Sentinel designed a phased engagement that moved the customer from assessment through strategy to implementation-ready governance:

Readiness Assessment - The customer participated in Sentinel's Microsoft AI Readiness Workshop, which evaluated the organization across five dimensions: use cases and education, governance, information landscape, adoption readiness, and technical infrastructure. The workshop output provided both a benchmark against industry peers and a clear picture of what needed to be addressed before scaling AI adoption.

Strategic Roadmap – Sentinel then conducted a three-month strategy engagement to build a prioritized AI roadmap for the customer. The process involved stakeholder interviews to gather input from research scientists, commercial teams, legal, compliance, IT, and executive leadership. The goal was to surface use cases grounded in actual business needs.

The resulting roadmap included machine learning initiatives to extract value from historical clinical datasets, automated research assistance tools deployable across business units, predictive analytics for patient outcomes, and administrative automation with contract review. Each initiative was evaluated for feasibility, regulatory implications, and expected impact, then sequenced into a multi-year execution plan.

Governance and Security Infrastructure - The final phase focused on building the structures necessary to execute the roadmap safely. This included AI-specific policies governing data classification, model development and validation, and acceptable use. Security controls were implemented to protect sensitive data while enabling appropriate access for AI workloads. Training programs were developed to build organizational capability, ensuring teams could work effectively within the new framework rather than around it.

The structured assessment, strategy, and governance solutions presented by Sentinel have changed the customer’s approach to AI. They are following the adoption roadmap, and with supporting infrastructure, continue to advance dozens of initiatives that include machine learning projects analyzing historical data, research automation tools for multiple units, and administrative applications such as contract review moving toward deployment. Internally, customer teams now have clear guidelines and approved pathways for AI adoption, while leadership has visibility and controls to support continued innovation while minimizing risks.