For leaders who have been asked for an AI transformation plan

The board asked for AI transformation. Nobody handed you a first move.

The pressure is real. Sixty-one percent of CEOs told BCG this spring that their boards are rushing AI transformation, and CEOs now put about a third of their own performance review on AI returns. That pressure lands on the revenue leader, the COO, and the CTO, usually with a set of pilots that never reached production and no agreed view of what to do next.

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Who this is for, and the question each of them brings us

AI transformation

2026 was the year to experiment. 2027 budgets are being written against results.

We work alongside revenue and technology leaders to understand the system as it runs today, find the friction, decide what to fix first, and lead the change so that the results are measurable and your people own them. Here is why that is the work, and not another tool.

Most AI pilots die in the handoff. The reasons AI initiatives stall are well documented and they are rarely about the technology. Gartner’s sales research put it plainly this summer: agents “are only as effective as the systems they operate within.” Fragmented data, workflows that were never redesigned, and tools layered onto unclear ownership are what consume the return, and adding more AI to a disconnected revenue system makes that worse rather than better.

So the first move is not another tool and not a twelve-month program. It is a clear picture of how your revenue system actually runs, an honest view of where the friction is, a sequenced plan for what to fix first, and a first change that is live and measurable within weeks. AI automation is one of the instruments in that plan, applied where the foundation can hold it. Read how we work.

  • Where do we start?

    With the system as it exists today. We map how revenue moves through your company, identify the friction points and what each one is costing, and sequence the fixes so that each change makes the next one easier.

  • What do we tell the board?

    Three measures they can check without a briefing: one named use case with a named owner, something running on real data within weeks, and by quarter end one decision that AI informed. Easy to measure, hard to fake.

  • Will our people actually use it?

    Every change lands on people, so change management, facilitation, and executive coaching are inside the engagement rather than sold beside it. A process or a tool that nobody adopts is a failed one, whatever the demo looked like.

Sources: BCG, CEO and board survey, May 2026 · Gartner Sales, July 2026

A distinction worth making

AI adoption is not AI transformation.

Most companies that feel stuck have done the first and are being asked for the second. The difference is not how many tools are in use. It is whether the way the business runs has changed.

Adoption

People are using AI tools. Licenses are active, a few teams have pilots, and there are productivity stories in the all-hands. The org chart, the process, the handoffs between teams, and the numbers the board looks at are the same as last year. If this describes you, you are in good company: it describes most companies right now.

Transformation

The way the business runs has changed. A process is different, a handoff between two teams has been redesigned, decisions are made on better information, and some of the work is done by automation rather than by people. Something measurable moved, a named leader owns it, and the next change is easier because of the first one. This is what the board is asking for.

In a revenue organization, the changes that matter most are in the handoffs between functions, because that is where the hours and the errors accumulate. See how we find them.

Why us

The world is full of AI services. Three things separate us.

Three things have to be true for AI to pay in a revenue engine: someone who knows where the friction is, someone who can build across the systems you already own, and someone who can get a leadership team to run differently. We have done all three from the operator seat. Both of us are on every engagement, and nothing we do adds a SaaS contract to your shelf.

  • Operators, not consultants

    We ran revenue teams before we advised them. Customer success, revenue operations, and go-to-market leadership inside companies scaling from twenty million to several hundred million. We know where the friction hides because we have lived inside it, with our own numbers on the line.

  • Architects, not another engineering shop

    Plenty of firms will build you an automation. Far fewer can tell you which change is worth making, in what order, and where a human still has to stay in the loop. That judgment is the work, and it is what decides whether any of it pays.

  • Adoption is part of the build

    A change nobody adopts is a failed change. Change management, facilitation, and executive coaching sit inside the engagement rather than beside it, because what stalls these efforts is almost always people and process rather than pipelines. One of us is an ICF-certified executive coach because that turned out to be the job.

Who we are

Two operators who ran the revenue systems we now help you change.

Both of us are on every engagement, from the first working session through the handover. There is no delivery team behind a pitch team.

Beth Yehaskel

Principal

Twenty-five years running revenue teams. VP Customer Success and then VP Business Operations at Spredfast as it scaled from roughly $20M to $60M ARR and integrated two acquisitions. Head of Customer Success for the Americas at Dropbox. VP Customer Success at Jungle Scout through a churn turnaround during hypergrowth.

Revenue Architect at Winning by Design from 2020 to 2025, teaching more than 3,000 go-to-market leaders and working on revenue architecture for companies from $20M to $350M ARR. ICF-certified executive coach.

At Cognitive Edge: mapping the revenue system and its friction points, post-sale and retention mechanics, and the leadership coaching and change management that decide whether any of it gets adopted.

Ernesto Humpierres

Principal

Over a decade across B2B SaaS in operating and advisory roles. Formerly at Atlassian in revenue and go-to-market roles, then commercial leadership inside private equity backed portfolio companies, working directly with PE operating teams on revenue performance.

At Cognitive Edge: revenue engine analysis, deciding which changes and which automations are worth making and in what order, the technical architecture underneath them, and the operating changes that make them stick.

FAQ

Questions revenue and technology leaders ask us.

Answered the way we would answer them in the room. Click a question to open it. Questions about the technical side of an engagement are on the How we work page.

What is AI transformation, and how is it different from AI adoption?

Adoption means people are using AI tools. Transformation means the way the business runs has changed: the process is different, the handoffs between teams are different, decisions are made on better information, and some of the work is done by automation rather than by people. In a revenue organization the changes that matter most are in the handoffs between functions, because that is where the hours and the errors accumulate. If the org chart, the process, and the numbers look the same as last year, what you have is adoption.

Why do most AI pilots fail to reach production?

Because of the system they were dropped into, not the technology. Deloitte’s 2026 survey found only a quarter of organizations have moved even 40 percent of their pilots into production. Gartner’s sales research names the causes: fragmented data, poor workflow integration, and agent sprawl. A pilot on demo data proves the model works. It says nothing about whether your data holds, whether the decision is decidable, or whether anyone owns the outcome. Understanding that system first is where our work begins.

Our board wants an AI transformation plan. What should be in it?

Three success measures the board can check without a briefing. One named use case with one named owner and the metric it is meant to move. Something running on your own real data within weeks, rather than a vendor demo. And by the end of the first quarter, one decision made by a named leader that AI informed. A plan built on those three measures shows progress the board can fund again; a plan without them is a list of tools. We gave the same three measures to a private equity operating partner who was evaluating a portfolio company’s progress.

How long does it take to show AI results in a revenue organization?

A clear map of the revenue system and its friction points in two to three weeks. A first change live on your own data within roughly ninety days. A measured result against a matched control group by the quarter after that. Fast enough for a board that wants results before the next budget cycle, and slow enough to be real.

How do we measure AI ROI for the board?

Two numbers. The metric the change was chosen to move, compared with a matched control group so the result cannot be explained by seasonality or a strong quarter. And revenue per employee, which is how investors already compare portfolio companies. McKinsey’s 2026 analysis of 471 private equity backed companies found the most advanced AI adopters run at a median of $180,000 revenue per employee against $118,000 for the level below.

Should we fix our data and processes before deploying AI?

Fix what the first change needs, not everything. Every candidate for automation is run against four tests: usable data, a decidable decision, a defined action, and a named owner. Where it fails on data, that specific gap closes first as a scoped piece of work. Trying to fix the whole foundation before changing anything is how companies spend a year on data hygiene and ship nothing. Building on top of a broken base is how they ship something nobody trusts.

Do we need a Chief AI Officer or a fractional AI executive for this?

Not to start. IBM’s 2026 CEO study found three quarters of large companies now have one, and most still say adoption depends on people more than technology. A mid-market revenue organization needs a clear first change, a named owner, and a leadership team willing to run differently. If you already have an AI leader, we work with them. If you do not, the first change shows you what the role would need to be.

What is the role of change management in AI transformation?

It is most of the work. BCG puts it at seventy percent people and process, twenty percent data and technology, ten percent algorithms, and our years running revenue teams agree. So it sits inside the engagement: who owns the change, what the team stops doing, how the weekly cadence changes, and what leadership has to model. One of us is an ICF-certified executive coach because that turned out to be the job.

Talk to us.

Tell us what the board is asking for, where you think the friction is, and what you have tried so far. Both principals read every one of these, and you will hear back from one of us, not a sequence.

The first step is a thirty-minute conversation. Share your goals and the challenges in front of you, and we will explore together how we can help. Prefer email? engage@cognitive-edge.ai reaches both of us.