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Strategic AI Advisory
Identifying the right AI bets for sustained business value
Most enterprises have more AI ideas than budget, data or people to support them. We help you choose the few that are essential and will deliver value, make sure your data and platforms can carry them, and plan the path from first pilot to production.
Client result
55countries under automated compliance monitoring, on one governed agentic platform
Global insurance intermediary.
Read the case studyfrom idea to impact, across the systems lifecycle, this service sits here
How we prioritize
Business value vs Your readiness
Every idea is scored on what it is worth and on how ready your data, platforms and people are to support it.
Select an AI use case to see why it belongs to where it does.
Claims triage agent
High volume, clear rules and years of clean history make this a strong first production candidate. It needs a named owner and an audit trail from day one.
Customer service copilot
Agents feel the benefit within weeks. Ground it on approved knowledge only, and measure handle time before and after.
Demand forecasting with external signals
Large upside, but sales history sits in three systems with different definitions. Fix the data model first.
ML driven Automated pricing
Valuable and risky. Start with recommendations a person approves, and build the controls before any automation.
Contract clause extraction
Quick to stand up on documents you already hold. The payback is real, but limited to one team.
Internal policy chatbot
Easy to build and low value on its own. Bundle it with HR or IT service work instead of funding it separately.
Gen AI based meeting summaries
Packaged tools already do this well. Buy it rather than build it.
What we do
Advise, build and run
Taking into consideration the current state.
AI-accelerated
AI strategy and target architecture
Where AI should sit in products and operations, and the architecture that lets it scale without lock-in.
AI vision tied to business goals
Two or three outcomes leadership will fund and measure, rather than a list of technologies.
Target architecture
Models, data, integration and controls drawn as one system, so pilots stop inventing their own stacks.
Build, buy or partner
A clear view of where packaged AI is good enough and where your advantage needs custom work.
AI architecture audit
A review of your current stack for the scalability, cost and safety gaps that stall pilots.
AI-acceleratedData and AI maturity assessment
A plain read on data, platforms, skills and governance, measured against the applicable use cases.
Data readiness by use case
Quality, ownership and access checked for the data each priority use case needs.
AI-acceleratedPlatform and MLOps review
Whether your pipelines, environments and monitoring can run models in production.
Skills and operating model
Who will build, own and run AI, and where you need outside help.
Governance baseline
The policies, controls and approvals already in place, and the gaps a regulator would find.
Use-case prioritization and business cases
Ideas scored, ranked and turned into business cases.
Discovery workshops
Sessions with business and technology leads to surface ideas, constraints and owners.
Value and readiness scoring
Each idea placed on the matrix, with the evidence behind its score.
AI-acceleratedBusiness cases
Costs, savings, risks and time to value, modeled from your own volumes.
AI-acceleratedSequenced roadmap
What starts now, what waits for foundations and what to drop.
What we deliver
Deliverables built for execution
Each deliverable builds on the one before it.
Current-state readout
Where your data, platforms and governance stand today against the use cases on the table, and which gaps to close first.
Ranked use-case portfolio
Every candidate scored on value and readiness, so you can fund the strongest and park the rest.
Business cases
Cost, savings, risk and time to value for the top candidates, built on your own volumes.
Target architecture and 90-day plan
The architecture the first wave needs, and the order of work to get there.
Case study
Insurance | Global insurance intermediary
From AI pilots to governed production
The broker had approved a major AI budget with an IPO on the horizon. Pilots were stalling before production, tools sprawled with no shared context or guardrails, AI governance was very new and regulatory monitoring across 55 countries was too dynamic for rules.
We ran a five-part transformation across people, process, technology, governance and execution, built on one governed agentic platform: orchestration, shared context and ontology, evals, guardrails and observability. Agents for compliance monitoring, submissions, capacity marketing and client intelligence run across the broking value chain.
Bring us your AI wish list
We’ll tell you which ideas to fund first, which to park and what has to be true before the rest can work.