MassMutual's AI strategy focuses on flexibility and measurable outcomes

Enterprise AI teams face a dilemma: The best models today might not be the best models a year from now. MassMutual's answer is to stop making long-term bets — and build infrastructure that can swap models as the market shifts. "The world of AI today is extremely dynamic," Sears Merritt, MassMutual CIO, explained in a new VB Beyond the Pilot podcast .

"We wanted to make sure we were positioned to ride that wave of dynamism." The strategy appears to be paying off in a big way. MassMutual has measured a roughly 30% increase in developer productivity, while AI-powered contact center workflows have reduced resolution times from 10 minutes to one and cut costs from dollars to cents. But the broader lesson for IT leaders may be less about the results and more about how the company is thoughtfully building its AI infrastructure and keeping users at the center.

Maintaining optionality for the possibilities of tomorrow MassMutual works with vendors at the leading edge, but keeps those relationships on a clock. "Those relationships are capped so that we maintain optionality for best-of-breed tools as things mature in this space, and at some point, settle down and stabilize," Merritt said. That philosophy extends to open-source models.

Merritt says his team is "100%" looking at open-source tools, and sees the technology playing a big role in how MassMutual (and similar companies) use AI. "We're certainly going to need frontier models and leading edge capabilities to do what today is impossible, and tomorrow will be possible," he said. Measuring outcomes from the start MassMutual's AI efforts fall into two broad categories.

The first focuses on enablement: Putting productivity-enhancing tools such as Copilot and virtual assistants into the hands of all employees. The second involves what Merritt describes as "deepen and focus" initiatives, where teams target a specific workflow or business process that will have a strong impact on advisors, policyholders, or employees. Rather than focusing on adoption metrics, these projects begin with predefined success criteria.

"Everything we do is measured," Merritt said. "There's always a success metric that we define upfront to determine whether or not we're going to scale up some of these things." The company is also deliberately encouraging experimentation, giving employees access to a range of best-in-class models, "token-consumptive workflows" and other possible capabilities so they can weigh the benefits relative to "simpler, lower cost" large language models (LLMs). At the same time, MassMutual is collecting increasingly detailed analytics around usage patterns, developer workflows, model performance, and costs.

The goal is to reduce spending while also building operational intelligence to eventually route workloads to the right model based on cost, response quality, and user experience. Those insights will eventually drive optimization decisions around model routing, prompt selection, response times, and infrastructure design. "We're gaining access to analytics that let us, in a very granular way, look at usage patterns, developer workflows, and begin to make sense of who's using what, when, and for what types of tasks," Merritt said.