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Module 02 Β· ~8 min

Competing in the Age of AI

Iansiti & Lakhani's frame, translated to your clients' scale.

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The big idea

πŸ’‘Key idea
The competitive moat isn't the AI model itself β€” everyone rents the same models at the same price. The real advantage belongs to whoever owns the feedback loop: proprietary workflow data flowing in, corrections flowing back, the system quietly improving with every cycle. This is also exactly why MIT's research shows a 95%/5% split. The failing 95% use AI as a static tool β€” plug it in, get output, done. The winning 5% treat AI as something that learns: it has memory, captures feedback, and adapts over time. Two Harvard frameworks and one MIT dataset, one conclusion: build the loop, not the demo.
Quick check
1 question Β· instant feedback
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  1. In Iansiti & Lakhani's frame, sustained advantage comes from:

Numbers that matter

5Γ—
more likely to realise significant financial benefits when combining machine-learning, human→machine teaching, and machine→human teaching
MIT SMR Γ— BCG
Quick check
1 question Β· instant feedback
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  1. Right consulting move for a 40-person services firm intrigued by 'AI transformation'?

Deep dive

3/3 open

Iansiti & Lakhani describe an 'AI factory' β€” an operating model that industrializes the process of turning raw data into predictions, insights, and decisions that guide (or fully automate) business operations.

It has four components: a **data pipeline** that gathers, cleans, and connects data; **algorithm development** that builds the actual prediction engines; an **experimentation platform** so changes are validated by evidence, not intuition; and **software infrastructure** that wires all of it into the real business.

The strategic shift this enables: instead of choosing a market position and defending it, you design a learning loop and compound it. More usage β†’ more data β†’ better predictions β†’ better outcomes β†’ more usage. That cycle, not the model, is the moat.

The AI factory isn't an abstract concept β€” you're already building one, piece by piece.

Data pipeline = your client's existing systems plus the integrations you build. Algorithm development = model calls shaped by your prompt engineering and eval discipline. Experimentation platform = the eval harness from Volume 1, literally. Software infrastructure = your MCP/n8n/Supabase layer connecting it all.

You are not selling the metaphor. You are selling a small, concrete instance of it β€” one workflow at a time β€” and pointing out that each instance is a stepping stone to the next.

When a company running an AI-driven operating model goes head-to-head with a traditional competitor, the dynamic is asymmetric: the digital firm updates its models daily, owns the data-rich customer interface, and quietly expands into adjacent offerings. The traditional firm scales headcount.

For your prospects, the honest version of urgency isn't 'AI will replace your employees.' It's more precise: 'A competitor who instruments these decision loops before you will compound β€” improving week over week β€” while your firm stays flat.' That's a compounding gap, and it widens quietly until it's obvious.

Quick check
1 question Β· instant feedback
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  1. Best discovery question, per this section:

In the field

πŸ”¬Worked example
Every client has candidate learning loops. A lead-qualification workflow, a proposal pipeline, a claims process β€” each is a decision process that currently runs on human judgment, produces data that evaporates, and improves only when someone happens to notice a pattern. The consulting question is never 'where can we add AI?' It is 'which decision process, if instrumented, would compound?'
🚫When not to reach for it
Do not sell 'become an AI-first firm' to a 40-person services company β€” the full rearchitecting program in the book presumes resources and data volumes your clients don't have, and pitching it is exactly the over-scoped 'transformation' that produces stalled programs. The mid-market version is one instrumented decision loop at a time, proven with evals, compounding into the next.

Pitfalls & takeaways

Failure modes

  • Selling the metaphor instead of a small real instance of it.
  • Confusing model advantage with workflow-data advantage β€” the former is rented, the latter compounds.
  • Ignoring the collision case: an AI-driven operating model updating daily against an incumbent scaling headcount.
  • Full transformation programs pitched into 40-person services firms.

Durable takeaways

  • The moat is the loop, not the model.
  • The 'AI factory' components map onto your existing delivery stack.
  • Urgency = competitor compounding, not AI replacing.
  • Sell one instrumented decision loop, not a transformation.

Do the work

πŸ“¦Artifact to produce
Learning-loop audit lens for discovery β€” decisions made, data generated, where it goes, what would improve if closed.

Sources

  • Β· Marco Iansiti & Karim R. Lakhani, Competing in the Age of AI (Harvard Business Review Press, 2020)
  • Β· MIT Sloan Management Review Γ— BCG, Expanding AI's Impact with Organizational Learning