Services
AI Center of Excellence consulting for regulated financial services.
A working AI Center of Excellence with clear ownership, intake, standards, and a delivery cadence.
Who it's for
Organizations with scattered pilots and no central operating model.
What you get
The controls regulated firms need before scaling AI.
Who it's for
Firms facing audit, regulatory scrutiny, or unmanaged AI adoption.
What you get
A ranked portfolio of AI use cases tied to measurable value.
Who it's for
Leadership teams that need a credible AI roadmap.
What you get
Production-grade agentic and generative AI systems, delivered on time and governed.
Who it's for
Firms building in-house or managing delivery partners.
What you get
How we engage
A fixed-scope program that ends with an operating CoE: charter, structure, intake, and a first wave of use cases in flight.
A fixed-scope engagement that ends with a complete AI inventory, a controls baseline, and a prioritized remediation plan.
A fixed-scope engagement that ends with a ranked use case portfolio and a 12-month roadmap.
Ongoing fractional AI leadership and delivery oversight.
Timelines are set together during discovery, based on your scope, your data, and your team's availability.
Engagement scenarios
Illustrative scenarios. These show how we approach common situations. They are not client case studies.
Business units adopted GenAI tools on their own, and audit flagged it.
Approach: A rapid AI inventory and risk triage, a governance baseline, then a CoE charter that turns shadow usage into a governed intake pipeline.
Pilots work, but no one can explain agent decisions to regulators.
Approach: An evaluation framework with golden datasets and human review thresholds, audit logging, and a model risk process aligned to state insurance AI guidance.
The CEO needs an AI strategy within one quarter.
Approach: A maturity assessment, a prioritized use case portfolio with value estimates, and a 12-month roadmap with a funding model.
A dozen vendors are pitching AI products, and there are no criteria to compare them.
Approach: A vendor-neutral evaluation framework, a due diligence scorecard, and pilot designs with clear exit criteria.
Common questions
An AI Center of Excellence is the team and operating model that decides which AI work gets done, sets the standards it must meet, and supports it in production. It owns intake, prioritization, validation, and the path from pilot to production. It does not have to build every use case itself. Its job is to make AI delivery repeatable and governed across the business.
Most regulated firms need both. A small central team owns standards, the governed platform, risk review, and the portfolio view. Business units own their use cases and outcomes. Fully decentralized AI produces duplicate tools and untracked risk. Fully centralized AI becomes a bottleneck. We design the split around your size, talent, and risk appetite.
At minimum: an inventory of every AI system in use, including AI inside SaaS tools; due diligence on foundation models and vendors; a risk review and approval workflow; validation and monitoring standards; and documentation an auditor can follow.
We score each candidate on value, feasibility, data readiness, and risk, then separate quick wins from longer-term bets. We also check whether fixing the process would deliver most of the gain without AI. Often it does. The result is a ranked portfolio tied to measurable outcomes, not a list of ideas.
Nothing ships without three checks. The output must be grounded in an approved source. It must pass a golden evaluation set built from real cases with known answers. And an independent model, acting as judge, reviews it for hallucination and risk. Agentic systems also need human review thresholds, audit logs, and clear limits on what an agent can do without approval.
Start with an inventory. You cannot govern what you cannot see. Map every AI system in use, triage each by risk, and put a lightweight approval path in place for new work. Then stand up the operating model that turns scattered pilots into a governed intake pipeline. Build governance and the CoE together, not one after the other.
No. We design the operating model, set the standards, and oversee delivery. Your teams and partners do the building. We are vendor-neutral.
Map it. Our free AI Capability Maturity Framework assesses capabilities across three tiers, Foundation, Value, and Advanced, over five levels. Most organizations get most of their value from solid mid-level capability. Full autonomy is a choice, not a requirement. The framework shows which gaps to close next.
Let's begin
