Use Case
AI Strategy - Microsoft Copilot
The executive team faced mounting pressure to define a coherent approach to artificial intelligence. Microsoft Copilot licenses had already been purchased, but adoption was fragmented and ad hoc. Individual teams experimented with document drafting, meeting summaries, and email automation, yet no consistent standards, guardrails, or measurement existed. Security leaders raised concerns about uncontrolled data exposure, while operations leaders worried that benefits would remain incremental if Copilot was not embedded into core workflows. Without a unified strategy, the company risked paying for licenses without realizing tangible productivity or compliance gains.
The program began with a structured AI strategy and governance framework anchored on Microsoft Copilot. The first phase assessed current license usage, interviewing end users across functions to identify how Copilot was being applied and where adoption stalled. These findings informed a prioritized roadmap that linked Copilot use directly to business outcomes—improving bid response times in sales, accelerating financial reporting in accounting, and streamlining code reviews in IT.
The second phase established guardrails. A governance model defined what data could be exposed to Copilot, set rules for sensitive document handling, and created an approval process for new use cases. Training programs were launched to help employees integrate Copilot into daily workflows, with role-specific playbooks showing how to use prompts effectively in Word, Excel, Teams, and Outlook. Pilot groups were tracked for measurable impact before scaling across the organization.
By the third phase, the company embedded Copilot into core business processes. Finance teams automated monthly close packs, HR used Copilot to draft policies and job descriptions, and project managers applied it to summarize meeting actions and risks. Dashboards measured adoption rates, productivity improvements, and error reduction, allowing leadership to track return on investment and continuously refine the strategy.
Solution
Key Features
The AI strategy introduced several foundational elements. A governance and risk framework ensured that Copilot was used responsibly, with oversight on security and compliance. Role-based playbooks provided practical guidance tailored to business functions. A pipeline of prioritized use cases linked Copilot to measurable outcomes rather than general productivity promises. Adoption dashboards gave leadership clear visibility on usage patterns and benefits captured. Together, these features turned Copilot from a generic tool into a structured enterprise capability.
Outcomes
Clear enterprise AI strategy anchored on Microsoft Copilot
License utilization increased from fragmented use to 80%+ active adoption
Measurable productivity improvements across finance, HR, IT, and sales
Compliance and risk concerns mitigated through governance and guardrails
Payback achieved through avoided license waste and faster cycle times on core workflows
Business Drivers
The strategy was shaped by several pressing drivers. Executives needed to demonstrate ROI on AI investments already made and avoid license waste. Employees demanded productivity gains but lacked clarity on best practice. Security and compliance teams needed assurance that Copilot use would not create data leakage or regulatory breaches.
Finally, leadership wanted to position the company as forward-looking, ensuring that generative AI was not just a technology experiment but a core enabler of operational efficiency and competitive advantage.
Technologies
The solution centered on Microsoft Copilot, deployed across Office 365 applications including Word, Excel, Teams, and Outlook.
Governance leveraged Microsoft Purview for compliance and sensitivity labeling, while Power BI dashboards tracked adoption and usage patterns.
Integration with Dynamics 365 extended Copilot use into finance and operations workflows. Together, the Microsoft ecosystem provided both the productivity layer and the control framework needed for enterprise-wide scale.