Sodexo Exec Highlights Data Cleanup As The Key To Unlocking AI Value In Facilities Mgmt

For years, facilities leaders have operated under a simple idea: more data should lead to better decisions. In practice, the picture hasn't been so tidy. The industry is rich with data, yet turning that raw information into actionable intelligence has remained a stubborn challenge. With AI now entering the mainstream, that gap is getting harder to ignore, prompting a fresh look at how organizations think about efficiency, risk, and the value they expect their data to deliver.

Chris Hammond, Director of Business Analytics at Sodexo, a leading provider of high-quality dining and facilities management solutions, sees this moment as a turning point. His work sits at the intersection of data analytics and AI implementation, giving him a clear view of both the opportunity and the volatility. To him, real progress comes down to a balance: use AI in ways that deliver measurable financial returns, and use it in ways that keep the organization safe. In his view, that balance is the only path to unlocking AI's promise without stumbling into its risks.

"Data is the lifeblood of AI, but it only creates value when it's clean, centralized, and used with intent. Real impact comes from knowing where AI fits, keeping a human in the driver’s seat, and making sure every use case is both effective and safe," says Hammond. He sees this as the difference between chasing trends and building technology that actually moves the business.

• Data treasure hunt: Hammond says the first hurdle is untangling years of scattered spreadsheets and siloed notes that make it difficult for any advanced system to function. "One of the biggest obstacles in getting AI to work is simply figuring out what data we actually have and where it’s hiding." He explains that this requires coordination across the business. "No single team can fix this alone because every group holds a different piece of the picture. AI can flag inconsistencies and show us exactly where the gaps are so we know what needs attention before we move into more complex automation."

• Building on the foundation: The real value of AI shows up after the data foundation is in place, Hammond explains, because dashboards alone rarely move a team to act. AI can bridge that gap by turning information into direction, surfacing patterns, and pulling insights from years of technician notes. "AI can empower teams by suggesting the specific actions they can take to improve the business based on that data. When we aggregate all that information, we can see problems coming before they happen and keep our clients from experiencing downtime."

That evolution is pushing the industry into what Hammond calls the "agentic era," where AI can perform semi-autonomous actions. The capability, however, comes with risks, stemming from AI's probabilistic nature. To navigate this, he offers a guiding philosophy: "Automate the transactional, enhance the relational."

• The 80% rule: Hammond champions using AI for assistance, where it takes over repetitive, low-risk work. The approach frees human teams to focus on high-value, client-facing activities like building relationships—work that AI can't do. "In many cases, AI can get you 60 to 80% of the way there almost instantly. A human expert then puts in the finishing touches, saving a ton of time. They're spending 20% of the time to get 100% of the results."

Of course, none of this matters if you can't prove it to the C-suite. Any major AI initiative typically needs to answer to the executive suite, which calls for a nuanced approach to proving its business value. Hammond advises leaders to think about ROI in two distinct categories. Unlocking either, he notes, often depends on a deliberate investment in people.

• Soft sell: Hammond says the rollout matters just as much as the technology itself. "With tools this new, you can't just roll them out and hope for the best. Continuous training is key so people can understand the technology, use it effectively, and evolve with it."

• Hard numbers: For a project like automating a call center with an AI bot, he advises building a clear business case that demonstrates how the initiative will generate savings that can then be reinvested to fuel business growth, tying the technology directly to a tangible financial strategy. "If we can show exactly where the money comes from and where it goes, the value of the AI becomes real for the business."

In his final analysis, Hammond returns to a point he believes is important yet frequently overlooked as companies adopt AI: safety. Data leaks, shadow tools, and unchecked experimentation can expose an organization long before the benefits show up, and he warns that many teams still misunderstand how easily sensitive information can escape their systems.

The fix, he says, is firm human oversight. Strong governance, careful validation, and disciplined training are what turn AI from a risk into an advantage. "Leaders have to stay in the driver's seat 100% of the time if they want AI to be powerful, safe, and worth the investment," Hammond concludes.