AI Use-Case Strategy & Investment Case
Turn a portfolio of pilots into a funded, sequenced roadmap the board can hold to account.
AI & Automation
Data Readiness & AI Foundations
AI only works where the data is complete, accessible, controlled and connected to the workflow.
Overview
Most AI programmes stall on data, not models. Use cases are funded, pilots run on extracts, and production stops when the underlying data turns out to be incomplete, unowned, hard to access or disconnected from the workflow the AI is meant to improve.
Capmark helps institutions build the data foundations their AI portfolio needs. We assess lineage, quality, access, integration and ownership for the priority use cases, then fix the gaps in order: source data, entity resolution, pipelines, permissions and the connections that carry outputs back into the process.
The work is scoped from the use case backwards, not the estate upwards. Data is fixed to the standard the use case needs, with ownership and controls that keep it fixed after go-live.
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.
Engagements run from a use-case data assessment to full delivery of the data foundations behind an AI programme.
01
Assess
Establish the current state, the constraints, the risks and the value at stake.
02
Define
Shape the target model and the business case with the executives who own the outcome.
03
Mobilise
Stand up the team, the plan and the governance around the outcome.
04
Implement
Design, build and test the change, with the business alongside.
05
Sustain
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
Proven in the work, not the pitch.
Asset & Wealth Management · Financial Crime
Financial-crime controls rebuilt for the world's largest alternative asset manager
The World's Largest Alternative Asset Manager · Financial Crime Transformation
The firm partnered with us to convert regulatory findings on AML control deficiencies into a resilient financial-crime framework: customer risk assessment, screening, enhanced due diligence and AI-assisted alert reduction, remediated to completion under independent scrutiny.
- 5
- Interlocking initiatives directed
- AI/ML
- Screening false positives reduced
- 2 clouds
- Azure and AWS delivery
- Evidenced
- To independent monitors
Read the case study
Insights
Related thinking.
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