Our competitors bolted AI on. For DigiNom it has been part of the operating model from the start, which is why it appears inside every capability we deliver rather than as a line item on top of them. That is a structural difference, not a positioning one, and it changes what we are able to recommend.
Why the distinction matters commercially
A consultancy with a separate AI practice has a revenue line that depends on the answer being AI. It will not tell you that a process redesign would have solved the problem for a tenth of the cost, because that answer belongs to a different team with a different target.
We hold AI inside the four capabilities deliberately, so that the recommendation is not pre-committed. In a procurement review, AI appears as spend classification across fragmented supplier data. In a DevSecOps programme, as augmented triage across a volume of findings no human team can process. In a digital operating model, as embedded automation in the workflow rather than as a tool bolted to the side of it.
Where AI is the right answer, it should be arrived at rather than assumed. Those two routes produce very different programmes and very different invoices.
Inside Business Transformation
AI-driven spend and commercial analysis inside procurement and cost reviews. Mid-market procurement data is typically fragmented across functions, systems and formats — the same category bought four ways, supplier records that do not reconcile, contract terms held in PDFs nobody has read since signature. Classifying and analysing that estate is genuinely hard for a human team and genuinely well suited to machine assistance. More on Business Transformation →
Inside Technology Transformation
AI-augmented security pipelines behind our DevSecOps delivery. Security tooling produces more findings than any mid-market team can triage, which is why remediation is so often measured in release cycles rather than days. Prioritisation is the bottleneck, and it is a good machine problem. Autonomous remediation is not — that boundary is where governance earns its keep. More on Technology Transformation →
Inside Digital Transformation
AI-powered automation built into digital operating models and data platforms. Intelligent document processing where the input is genuinely unstructured, straight-through processing for transactions that do not need a human, and forecasting where the data is clean enough to support it. That last condition does most of the work: applied to poor data, these produce wrong answers faster. More on Digital Transformation →
Before we recommend AI
Most consultancies start with the technology. We start with your problem, and the sequence is not negotiable.
The three-step test
Applied before any AI investment is proposed.
- Understand the challengeWhat is actually costing you time, money or competitive position — named as a transaction and a measured baseline, not as a function or an ambition.
- Test whether AI is the leverA large share of what gets scoped as an AI problem is a data quality problem, a process problem, or a master data problem wearing a more fashionable label. Fix the underlying issue and the AI case frequently evaporates. Not every problem needs AI, and we say so when it doesn’t.
- Apply it with governanceReadiness, governance, data and privacy, security and controls assurance — built in from day one rather than retrofitted when a client, an auditor or a regulator asks.
The governance that comes with it
Adoption has outpaced governance across the mid-market: most AI tools were deployed without formal risk review, fewer than half of organisations monitor their systems for accuracy or drift, and only around 20% have a mature governance model for AI agents. That gap is now a commercial vulnerability as well as a compliance one, because enterprise clients and investors have started asking about it in procurement and diligence.
Everything we deploy carries readiness assessment, named ownership, defined human oversight per use case, and an evidence trail built during delivery rather than reconstructed afterwards. For businesses with EU market exposure that includes classification under the EU AI Act, whose high-risk obligations take full effect in August 2026. More on AI & Automation →
What this means in practice
- We will tell you when the answer is not AI, including when we have already been engaged to deliver it
- Every AI system we put into production has a named human owner and a working override route
- Governance is sized to the risk of the use case, not applied as a blanket policy nobody follows
- Consumption-priced platforms get a modelled cost ceiling before commitment, not after the first invoice
- The audit trail is built as part of delivery, because reconstructing it later costs several times more
AI may well be the answer. But you need to make sure you have the right question first.