Digital & AI Strategy
GenAI, Agentic AI and AI value creation for CEOs, boards and private equity. Use-case portfolio, target operating model, responsible governance, and M&A tech due diligence — with a written exit criterion, not a dependency.
AI and GenAI strategy, value-case design and delivery for companies that need pilots to reach the P&L — across the DACH region, Poland and wider Europe.
What you get
- Digital and AI strategy aligned to the value agenda of the CEO, board, or investor.
- GenAI and Agentic AI use-case portfolio with feasibility, risk, and ROI scoring.
- Target operating model for data, ML, product, and GenAI platform delivery.
- AI delivery roadmap with change management and upskilling plan, sequenced by value and by delivery readiness.
- Commercial and tech assessment for M&A: AI-enabled value creation, disruption risk, synergy potential.
- Responsible AI governance — risk controls, model oversight, EU AI Act alignment.
How we work
- Align leadership on outcomes, value levers, scope, and risk appetite.
- Assess data, platform, talent, and delivery readiness against the AI ambition.
- Prioritise GenAI and Agentic AI use cases by value, feasibility and delivery readiness.
- Design operating model, governance, and delivery rhythm; mentor the client team along the way.
- Support go-to-market, board discussions, and investor narrative around AI-enabled value.
Sectors
Delivered across TMT, industrials, consumer, and financial services — both corporate transformations and private-equity portfolio value creation.
Delivery model
AI bets ship through a delivery layer — DORA-instrumented squads, a named pilot before scale, and engineering managers mentored to run the rollout. We run this layer inside the same mandate, not as a separate engagement. See Agile Engineering & Delivery Performance for the standalone shape.
Ideal for
- CEOs and boards turning AI ambition into measurable outcomes.
- Private equity and investors pressure-testing AI-driven value creation in the portfolio or during due diligence.
- CIOs, CDOs, and digital leaders scaling GenAI and Agentic AI beyond pilots.
- Public sector leaders seeking accountable, auditable AI delivery.
Typical engagement shape
- Duration
- 12–20 weeks for a full strategy + operating-model mandate; 4–6 weeks for M&A tech due diligence
- Team
- 1 principal + 1 senior specialist, scaled with named associates for programmes beyond 16 weeks
- Cadence
- Weekly exec + board-ready review at week 6 and at exit
- Starts with
- A 2-week readiness diagnostic — data, platform, talent, governance — before the big commitment
- Exit
- Prioritised use-case portfolio live or on a dated delivery plan, owned by a named client lead
Related reading
- From GenAI pilots to production: AI value creation for CEOs, boards and private equity — our point of view on where the real value lever sits, and how to avoid the common GenAI traps.
- The Big Consulting AI frameworks, compared (2026) — a sourced benchmark of BCG, McKinsey, Deloitte, EY, PwC, KPMG, Accenture, Bain, Capgemini and IBM.
- AI Strategy Workshop — a faster way in: turn an AI mandate into a prioritised, board-ready roadmap in a single facilitated session.
Common questions
What is AI transformation?
Using AI, including generative AI, to change how the business actually operates and makes money — not running another pilot. It is the operating changes, data foundations and delivery discipline that turn AI from a demo into measurable P&L impact.
Why do most AI projects fail?
They stall before value: pilots that never reach production, no clear value lever, missing data foundations, and no delivery layer to ship and run them. We start from the P&L lever and the delivery prerequisites, not the model. See our AI value-creation playbook for the 90-day pattern.
Where should a company start with AI?
With the one or two decisions or workflows where AI moves a real number, and an honest read of whether the data and delivery foundations exist. A focused 90-day pattern that turns a pilot into measurable impact beats a broad roadmap nobody executes.
What does an AI transformation partner actually do?
Set the value thesis, pick the few use cases that pay, build the data and delivery foundations, and run the cadence that gets them into production and keeps them there. Operator work in the room, not an advisory report.
How do you measure ROI from AI?
Against the value lever you chose up front — cost, revenue, risk or speed — with a baseline and a target set before you build, so "is it working" has a number, not a vibe.