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Most books in this space deal with how to develop a strategy for AI: The Intelligence Multiplier focuses on how to develop a strategy with AI.
Combining practical advice, real-world examples, and a focus on actionable insights, it’s designed to help you make smarter decisions, build competitive advantage, and future-proof your organization in an AI-driven world.
It covers topics such as: - Gathering and managing increasing volumes of information - Best practices for working with customer/personal data - Using AI to synthesize data and generate insights, using the strategy models you know and the technology you might not - Making smarter, faster decisions - Communicate your strategy to stakeholders, with more personalized but consistent communications - Responding more quickly when your strategy is overtaken by events
There is no single, correct approach to managing the risks and opportunities of AI; this book gives you a clear and structured way to determine your own approach.
George Walkley is an independent strategy consultant and recognised expert on the strategic implications of artificial intelligence. He advises organizations across sectors including publishing, education, manufacturing and professional services on how AI is transforming strategy, decision-making and competitive advantage. He has delivered training and consultancy to more than 300 organizations in 15 countries.
George is a regular speaker on AI and strategy at conferences and corporate events internationally. He serves as non-executive chairman of the communications group Midas, as a non-executive director of the scientific publisher and AI pioneer Burleigh Dodds, and as an advisory board member of Bristol University Press. He holds an MBA with distinction from Bayes Business School and is a Fellow of the Royal Society of Arts.
George is based in Wiltshire, UK.
Introduction
Who this book is for
How this book works
AI usage statement and updates
Chapter 1: Strategy is broken
Volatility
Uncertainty
Complexity
Ambiguity
Figure 1: VUCA in Practice
Bringing it together
Reflective Questions
Chapter 2: Strategy
A working definition
Frameworks for good and bad strategy
Strategy and execution
Strategy in SMEs
Strategic thinking and the promise of AI
Reflective Questions
Chapter 3: How AI actually works (and why it matters for strategy)
A short history of AI
How LLMs work
Understanding AI's limitations
Getting the best from AI
Reflective Questions
Chapter 4: Working effectively with LLMs
Prompting fundamentals
Figure 2: The four dimensions of an effective prompt
Iterative refinement
Context window management
Retaining and organising your work
Verification and quality control
Figure 3: warning signs of unreliable output
Cognitive practices
Sequencing and selective use
Reflective Questions
Chapter 5: Prerequisites and risk management
Getting stakeholder support
Selecting appropriate tools
Data readiness
Understanding data boundaries
Figure 4: Information risk levels
Governance structures
Legal and compliance considerations
Cultural readiness
Risk management
What comes next
Chapter 6: Using AI for research and competitive intelligence
From hunting to framing
Three tiers of information
Practical research capabilities
Primary research design
Verification
Figure 5: Choosing the right tools
Reflective Questions
Chapter 7: Using AI for analysis and market analysis
Types of analysis
Frameworks: From scarcity to abundance
The decision focus
Frameworks in practice
Synthesis, prioritisation and insight
Reflective Questions
Chapter 8: Using AI to see what you're missing
A practical sequence
Grounding perspectives in evidence
What perspective-taking actually looks like
Separating what you know from what you're guessing
Red teaming: reframing and challenging assumptions
From perspectives to decisions
Reflective Questions
Chapter 9: Thinking about the future
Three kinds of uncertainty
Start with rough models
Decision trees and strategic options
Simulate responses
Explore alternative futures
From insight to action
Monitor signposts
Limits and discipline
Reflective Questions
Chapter 10: Strategy formulation and decision-making
The stack shift
The top-of-stack rule
Owning the process, not just the outcome
From analysis to options
The explainability test
The politics of AI-informed decisions
Decision rights
Resource allocation
Cognitive biases
From decision to commitment
After the decision
Reflective Questions
Chapter 11: Implementing strategy, managing change
Strategic communication
Dialogue and resistance
Frontline reality checks
Implementation planning and execution
Prioritisation and sequencing
Building implementation capability
Tracking progress and maintaining momentum
Sustaining energy over time
Change management and stakeholder engagement
Tools and templates
Common implementation pitfalls
Reflective Questions
Chapter 12: Course correction and dynamic strategy with AI
Is strategy intrinsically long-term?
You still need the fundamentals
VUCA demands a different rhythm
Signal-to-noise discipline
When to hold and when to fold
Figure: Example dynamic strategy rhythm
The governance challenge
Reflective Questions
Conclusion: The work ahead
Glossary
Acknowledgements