Everything You Must Know to Run Your Brand 🌟
Sunday, September 28, 2025
Brand Key Model
The 𝗕𝗿𝗮𝗻𝗱 𝗞𝗲𝘆 𝗠𝗼𝗱𝗲𝗹 (𝘰𝘳𝘪𝘨𝘪𝘯𝘢𝘭𝘭𝘺 𝘧𝘳𝘰𝘮 𝘜𝘯𝘪𝘭𝘦𝘷𝘦𝘳) is one of the best tools to lay out your Unique Selling Proposition with clarity and focus.
Sunday, September 21, 2025
AI in Advertising Use Case Map
https://www.iab.com/guidelines/ai-in-advertising-use-case-map/
As AI transforms advertising, organizations need a clear, structured understanding of its applications across the marketing and media value chain. The IAB AI in Advertising Use Case Map, developed with industry leaders, provides a comprehensive guide to current and emerging AI applications.
What it is:
Real-world AI use cases organized by category, maturity (established vs. emerging), and description.
Why we built it:
To demystify AI in advertising, help members prioritize focus areas, and provide shared language for evaluating opportunities, risks, and investments.
How to use it:
- Benchmark your current AI adoption
- Identify relevant capabilities for experimentation or deployment
- Inform strategic planning, education, product development, and policy workstreams
We welcome your feedback as we continue to refine it in the updates to come!
How AI supercharges strategy and planning
Fish Food 657: How AI supercharges strategy and planning
Stretch use cases for AI in strategy, is AI really taking jobs, the shadow AI economy, Nano Banana and what 1910 tells us about modern technology anxiety
This week’s provocation: Stretch use cases for AI in strategy development
I ran a session with a leadership team this week in which we were working through how AI could be a true partner throughout the strategy development and deployment process. One of the issues in discussing this subject is the sheer breadth of application. Similarly to the innovation process, there’s just so many ways in which AI can be integrated at every stage and so to get the true benefit we need to truly think about it as a thought partner throughout.
The value of AI in this context is not only in efficiency but in how it can help you get to places that you probably wouldn’t have got to on your own. It ‘supercharges’ strategic processes because it both catalyses planning but also enables entirely new possibilities. Over the past few months I’ve written about using AI project spaces to develop strategy, using AI as a thought partner to challenge assumptions and think differently, using synthetic personas and research to explore ideas and using AI for simulation and scenario planning in strategy.
I’ve set out a (far from comprehensive) list of the more obvious AI use cases above, using Stephen King’s classic strategy cycle to give it some structure, but I thought it may be fun to dwell on some of the more unusual or thought-stretching ways of using AI for each stage, some of which came up in my recent workshop.
Situational awareness: Where are we?
Alongside the obvious ways in which AI can synthesise and deepen customer, category, cultural and company understanding, I’m fascinated by the whole idea of being able to better interpret weak and emerging signals. Some examples include:
Competitive intent: Beyond more obvious variables (market share, pricing, distribution) AI can infer competitive intent by analysing hiring patterns, leadership speeches, company report language, patent filings, customer reviews.
Synthetic future consumers: There are some very useful ways of using synthetic personas to explore ideas, but I like the idea of creating future AI personas trained on demographic and cultural data which can simulate how emerging customer groups might behave.
Fusing signals across domains: We often analyse markets in isolation but AI can also be used to connect faint signals across unrelated domains (science, patents, culture, policy). This can reveal the first signs of different forces colliding (for example AI + regulation + consumer activism) before they converge into disruptive change.
Clustering anomalies: Rather than just tracking trends, AI can detect clusters of anomalies across customer behaviour, competitor actions, or market data which may indicate the first ripples of disruption.
Current state analysis: Why are we there?
I’m a fan of using AI to help with root cause analysis and iteratively using AI in a ‘5 whys’ approach to surface fundamental drivers and refine problem statements. But there are some other stretch use cases here too:
Causal simulations: We usually think about simulations in the context of future possibilities, but it can be interesting to feed a bunch of historical performance, leadership decisions, and cultural indicators into an AI and ask it to simulate ‘alternate histories’. What would have happened if a different decision had been made? This helps to uncover the real drivers of causality and can inform future scenarios.
Decision-path archaeology: similarly, AI can analyse board papers, financial reports, and outcomes, reconstructing past strategic decisions and surfacing decision biases, recurring blind spots, or patterns of over/under-reaction.
Narrative mapping: AI can process years of internal comms, reports, and leadership messaging to reveal the implicit stories that have guided behaviour. These hidden narratives can help explain inertia or misalignment.
Objective setting: Where could we be?
AI is pretty good at enabling better objective and goal definition but again, there’s also some more imaginative ways in which we can use it:
Counterfactual stretch goals: Asking AI to generate objectives that assume one core constraint is no longer a factor (‘If capital were unlimited, what would our 5-year goal be?’) can open up new possibilities which can then be scaled back more pragmatically.
Inverse benchmarking with synthetic competitors: We’re already familiar with developing synthetic personas but I’m also fascinated by the idea of using AI to generate ‘synthetic competitors’, or hypothetical firms which can be used to stress-test strategies or expose overlooked goals or possibilities.
Values-aligned objective generation: AI is of course pretty good at recommending financial or market-driven goals, but don’t forget that it can also synthesise (potentially more inspiring) objectives based on stated values, purpose and to a degree the cultural DNA of a business.
Strategy formulation: How could we get there?
Some of my favourite stretch techniques here involve constraints-driven and recombinant thinking:
Constraint-flipping: Inverting assumptions (‘what if we had to achieve this with half the resources?’) using AI can help to generate unconventional pathways or creative shortcuts.
Adjacency recombination: I love norm-switching as a way to break out your sector assumptions, and cross industry analogies using AI (like applying logistics optimisation techniques to healthcare strategy) can open up entirely new thinking.
Strategy sparring: Deploying multiple specialised AI personas (tech optimist, skeptic, regulator, innovator) to debate strategy options gives you a helpful range of divergent perspectives.
Measurement and tracking: Are we getting there?
Strategic drift detection: AI can be set up to continuously compare progress against stated goals and strategy both in terms of metrics tracking but also actions taken (meeting minutes, budgets, project outputs) to flag potential deviations early.
Emergent KPIs: As progress is made KPIs may need to change, so AI can propose new KPIs that may be better than pre-defined ones.
Future-back metrics: Rather than just starting with the KPIs that may seem right today, AI can generate different metrics by working backwards from desired long-term outcomes, which can help with the tracking of leading indicators of future success.
A lot of these stretch techniques are pretty nascent but they show how we’re only really scratching the surface of how AI will change strategy development and implementation. They are less obvious but their value comes from just that - they go beyond efficiency (where many strategists stop right now) and push AI into different territories like reframing, imagination, and discovery. Whilst these remain the parts of strategy that humans are strongest at, they are also ones that AI can now take to a whole new level.
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Building and running a strong brand isn’t just about a logo or tagline — it’s about creating a complete ecosystem that connects your promise, people, and products with your consumers.
Here are the 7 key pillars every brand leader should master:
🔹 Brand Story – Craft authentic narratives, creative assets, and a clear tone of voice that resonate across owned, paid, and shared media.
🔹 Brand Promise – Define your emotional and functional benefits through a strong positioning statement and brand concept.
🔹 Consumer Knowledge – Understand your target consumer deeply: their needs, insights, and desired responses to your brand.
🔹 Product Innovation – Constantly ideate, test, and launch new products based on research and consumer feedback.
🔹 Purchase Moment – Optimize pricing, shelf space, merchandising, and e-commerce to ensure your product is at the right place, at the right time.
🔹 Consumer Experience – Deliver consistency, personalization, and emotional connection at every touchpoint.
🔹 Brand Culture & Business Operations – Align your internal purpose, values, and behaviors with strategic plans, financial tracking, and supply chain to drive profitability.
💡 When all these elements work together, you don’t just sell a product—you create a brand people love and trust.
What do you think is the most important pillar for a successful brand today?
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