Three enterprise data leaders described concrete agent deployment outcomes at Snowflake Summit 2026 in San Francisco. Their accounts, reported by ZDNET, focus on measurable results rather than projected capabilities.
Fanatics: Benchmarking Agent Value Systematically
Madeleine Want, VP of data at Fanatics, told ZDNET her organization tracks agent impact across its data practitioner community through structured benchmarks: “We benchmark how you are using these tools, what type of tasks you are using them for, how much time you feel that they are saving you, and what you are doing with the time.”
The benchmarks show agents absorb routine reporting tasks. “Every business analyst out there will tell you some version of, ‘I wish I could be doing more strategic work, but I am bogged down in routine reporting,’” Want said. “What we are seeing is that the more routine reporting tasks are the ones that often lend themselves best to automation through AI.”
Want also flagged a reality that enterprise agent adopters rarely discuss publicly: agentic AI tooling is unstable. “We are not adopting well-tested, well-trodden technologies that, once rolled out, will never be rolled back. We’re in an experimental phase right now, and so, adopt early and try things, but also hold it lightly, because we’re going to need to stay agile.”
Whoop: Quantifying the Ad Hoc Question Tax
Matt Luizzi, VP of analytics at Whoop, put a number on the problem agents solve at his company. “People were saying they’re spending between 50% and 60% of their time just answering random questions from around the business,” he told ZDNET. Questions like “what were sales yesterday” and “how does that differ by region” are disruptive and repetitive. Agents now handle them.
The result is bandwidth reallocation, not headcount reduction. “We’ve also seen real revenue impacts from this technology already, with people being able to identify things proactively, root cause them with AI, troubleshoot what’s going on, and take action much faster before the ship has left the station,” Luizzi said.
Synopsys: Smaller Teams, Larger Data Sets
Sriram Sitaraman, CIO at Synopsys, described a different pattern. His company uses agents to reduce the number of people required for data-driven decisions. “You don’t need a team of people having the conversation. It’s a smaller team of people looking at a large amount of data,” he told ZDNET.
Sitaraman framed agent capability progression as hierarchical: “The models will keep pushing tasks downstream to AI, and the complexity of tasks AI can manage will increase as the models get better. So, in six months, I see AI solving different types of problems, not the same types of problems as now.”
The Consistent Pattern
All three leaders described the same dynamic: agents absorb structured, repetitive tasks (reporting, ad hoc queries, data sorting), freeing human workers for judgment-intensive work. None described agents replacing roles entirely. The shift is in work composition, not headcount.
Gartner projects AI agent software spending will reach $206.5 billion in 2026, rising to $376.3 billion by 2027, according to ZDNET. The Snowflake Summit accounts suggest that spending is producing measurable productivity shifts in organizations that benchmark rigorously, rather than deploying agents and hoping for the best.