Four ways to work together.

Should you launch your AI initiative?

Before budget is committed, the most valuable thing you can do is test the decision to proceed. Most AI projects that run into trouble aren't stopped by the technology — the groundwork was never ready. This is an independent, structured assessment of whether a specific project is genuinely ready for AI-enabled delivery: the right work, the right foundation, the right conditions. You receive a clear go / no-go verdict, a readiness score you can track over time, the delivery risks that matter most with concrete mitigations, and a one-page summary your board can act on.

How it works

• A short call to confirm fit and agree scope.
• A structured review of the project and conversations with your team.
• A readiness scorecard covering suitability, the organisational dimensions that decide success — business intent, operating model, governance, people and skills, and technology and data — and the context foundation the project will rely on.
• A written report and a joint walkthrough, so you know exactly what to address first.

Good fit when

• You've been asked to proceed with AI but the case isn't yet clear.
• Ownership of delivery is unclear, or the ROI case rests on untested assumptions.
• The information the project relies on is scattered across documents, tools, and people's heads.
• Governance, risk, or compliance is a live concern.

Will your operating model hold?

When AI-generated output arrives faster than people can validate it, the familiar delivery model breaks — quietly, and expensively. The constraint is never the technology; it's validation capacity. This engagement stands up a way of working built around that reality: clear roles and accountability, quality gates your own experts run, sizing that reflects what validation actually demands, and the metrics and governance to keep it manageable.

What you get

• A defined role model with clear ownership and no accountability gaps.
• Validation gates and a quality mechanism your team controls.
• A capacity and sizing approach built around the validation bottleneck, not raw output speed.
• A metrics and health-signal framework — and the governance to act on what it tells you.
• A short runbook your delivery leads keep and reuse.

Is your delivery model still working?

Delivery models rarely fail at launch — they drift. Validation slows, rework climbs, costs rise, and by the time it shows in the numbers you've already lost months. This retainer is a regular, structured check on the early-warning signals that predict trouble, with concrete adjustments agreed each cycle — not another dashboard or status report.

What it covers

• A reading of the health signals and what they mean for your programme right now.
• Concrete adjustments to roles, gates, sizing, or governance — agreed and tracked.
• An independent challenge as the work evolves and the delivery model has to keep up.
• A short summary for your sponsor after each review.

Can your team run AI project delivery without outside help?

There's a difference between a team that can follow a process and a team that can lead one. The gap is judgement — knowing what to adjust when the work changes, which signals matter, how to make the call when the situation isn't in the playbook. That doesn't transfer through documentation; it transfers through doing. This engagement runs working sessions on your real projects, building the judgement your delivery managers need to run AI-enabled delivery independently.

What it covers

• Working sessions on your live projects — not generic exercises.
• The mindset shift experienced delivery managers need to lead AI-enabled work, not just follow it.
• A capability baseline at the start, so you can see the progress.
• Reference material and decision guides your team keeps and reuses.