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I’ve been disappointed with HBR articles on LLMs of late and been openly critical. This one’s a notable corrective.
Credit where it’s due, The Gen AI Playbook for Organizations is a far more credible, grounded lens. It’s aware of epistemic limits in a way most recent HBR AI pieces simply haven’t been.
The authors introduce two genuinely useful axes - cost of errors and type of knowledge (explicit vs tacit) - to decide where Gen AI can be used well.
That simple distinction reframes the whole conversation from “how smart is the model?” to “what kind of knowing does this task demand, and what’s the price of being wrong?”
It’s still classic HBR - managerial tone, quadrant chart, actionable checklists - but it earns the framework.
Think of it as McKinsey meets tacit knowledge, competently.
What it gets right:
- Reintroduces human judgment: empathy, ethical reasoning, and context as irreducible.
- Offers a practical map of where Gen AI belongs today and where it doesn’t.
- Frames the paradox of access: when everyone can use the same tools, advantage shifts to how you use them.
- Grounds strategy in difference, not speed.
What leaders can take from it:
If you’re wrestling with Gen AI adoption, three questions here are worth carrying into the boardroom:
1️) Where is the cost of being wrong low enough to let Gen AI run?
Start there. Don’t chase perfection; chase learning.
2️) Which tasks rely on tacit judgment, context, or ethics?
Keep humans squarely in the loop, that’s where trust and reputation live.
3️) How can we use the time Gen AI saves to improve coordination, not just efficiency?
Free hours mean nothing if feedback loops stay broken.
Those three prompts alone could help many reshape how leadership teams approach pilots, governance, and ROI.
The piece reveals where corporate AI thinking still sits, caught between epistemic humility and managerial control.
Where it falls short:
- They echo Polanyi without ever naming him, invoking tacit knowledge while designing as if it could be made explicit.
- They say strategy beats speed, then prescribe speed as strategy.
- Their “cost of errors” model assumes organisations know that cost, when it’s often emergent, reputational, or discovered too late.
- They keep humans in the loop but forget humans are the loop, the source of coherence itself.
That’s where it overlaps - and diverges - from my own positioning.
Where they speak of knowledge types, I speak of rails: the organisational feedback systems, semantics, and judgment structures that turn intelligence into ROI.
Where they stop at “human oversight,” I push toward management discipline over model fascination.
Still, it’s a step in the right direction, for once, an HBR article on AI that recognises strategy is about where humans remain essential, not how fast machines improve. | 15 comments on LinkedIn
The piece reveals where corporate AI thinking still sits, caught between epistemic humility and managerial control.
