AI Use-Case Strategy & Investment Case
Turn a portfolio of pilots into a funded, sequenced roadmap the board can hold to account.
AI & Automation
AI Model Risk & Governance
One model-risk framework that covers statistical models, machine learning, LLMs and agents.
Overview
AI enters the institution faster than most governance frameworks can absorb it. Models, copilots, embedded vendor tools and agents can affect decisions, data and customers before they are visible in the inventory.
Capmark helps institutions extend model risk and AI governance frameworks without creating a parallel control regime. We define inventory treatment, tiering, validation standards, human oversight, monitoring and evidence for machine-learning models, LLMs, copilots and agents.
Where AI, model risk, third-party, privacy or operational-resilience obligations overlap, we map them into the framework the institution already operates and build the evidence needed to show the controls working.
What we do
We extend model risk policy, tiering, validation standards, monitoring and review cadence to cover AI systems that learn, drift or resist simple explanation. The framework is designed with second line and tested against live use cases.
We define what belongs in the inventory, including AI assistants, vendor-embedded tools and agents that act across systems. Each use case receives a tier, owner, review cycle and control path proportionate to its impact.
We build evaluation methods for LLM and AI systems, including curated test sets, accuracy and error measurement, adversarial testing, change testing and reproducible evidence. The standard is designed to support second-line review.
We map applicable AI, model risk, third-party and operational-resilience obligations into the control framework. The aim is a single practical control set, not overlapping governance that slows delivery.
We review individual models, AI use cases and full frameworks with documented methodology. Where findings already exist, we sequence remediation and build the evidence needed to close them.
What to expect
A Senior Practitioner leads from day one. The first weeks review the model inventory, AI use cases, policies, validation capability and open findings, with particular focus on AI systems operating outside the current framework.
We then draft the policy, tiering and validation standards with second line, test them against live use cases, and establish the monitoring and review cadence.
Engagements range from a framework review to independent validation of a model, AI use case or model family.
Engagements run from a framework review to independent validations scoped per model or model family.
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.
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Read the case study
Insights
Related thinking.
Get in touch
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