who we work with: leadership teams.

For leadership teams
facing a post-AI P&L.

Your customers will reprice the labour in what you sell before your finance team does. The leadership job is to decide where AI pays, rewire the organisation to capture it, and prove it to the board.

in short.

For executive teams facing a post-AI P&L: find where AI will pay, rewire the organisation to capture it, and prove the value to the board.

what we hear.

Leadership teams come to us when...

  • AI spend is growing and nobody can say what it has returned
  • pilots work, then stall before they reach core workflows
  • the board wants an AI plan and evidence of value at the same time
  • competitors are running the same revenue on leaner teams
  • people are using AI the organisation doesn't know about
who owns what.

AI changes every seat at the table.

  • chief executive.

    Sets the direction: where AI changes the economics of the business, and which bets the organisation is making.

    the value framework →

  • chief financial officer.

    Owns the number: the value case for each use case, cost per outcome, and AI spend split fixed and variable.

    measuring AI ROI →

  • chief operating officer.

    Owns the work: which workflows change, execution accuracy and rework.

    rewiring the operating model →

  • people and HR.

    Owns the roles: task-by-task redesign, skills, and how many agents a person can supervise.

    how AI changes work →

  • technology.

    Owns the platform: model choice and routing, data that is fit for the decision, and building so a model can be swapped.

    what agents can do today →

  • risk and legal.

    Owns the exposure: controls matched to risk, and the value at stake where an agent acts without review.

    how to govern AI →

also.

We also work with private equity and ai-native start-ups.

questions.

Straight answers.

What should a board ask about AI?

What each AI use case is meant to move in the P&L, who owns that number, what it has returned against its baseline, what the full cost is including compute and rework, and which controls apply where AI acts on customers, money or regulated decisions.

Where should a large organisation start with AI?

With a small number of use cases close to its core workflows, each with an owner and a baseline, rather than a broad licence rollout. A focused review sizes them and designs the first wave.

How does AI change the P&L?

Human delivery costs fall, compute starts to behave like a cost of goods, and new lines appear for model refresh, AI liability and compute buffers. Benchmarks built on headcount stop carrying information. the post-AI P&L →

next step.

Start with where AI will pay.

A fixed-price review that ends in a first wave the board can approve.

talk to us →