AI Use-Case Strategy & Investment Case
Build and prioritise an AI portfolio against business value, feasibility, data readiness, control requirements and total cost.

AI & Automation
Prepare the data foundations your AI use cases require.
Overview
An AI application needs information that is relevant, accessible and controlled for its intended use. The requirements should be defined around the workflow and decisions the application will support.
Capmark assesses source data, quality, entity resolution, lineage, ownership and access for priority use cases. We design the pipelines and integration needed to connect AI workloads with enterprise systems.
We help establish reusable data foundations and practical controls, proportionate to the use case rather than a broader data programme without a clear business purpose.
What we do
We test each priority use case against the data it needs: completeness, quality, lineage, access, latency and how outputs reach the people or systems that act on them. The output is a costed gap list, sequenced by the use cases it unblocks.
We fix the upstream defects that break AI outputs: duplicate and unresolved entities, stale records, weak identifiers and fields never designed for the use they are now put to. Fixes are made at source, with controls that stop the defects returning.
We establish where critical data comes from, who owns it and who may use it for what. Lineage is documented to the depth governance needs, and access is controlled so AI systems inherit the same permissions as the people they support.
We build the pipelines that feed AI systems reliably: sourcing, transformation, refresh, monitoring and the integration that carries outputs back into the workflow or system of record. Pilots move off manual extracts and onto governed feeds.
Where several use cases share the same data, we build the foundation once: shared sources, common entity views, reusable pipelines and platform patterns, so each new use case starts further ahead.
What to expect
A Senior Practitioner leads from day one. The first weeks assess the data behind the priority use cases: where it comes from, who owns it, what condition it is in and what is blocking production.
We then fix the gaps in priority order, working with data, technology and business owners, and evidence each fix against the use case it unblocks.
Engagements range from a use-case data assessment to full delivery of the data foundations behind an AI programme.
The scope is shaped by the data requirements of the priority use cases and the systems involved.
Establish the current state, the constraints, the risks and the value at stake.
Shape the target model and the business case with the executives who own the outcome.
Stand up the team, the plan and the governance around the outcome.
Design, build and test the change, with the business alongside.
Cutover, hypercare and handover, so the business runs it under its own control.
The same five stages on every engagement, led by senior practitioners end to end. How we work
Client result

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