AI transformation has ten steps and only one of them is AI. · VANDAM

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# AI transformation has ten steps and only one of them is AI. · VANDAM

> The ten steps of making a process AI-native, walked through one quote process: why mapping, data and adoption take the time and the AI step is short.

URL: https://vandam.ai/notes/ten-steps-one-is-ai  
Contact: hendrik@vibelab.be

# AI transformation has ten steps and only one of them is AI.

Alex Lieberman of Tenex listed the ten steps it takes to make a process AI-native. I walked them through one quote process at a parts distributor to show where the weeks go, and why the map pays off before any model does.

Hendrik De Winne 25 October 2026 7 min read

In May, [Alex Lieberman](https://x.com/businessbarista/status/2054684459279012069), co-founder of Morning Brew and of [Tenex](https://www.tenex.co), an AI transformation firm, put my whole job in one sentence: “AI transformation is just a cooler way of saying data engineering, process mapping, change management and intelligent automation.”

A month later [he spelled it out](https://x.com/businessbarista/status/2064089261121626531). “There are 10 steps in the sequence of making an internal process or external product AI-native. Only 1 step is AI, and ironically, the other 9 steps are the far harder part.” His list is the best checklist I have seen for this work, so I want to walk it through one process from start to finish and show where the calendar actually goes.

The process is an illustration, built from how this kind of work usually runs, and no client is behind it. Take an industrial parts distributor with a few hundred people. Customers send quote requests by email, as typed lists, PDFs or photos of a worn part. An inside-sales rep reads each one, translates the customer’s own part names into catalog numbers, checks stock in the ERP, looks up the customer’s negotiated prices in a spreadsheet the sales manager keeps, asks a product specialist when a part is discontinued, types the quote into the ERP, sends a PDF and follows up a few days later. Everyone agrees quotes are too slow. Nobody can say exactly why.

## The first three steps take most of the time

1. **Identify the problem.** Lieberman’s instruction is to “find the manual process worth automating” and to turn on your “suck meter.” For the distributor that part is easy, because sales already complains about it. The harder part is writing the goal down. Tenex teaches a template for this in [its workshop for CFOs and CHROs](https://x.com/businessbarista/status/2101659025788031152): “Improve [measure] from [baseline] to [target] by [date].” Here that becomes: improve median quote turnaround from whatever it is today to the same day, by the end of next quarter. Nobody knows the baseline yet, which tells you what step two is for.
2. **Understand the workflow.** “Map how people actually work today,” on paper or a whiteboard, from beginning to end. Lieberman calls it the least sexy part and says it is “generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time.” At the distributor that means sitting next to three reps for a few days and timing every hop. The map usually surprises people. In this example it shows that the slow step is the price spreadsheet: only the sales manager edits it, and reps wait for his answer before they can send anything. It also shows that a large share of requests repeat an order the customer placed before.
3. **Collect the data.** “Gather sample inputs, documents, edge cases.” Here that means a few hundred past requests together with the quotes that answered them, the photos nobody could read, the discontinued parts and every customer who uses their own codes. The most valuable thing this step produces is a table that maps each customer’s part names to catalog numbers. Right now it lives in the heads of the two longest-serving reps.

Two fixes fall out of the map before anyone writes a prompt. Agreed prices move from the spreadsheet into the ERP, where any rep can read them, and a repeat request can be answered by copying the last quote. Lieberman notes this in his first step: “funny enough, your company becomes more efficient just by mapping out your processes even if you don’t introduce AI.” In [an April training](https://x.com/businessbarista/status/2047488697885413391) for executives from a large financial institution, his team had every exec write out a key process and mark each step E (eliminate), A (automate) or D (delegate). He reports it “revealed opportunities for AI, but also existing inefficiencies that have gone undetected.”

This is old knowledge. In 1990, [Michael Hammer](https://hbr.org/1990/07/reengineering-work-dont-automate-obliterate), then president of the consulting firm Hammer and Company, described how Ford looked at its North American accounts payable department of more than 500 people. Management hoped new computers would cut headcount by some 20%. Then it saw that Mazda ran accounts payable with a total of 5 people. When Ford mapped the old process, it found the department “spent most of its time on mismatches” between purchase orders, receiving documents and invoices. Ford stopped asking for invoices altogether.

- **143**
  Data items accounts payable had to match before paying a vendor, before and after Ford’s redesign, per [HBR](https://hbr.org/1990/07/reengineering-work-dont-automate-obliterate).
- **75%**
  Headcount reduction where the new process ran, against the 20% the original plan aimed for.

It is time to stop paving the cow paths. (Michael Hammer, [Harvard Business Review](https://hbr.org/1990/07/reengineering-work-dont-automate-obliterate), July 1990)

## The AI step is the short one

1. **Build the prototype [The AI Part].** Lieberman’s brief is to “just worry about proving there’s a there there.” For the distributor, the prototype is an agent that reads a request in any format, matches each line to a catalog number using the table from step three, pulls stock and price, and drafts a quote for the rep to approve. With the map and the data in hand, this is the step measured in days. Without them, it is where projects go to die, because nobody can say what a correct quote looks like.

> Before you reimagine a process you have to become an expert in that process.
>
> Alex Lieberman, Co-founder, Tenex, June 2026, [Source](https://x.com/businessbarista/status/2064089261121626531)

## From one user to a whole sales team

1. **Test & iterate.** “Validate with real users and edge cases.” The past requests from step three become the test set, because the right answer is already known. A rep runs the agent next to their normal work for a few weeks and corrects every draft. One question from [the list his team collects from enterprise executives](https://x.com/businessbarista/status/2077143133049995619) belongs here: “When an agent does eight hours of work, how does a human check it in eight minutes?” For quotes, the answer is a draft that shows which lines it was unsure of, so the rep checks three lines and skips the forty it matched cleanly.
2. **Integrate with systems.** “Point-in-time data is good for testing the workflow, but live data is necessary before going into production.” Now the agent needs read access to the ERP, the shared mailbox and the CRM, and someone in IT has to approve each of them. [Aaron Levie](https://x.com/levie/status/2067455756795039957), CEO of [Box](https://www.box.com), named this work in June as a big reason the applied AI layer works at scale: “Data has to be cleaned up and moved to modern systems, processes have to be re-engineered and documented, workflows have to be evaled, SLAs have to get achieved, and so on.”
3. **Roll out & train.** The next step is “hand-holding your peers/users through the onboarding process.” At the distributor that is twelve reps, each with their own habits and a few customers they guard closely.

## The last three steps are where companies stall

Lieberman also posted [notes from a call](https://x.com/businessbarista/status/2056879209583349801) with the Chief HR Officer of a $500m consumer business, and they read like a description of a company that stopped after step four. Everyone has ChatGPT access and basic prompting training. A smaller group has Claude with deeper permissions. The CHRO’s biggest worry is “a widening gap between power users and the rest of the org,” and the most advanced users are “building agents for personal workflows, not yet for scalable company processes.”

1. **Drive adoption.** His rule is to introduce a workflow that saves time “and integrates with their already existing behavior so they don’t have to deal with re-education.” For the reps, the drafted quote lands in the same inbox where the request arrived, ready to approve. They never open a new tool.
2. **Empower contribution.** “Treat your new process like an opensource project.” Every time a rep corrects a match, the correction goes into the customer part-name table. The two senior reps who carried that table in their heads now maintain it for everyone, which is a better job than retyping PDFs.
3. **Measure & capture value.** Lieberman is relaxed about ROI while a company is still experimenting. In the scale-up phase he is blunt: “you need to reskill employees attached to this process, undershoot your approved hiring roadmap, or measurably increase ACV/conversion rate/sales cycle speed.” For the distributor this is where the template from step one pays off. The baseline was measured in step two, so the team can show the change in turnaround and decide what the saved rep hours are for: more customers per rep, or fewer new hires next year.

Counted honestly, the AI step is a short stretch in the middle. Mapping, data, integration and getting twelve people to change a habit take up the rest, and the first fix, prices in the ERP, ships before any model is involved. That is why I start every engagement with the map and keep the build small until the baseline exists. [The team that does that work](https://vandam.ai/notes/ai-needs-hr-and-finance) needs people who can sit next to a rep for three days and come back knowing where the time goes.

If you want to try it on your own company this week, take one process your team complains about, write every step on a single sheet of paper, and put an E, an A or a D next to each one. Some of the Es you can fix on Monday.

## More notes

29 October 2026 6 min read

## Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.