The Strategist's Edge
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The Strategist's Edge
In the autumn of 1999, John Doerr walked into a two-story building off the 101 freeway and made the largest bet of his career to that point: $11.8 million for a 12% stake in a company founded by two Stanford dropouts (Wired).
Then he stood in front of roughly forty people — pretty much the entire company at the time — and presented something he'd learned from Andy Grove at Intel in the 1970s. Objectives and Key Results.
Here's what matters about that story, and it's the part that usually gets skipped: John Doerr could not write code better than Google. He could not build a search algorithm better than Larry Page and Sergey Brin. He wasn't going to out-engineer the two people who had just invented PageRank.
The edge he brought wasn't technical. Kleiner Perkins' own account of the investment describes it plainly — Doerr "introduced resources to help Google deal with its rapid growth, such as Objectives and Key Results (OKRs) for establishing priorities and stretch goals" (Kleiner Perkins). He gave them an industry-agnostic framework for implementing strategy. It became part of Google's operating DNA, and it spread from there to thousands of organizations worldwide.
That is the strategist's edge. And almost nobody in private capital is building it deliberately.
Investment theses are built in the engine room
I come from a private equity background, so let me describe how the machine actually works.
Buy-side investors build an investment thesis around areas of expertise. Those areas of expertise come from the GPs' past experience in specific industries. And that experience, almost always, comes from heavy operational involvement — years spent inside manufacturing, or healthcare services, or logistics, or fintech, learning where the bodies are buried.
This is real expertise. It produces genuine pattern recognition: which margins are defensible, which suppliers will squeeze you, which regulatory shift is actually a threat versus noise. A GP who ran distribution operations for fifteen years will see things in a distribution business that a generalist never will.
But notice what kind of expertise it is. It's operational. It's product. It's engineering. It's process.
It's still engine room talk.
And here's the uncomfortable part: the portfolio companies these investors back are usually already good at that. They're operationally solid. They know their products cold. Their engineering is competent. Their operations run. That's typically why they got funded in the first place.
So the GP arrives with deep operational expertise — and hands it to a management team that is already operationally strong. Two parties, both excellent in the engine room, both looking at the same gauges.
Nobody is on the captain's bridge.
The plan will change. That's not the failure — that's the job
Now consider what the research says about what happens next.
Professor Amar Bhidé tracked Harvard Business School graduates who started their own companies. Most failed, as expected. But among those that succeeded, 93% had to abandon their original strategy — because the original plan proved unviable. The finding was popularized by Clayton Christensen, who used it to make a sharp point: successful companies don't succeed because they had the right strategy at the beginning. They succeed because they still had resources left to pivot when the first strategy failed (Bhidé, via Christensen's How Will You Measure Your Life).
Read that again through an investor's eyes.
The strategy in the investment memo — the one the deal was underwritten on, the one the LPs saw — will almost certainly not be the strategy that produces the return. Ninety-three percent of the time, something has to change.
Which raises the question nobody in the deal process wants to sit with: who, specifically, is going to detect that the plan is wrong, decide what replaces it, and re-point the organization?
The CEO is running the company. The CFO is running the numbers. The GP is on the board, reviewing the numbers. All of them are watching lagging indicators of a strategy nobody is actively re-authoring.
McKinsey ranks it first. Not fifth. First.
McKinsey identifies six capabilities critical to successfully implementing a transformation. Here is the order they publish them in:
- The ability to craft a clear strategy focused on business value
- A strong talent bench with in-house engineers
- An operating model that can scale
- Distributed technology that allows teams to innovate independently
- Access to data that teams can use as needed
- Strong adoption and change management
Capability number one is not talent. It is not technology. It is not data or tooling or change management. It is the ability to craft a clear strategy focused on business value — and McKinsey is specific about what that means in practice: focusing the transformation on the specific domains, journeys, processes, or functions that generate significant value, guided by a road map detailing the solutions and resources needed to deliver change to those prioritized domains (McKinsey & Company).
Now look back at that list of six and ask which ones a well-run portfolio company already has.
Engineers? Usually. Operating model? Functional. Technology and data? Adequate, often good. Change management? Passable.
The one at the top — the strategy capability — is the one nobody owns. It's the capability McKinsey ranks as most critical, and it's precisely the capability that neither the operationally-expert GP nor the operationally-expert management team was ever trained to provide.
Meanwhile roughly 70% of transformations fail (McKinsey & Company).
Put the two research findings side by side and the picture sharpens considerably. The number one capability for transformation success is crafting a value-focused strategy. The plan built on that strategy will still need to change 93% of the time. And most transformation efforts fail anyway.
That is not a gap you close with better operational expertise. It's a gap you close with a strategist.
What it would look like if GPs actually did this
Imagine a fund where the value-add wasn't just industry pattern recognition and a rolodex, but a repeatable capability:
Build a value-based strategy for the portfolio company. Not a growth plan reverse-engineered from the return target — an actual strategy grounded in where value is created and captured, tested against the alternatives.
Execute it with a real implementation framework. This is the Doerr move. OKRs weren't domain knowledge about search engines; they were a mechanism for making sure forty people, and later a hundred thousand, pushed in the same direction every quarter.
Run a genuine annual review to determine tactics and pivots. Not a board deck that reports variance against a plan everyone privately knows is stale — a structured re-examination that decides what changes, based on evidence. If 93% of successful strategies end up substantially different from the original, then the review process is the strategy process. It's not administrative overhead. It's the main event.
A fund that could do that across every portfolio company would have an edge that has nothing to do with which industries its partners used to work in. It would be industry-agnostic — exactly like the framework Doerr carried from Intel to Google.
And no, you can't just point generic AI at it
The obvious objection: if strategy is a method, can't a language model just run it?
We tested exactly that. Three real businesses — a regional F&B chain, a B2B SaaS startup, and a family-owned logistics firm — were submitted to both approaches without modification. Outputs were scored by a blind evaluation rubric, where evaluators did not know which method produced which strategy, across nine dimensions of strategy quality.
Generic AI averaged 25%. TheStrategist.me method averaged 92%. A 3.7× quality advantage across 27 individual scores.
The gap wasn't in writing quality. The AI outputs were grammatically clean and well-formatted. The gap was structural, and it showed up most severely exactly where strategy actually lives:
- Execution readiness: 16%
- Competitive positioning: 19%
- Customer specificity: 20%
- Decision-ready quality: 20%
That last one is the verdict. Asked whether a board of directors would accept the output as a credible, evidence-based plan, the generic AI strategies scored 20%. One of them described its competitive advantage as "innovative and easy to use." Another listed five priorities — invest in technology, develop leadership pipeline, improve customer service, reduce costs, expand market share — with no data, timelines, resources, or KPIs attached to any of them. Four pages. It read as a brainstorm, not a strategy.
So why does a model that can pass the bar exam produce a 25% strategy?
Because there is no published, sequenced, methodical path to quality strategy work in its training data. The frameworks are all in there — Porter, VRIO, 7S, Kaplan-Norton, Christensen, Simons, OKRs, ADKAR. A model can define any of them on request. What was never published, and therefore never trained on, is the order: which tool runs first, what its output feeds into, which processes must complete before the strategy map can be drawn at all.
Ask a model for a strategy and it returns a strategy-shaped document — a competent-sounding assembly of framework names with no sequence connecting them. It skips value discovery entirely. It skips change management. It skips leadership sponsorship. Not because it lacks the concepts, but because nothing in its training data told it those steps come next.
The pantry is fully stocked. There is no recipe.
"But the next model will fix that."
It's the right objection, so we tested it directly. When Anthropic released Claude Fable 5 — a genuinely frontier-capable model — we ran the same evaluation within days of release, unguided, against the method.
Fable 5 scored 54. Generic prompting had scored 25. So raw capability gains are real, and worth conceding plainly: the frontier moved 29 points and more than doubled the earlier result.
It still finished 38 points behind the method's 92.
That is the whole argument in one number. The model got substantially smarter and the gap barely closed, because the missing ingredient was never intelligence. It was sequence. A better engine doesn't make it enforce the order a strategy has to be built in — it just produces a more articulate version of the same unsequenced assembly.
Which makes this a claim about the next model too. Expect it to beat Fable 5. Expect it to fall short of a method, for the same structural reason. The full Fable 5 comparison is here.
So become the strategist. But how?
Here's where most people hit the wall.
The tools exist, and they're not secret. Porter's Five Forces. The Value Stick. Business Model Canvas. Kaplan-Norton Strategy Maps. Simons' Four Levers. OKRs. Blue Ocean. Jobs to Be Done. Any competent MBA can name a dozen frameworks and explain what each one does.
What doesn't exist — what nobody hands you, and what no model was trained on — is the method. The sequence. Which tool comes first, what its output feeds into, when you're done with one phase and ready for the next, and how the whole thing connects from mission all the way through to an annual review that decides the pivot.
Most executives are in the same position as the model: a pantry full of ingredients, no recipe.
That's the gap I wrote From the Engine Room to the Captain's Bridge to close: four phases — Mobilize, Develop, Execute, Review — broken into twelve sequenced processes and thirty-six tools, arranged in the order you actually use them.
Not a theory of strategy. A step-by-step method for producing one, executing it, and revising it when reality disagrees with your plan — which, 93% of the time, it will.
Abdulla Al-Awadi is the founder of TheStrategist.me and ESSAM.AI, and Chief Strategy Officer at KIB. TheStrategist.me gives business leaders the strategy OS they need to develop a rigorous strategy and execute it.