The Heartshaped Method

I use AI to accelerate
the thinking.
Not replace it.

Most organisations use AI to automate tasks. I use it as an accelerant for thinking — and for the kind of change that actually sticks. It's not about the tools. It's about using AI to help you think faster, decide better, and change more deliberately. Every talk, workshop and programme I deliver has this running underneath it.

More than a keynote

How an AI-powered talk works

Every Heartshaped session is AI-powered. Your people contribute live input, AI helps analyse and aggregate the room, and the output comes back as individual insight, group patterns and practical next steps.

The Heartshaped process: people give live input, AI analyses and aggregates the room, and insight returns as individual and group outcomes
01

Live input from the room

02

AI-assisted analysis and reasoning

03

Practical insights and decisions as takeaways

Some people just teach the tools. We teach better thinking too.

The five steps

From insight to
real decisions.

These aren't abstract stages. They're what I actually do — in every room, at every stage of preparation, and in how your team moves from raw thinking to action. AI runs through all of them.

01
Capture
Gather everything that matters — before you start jumping to answers
02
Understand
Surface what's actually going on, not just what's visible on the surface
03
Decide
Make the call with confidence — not certainty, but real clarity
04
Act
Move faster than you would have. Better than you could have alone
05
Measure
Track what changes. Compound what works
The Heartshaped method: a five-step continuous loop — Capture, Understand, Decide, Act, Measure
What your people get

Capability built
across every programme

Behaviour, thinking and practical skills — mapped to each programme

 The Human Dividend “Save My Job
from AI” 		Judgement Advantage “Stop Messing
Around with AI” 		AI Fluency “The New
Literacy”
Behaviour
Adaptability to AI-driven change
Curiosity and willingness to experiment
Confidence under uncertainty
Resilience when things don't work first time
Decision ownership and accountability
Intellectual humility — knowing what you don't know
Learning agility — updating beliefs when evidence changes
Thinking
Judgement — knowing when and what to decide
Critical evaluation of AI-generated output
Problem framing — setting AI up to genuinely help
Strategic thinking — seeing the bigger picture
Assumption testing — questioning what you take for granted
Structured reasoning — thinking in steps, not instincts
Risk awareness — seeing what could go wrong before it does
Practical
Prompting — getting better AI outputs, reliably
Context creation — briefing AI so it actually understands
Task decomposition — breaking complex work into AI-ready parts
Building reusable AI workflows for real tasks
Evaluating and editing AI output before it goes anywhere
Human value mapping — plotting where people add irreplaceable value
Role redesign — rebuilding work around human and AI strengths
Decision mapping — structuring when and how decisions get made
Identifying bias and gaps in AI-generated analysis

Want to see it
in the room?

See the talks