Solutions / AI Consulting
Choose where AI fits your business.
We assess where AI can help your business and where other approaches fit better before any build begins.
We start AI consulting by checking that the work suits the technology. Most programs fail on that decision, rather than the model itself. MIT's 2025 research found about 95 percent of enterprise generative AI pilots delivered no measurable P&L return, despite spending in the tens of billions. The technology was rarely the problem. Businesses had put it to work on tasks it was never suited to handle.
Before we design a pilot, you get an assessment of which work suits AI. We check whether the task can be clearly described and whether it repeats enough to justify automation. Errors must be inexpensive to correct. Work involving judgment or relationships, and decisions you cannot reverse, stays with people. We reduce the engagement's scope where needed, so the proposal reflects the work the technology can usefully do.
We first review your existing systems, including unresolved integration work that can be difficult to document. We then record what a pilot must prove and the results that would stop it. We build into the systems of record your team uses and agree how it will be maintained after we leave. Following this order matters more than the tools. Most programs fail when they skip these steps.
Six comparisons of repeatable tasks with inexpensive errors and decisions that require human judgment.
AI use cases and decisions
Figures carry their source and year.
Evidence for AI investment
of enterprise generative AI pilots delivered no measurable return
MIT NANDA, The GenAI Divide: State of AI in Business 2025of organizations report financial returns from AI in McKinsey's 2025 survey findings
McKinsey, The State of AI 2025companies have mature governance for autonomous AI agents, while the others develop rules as needed
Deloitte, State of AI in the EnterpriseWe manage each service through its specialist practice.
Our nine AI consulting services
We identify workflows worth automating and mark those that should remain manual.
We check your data, systems and unresolved integrations before approving a pilot.
We compare cost, control and time to value without selling you more.
We document success and stop thresholds before your first sprint begins.
We put AI in your systems of record, beyond a separate test environment.
We define data handling and model use rules, including limits on agent autonomy.
We assess providers impartially and review your selection as the market changes.
We train the people who will use your system before it goes live.
We arrange support, a runbook and a maintenance plan before we step back.
We assess suitable AI use cases before recommending a build.
Before starting an AI consulting project
Many AI consulting offers focus on hype. Most programs fail, as the figure above shows. Before you sign, we assess what your program needs to succeed and document those conditions. You can then decide whether to proceed with a clear view of the risk and the requirements your business must meet for the work to deliver results.
You receive clear decisions and, when justified, a production system connected to your systems of record. Your contract distinguishes the advisory work from the system we deliver and gives a due date for each. Sometimes the assessment shows that a document should be the final deliverable. We tell you that upfront. We do not charge you for a build the assessment does not support.
Messy data and workflows are common. Most programs fail because records disagree, work stays in email or systems cannot accept updates. Model quality is rarely the main problem. We audit these integration issues first and report what we find, including problems cheaper to fix than work around with automation. Your data usually changes the order of the work rather than making it impossible.
Vendors do have that incentive. To address it, every assessment includes a list of places where we advise against AI, with a reason for each. You receive that list as a deliverable and can challenge our conclusions. It protects your budget from being spent on experiments whose answer could have been established through a week of careful assessment before a build.
That happens often, usually because of how the pilot was set up. A generic tool is added to an unchanged workflow. Support stops after the demo, and success is too loosely defined to identify failure. We change three things. Your pilot covers one workflow with a written stop threshold. It updates the system your team already uses, and ongoing support is assigned when it goes live.
Questions about AI consulting
How is AI consulting different from hiring a software or data science vendor?
A software or data science vendor assumes you need something built, because building is the service it sells. Our AI consulting starts earlier. We assess whether the work should be automated and who should do it. The services can overlap in practice. The distinction is which decision comes first and whether the people making it can recommend no build.
How do you decide which processes should use AI and which should not?
We apply three tests. The inputs and correct output must be clear enough to document. The task must repeat enough for accuracy to add value over time. A wrong answer must be cheap to fix and reversible. Passing all three makes work suitable for consideration. If a single error would be costly or public, people retain the work even when a model could perform it.
What is a realistic ROI timeline?
You can get an initial assessment in weeks; financial returns take quarters. A narrowly scoped pilot tests whether the workflow performs as expected within a few weeks, keeping the cost of failure low. Returns require production integration and adoption, where most programs stall. A promise of payback within a quarter describes the pilot, rather than the financial outcome your business is seeking.
Do you build custom models or configure existing platforms?
We configure existing platforms first and build custom models only when justified. An existing model, correctly connected to your systems, solves most business problems. The main custom model cost is usually maintenance five years later by people who did not build it, rather than training. Proprietary data and a lasting business advantage are the reasons to consider custom work for your business.
How do you handle data privacy, security, and model risk?
We define these requirements before choosing tools. We agree what data can leave your systems and what an agent can do without human approval. We also specify what gets logged and who reviews it. Deloitte's 2025 work suggests only about one in five companies has mature governance for autonomous agents. We therefore include the governance document in the build, so it is ready with the system.
Will this cost people their jobs?
Roles do sometimes change. Denying that damages trust faster than the technology itself. We favor removing work that people do not want to keep, such as copying and rekeying information or waiting for it. If a proposal could reduce headcount, your leadership needs to decide that before the pilot. People affected should hear it clearly at that stage, rather than finding out when the system launches.
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Describe your workflow and we will explain where AI fits, including where it does not.
