Enterprise AI Strategy & Center of Excellence
Fortune 1000 Insurance Group
Business Context
The organization and its strategic environment
A $2.8B insurance group had invested $4M in AI pilots over 2 years with zero production deployments. The board was losing confidence in AI as a strategic lever, and the CTO needed a credible path from experimentation to enterprise-scale value.
Technology Landscape
Systems and infrastructure before DGT
Azure cloud infrastructure, 6 failed AI pilots (claims, underwriting, fraud), no MLOps pipeline, data science team of 8 with no production deployment experience, and no AI governance framework.
The Challenge
What the client was facing
$4M invested in AI pilots with zero production deployments. Data science team operated in isolation from business units. No MLOps infrastructure, no model governance, and no clear connection between AI initiatives and business KPIs.
The DGT Solution
How DGT addressed the challenge
DGT designed and stood up an AI Center of Excellence with governance framework, MLOps pipeline on Azure, and a prioritized use case portfolio. We rescued 2 of the 6 failed pilots and deployed them to production within 12 weeks.
DGT Accelerators Used
Delivery Approach
How DGT executed the engagement
DGT's Assess-Align-Activate-Scale methodology. Week 1-2: AI maturity assessment across 12 dimensions. Week 3-4: Use case prioritization with business stakeholders. Week 5-16: CoE build, MLOps pipeline, and first 2 production deployments.
Governance Model
How the engagement was managed
AI Steering Committee (CTO + 4 business unit heads), AI Ethics Board, model risk management framework aligned to regulatory requirements, and quarterly AI value review.
Timeline & Phases
The execution roadmap
AI Maturity Assessment
2 weeks12-dimension scoring, stakeholder interviews, pilot post-mortems
Strategy & Roadmap
2 weeksUse case prioritization, CoE design, governance framework
CoE & MLOps Build
6 weeksTeam structure, Azure MLOps pipeline, model registry, monitoring
Production Deployments
6 weeksClaims automation and fraud detection to production
Scale & Transfer
4 weeksTeam upskilling, 6-month roadmap, governance handover
Risks Addressed
Key risks DGT mitigated during the engagement
Why DGT Won
What set DGT apart in this engagement
Outcome Metrics
Measurable before-and-after results
| Metric | Before | After DGT |
|---|---|---|
| Production AI Models | 0 | 2 (4 in pipeline) |
| Claims Processing (AI-assisted) | Manual | 65% automated |
| Fraud Detection Accuracy | 72% | 94% |
| Model Deployment Cycle | Never completed | 8 weeks |
The Impact
Headline results delivered
"DGT turned our AI embarrassment into our competitive advantage. In 16 weeks, we went from zero production models to a functioning AI CoE that the board now champions."
Thomas Wright
Chief Technology Officer, Fortune 1000 Insurance Group