Gartner Told 7,500 IT Leaders That AI Value Runs Through People

This post is a part of the AI Corner series: a weekly read on the AI news that matters for regulated commercial teams, and what to do about it.

Gartner's message to more than 7,500 IT leaders this week was to make AI value compound faster than AI risk, with aligned governance and people as the engine. The warning underneath it is real: CIOs and IT leaders report that risk is growing faster than value in their organizations. The answer Gartner gave was not to pull back. For regulated commercial teams, the implication is practical: scale the AI applications that improve the work, establish clear accountability, and invest in the people who turn those tools into results. The teams that get this right will use AI twice: once to do the work, and once to build the people.

What Did Gartner Tell IT Leaders About AI at Its 2026 Symposium?

Gartner told IT leaders to make AI value compound faster than AI risk, and named people as the variable that decides whether it does. At the IT Symposium/Xpo keynote in Orlando on October 19, analysts told more than 7,500 CIOs and IT executives that 58% of leaders report at least some positive value from AI initiatives, and that 71% of organizations expect to deploy agentic AI within twelve months while only 21% have agentic AI governance capabilities in place. Daryl Plummer noted that "the right kind of drag can be beneficial," and was blunt about what governance alone delivers: "What it will not do is eliminate risk, and it will not drive value. The only thing that can do that, is people." Gartner's recommendations included rewarding the advanced users it calls AI artisans for reinventing work. Per CIO Dive's reporting from the keynote, roughly one in five AI projects is succeeding, unchanged from a year ago.

Why It Matters if You Run a Regulated Commercial Team

Gartner was speaking to IT leaders across every industry, so hold the findings at that level: pockets of value that do not scale, agentic ambition outrunning governance readiness, and people as the deciding variable. In a pharma commercial organization, those patterns could look like this. A roleplay or coaching pilot proves out in one therapeutic area and never reaches the rest of the field force before the launch window closes. AI moves into field workflows faster than anyone has mapped the MLR touchpoints, the data boundaries, and the ownership, which is the commercial version of the 71-versus-21 gap. And the people variable shows up as rep variability: the distance between your best rep and your fiftieth, which no tool closes on its own.

The one-in-five success rate should prompt a harder question: which deployments are producing measurable outcomes, and what would it take to repeat that success? That is a question about outcomes, ownership, and people. It is not a question about whether to adopt.

What to Do Monday Morning

Four moves, usable this week.

• Run a value-over-volume audit of your commercial AI stack. List every AI use case touching training, coaching, or the field, and keep what ties to a measurable readiness or performance outcome: time to certification, message mastery, demonstrated skill gains. Scale those and park the rest. Where MLR review and launch calendars make false starts expensive, Gartner's advice to scale existing successes rather than add use cases counts double.
• Name your AI artisans. Look for the trainer who rebuilt a certification workflow with AI, or the manager who preps coaching conversations with it. Give those people standing, a channel, and the explicit job of spreading what they know. Gartner told CIOs to reward them for reinventing work; do it inside the commercial organization first.
• Make sure every AI tool your field relies on has an owner. Before reps or managers depend on an AI capability, know who owns it, where MLR and compliance review sit, and who answers for the output. Training and commercial leaders don't need to design the governance framework, but they do need a seat in it. This is the good drag Gartner described: a small cost paid up front so the program can scale.
• Measure adoption and impact separately. Track usage to understand adoption. Measure demonstrated skills and field behavior to understand whether that adoption is helping: certification on demonstrated behavior, objection handling that improves across the team, and pull-through of approved messaging. If you track coaching coverage, pair it with coaching quality or observed improvement, since coverage on its own measures activity.

Use AI Twice: Once to Do the Work, Once to Build the People

When AI takes work off a rep's or a trainer's plate, the freed hours are among the most valuable resources a commercial leader controls. Decide what they are for before they disappear into the inbox. Reinvest them deliberately: more practice before the launch, more coaching conversations per manager per cycle, and better preparation for the HCP meeting where access is short and the message has to land. This is where Gartner's people argument becomes operational. If the pattern it describes holds in commercial teams, reinvested capacity is how a pocket of AI value becomes a repeatable one.

The teams that get the most from AI build more capable agents and more capable people, on purpose. As AI takes on more of the work around the conversation, the conversation itself matters more: the ability to listen, adapt, exercise judgment, build trust, and respond in the moment.

AI coaching is how you build those people at scale. Quantified is the AI Roleplay and Sales Coaching Platform for life sciences and regulated commercial teams. Reviewing your own AI training stack? Our AI Sales Training Buyer's Diagnostic Guide offers ten vendor questions and 30-60-90-day pilot checkpoints to help decide what deserves to scale.