It was a standard unit that turned loading, unloading, insurance, theft, and scheduling into something predictable and industrial. Costs collapsed because friction collapsed.

Those examples are a map. They tell us what comes next for AI.

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From assistance to autonomous execution

AI’s first phase was assistance.

Assistance improves output. It helps people write faster, search better, summarise more cleanly, and draft more confidently.

But the operating model stays the same. A person sits at the centre, receives suggestions, and then does the work: moving information between tools, reconciling inconsistencies, coordinating with others, and pushing tasks over the finish line.

The second phase is autonomous execution. Not full autonomy in the abstract, but governed autonomy in the real world.

In enterprise terms, this is the shift from systems of record (software that stores truth) to systems of action (software that can reliably move that truth through workflows, across tools, to a finished outcome - with accountability).

Systems of action do not just inform decisions. They carry work through, including the messy exceptions that make up real operations.

The goal is not to automate only the happy path. It is to resolve most variance inside guardrails, and escalate only the few cases that truly require human judgement.

Why this matters

The constraint in most organisations is not ideas. It is execution across imperfect systems.

Most companies run on a patchwork of legacy applications, portals, spreadsheets, email threads, PDFs, ticketing systems, and processes that evolved through years of practical compromise.

Even when processes look standard, exceptions are the rule: missing fields, ambiguous documents, late updates, policy nuance, edge cases, local variants, and systems that disagree about what is true.

So much capacity gets consumed by work that looks minor but is strategically expensive: chasing inputs, rekeying data, reconciling discrepancies, updating multiple systems, nudging stakeholders, documenting decisions, and creating proof that the work was done correctly.

This is why transformation is so often described as an execution problem, not a strategy problem. McKinsey has repeatedly highlighted that large-scale transformations fail roughly 70% of the time.

And it’s why “single system” programs routinely underdeliver. Modern ERP can be invaluable - but deployments, upgrades, and replacements are still prone to major disruption, cost blowouts, and long recovery cycles, with highly visible failures even in sophisticated organisations.

For the core, engineering remains non‑negotiable. Financial controls, high-volume transactions, security boundaries, and systems of record must remain authoritative.

But much operational work lives at the edges - where requests arrive in uncontrolled formats and counterparties change behaviour without warning. In those conditions, perfect definition becomes a moving target. Perfect data becomes a multi‑year program that often costs more than the work is worth.

Where “automation” has hit the wall

We’ve tried to close the execution gap for years.

We pushed workflow automation inside systems of record. We built integration programs. We adopted iPaaS. We rolled out RPA. We built enterprise data lakes to “centralize truth.” These tools created value - especially in closed-world processes.

But they also exposed a pattern: most automation approaches assume stability.

Even integration platforms acknowledge the reality: every system has different workflows, protocols, and data formats, and integration still requires mapping fields and handling compatibility and exceptions.

RPA tried to bridge the gap by mimicking human clicks and keystrokes. It can work in stable flows, but scripted automation inherits brittleness. When a portal changes or a process deviates, the bot breaks. As practitioners put it: these solutions often require more maintenance than traditional IT.

And scaling is where it commonly stalls. McKinsey was already warning years ago about organisations getting “burned by the bots” when they tried to move from demos to bot armies.

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Deloitte’s surveys have consistently found “process fragmentation” and lack of end‑to‑end coherence among the top barriers to scaling automation.

Even data platforms show the same lesson. Data lakes promised flexible, centralized, self‑service truth - but “many of the promises… have not been realized” without transactions, quality enforcement, governance, and performance optimizations; the result, by Databricks’ own description: enterprise lakes turning into “data swamps.”

The recurring theme isn’t that the tools were “bad.” It’s that they were designed for a world that was more closed than reality.

Closed-world vs open-world work

Some work is closed-world and some is open-world.

Closed-world work has stable interfaces, structured inputs, enumerated exceptions, and deterministic outcomes.

Open-world work is what most organisations quietly run on: ambiguous inputs, changing screens, incomplete information, shifting rules, and novel exceptions.

Scripted automation struggles in open-world conditions because the moment the environment changes, the system has no interpretation layer - only failure modes.

AI agents change the calculus because they can interpret variability rather than collapse when it appears. They can:

• observe state

• choose actions toward an outcome

• verify results

• recover when the environment deviates

• escalate when confidence or authority runs out

They introduce a control loop: perceive → act → check → correct → escalate.

That loop is what makes autonomous execution possible in open-world conditions.

But it also explains why hype collapses without redesign. Even CEOs running multi‑billion‑dollar AI companies have publicly cautioned that you can’t “just unleash the agents, and it just works” - it takes engineering, evaluation, and operational discipline.

And the failure mode is now measurable. Gartner has predicted that over 40% of agentic AI projects will be canceled by the end of 2027 due to costs, unclear business value, or inadequate risk controls.

This doesn’t argue against autonomous execution. It argues for taking governance seriously as part of the operating model.

Vertical operators, not generic copilots

Here is the catch many organisations will learn the hard way. The hardest part of open-world work is not clicking the UI. It is knowing what should happen when inputs are ambiguous and consequences are real.

That knowledge is vertical. It lives in industry-specific policy, regulation, commercial norms, and risk appetite.

It also lives as domain skills and tools: specialised checks, calculations, reference data, decision frameworks, and workflows that encode how the industry actually works.

The systems of action that matter will not be generic copilots that are “good at language.” They will be vertical operators: grounded in a company’s rules, shaped by its exception patterns, equipped with domain tools, and constrained to act safely inside the organisation’s control environment.

The engineered spine and the agentic edge

The most effective posture is not agents versus integration. It is a stable digital spine plus an agentic edge.

The spine remains engineered: clean APIs where available, data contracts, access controls, event logs, and systems of record that stay authoritative.

Systems of action at the edge operate across surfaces that will not be perfectly integrated for years: portals, email, documents, tickets, spreadsheets, and UI workflows.

They stay constrained by the spine’s policies.

They also become a discovery mechanism. They show where variability is highest, where exceptions concentrate, and where an integration or product change will produce the highest leverage.

Autonomous execution requires governance

The moment you allow software to act, the problem stops being “Can it do the task?” and becomes “Can we govern the task?”

Autonomous execution is not just capability. It is accountability.

Autonomy becomes valuable only when it becomes governable.

Autonomy is not something you turn on. It is something you make safe.

That requires redesign around three ideas:

First: redesign work around outcomes, not activities.
Systems of action need explicit outcomes, a definition of done, acceptance criteria, and boundaries. Without that, you do not have a process. You have a collection of habits.

Second: redesign the control environment for machine action.
Treat AI like a new class of operator, with identities, roles, permissions, segregation of duties, and audit trails. The hidden blocker is often operational plumbing: access control, logging, monitoring, and flight-recorder visibility into what happened, when, and why.

Third: redesign management for exception-driven work.
When execution becomes cheaper and faster, management shifts from supervising activity to managing exceptions, trade-offs, and quality. Cycle times compress. Queues shrink. Decisions become the bottleneck.

A useful parallel is the disappearance of elevator operators. Autonomy scaled only when safety mechanisms, standards, inspections, and interfaces made operation safe for ordinary people.

The point is not fewer people. It is better use of people.

In a world of systems of action, the operational layer runs continuously. Cross-system work happens overnight and across time zones. Teams spend more time on judgement, negotiation, customer relationships, and improvement—and less time copying, checking, and chasing.

A third phase: initiative

Assistance helps you produce better output. Autonomous execution helps you finish work to an accountable outcome.

The third phase is initiative.

Initiative is when systems of action do not just respond to requests. They start the right work before the queue forms. They watch for signals, detect what is missing, anticipate next steps, and prepare decisions for humans to approve.

Not because the system is “smart,” but because the operating model has been redesigned so proactive work is safe, legible, and auditable.

If this sounds ambitious, remember the red flag. It was a transitional rule for a world that had not yet adapted. Roads improved. Standards emerged. Drivers became trained. The law changed.

Work is going through its own version of that transition. Many organisations will keep AI behind a flag for a while, and that caution is understandable.

But history suggests the long-term answer is redesign: systems that make autonomy safe, accountable, and useful at scale.

Autonomous execution is not a feature. It is the new operating model.

Alongside my role as Founder & CEO of Rohlik, I’ve co-founded duvo.ai. We’ve just raised our seed round and are opening Duvo up to more retailers and consumer brands.

We’ve put together a short launch video that shows Duvo in action

The problem I couldn’t ignore

For years at Rohlik I’ve watched brilliant people spend most of their week not on negotiations, ideas or customer experience, but on:

• Moving data between internal systems, spreadsheets, emails and supplier portals

• Chasing the same confirmations on the phone again and again

• Fixing the same issues every week in slightly different formats

We hire people for judgment and relationships — and then we bury them in copy-paste.

At some point that stopped feeling like an “efficiency project” and started to feel like a personal calling. I knew I wanted to work in AI, but not as a buzzword or a chatbot. I wanted to create an AI workforce that gives people their time back and lets them focus on meaningful work again.

What Duvo is

Duvo is an AI workforce for retail and consumer-goods operations.

Duvo agents:

• Log into SAP and other ERPs

• Work in supplier portals and spreadsheets

• Read and send emails

• And when a process needs a conversation, they pick up the phone

They can call suppliers, carriers or stores in natural language, follow governed scripts, capture outcomes, update your systems and escalate to humans only when judgment or relationship really matters.

The intention is clear:

AI does the repetitive digital and phone work end-to-end.
Humans focus on decisions, creativity, customers and partners.

Built for messy, exception-heavy retail

Retail and consumer-goods processes are full of exceptions. Every supplier, category and market has its own quirks, file formats and unwritten rules.

Simple flow-based automation is great for tidy diagrams and “happy-path” processes. But in the real world, that happy path is often 20% of the work. The other 80% is messy:

• A supplier sends a slightly different template “just this once”

• Data is missing or formatted wrong

• Someone needs to pick up the phone to clarify what’s really going on

That messy 80% is what still lands on people’s plates.

Duvo was built for this reality. Agents can click, type and speak across SAP, portals, spreadsheets, emails and calls, handling exceptions end-to-end with humans firmly in the loop. Instead of breaking on the first weird case, they’re designed to operate in it.

IT bottlenecks and why Duvo is IT-aligned

A big part of the founding story is IT bottlenecks.

At Rohlik, every serious automation idea ran into the same constraints: scarce engineering capacity and long integration roadmaps. It wasn’t that people didn’t want to automate — they simply didn’t have the time and resources to integrate yet another system.

With Duvo, agents run directly in:

• SAP and other ERPs

• Supplier and logistics portals

• Emails and shared spreadsheets

That means teams can start in weeks, without big integration projects or workflow redesign.

Crucially, this is not “shadow IT.” We designed Duvo to be IT-aligned:

• Central governance

• Controlled access

• Full audit trails

• Enterprise-grade security

Business users get speed. IT keeps control.

Why I’m staying at Rohlik

I am keeping my role as Founder & CEO of Rohlik.

Rohlik is my life’s work - and at the same time, it’s the perfect proving ground to battle-test Duvo in one of the toughest environments: time-critical, low-margin, high-complexity grocery operations.

My belief is simple:

If Duvo can transform Rohlik’s cost base and ways of working, it can transform how retail is run in general.

The next decade in retail (and work)

I’m convinced the next decade in retail will be defined by cost transformation.

But for me this is not a story about “cutting FTEs.” It’s about changing the mix of what people actually do all day. I don’t think anyone wakes up excited to reconcile spreadsheets, dig through portals for missing data or chase the same confirmation for the fifth time.

Retailers who truly rewire their cost base with AI - while elevating their people to higher-value work instead of burying them in mundane tasks - will be structurally cheaper. And in retail, structurally cheaper usually wins. But the part I care about just as much is what happens to the humans in that equation.

What this means for me personally

I’ve spent most of my career building companies where operational excellence is everything. I’ve also spent a lot of time watching talented people spend their days on work that doesn’t match their abilities.

Co-founding Duvo while staying at Rohlik is my way of trying to solve that tension from both sides:

• From the operator side: proving that AI can fundamentally change cost structures and execution.

• From the human side: proving that we can use AI to take away the mundane parts of work, not the meaningful ones.

I don’t pretend to have all the answers about the future of work. But I know this: if we are going to bring AI into real operations at scale, we should do it with the explicit goal of giving humans better work, not just cheaper work.

Duvo is my attempt to push in that direction - while still being very much in the arena at Rohlik, where all of these ideas have to survive contact with reality.

This week we dropped a Christmas spot made by one talented colleague and a bunch of AI tools. It cost about €400 and took 80 hours - down from the €300,000 and a small army it took us last year. We thought it was a neat experiment. Judging by the reaction, some of you thought we’d personally cancelled potato salad.

Was it perfect? Nope.
Could you tell it was created with AI? Yes.
Did some people feel it lacked heart? Absolutely.
A few even said it made them want to vomit - thanks for the festive spirit.

But the technology and the outcome are freaking awesome. Before we told people it was AI, people wept when watching the ad.

The Outrage Olympics

Reading the comments, you’d imagine we’d asked baby Jesus to work through lunch.

Some of the loudest critics work in marketing and, if we’re honest, their career highlight is a 30-second ad for toilet paper. If an ad about presence and family doesn’t move you, but slow-motion pasta or a shiny teeth close-up does, maybe your problem isn’t AI.

What makes me smile is the split:

• On one side, people carefully circle every uncanny detail and British-looking kitchen frame by frame.

• On the other, people messaging, “Wow, this is clever,” and “For €400, that’s impressive!”

I see emotionally flat ads made by humans every day - often by the same agencies lecturing us on authenticity. If the bar is “don’t be boring,” AI did not invent that problem.

Also, let’s be honest about emotion: disgust, anger, envy… those are emotions too. We clearly stirred something. We stirred more passion than Slavia vs. Sparta on a bad refereeing day.

Real People Behind the Pixels

Behind those pixels are real people.

Veronika burned 80 hours and a few brain cells making this. Our colleagues are scrolling through those comments. They didn’t even pick up a mouse, yet they’re watching strangers unload on something they care about.

Save the vomit emoji for the next time you present a ‘safe’ TV spot nobody remembers.

You Weren’t the Target, Josef

Can we talk about the demographic breakdown? Most negative comments came from guys. That’s like me complaining the final season of Bridgerton didn’t have enough car chases. It wasn’t for you, Josef.

The spot was aimed at the unsung heroes who prep Christmas dinner, do the shopping and keep the whole thing from collapsing, while you’re Googling “what to do with the kids for longer than two hours.” If you didn’t cry, that’s okay. You’re allowed to be unmoved by other people’s joy. Just don’t pretend that means nobody else felt anything.

And by the way: people talk about “lack of emotion” in the film, but the emotions in the comments are very real. Disgust, anger, fear, ego… AI didn’t kill feelings. If anything, it dragged them all into the open.

Change Is Coming Either Way

We didn’t set out to prove that AI is better than people. We set out to prove that it’s a tool worth exploring, especially when budgets are tight and curiosity is high. Some marketing folks feel threatened by that, and I get it. Change is scary when your livelihood depends on the old way of doing things.

But clinging to past methods and throwing stones at experiments is a very reliable way to make yourself irrelevant. The world will move on with or without your approval.

I’ve been told by professional marketers more than once that my ideas are “shit.” Yet here we are, still growing, still innovating. It’s easy to sit on the sidelines and critique. It’s harder to build something new, take the heat, and come back next year with something better.

So throw your peanuts, write your hot takes. We’ll keep experimenting. Maybe next year’s ad will be entirely sung by AI dogs. Or maybe we’ll go old school and hire real elves. Either way, we’ll have fun, we’ll learn and we’ll keep delivering your groceries on time.

Carry On Heckling

So, to those who gave constructive feedback: thank you.

To those who shouted “Bliju” and moved on: I hope St. Nicholas brings you a nice lump of coal.

And to the rest of you who watched, smiled, rolled your eyes, or argued in the comments: cheers. We love the conversation.

Merry Christmas from Team Rohlik - whether your ad of choice is AI-generated, hand-crafted, or somewhere in between. We’ll keep doing things our way, and we’re okay if it ruffles a few feathers along the way.

Key Takeaways:

• Elena Verna’s approach turns complex operational challenges into memorable, actionable frameworks

• The core principle: The product isn’t just part of the growth strategy—it IS the strategy

• Success requires building growth directly into the product for repeatable, compounding results

• Moving beyond buzzwords to practical implementation

Who This Is For:

• Founders seeking product-led growth strategies

• Product Managers looking to integrate growth into product development

• Executives wanting to understand modern go-to-market approaches

Sources: Elena Verna’s Growth Scoop newsletters (2024-2025)

This episode serves as a practical shortcut to mastering modern product-led growth, with frameworks you can actually implement rather than theoretical concepts.

A few word prompts were cute. Now they burn cycles. As models get better at reasoning, they reward you for tightening the brief. Give them context, and they stop drafting fluff and start producing decision-ready work.

What "context" actually means

By context, I do not mean paragraphs of lore. I mean the minimum scaffolding that lets a model deliver the thing you need, the way you need it.

• Intent: The decision you are trying to make or the job to be done.

• Audience: Who it is for and where it will land (board, customers, sales, internal Slack).

• Scope: In: What must be covered.

• Scope: Out: What must be excluded.

• Deliverable: Type, format, length, and channel (one-pager, 500 words, deck outline, email).

• Constraints: Tone or voice, quality bar, resources or timebox, tools it may assume.

• Evidence: Sources, attachments, examples to mirror (short excerpts or bullets, not just titles).

• Acceptance: Success criteria and reviewers ("passes PM and CEO in one read", "no superlatives").

• Risks and assumptions: What is uncertain, what not to contradict.

• Timeline: Deadline, milestones, and timezone.

• Privacy: What to redact and any consent constraints.

Write this as a tight brief, not a novel. Two screens, tops.

Why now

Here is the pattern I observe in my own work: the more I refine the context, the fewer back-and-forths I need, and the more "ready to use" the initial output becomes. This is not a technical claim. It is an operator's observation. When your prompt encodes intent, constraints, and acceptance criteria, you get less speculation and more judgment.

Founders are allergic to latency. Every extra turn is another meeting that slips. Context front-loads the thinking a human would otherwise ask you for. It is the difference between a junior "having a go" and a senior bringing a proposal with options, trade-offs, and a clear recommendation.

Treat the model like a sharp operator who missed the last meeting. If you give them the room temperature, they will guess. If you give them the recipe and the diners' allergies, they will plate dinner.

Three founder grade takeaways

From tasking to deciding

Rich context turns "do it" into "decide it".

You do not need ten drafts. You need a recommended path with risks and a rationale. Ask for a decision, define what "green" looks like, and require the trade-offs. That pushes the model past description into choice. You still apply judgment, but you start from an 80 per cent proposal, not a blank page.

Scope beats fluff

Say what is in, what is out, and how you will judge success.

Most generic output is on us. When the scope is unclear, the model fills it in. When the scope is clear, the model is trimmed. Write "include X, Y, Z" and "exclude A, B". Add acceptance criteria like "one page, plain English, no acronyms without a gloss". The voice tightens fast.

Evidence over vibes

Give the model ground truth to stand on.

If you want your voice, show your voice. If you want positioning, give the three facts that cannot contradict. Paste short quotes from interviews. Include a two-line style sample. The model still writes, but now it is anchored to reality rather than a vibe you hoped it would infer.

Before and after: two founder use cases

Example 1: Product launch announcement

Before

"Write a launch post for our new feature."

After

Goal: Publish a product launch post that gets existing customers to try "Routes" within 7 days.

Audience and where: Existing power users on email and LinkedIn. Secondary audience is industry analysts.

Positioning:
- "Routes" = faster, safer handoffs for ops teams.
- Emphasise reliability and fewer manual steps.
- Do not claim new categories or market leadership.

Must include facts:
- Beta with 42 customers over 3 months. Median handoff time cut from 12 min to 4 min.
- Available today on Pro and Enterprise. EU rollout on 1 October.

Non goals:
- No forced migration messaging. No roadmap beyond the next release.

Deliverable:
- Substack post of 600 to 700 words.
- LinkedIn summary of 120 words.

Tone and voice: Confident, plain English, British spelling, no superlatives, no fluff.

Evidence to anchor: Two short customer anecdotes about speed and fewer retries. Anonymised.

Acceptance criteria:
- Provide 3 headline options and 3 email subject lines.
- Include one 5 bullet section: What is new, Why it matters, Who it is for, How to try, What is next.
- Include a 3 question FAQ.
- No jargon without a one line gloss.
- Reads cleanly in one pass for PM and CEO.

Example 2: Customer interviews to the Jobs to be done brief

Before

"Summarise 5 interviews and create jobs to be done brief."

After

Intent: Inform a go or no go on automated invoice matching in Q4.

Stakeholders: PM owner. CEO final decision. Finance lead reviewer.

Deliverable:
- One page JTBD brief with a simple table.
- 3 core JTBD statements in first person, each with trigger and context.
- Pains and Gains per job in bullets.
- Opportunity size as a range with assumptions.
- Clear recommendation: Go, No Go, or Explore. Include top 3 risks.

Evidence: Use the 5 interview notes. Paraphrase. Include at least 1 must cite quote per job.

Constraints: Plain English. British spelling. Maximum 450 words. Include the table header: Job, Pains, Gains.

Acceptance criteria:
- Recommendation stated in the first 2 lines with rationale.
- Unknowns listed with a 2 week validation plan.
- Any contradictions in interviews flagged explicitly.
- Table present and readable without scrolling on desktop.

Risks and assumptions: Customers differ in ERP maturity. Do not assume OCR quality beyond what is in notes.

How to set context without slowing down

• Use a template. The first minute you spend writing intent, scope, and acceptance criteria saves ten minutes of ping pong later.

• Bias to constraints. If something would cause you to reject the work, say it upfront. "No superlatives", "one screen", "one chart, no colours".

• Attach truth. Include the key metrics, a product doc, or three bullets from sales calls. Do not make the model guess.

• Request options, not volume. Ask for two or three strong alternatives with trade offs, not five fluffy variations.

• Keep it human. Specify tone and audience like you would brief a senior operator. The model will match the register you set.

Common failure modes and fixes

• The answer is generic. Fix: Add scope in or scope out and one acceptance criterion.

• It argues with facts. Fix: Paste the facts and mark them "cannot contradict".

• It is too long. Fix: Set a length cap and a target channel.

• It is polite but useless. Fix: Ask for a recommendation with risks and a next step you could actually take tomorrow.

• It is missing your voice. Fix: Paste a short sample of your writing and say "mirror this cadence".

Bonus: a tool I use

If you want a faster way to capture context without writing from scratch, I've built Your Personal Context Engineer, a simple helper that guides you through the brief and outputs a clean specification you can paste into any model. Use it here:

https://chatgpt.com/g/g-6898371603b08191b9d49a655b2eee95-your-personal-context-engineer

Copy-paste context template

# Copy or Edit
Goal:
Audience & stakeholders:
Scope: In:
Scope: Out:
Deliverable: # type, format, length, channel
Constraints: # tone or voice, quality bar, resources or timebox, tools
Evidence: # sources, attachments, examples
Acceptance: # criteria, reviewers
Risks & assumptions:

Few word prompts are over. Context is the new leverage.

Then AI happened.

Or more precisely, AI started changing things fast enough that a lot of the old assumptions started breaking. Bottom-up still works in many places. But if you’re building AI or adapting your company to it, you’ve probably started noticing something: the companies that are winning aren’t the ones with the flattest orgs. They’re the ones with the clearest direction.

Not necessarily the biggest or the richest. Just the ones where someone is saying, "This is where we’re going," and everyone else moves.

That used to be called top-down management. It’s coming back. But not the way people remember it.

The real reason

AI didn’t just introduce a new technology. It introduced a new tempo.

The field moves fast. Research changes monthly. APIs update weekly. Interfaces shift, models get cheaper, the ecosystem refactors itself in real time. You don’t just need to move fast — you need to choose quickly, and often.

In that kind of environment, decision-making becomes more valuable than consensus. Not because collaboration is bad, but because hesitation costs more.

Direction is not control

When people hear "top-down," they think bureaucracy or command-and-control. That’s not what’s returning.

What’s returning is direction.

The best leaders right now are not managing every detail. They’re doing something harder. They’re setting a direction and saying no to everything that doesn’t fit. They’re making bets. They’re repeating the mission over and over until people get bored of hearing it — and then repeating it again.

They don’t tell everyone what to do. They tell them why they’re doing it. That’s enough.

Coherence vs. chaos

In 1999, Garry Kasparov played a game of chess against the internet. Thousands of people voted on every move. They had access to engines, commentary, and experts. He still won.

Why? Because the crowd had no plan. They argued at every step. Kasparov had a strategy.

That’s what happens inside companies too. A hundred smart people pulling in slightly different directions doesn’t give you innovation. It gives you noise. Great products feel like they came from one mind. Because they did.

That mind might listen to others. It might even be wrong. But it moves.

Not forever

Top-down isn’t a permanent solution. It’s a tool.

You use it when the terrain is changing fast and you need everyone aligned. You don’t stay in that mode forever. Once the shape of the problem becomes clearer, you can open things up again.

The best founders I know switch between modes. When the team knows where to go, they delegate. When the team is confused, they zoom in. They don’t get stuck in one style. They manage based on the situation, not the fashion.

What’s actually happening

People assume flat orgs are fast. Sometimes they are. But many of the companies that look flat on the outside are sharp on the inside.

Nvidia looks like a normal public company, but it isn’t. Jensen Huang decides what matters. And everyone else orients around that.

OpenAI isn’t run by consensus either. Sam Altman sets direction. He’s not always right. But his team doesn’t spend six months in debate. They ship, then adjust.

Even Google merged its AI teams under DeepMind to cut the politics and accelerate launches.

The pattern is the same. Not more meetings. Fewer decisions, made faster, with more focus.

The hard part

The hard part of top-down isn’t making decisions. It’s being accountable for them.

Consensus feels safe. No one owns the outcome. Everyone shares the blame. That’s why people like it. But it also means no one drives. The car just coasts.

Top-down means picking a direction when others aren't sure. It means making calls that might be wrong. But if you don’t, the opportunity passes you by while you’re still aligning calendars.

What I’m seeing

At Rohlik, we’re living this. We’re not a legacy company in the classic sense. But we’ve been around long enough to build legacy behaviors. And when the vision isn’t sharp, those behaviors take over. Committees form. Energy dilutes. Momentum stalls.

Changing that has been hard. Harder than I thought. And if it’s this hard here, I can only imagine what it’s like in older, slower organizations.

But what I’m seeing is this: if you want to move fast, someone has to say where you’re going. Someone has to own the hill you’re trying to take. If no one does, the hill stays untaken.

So what?

If you're building in AI, or trying to adapt to it, you probably need to be more top-down than you’re comfortable with.

Pick three things that matter. Say them out loud. Kill the rest. Assign owners. Get out of their way. But stay close enough to steer.

And when it feels awkward to take the lead, remember: the only thing worse than being wrong is being directionless.

Imagine the inefficiency clearly: a human notices an issue, requests a report, someone else wastes hours, sometimes days, pulling data together manually. Then, the requester analyses it, decides what to do, and manually implements the action—more wasted time. Each step introduces delays, inefficiencies, and potential errors.

AI-Powered Efficiency

Contrast this with an AI-driven scenario: an AI agent or even a simple algorithm monitors data continuously, spots an issue instantly, analyses it, makes a decision within seconds, and automatically executes the required action without any human intervention. The difference in speed, accuracy, and efficiency is staggering.

Companies adopting AI-driven BI solutions report reducing analytics workload by up to 80%, turning what previously required hours or days into mere minutes. Gartner research highlights companies saving approximately 110 hours per week on analytics tasks, accelerating decision-making by over 60%. Furthermore, AI adopters in retail often experience double-digit sales growth and roughly 8% higher profits compared to non-adopters, clearly demonstrating tangible business advantages.

Rohlik's AI Journey

At Rohlik, we've already taken significant steps towards this transformative approach, moving from traditional reports on pricing and shrinkage to fully automated solutions. Our demand planning is also fully automated. During our recent hackathon, we developed a powerful internal tool that enables employees to communicate directly with our data, eliminating the need for static reports altogether in the near future.

This democratisation of data means faster, more accurate decisions at every level of the company. Instead of static historical reports, AI enables predictive and prescriptive analytics, proactively alerting us when key performance indicators deviate from expected trends.

With AI handling routine tasks, our BI teams can shift their focus to strategic roles—enhancing data quality, refining AI models, and discovering new data sources. This transformation not only boosts efficiency but also elevates human roles from mundane report generation to innovation and strategic oversight.

Navigating AI Challenges

However, this transition to an AI-driven BI future isn't without challenges. We must prioritise robust data governance, transparency in AI algorithms, and ethical considerations. Training and organisational adaptation are crucial investments to harness AI's full potential.

Ultimately, the move towards AI-driven BI offers Rohlik—and businesses across industries—significant competitive advantages, streamlining operations and enhancing strategic agility. The days of manual, static reports are numbered, and AI-driven analytics will soon become deeply embedded in our daily business decisions and strategies.

Are you ready to say goodbye to manual reporting and fully embrace AI-driven decision-making?

The interview is in Czech, but YouTube's auto-generated subtitles work well for English speakers.

I am looking forward to your thoughts and feedback.

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I recently had the privilege of presenting at the Google Cloud Summit with Pavel Doležal, CEO of Keboola. We shared some important insights on unlocking the full potential of enterprise data.

In our presentation, we discussed how effective data utilisation can drive business decisions and fuel the AI revolution. Our journey at Rohlik Group, from hundreds of daily decisions to becoming a unicorn, showcases the transformative power of data automation.

I've attached the presentation for those who couldn't attend. Don’t miss the amazing video linked on the list page, which highlights what’s next in the world of data.

Stay tuned for more updates and insights and feel free to ask any questions :)

Tom

Unlock The Full Potential Of Your Enterprise Data

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Firstly, my vision for TCF Capital came from a pressing need I observed in the e-commerce and e-grocery industry – the necessity for a robust, modern ecosystem that facilitates a faster transition to a sustainable future. This ecosystem encompasses everything from goods production, logistics and automation to retail technology and retailing. Technology is changing how we shop, making it more convenient, efficient, and eco-friendly. But to truly reap the benefits of this revolution and bring it to the masses, we need a thriving ecosystem that can scale these solutions and navigate the sector's complexities.

Secondly, I didn't jump into this adventure empty-handed. My journey of building and scaling my last three companies (Slevomat, DameJidlo turned Foodora, Rohlik) has equipped me with a playbook of sorts. These experiences, coupled with the invaluable lessons from earlier failures, have taught me about the dynamics of growth, the intricacies of the market, and the art of leadership in a rapidly evolving industry. At TCF Capital, I'm excited to apply this knowledge to aid founders in their quest for success.

Next, having raised substantial funds over the years (over EUR 800m), I've recognized the pivotal role of capital in breaking artificial ceilings. Adequate funding empowers startups to aim higher, scale faster, and go further. It's an indispensable resource, and at TCF Capital, we're committed to connecting promising entrepreneurs with the right capital to accelerate their growth.

Furthermore, I've always believed that great teams build great companies. Over the years, I've been fortunate to work with exceptional people who’ve been integral to our collective successes. At TCF Capital, we're building a powerhouse team to support companies as they scale, offering them our collective expertise and insights.

Lastly, the mission of TCF Capital goes beyond just funding and scaling startups. It's about sharing what we learn along the way. It's about fostering a community of founders and investors (we will also open our funds to external investors) in Central and Eastern Europe. It's about catalyzing growth and prosperity in the region. It’s about the ambition to grow beyond the region.

In a nutshell, TCF Capital is my commitment to building a better and brighter future for e-commerce, e-grocery and CEE region. It's a platform where we merge vision with expertise, ambition with resources, and innovation with scalability. I look forward to making a tangible difference in the e-commerce landscape through TCF Capital, driving the sector towards a sustainable future.

Here's to the exciting journey ahead!

P.S. If you want to support us, please follow us on LinkedIn. And if you are a founder in need of support, email peter@tcfcap.com.

Tom

I wish I were writing my first newsletter in more joyful times, but we are currently living through difficult times for many businesses. Restricted funding, soaring inflation, and bank collapses are making things challenging. It's not easy on anyone. Let me reflect on what I believe are the critical aspects of successfully managing a business through a crisis, with some examples from Rohlik Group life.

1. Cash Management

   Effective cash management is essential during an economic crisis. It's important to clearly understand your company's financial position, monitor cash flow, and take appropriate steps to conserve cash. We run cash flow forecasts weekly at Rohlik Group, despite having a 12+ month-long runway. Having a great CFO is more important than ever, and we just hired one in January.

2. Agility

   During an economic crisis, it's essential to be agile and responsive to changes in the market. This may involve adjusting your business model, products or services, or pricing strategies. Plan for different scenarios and be ready. We closed Series D funding with 17 days runway, but there was always a plan B and C. Work on those plans before it’s too late, and be ready to react. A “what if” and “what could kill us” discussion within leadership is vital.

3. Cost Management

   Managing costs is essential for a business but especially critical during an economic crisis. It’s hard, it involves letting go of people you value, but the primary job of a CEO is to make sure the company prospers long-term. This may include reducing non-essential expenses or renegotiating contracts with suppliers. We cut our costs by more than EUR 10m annually recently. For example, we realised we had EUR 1m annually in non-essential travel. We looked deep and left no stone unturned. We established governance where even small expenses must be approved by someone senior, incl. myself, for large costs. We were trying to avoid death by thousand papercuts. Small expenses amounted to millions per year.

4. Innovation

   Innovation can be a powerful tool during an economic crisis, especially with reduced team size. It can involve finding new ways to use existing resources, developing new products or services, or creating new partnerships. The company needs to find a way to innovate, especially with limited resources. Necessity and scarcity foster innovation more than anything else.

5. Communication

   Effective communication is essential during an economic crisis. Communication should be timely, transparent, and consistent. It's critical to keep all stakeholders informed and updated on the situation and any actions being taken. This includes employees, customers, investors, and other partners. Especially employees. Setting the proper context is key. We communicated layoffs in one wave and gave everyone who remained at the company security for the next 12 months at least. People don’t perform well when they are worried that another round of layoffs is coming soon. Top performers leave in anticipation. We wanted to stop this.

6. Resilience

   Running a business through an economic crisis requires resilience. It's essential to stay focused on the business's long-term goals and not get too caught up in short-term challenges. This may involve making difficult decisions, such as layoffs or restructuring, but staying optimistic and focused on the future is important. This is the hardest. You will be under pressure to compromise on your values. Don’t! Do everything above, but don’t compromise your values and long-term goals.

I hope this was helpful. Don’t hesitate to ask any questions; I will try to reply to everyone.

Have a great Sunday!

Tom

I am thrilled to introduce my new Substack newsletter! As an entrepreneur, I know how challenging and rewarding this journey can be, and I want to share my thoughts and insights with you on the latest trends and strategies for building successful businesses.

In this newsletter, I'll discuss various essential topics for entrepreneurs, such as marketing, product development, fundraising and scaling. I'll also share success stories from other entrepreneurs and provide exclusive access to valuable resources and tools. My goal is to provide you with the information and inspiration you need to take your business to the next level.

I imported contacts from my old Tinyletter newsletter, so please unsubscribe if you are not interested anymore. I will send my first content within a week.

In addition to sharing my knowledge and experiences with you, I also see this newsletter as an opportunity to learn from my subscribers. I believe that the best way to grow as entrepreneurs is by constantly learning from others, and I hope that by starting this newsletter, I'll be able to create a community where ideas can be shared and everyone can benefit from each other's experiences.

My main goal is to give back and help others on the same journey. I've had the opportunity to learn from some of the most successful entrepreneurs in the industry, and I want to pass on what I've learned to help others achieve their goals.

I'm excited to start this conversation with you and see where it takes us.

Please don't hesitate to reach out and share your feedback and thoughts. I'm looking forward to hearing from you.

Best,

Tom

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