This essay is available to the entire AI Strategies for CEOs community. If you find it valuable, please consider sharing it with peers facing similar decisions.
The strategic deliverable used to mean a five-figure engagement and a three-month wait. That era is ending for decisions whose shape is already known. Here’s what changes — and what it doesn’t replace.
In 2025, MIT’s The GenAI Divide (NANDA initiative) reported a number that should have made more noise in boardrooms than it did on LinkedIn: 95% of enterprise generative-AI pilots produce no measurable impact on the P&L. Not weak impact. None. That matters because the failure is almost never technical. The model works. What’s missing is the link between the AI capability and the business decision it’s meant to serve.
So the real bottleneck for enterprise AI is strategic, not technological. And that’s exactly where leaders are least well equipped.
The false choice
A CEO who wants to answer a deceptively simple question — build, buy, or partner? What’s the business case for this initiative? Where do we actually stand on AI versus our sector? — faces a sharper split.
The first: a bespoke consulting engagement. Several weeks, a five-figure budget, a scope to negotiate before you even know what you’ll get. For many decisions, that’s disproportionate.
The second: do it internally. The team produces a deck. The deck doesn’t survive the CFO’s first round of questions because it lacks the method — the sizing, the value drivers, the baseline, the attribution. Straight back into the 95%.
Between the two, there was nothing. That gap is what’s closing — between bespoke consulting and productized skills.
What a productized Skill actually is — a definition
A productized Skill is a strategic deliverable with a fixed scope, price, timeline, and partner review. Three distinctions show why it differs from something else:
• It is not a template you fill in yourself. It’s a finished artifact — a diagnostic, a business case, a decision matrix — executed and delivered.
• It is not an open-ended engagement. You know, before you start, exactly what you get, by when, and at what price.
• It is not a chatbot output. A partner stakes their judgment on the result. Productization industrializes the method, not the accountability.
Why now
Two things changed at the same time, and together they enabled productization.
First, the method matured. After enough engagements, the good strategic questions repeat, and the way to answer them becomes a repeatable protocol. What once required a dedicated team can now be specified.
Second, AI executes that protocol — faster and more consistently than a team starting from scratch each time. Sizing, modeling, structuring an argument, benchmarking against a reference set: these are tasks AI accelerates massively, provided a method and human judgment frame them.
There’s an intended irony here: we productized our own AI-strategy practice using AI itself. It’s the most honest demonstration we can offer of what we advise clients to do.
Why it’s better for the buyer — not just cheaper
The cost reduction is the least interesting consequence. What matters most to a leader is this:
Price certainty. You know the cost before you commit. No drift, no change orders.
Scope certainty. The deliverable is defined up front. You buy a result, not billable days.
Speed. A decision that doesn’t wait for next quarter. Strategy that operates by Monday.
Judgment preserved. The partner who reviews the deliverable is what separates you from the 95%.
Entry without commitment. Buy a single artifact to test the method — no retainer.
That last point echoes another under-cited MIT finding: externally built solutions succeed roughly twice as often as internally built solutions. On a field this young, a proven outside method beats well-meaning improvisation.
What productization doesn’t replace
Be honest about the limits, or the promise rings false. Here’s where the line is:
A productized Skill is the right tool when the problem’s shape is known: a business case, a maturity diagnostic, or AI due diligence. These follow a structure you can systematize. It is the wrong tool when the question is genuinely novel, highly ambiguous, or politically charged internally — when much of the work is defining the problem before solving it. Those stay irreducibly bespoke, and that’s where advisory keeps its full value.
The rule: use a Skill when the shape is known, and an engagement when it isn’t. They don’t compete; they serve different moments.
What we’re opening
So we productized our practice into a catalog of Skills — from a one-day readiness audit to a 21-day Value Realization Framework — structured on our framework, Frame / Decide / Create / Measure.
Most leaders start at the same door: a 60-minute, self-serve AI readiness audit that tells you where you actually stand and names your three priorities. It’s deliberately the lowest step: the way to test a method is to run it on a real question, not read its brochure. For the buyer, it is the simplest first decision.
The full catalog is here: odyssey.tech/skills.
FAQ
Is a Skill just an expensive template? No. A template leaves you the work; a Skill delivers the finished, partner-reviewed artifact. You’re not buying a frame to fill in — you’re buying a result you can take into a board meeting.
How is it different from a consulting engagement? An engagement has open scope, a negotiated price, and a variable timeline. A Skill fixes all three in advance. The engagement stays relevant when the problem is novel or ambiguous; the Skill, when its shape is known.
Isn’t an AI-produced strategy of lower quality? AI executes the method; it doesn’t carry the judgment. The partner review is what makes a deliverable survive the CFO’s questions — the exact difference between the 5% of AI projects that create value and the 95% that don’t.
Where should I start? With the 60-minute readiness audit. It clarifies your priorities and points to the next Skill that makes sense — with no commitment. For the buyer, that means a clear first move.
Axel Tombereau is founder and managing partner of Odyssey, an independent AI-strategy consultancy. Odyssey designs leaders’ AI strategy, measures its ROI through its proprietary VET-IA™ framework, and runs pre-investment strategic due diligence.
Comments
Nothing yet. Say the first thing.
Sign in to join the conversation.