Situation
The business context
A large restaurant company was undergoing a significant business turnaround driven by declining sales, traffic, and earnings. Leadership was tasked with materially reducing G&A while simultaneously increasing organizational productivity. Employees were spending significant time on repetitive tasks and searching across a large Microsoft information estate for the data and knowledge needed to perform their jobs. AI created opportunities to improve enterprise productivity, but capturing that value required focused use cases, clear ownership, and guardrails that could support responsible adoption at scale.
Task
The leadership mandate
As a senior IT leader, I was responsible for developing and executing an enterprise AI strategy and translating it into practical capabilities that would improve employee productivity, reduce manual effort, and create organizational capacity. The objective was to help the company operate more efficiently without relying solely on traditional cost-cutting measures.
Strategy
The strategic approach
My strategy was to anchor AI adoption in the company’s turnaround and productivity objectives rather than pursue AI as a standalone technology program. The approach combined two complementary paths: broadly available tools that could improve everyday knowledge work and targeted agents that could remove effort from high-value business processes. Governance, employee enablement, and shared learning would be established alongside the technology so adoption could scale responsibly. Investment would progress through practical use cases, employee feedback, and observable workflow improvement rather than experimentation without a defined business outcome.
Action
How the work moved forward
- Established an outcome-led AI strategy and governance model: Connected priorities to enterprise productivity and G&A objectives, created guardrails for responsible adoption, and focused investment on use cases with a clear workflow and business need.
- Built a broadly available productivity foundation: Deployed Microsoft 365 Copilot, Copilot Chat, Copilot Studio, and Azure AI Foundry capabilities to reduce repetitive work and improve employee access to information.
- Made adoption an organizational capability: Partnered directly with Microsoft on demonstrations, training, and practical guidance, then established a Teams-based AI Community of Practice and prompt-sharing forum where employees could exchange use cases, practices, and lessons learned.
- Embedded AI into everyday knowledge work: Enabled employees to use Copilot across Teams and the Microsoft ecosystem to find, summarize, and synthesize enterprise knowledge, including information stored in SharePoint.
- Expanded from individual productivity into process automation: Developed purpose-built agents for labor-intensive workflows including brand-specific marketing content generation and guest-feedback classification and routing.
- Used observable value to guide further investment: Combined employee feedback with demonstrated workflow improvement to identify where AI could eliminate repetitive effort and redirect capacity toward higher-value work.
Result
The measurable difference
- Established AI as an enterprise productivity capability supporting the broader turnaround strategy, improving how employees accessed information, completed routine work, and leveraged organizational knowledge.
- Dramatically accelerated marketing content creation, reducing a weeks-long process involving 10+ employees to a matter of days with fewer than five people required from creation through approval.
- Nearly eliminated manual guest-feedback routing, transforming a responsibility requiring approximately one full-time employee into an automated process requiring only a few hours of human oversight per week.
- Returned meaningful productive capacity to the organization by automating repetitive work and enabling employees to redirect time toward higher-value activities, supporting the company's mandate to improve productivity while reducing G&A.
- Generated strong employee adoption and positive feedback, particularly around Copilot in Teams and Copilot Chat, where employees reported significant value from being able to rapidly find, summarize, and synthesize information across the Microsoft estate.
- Demonstrated AI's value beyond experimentation, establishing practical, repeatable use cases for both individual productivity and process automation that created a foundation for broader enterprise AI adoption.
Leadership insight
Transformational leadership requires more than deploying new technology; it requires creating the conditions for adoption and connecting innovation to business outcomes, even when the full impact is difficult to quantify but clearly demonstrated through observable results and employee feedback.