AI Strategy, Action Plan & Execution

The context

Within Essent's commercial organization, over half of the employees used their own AI tools for work, selected outside of the central offering. This was a strong signal of intrinsic motivation and innovative power, but also of an organization where individual adoption took precedence over frameworks, governance, and secure access. The question was not whether AI should be deployed, but how to move from fragmented experiments to a structured, scalable, and responsible approach with a direct impact on commercial performance, customer interaction, and work processes.

The approach

We were the driver of the change. The role encompassed drafting the strategic rationale, building the business case, connecting stakeholders at the executive, management team, and operational levels, and helping to establish an implementation structure that enables the organization to continue independently.

• AI action plan drawn up with urgency justification, development themes, preconditions, and a phased roadmap for 2025-2026
• Business case developed for the investment in central, secure AI access and the associated support program
• Baseline measurement conducted among over one hundred employees regarding knowledge, usage, ambition, and attitude towards AI, using a persona approach that reveals the diversity within the team.
• Stakeholder management at executive and MT level, with approval of the plan and commitment for the implementation phase.
• Implementation structure set up with Practice Leads as change agents per discipline and AI Communities as knowledge networks that accelerate adoption and innovation.
• Five strategic action lines have been developed: accelerating secure access, developing a practical learning program, establishing a central AI hub for knowledge sharing and governance, working from concrete use cases, and strengthening communication and frameworks.
  
  

What it yielded

Essent has a widely supported AI action plan that marks the step from standalone experiments to structured adoption. The baseline assessment provides a substantiated starting point and reveals that the diversity in AI maturity requires a tailored approach: early adopters want room to experiment, a large middle group requests practical training, and a critical group first wants certainty regarding ethics and reliability. The combination of strategy, substantiation, and implementation structure gives the organization the foundation to responsibly and scalably embed AI in commercial performance, customer interaction, and work processes.