AI
Choose the AI use cases worth building.
Use case selection, data and infrastructure readiness, build or buy decisions and a roadmap a board can approve.
Sound familiar
Where this usually starts.
The first AI decision is not which model, it is which problem. We assess candidate use cases against the value they would create, the data they would need, the risk they would carry and the infrastructure they would run on, then produce a roadmap that starts with something measurable. The people writing the recommendation also build and operate AI systems, so the plan survives contact with delivery.
Scope
What is included.
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Use case discovery
Working sessions with the business to find tasks where AI changes a measurable outcome.
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Value and feasibility scoring
Each use case scored on value, data availability, risk and effort, on the same scale.
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Data readiness
Where the needed data lives, its quality, who owns it and whether it may be used.
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Infrastructure options
Hosted models, cloud accelerators or owned GPUs, compared on cost, control and timeline.
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Risk and policy
An acceptable use policy and a risk classification for each use case.
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Roadmap
A sequenced plan with the first production use case, its success measure and its budget.
Reference architecture
AI that answers from your data, on infrastructure you control.
Strategy sits with the people and oversight side of the AI architecture: the users whose work would change and the governance that decides what may be built. It sets which parts of the rest of the diagram are worth building first.
What this service delivers here
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Users
Staff and customers, through the applications they already use.
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Governance and audit
The inventory, risk classification and logs a regulator can ask for.
Before you start
Decisions to settle early.
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Build, buy or wait
Many AI needs are met by features already in software you own; some justify a build; some are better left until the technology or the data matures. Each use case gets one of the three.
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Hosted models or your own
Commercial models are quickest to start; private models give more control over data and cost at volume. The data and the regulation usually decide.
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Central team or federated
A central team keeps standards and avoids duplicate spending; business teams know where the value is. We recommend a model that suits your size.
Approach
How the work runs.
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Discover
Candidate use cases from the business
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Assess
Value, data, risk and infrastructure
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Decide
Build, buy or wait, with reasons
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Plan
A roadmap with the first measurable win
Commercials
How it is bought.
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A fixed-fee assessment
We hold short sessions with the teams whose work could change, review candidate use cases and the data behind them, and give you a scored shortlist with a fixed price for the full readiness program.
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A fixed-price build
Use case discovery and scoring, data readiness checks, infrastructure options, an acceptable use policy and a costed roadmap, delivered at a fixed price for the departments in scope.
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Running it afterwards
Once the roadmap is agreed, the first use case moves to delivery under its own price, and a periodic review keeps the portfolio current as models and regulation change.
Start with the assessment.
Worked example
A list of ideas and no plan.
Say an insurer's leadership has collected AI ideas from every department, two pilots are running with no route to production, and the board has asked for a strategy.
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Discover
We run short working sessions with claims, underwriting, finance and service teams to find tasks where AI would change something measurable.
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Score
Each idea is scored on value, data, risk and effort. Document triage for claims rises to the top, while a customer-facing chatbot is deferred.
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Check data
For the leading use cases we confirm where the documents live, who owns them, and whether policy and law allow their use for this purpose.
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Plan
The roadmap starts with one production use case, its success measure and its budget, and each pilot is either folded in or stopped.
At the end, the board has a plan it can fund and measure, staff have a policy for AI tools, and the first project starts with its data question already answered.
An illustration
Where it goes wrong
What we design out.
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Starting from the technology
Choosing a model or platform before the problem leads to pilots that prove nothing. Every use case starts from a business measure it would move.
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Pilots without an exit
Experiments that never reach production consume budget and goodwill. Each one gets a success measure and a decision date, and is stopped if it misses.
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Ignoring the data question
Many use cases fail because the data is missing or may not be used. Data availability and permission are checked before anything is scored as feasible.
Outcomes
What it changes.
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A short list, not a long one
Effort goes into the few use cases with clear value and usable data.
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Decisions with reasons
Build, buy and wait decisions are written down with the evidence behind them.
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A first win on a date
The roadmap starts with a use case small enough to ship and important enough to matter.
How you will know it is working
- The board can see which AI projects are funded and why.
- Each pilot has a measure, an owner and a date on which it continues or stops.
- Staff know which AI tools they may use and with what data.
- The first production use case is measured against the outcome it promised.
You keep
What you have at the end.
Handover pack
five items
- Scored use case portfolio Every candidate use case scored on value, data, risk and effort on one scale, so they can be compared fairly.
- Data readiness assessment For each shortlisted use case, where its data lives, its quality, its owner and whether it may be used.
- Infrastructure and sourcing options Hosted models, cloud accelerators and owned GPUs compared on cost, control and timeline for your use cases.
- AI acceptable use policy What staff may and may not do with AI tools and company data, written to be followed rather than filed.
- Roadmap with budgets and success measures The first production use case and the sequence after it, each with a budget and a measure of success.
The team
Who you work with.
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AI lead
Runs the discovery sessions, scores the use cases and writes the roadmap.
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Data specialist
Checks where the data for each use case lives, its quality and whether it may be used.
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Risk and policy adviser
Drafts the acceptable use policy and the risk classification for each use case.
What we need from you
- An executive sponsor who can decide what is funded.
- Time from the teams whose work could change.
- Details of AI pilots and tools already in use.
- Your risk appetite, or the people who set it.
From the GPU to the governance, one team answers for the model.
Questions
Questions we get asked.
How long does an AI readiness assessment take?
Typically a few weeks, depending on how many business areas are involved and how quickly data owners can be reached. It ends with a written roadmap rather than a presentation.
Will you ever recommend not using AI?
Yes, where a rules engine, a better report or a process change would do the job more cheaply and more reliably. A recommendation not to build is a useful result.
Do you favor particular vendors?
No. We compare hosted and open-weight models, and cloud and owned infrastructure, on the same terms, and the comparison is written into the report so you can check it.
What do we need to prepare?
The ideas already in circulation, access to the people who own the processes and the data, and any constraints you already know about, such as data that may not leave the country.
How do you estimate the value of an AI use case before building it?
From the business measure it would move, estimated from how the task is done today: the time spent, the error rate, the cost of delay. Where the estimate is uncertain we say so, and the first build is designed to test the assumption cheaply before a larger investment is made.
What happens to the pilots we are already running?
They are scored like every other use case. A pilot with a clear measure and a path to production joins the roadmap; one without either is stopped, and what it taught is recorded so the next attempt starts further on.
Does an AI strategy need its own budget line?
The roadmap budgets each use case separately, because the costs differ: data work, integration, model usage and ongoing operation. A single AI budget hides which projects are paying for themselves and which are not.
Who in our organization should be involved?
An executive sponsor who can make decisions, the leads of the teams whose work would change, the owner of the data in question, and someone from risk or legal. The technical team matters too, but most early questions are about the business, not the model.
Ask about your situation
Answered from this site, with its sources.
Where it is bought
AI strategy and readiness, by sector and region.
Sectors that buy AI
Often paired with
What usually comes next.
CONTACT
Start with the use case worth building.
Bring the list of ideas and the constraints you already know. We will turn them into a roadmap with a first measurable win.
- Senior-led
- Accountable
- Direct
- Fixed-price options
- Compliance-ready
- 24/7 capable
Prefer to talk it through? Call +92 (333) 32 11011
What happens next
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You pick the time
Bring the list of AI ideas as it stands, however long; we arrange the call around your diary.
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Thirty minutes, with an engineer
An AI lead discusses which of them look measurable and which need data you may not have.
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A written assessment
You get a written view of the shortlist, the data questions and a fixed price for the full assessment.