Who we work with
Both AI-native companies and legacy businesses seeking to make artificial intelligence a material part of how they operate, compete and grow.
Leadership teams who see AI as important enough to affect strategy, operating models, customer propositions or enterprise value—not simply a niche technology initiative or chat bot deployment.
Relevant experience
We started scratching the surface of AI and machine learning around a decade ago. Since then, we have worked with several AI-native companies driving the agentic revolution, and with established businesses applying AI to update legacy operations, products and commercial models.
Our experience spans not only AI companies and adoption programmes, but also global breakthrough initiatives that set new standards for how AI technology is commercialised and valued.
When clients call us
When they want AI to contribute more clearly to revenue and margins. Making more sense of it, creating more momentum around it and ultimately increasing value from it. Sometimes that means we develop or red-team an AI strategy. It always means working out how customers, employees and other stakeholders could benefit—and what needs to change for that value to be realised.
When they need to build organisational momentum around AI. Shaping the narrative around AI so that employees understand where the company is going, leaders can make better choices and customers, investors and other stakeholders can feel the difference. Building and renewing organisational momentum around AI is often one of the hardest parts, and where we can bridge a gap.
When they need to identify high-value workflows to focus their AI adoption on. We also help companies move beyond scattered experimentation towards the few places where AI can materially change how the business works. Increasingly, that means looking at data, context, standardisation, workflows and agentic AI together.
When strong AI technology needs a stronger market position around it. AI-native companies tend to call us from the other side of the same question: how do we turn technical capability into a stronger company? That can mean sharpening positioning, defining a category, making a complex product easier to understand, accelerating enterprise adoption or building a narrative that gives customers, talent, investors and the wider market a clearer reason to care. The category itself provides momentum, but it also raises the bar for standing out within it.
Our perspective
AI is a microeconomic revolution as much as a technological one. When the cost of an input collapses, the value of what complements it changes with it. As generation and synthesis become dramatically cheaper, value moves elsewhere with scarcity: to data, context, judgement, taste and agency. As AI converges with other technologies—of varying maturity—such as robotics and quantum computing, the boundary between digital intelligence and the physical economy becomes increasingly porous, and more interesting.
AI adoption comes down to data governance and human behaviour
For established organisations, two of the hardest things to change are also among the most important: their data and human behaviour. Models improve quickly. Organisational memory is fragmented, permissions are inconsistent, processes are poorly documented and people do not change how they think and work simply because a better tool becomes available. Much of becoming AI-powered is therefore about making an organisation more legible to machines while helping human brains adapt to a different division of labour.
Agentic AI raises the stakes
As the cost of generation falls, the cost of verification rises. Producing another answer, analysis, image, recommendation or piece of code may approach zero: establishing whether it is correct, distinctive, safe and worth acting on does not.
The growing capabilities of agentic AI makes this more consequential. When AI moves from generating outputs to taking actions across systems, the question is no longer only whether an answer is good enough. It becomes what the agent can see, what it is allowed to do, which context it understands, how its actions are verified and when judgement returns to a human.
From AI tourism to durable value
This is why we tend to see the difficult work of AI adoption less in the model itself and more in data governance, access, context, standardisation, decision rights and behaviour. The risk is what we think of as AI tourism: organisations spending much time touring AI possibilities without ever changing the underlying conditions required to create value from it.
AI bubbles will burst along the way, and that will be necessary. The underlying economic and technological shift will continue regardless of market volatility, changing narratives or periods of doubt. The long-term opportunity is to redesign work around what AI makes possible, rather than layering AI onto processes built for a different era.
The new economics of AI advantage
We see our role partly in helping clients understand the new economics of AI—using technology to increase speed and intelligence without allowing the company itself to become more generic. The same principle shapes how we use AI in our own work: machines can expand the search space, perform more of the labour and increasingly take action, but the value still depends on what context they are given, what questions are asked and what humans choose to stand behind.
How we help
- Commercial AI adoption
- Identifying and implementing practical AI opportunities across marketing, B2B growth, investor relations, communications and other commercial workflows.
- Data governance and context
- Improving data ownership, access, permissions, definitions and process documentation so AI systems have the information and context they need.
- Learning and employee adoption
- Helping teams learn, experiment and adopt new ways of working, with clear roles, incentives and practical use cases.
- Company AI positioning
- Helping companies sharpen their positioning, category, differentiation, go-to-market strategy and sources of lasting advantage.
- Investor relations and critical matters
- Helping companies explain their AI strategy and capabilities to investors, partners, regulators and other important stakeholders.
- AI-powered data storytelling
- Turning proprietary and market data into clearer insights, narratives and visual communication for customers, investors and decision-makers.
- Second opinions and red-teaming of AI strategies
- Independently challenging AI strategies, business cases, assumptions and narratives before major commitments are made.
- Brand standardisation and AI skillification
- Turning brand principles, language, claims and knowledge into reusable standards and AI skills that keep output consistent and distinctive at scale.