AI Strategy & Advisory
Most enterprise AI strategies fail in the gap between the deck and the budget line. The use cases are plausible, the technology is available, and nothing ships — because no one owns the outcome, the ROI model measures accuracy instead of money, and legal finds out last.
Why AI strategies stall
The common failure is not picking the wrong use case. It is picking a use case nobody in the business is accountable for. A model that improves forecast accuracy by a few points changes nothing if the planner still overrides it, and no ROI model built on offline metrics will survive a CFO who wants to know which cost line moves. The second failure is sequencing: teams start with the use case that excites the board rather than the one whose data is actually usable, then spend two quarters on data remediation nobody budgeted for. The third is governance arriving at the end, when an AI system that was scoped as an experiment turns out to sit inside a regulated decision process.
The three calls that decide the outcome.
Made explicitly, with the trade-off written down, before anything gets built.
Build, buy, or fine-tune
The default answer for most enterprise use cases is now buy-then-adapt: a general model behind a gateway, adapted with retrieval and prompting, with fine-tuning reserved for cases where you have proprietary labelled data and a stable task. Building from scratch is justified far less often than vendors suggest, and the honest test is whether the capability is a durable differentiator or a commodity you are paying to re-implement.
Centralised COE, embedded pods, or federated
A central centre of excellence concentrates scarce senior talent but becomes a queue. Fully federated teams ship faster and quietly build nine incompatible retrieval stacks. The middle path — a thin central platform and governance function with delivery pods embedded in business units — is usually right once you are past the second or third use case, and premature before it.
Sequence by value or by data readiness
Ranking use cases by business value alone produces a roadmap that stalls at the first data gap. Ranking by data readiness alone produces a roadmap nobody in the business cares about. We score both axes explicitly and take the highest-value item that is actually buildable this quarter, which is rarely the one at the top of either list on its own.
The capabilities we bring on day one.
Each engagement assembles from this menu, sized to your scope, paced to your calendar.
Readiness audit
Data, platform, skills and governance assessed against the use cases you actually want, not a generic maturity grid.
ROI tied to P&L
Each use case modelled against a named cost or revenue line, with the assumptions written down and challengeable.
Operating model
Who owns models in production, who approves deployment, how funding flows past the pilot.
Regulatory classification
Every use case classified for AI Act risk tier and data-protection exposure before build, not after.
Governance, scoped early
The EU AI Act's obligations for high-risk systems have applied since 2 August 2026, and classification depends on what the system decides, not on how sophisticated it is — a modest classifier used in hiring, credit or access to essential services carries obligations that a far larger model used for internal drafting does not. We classify every candidate use case at scoping time, maintain an AI inventory you can hand to an auditor, and flag where a data protection impact assessment or a signed processor agreement has to exist before build starts rather than after.
From kickoff to live in four phases.
Each phase has named deliverables, named owners and a named gate, and every one respects the systems you already have in place.
Discover
Stakeholder interviews across business, data, security and legal. Data inventory and honest current-state assessment of what is usable today.
Score
Use cases scored on business value and data readiness independently, so the trade-off between the two is visible rather than buried.
Model
ROI model per shortlisted use case against a named P&L line, with sensitivity analysis and the assumptions stated explicitly.
Roadmap
Quarter-by-quarter sequence, operating model, governance framework and the first sprint scoped in enough detail to start.
What a good fit looks like.
Stated up front, so neither of us spends a call finding out this was the wrong conversation.
Engagement shape
A 60-day fixed-fee readiness audit, ending with a scored use-case portfolio, ROI models and a sequenced roadmap.
From your side
Access to business stakeholders who own the target outcomes, a walkthrough of your data estate, and whoever handles data protection.
When not to hire us
If you already have a validated use case, a business owner and clean data, skip this. Go straight to a production sprint — you do not need a strategy engagement to tell you what you already know.
AI Strategy & Advisory questions, answered.
The deliverable includes an ROI model tied to named P&L lines and a first sprint scoped in enough detail to start building. The people who write the roadmap are practitioners who will build against it, which tends to produce more conservative and more achievable sequencing.
Yes. Data readiness is scored as its own axis, independently of business value. A use case that scores high on value and low on readiness is reported as exactly that, with the remediation work sized, rather than being quietly promoted because it demos well.
We classify each candidate use case by risk tier at scoping time and flag where a data protection impact assessment or processor agreement is required. For formal legal sign-off you will still want counsel; we make sure the technical facts they need are documented and accurate.
That is a legitimate outcome and we will say so. Recommending a data remediation quarter before any model work is a cheaper answer than a failed pilot, and it is the reason the audit is fixed-fee rather than a gateway to a build contract.