Paris Keynote Speaker: AI Prediction Engines Explained

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SuperCollaboration·Paris, France·8:05

Paris Keynote Speaker James Taylor on AI Prediction Engines and Predicting the Future of Your Strategy

Filmed in Paris ahead of a session with senior leaders at one of the world's biggest AI companies, this is James Taylor at his most forward-looking: how AI prediction engines let you drop a strategy, policy or proposal into a simulated world of "synthetics" and see how the real people it affects are likely to respond.

By James Taylor MBA · FRSA Series: SuperCollaboration 5 min read · 8 min video

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SuperCollaboration · Filmed in Paris, France · Runtime 8:05

In This Episode

Approximate — single continuous take, no chapter markers in the source recording.

0:00Introduction from Paris, ahead of speaking to a major AI company's leadership

0:30The brief: partner managers, boundary crossers, and influence without authority

1:45Force field analysis: the classic model for mapping allies and resistance

2:45Introducing AI prediction engines and "synthetics"

3:45How governments use prediction engines instead of focus groups

5:00The Dubai real estate example: modelling market and stakeholder reaction

5:45The business use case: simulating client stakeholders before a pitch

7:00Practical payoff: finding resistance and hidden champions in advance

7:35Booking James as a keynote speaker for sales and business development audiences

Most AI keynotes cover the same ground. This one doesn't. Filmed in Paris just before a session with senior leaders at one of the world's biggest AI companies, this episode of SuperCollaboration introduces a tool most leadership teams haven't met yet: the AI prediction engine.

The brief for the session was specific. James had been asked to speak to a room of partner managers, the people who hold the relationships with the company's biggest clients, about where human-plus-AI collaboration is heading. Rather than repeat familiar AI talking points, he used the session to show them a new way to test an idea before it ever reaches the client.

Partner Managers: The Classic Boundary Crossers

Partner managers and senior enterprise sales roles are what James calls classic "boundary crossers". They carry high influence, often without direct authority. Their job depends on knowing the client's world as well as they know their own company's products, and on moving an idea across the line between the two organisations.

That makes them exactly the people who need to know, before a big proposal lands, who in the client organisation is likely to push back and who is likely to back it.

Force Field Analysis: The Traditional Way to Map Allies and Resistance

The classic tool for that job is force field analysis, the Lewin model. You map the forces driving a proposal forward against the forces resisting it, so you can plan how to strengthen one side and weaken the other before you walk into the room.

It works. But it is only as good as your own view of the stakeholders, and that view is always partial. That's where the next step comes in.

What Are AI Prediction Engines?

AI prediction engines, tools like Miro Fish, take force field analysis further. Instead of mapping stakeholders on a whiteboard, you take your strategy, policy or proposal and run it through a simulation.

"With AI prediction engines... you drop it into a simulated world, and that simulated world has what we call synthetics."

— James Taylor, Paris

Synthetics are AI agents built as digital twins of the real people, organisations or citizens your idea will affect. The simulation shows how they are likely to react, giving you a view of the reception your idea will get before you commit to it.

How Governments Are Already Using Synthetics Instead of Focus Groups

This isn't theoretical. Governments are already using prediction engines in place of traditional focus groups, testing policy against millions of synthetic citizens. The