#423 – April 19, 2026

Time for a New Playbook?

Hello, fellow strategists! 

The most costly mistakes in strategy often aren’t errors of effort — they’re errors of fit and focus. Using the wrong framework, trusting the wrong signal, or pushing analysis past the point where it stops helping.

In this edition, we look at why most bold projects miss their mark, and the principles that give you better odds; how AI tools develop subtle preferences that can substitute for reasoning; and why the classic strategy playbook may no longer match the dynamics of the game. It’s about knowing when more research stops earning its keep — and recognizing when the rules have changed before the scoreboard tells you. Enjoy!

 

Quick Takes

Why Big Projects Fail — And How to Avoid It

Big visions inspire, but most of them unravel. In fact, research across 16,000 projects by Oxford professor Bent Flyvbjerg found only 8.5% of projects hit the mark on both cost and time — and a mere half-percent nailed cost, time, and benefits.

To steer your own bold project around a fiasco, borrow some tips from How Big Things Get Done: The Surprising Factors That Determine the Fate of Every Project, from Home Renovations to Space Exploration and Everything In Between.

Flyvbjerg, the world’s leading megaproject expert, and author Dan Gardner have identified how errors in judgment and decision-making lead projects, both big and small, to fail. They also share research-based principles that will help you succeed with yours. For example:

• Understand your odds. If you don’t know them, you won’t win.
• Think slow, act fast. Getting to the action quickly feels right. But it’s wrong.
• Think right to left. Start with your goal, then identify the steps to get there.
• Find your Lego. Big is best built from small.
• Be a team maker. You won’t succeed without an “us”.
• Master the unknown unknowns. Most think they can’t, so they fail.
• Know that your biggest risk is you.

When Your AI Strategist Has a Favourite Answer

A new study in Harvard Business Review caught our attention recently — researchers tested seven leading AI tools (the kind powering ChatGPT, Claude, Gemini and similar assistants) across thousands of strategic scenarios, and the findings gave me pause.

Regardless of industry context or how cleverly they were prompted about strategy, the models reliably gravitated toward the same fashionable choices — whichever option sounded more exciting, more modern, more culturally resonant.

The researchers have a name for it: “trendslop” — AI’s quiet tendency to echo the optimistic vocabulary and trendy buzzwords of business culture rather than reason through strategies needed for your specific situation.

A few things to bring into your own AI-assisted work:

• Use AI to expand your range of options, not narrow it. Let it surface risks, blind spots, and stakeholder angles — then keep the decision in human hands.
• Deliberately prompt the counter-case. Ask for the strongest argument for the less glamorous option. The model won’t go there on its own.
• Treat hybrid recommendations as a red flag. “Do both” (trying to be all things for everyone) often means the model is hedging, not reasoning. Strategy is about choosing what not to do.
• Providing context helps, but doesn’t cure. Even detailed, tailored prompts shifted the bias only modestly. The worldview runs deep.

The deeper reminder: AI fluency includes knowing what these tools are quietly inclined to say — before you mistake their confidence for trustworthy judgment.

Does the Old Strategy Playbook Still Fit?

For decades, conventional strategy meant finding a strong position in your sector and holding it. Writing in Thought Sparks, strategist Rita McGrath notes that “virtually all the major strategy frameworks developed from the 1970s through the early 2000s were built for a world where the structure of industries was stable.”

You may have faced turbulence —budget shocks, shifting demands— but the playing field itself stayed fixed. That era rewarded positional moves: claim your ground, defend it, and optimize within the rules. But we don’t live in that world anymore.

To illustrate the archetype, McGrath relates a story from Thinkers50 award winner Sangeet Paul Choudary — about Apple moving from iTunes to iPod to iPhone: it was not a series of product decisions, but a deliberate migration of value across an entire ecosystem.

The old frameworks, focused on incremental positional moves rather than creating new types of value, would not have framed the scenario correctly — and likely would have led decision-makers to kill the iPhone before its launch, to protect the iPod.

What’s emerging today, McGrath also suggests, is a structure that’s more distributed. As AI enables small teams —even individuals— to create outsized value, “the rationale for large monolithic corporations starts to erode”, giving way to knitted-together networks where each node (or organization) does one thing exceptionally well.

The game didn’t get harder. It changed shape.

 

8020Info Drill-Down:

When More Research Isn’t the Answer

There’s a point in most planning processes where gathering more information stops improving the decision — and starts delaying it. Knowing where that point is may be more valuable than anything the additional research reveals.

• Good enough. Herbert Simon won a Nobel Prize for formalizing what good leaders already sense: in complex, unpredictable environments, satisficing — choosing the first option that clears a reasonable bar — often outperforms exhaustive optimization. Not because rigour doesn’t matter, but because the environment doesn’t reward the precision we’re trying to achieve.
• A surprising proof. Researchers once tested Harry Markowitz’s Nobel-winning portfolio optimization model — the gold standard of quantitative investing — against the simplest possible alternative: divide your resources equally across all options. No forecasts. No elaborate modelling. For a 25-variable problem, you’d need roughly 266 years of data before the sophisticated model reliably wins. The equal-split rule held its own against the complex model.
• Complexity’s hidden cost. Every complex model requires inputs, and each input carries uncertainty. In noisy environments — and most operating environments are noisy — that uncertainty compounds. The model doesn’t just fail to add value; it smuggles in false confidence.
• Simple beats optimal. Gerd Gigerenzer at the Max Planck Institute spent decades documenting the same pattern across domains: simple decision rules frequently outperform elaborate ones precisely in conditions of low predictability and shifting variables. Which describes most of the environments our organizations navigate.
• Build in the buffer. The practical implication isn’t to abandon environmental scans or risk assessments. It’s to resist refining the analysis past the point of usefulness — and to build enough margin into your decisions to absorb how wrong you might be. When conditions are genuinely uncertain, a clear enough signal and leaving room for error will serve you better than a perfect answer arrived at too late.

P.S. These ideas were drawn to our attention by the Polymath Investor’s essay The Cost of One More Hour — a thoughtful exploration of optimum research depth well worth your time if you want to go deeper.

For Your Reading List:  After the Cliffhanger

Jim Collins built his reputation studying what makes great organizations endure (e.g. Good to Great and Built to Last). His new book, What to Make of a Life: Cliffs, Fog, Fire and the Self-Knowledge Imperative, turns the lens inward — from the intrinsic encodings within you, waiting to be discovered through experience, to navigating the fog of life transitions and what he calls “cliff moments”. For some, the third section on sustaining inner fire late in a career may resonate most. (More than half the pages in biographies written about Benjamin Franklin, for example, speak to what he did after age 60.) Collins is a storyteller who has examined and learned from the long arc of remarkable lives.

Closing Thought:  No Need to Double Down

“You don’t have to make it back the way that you lost it.”  — Legendary investor Warren Buffett, advising us not to feel obligated to recover a loss using the same strategy that caused it. (Source: 1994 Berkshire Hathaway shareholder letter)