What feels impossible to crack?The Impossible Club
Growth / AI Growth Capability

AI Growth Capability

Build AI advantage.

Choose where AI creates advantage → build the future model → create the conditions to scale it.

Questions we help answer

How we work

Advise

Choose where AI can create material advantage.

  • Where AI can create growth or customer value, not just efficiency.
  • Which new propositions, business models or sources of growth are now possible.
  • Which parts of the business need fundamental redesign around AI.
  • Where human judgement should remain critical and where AI can take on more of the decision or work.
  • What to prioritise, scale or stop based on commercial impact and strategic fit.
Build

Turn the highest-value opportunities into something real.

  • AI-enabled propositions and customer experiences.
  • New business-model or revenue concepts enabled by AI.
  • Redesigned customer, decision and operating models.
  • Human + AI ways of working around priority decisions and activities.
  • Working prototypes, tools, agents or automations that make the future model tangible enough to test and improve.
Enable

Create the conditions to scale AI into enterprise value.

  • Clear ownership, decision rights and accountability.
  • The capabilities and ways of working needed to scale.
  • Governance proportionate to the value and risk.
  • Measures that connect AI activity to customer and commercial outcomes.
  • The operating rhythm to move from isolated pilots to repeatable, enterprise-wide value.

Ways to start

A few ways we could work together.

From prototype to production

We shape and test the proposition, customer model and ways of working. Production engineering, systems integration and enterprise architecture need the appropriate delivery partners.

AI growth capability, explained

Where can AI create growth beyond efficiency?

Start with customer value and the commercial opportunity: a new proposition, a stronger experience or a business model that becomes possible with AI. Then assess strategic fit and what would need to change to realise that value. Use those choices to decide what to build and test first.

What should people decide, and what can AI do?

Redesign around the decision or activity, then establish the role of people and AI. Consider the consequences of a mistake, the judgement required and who remains accountable. Make approval boundaries and decision rights explicit before testing how much of the work AI should take on.

What helps AI pilots become enterprise value?

Agree what customer or commercial improvement would justify wider investment. Then establish ownership, decision rights, capabilities and proportionate governance. Use an ongoing learning rhythm to decide what to improve, scale or stop, with accountable people responsible for the outcome.

Where growth gets stuck

What feels impossible to crack?

Let’s work it out.

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What feels impossible to crack?