AI strategy
AI strategy consulting that ends with working software
Pinpoint where AI delivers real business value and build the roadmap to capture it, from opportunity identification to enterprise-wide transformation, with governance built in from day one.
What is AI strategy consulting?
AI strategy consulting works out where AI will pay back in a business, in what order and under what controls. A good engagement measures the workflows that cost the most, models the return on each, designs the governance and proves the first use case on real data, so the plan arrives with working software behind it.
What the work covers
Six pieces of work sit inside an Elsewhen strategy engagement: opportunity discovery and a readiness assessment against your own workflows, a business case and ROI model for each candidate, agentic workflow design, rapid prototyping to prove value, governance and risk strategy, and the plan for taking AI across the enterprise once the first use case is running.
They overlap in practice, because the prototype tests the business case and the governance design shapes what the prototype is allowed to do, so we run them as one piece of work and the plan stays tied to evidence. It is also where every engagement across Elsewhen’s services begins.
Strategy and build in one team
At Elsewhen the senior squad that scopes the work is the squad that builds it, so the estimate in the business case is made by the people who will be held to it, and the first workflow goes live in four weeks.
| Output | What it is for |
|---|---|
| Ranked workflows | Volume, handling time and error rate measured for each candidate, so the first use case is chosen on evidence |
| Business case | The return on the first use case, modelled before any build begins |
| Proof of value | A working prototype on your data, running in your own cloud |
| Governance design | The approval gates, audit trail and data rules the build has to follow |
| Scaling plan | The order in which further use cases join the shared platform |
Why does your organisation need an AI strategy?
Spending on AI without a strategy rarely returns anything. MIT NANDA’s 2025 study of enterprise generative AI found 95% of the organisations it examined getting no measurable return, and attributed the gap to the approach organisations took. A strategy decides which workflows to change, how the return will be measured and what has to be true before agents act.
Pilots that never reach production
The pattern behind that number is familiar to anyone who has run an AI programme: pilots chosen because a tool was available with no costly workflow behind them, success measured in demos, and nobody owning the step from prototype to production.
Gartner predicted in June 2025 that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Those are the three things a strategy exists to settle before the build starts.
What a strategy settles
With a strategy in place, the first question is which work costs the most and could be done by an agent, and the tools follow from the answer. The return is modelled before the build, the controls are designed alongside it, and the people whose work changes help design it from the first week. A pilot built that way has an owner, a baseline and a route into production from the day it starts.
- 95%of organisations in MIT NANDA’s study saw no measurable return from generative AI
- 40%+of agentic AI projects Gartner expects to be cancelled by the end of 2027
Sources: MIT NANDA, The GenAI Divide: State of AI in Business 2025 (July 2025); Gartner, agentic AI prediction (June 2025).
How do you identify the right AI opportunities for your business?
Start by listing the workflows that cost the most time and money. Measure the volume, handling time and error rate of each, and test them against five conditions: tedious, repetitive and inefficient work, with accessible data and low risk. The candidates that pass, ranked by return, become the roadmap.
The TRIAL test
We call the five conditions the TRIAL test. Work that is tedious, repetitive and inefficient is where agents take the most hours off people. Accessible data means the agent can see what a person sees when doing the job, and low risk means a mistake can be caught and reversed before it reaches a customer or a ledger.
A workflow that fails one condition stays on the list further down the order, until the shared platform and the governance around it have been proven on something safer. For examples of what passes, by business function, see our guide to applied AI use cases.
Measuring the work as it is
Handling time comes from system logs where they exist and from a few days of observation where they do not, and error rates come from the rework queue, which is more reliable than memory. With those numbers for each candidate, the estimate of what an agent could take on stops being a guess, and a finance team can check the business case line by line.
- TediousWork people put off because it is dull
- RepetitiveThe same steps, many times a week
- InefficientHours spent moving information between systems
- Accessible dataThe agent can read what a person reads
- Low riskMistakes are caught and reversed before they land
What is agentic AI and why does it matter for enterprise?
Agentic AI describes systems that carry out a workflow end to end: an agent reads the case, applies the rules your experts set, calls the systems involved and hands the decisions that carry weight to a person. For an enterprise it matters because the return comes from work taken off people’s desks and done to the same standard.
From answers to actions
An agent works inside the process: it picks up the next item in the queue, gathers what it needs from your systems, does the work and records what it did, so people spend their time reviewing outcomes and deciding the cases that need judgement.
Agentic workflow design is the part of strategy that decides where in a process the agent stops and the person starts. Getting that boundary right, step by step, matters more to the return than which model the agent runs on.
The Agentic Enterprise
Taken far enough, this changes how a company runs. In what we call the Agentic Enterprise, people set direction and make the judgement calls while agents run whole operations, and every new use case builds on the context and controls the last one left behind. Most organisations get there one workflow at a time, so the strategy starts with one.
| Chatbot | Copilot | Agent | |
|---|---|---|---|
| Starts when | A person asks | A person is working | Work arrives in the queue |
| Produces | An answer | A suggestion | A finished piece of work |
| The person | Acts on the answer | Accepts or edits | Approves the decisions that carry weight |
| Return comes from | Time saved searching | Time saved drafting | Work taken off the desk |
How do you build a business case for AI investment?
Price the work as it is today, estimate how much of it an agent can take on and set that against the cost of building and running the system. Every input is measured: volume, handling time, error and rework rates, and the people cost behind them. The case is modelled before the build and checked against real numbers once it is live.
What goes into the model
The value side counts hours returned, errors avoided and, where the workflow touches revenue, the sales recovered or won. The cost side counts the build, the running cost of models and infrastructure, and the time people spend reviewing what agents do.
A case that only works if the agent is right every time is a weak case, so the review step goes in as a cost from the start. Where the return can be counted directly, it can also carry the fee: our outcome-based commercials sit against the result you asked for.
What the returns look like
On a credit management engagement, agents that onboard loan portfolios took £14m a year out of labour cost and cut source-to-servicing cycle time by 92%. At a global marketing group, an AI copilot that builds audiences from synthetic data returns £17m a year of planner time. Each began as one measured workflow with a business case written before the build.
- 1Measure the workVolume, handling time and error rate today
- 2Estimate the agent’s shareStep by step, with review time counted
- 3Price both sidesValue returned against build and running cost
- 4Prove it on real dataA working prototype tests the estimate
- 5Measure in productionThe case is checked against live numbers
What does AI governance and risk management look like?
In practice it is a set of controls built into the system: what data each agent can see, which actions it can take on its own, where a person has to approve before anything irreversible happens, and a record of every run. Designed alongside the first use case, those controls are what let security, legal and the regulator agree to go live.
Controls in the system
Controls written into the system apply to every run, whoever is on shift: permissions scoped to the task, approval gates on the actions that carry weight, versioned workflows that can be rolled back, and an audit trail that shows what the agent saw, what it did and who signed it off.
Your data stays in your estate, in your own cloud and under your own security controls, and Elsewhen holds Cyber Essentials Plus.
Autonomy in proportion to risk
Governance and risk strategy sets the level of autonomy for each step in a workflow. Reading and summarising a document can run unattended, a reply to a customer can wait for a person to approve it, and moving money or changing an entitlement waits for a named approver. Starting with low-risk work, as the TRIAL test does, builds the evidence that lets autonomy grow where it has earned it.
- 1Agent proposesDoes the work and prepares the action
- 2Rules checkPermissions and policy checked automatically
- 3Person approvesOn the actions that carry weight
- 4Action and auditThe system acts and records what happened
- Agent
- Policy
- Person
- System of record
How do you scale AI from proof of concept to enterprise transformation?
Build the shared parts once and reuse them. The first use case pays for the business context, permissions, integrations and audit that every later agent needs, so the second ships faster than the first and the tenth faster again. Scaling becomes a question of which workflow to add next, with no new programme to fund from scratch each time.
Three stages
Our Agentic Enterprise Framework runs in three stages. AI Squads are deployed first: an embedded team of a lead product manager, a data scientist, an AI engineer and agents finds the costliest workflow, ships working agents and measures the return.
With that use case proven, the Platform is built as a shared core and rented tools begin to retire. The third stage is the Agentic Enterprise, where agents run whole operations, people stay on the judgement calls and every use case compounds.
Why proofs of concept stall
Proofs of concept stall when each one is built as a standalone project with its own data pipeline, its own access rules and its own interface, so the tenth costs as much as the first. Treating the first build as the start of a platform, owned by you and model agnostic, changes the economics, because the second use case inherits everything the first one paid for. How that platform is put together is covered in our guide to enterprise AI engineering.
- 1AI Squads, deployedFind the costliest workflow, ship agents, measure the return
- 2The Platform, builtThe shared core built once, with rented tools retired
- 3Agentic Enterprise, reachedPeople set direction while agents run operations
What should you look for in an AI strategy consultant?
Look for a consultant who will be held to the numbers in their own plan. That means senior people who have shipped AI into production, a method that measures your workflows before recommending anything, commercials tied to the outcome, and an end point that includes working software in your environment, which you own outright along with the code and the IP.
Questions worth asking
Ask who will build what the strategy recommends, and whether they are in the room for the strategy. Ask for a production system they have shipped and what it returns, stated with how it was measured. Ask how they price the work and what happens to the fee if the return does not arrive, then ask who owns the code, the models and the data afterwards, and how a newer model would be swapped in.
Where strategy sits in the wider work
Strategy is one part of a broader engagement, and our page on enterprise AI consulting sets out the whole of it, from the first working session to the platform that later use cases run on. Elsewhen has delivered more than 200 client engagements since 2011 from offices in London, Manchester and Dallas, and the FT named it one of the UK’s Leading Management Consultants in 2025.
| Question | What a good answer sounds like |
|---|---|
| Who builds it? | The people scoping the work, named and in the room now |
| What have you shipped? | A system in production, with its figure and how it was measured |
| How is it priced? | Against the outcome, with the return modelled before the build |
| Who owns it afterwards? | You do: the code, the IP and the data, with no licence to renew |
| What if a better model arrives? | It swaps in without a rebuild |
Start with one workflow
Bring the workflow that costs you the most. Leave a working session knowing what it could return and what it would take to build.