AI Executives at Fortune AIQ Summit: Execution, Not Tool Count, Wins

Only 4% of ordinary organizations invest in ongoing AI upskilling, versus 57% of so-called pacesetters in ServiceNow's Enterprise AI Maturity Index. Diana David, Director of Futures at ServiceNow, delivered that gap at Fortune's AIQ Summit, held at the New York Stock Exchange. Her verdict on the difference-maker: "Talent investment. That was the biggest gap," David said.

The summit gathered executives from technology, finance, industrial operations, travel, and human resources to argue a single thesis. Broad access to generative AI does not produce productivity, business value, or organizational maturity on its own. What separates leaders from laggards is workflow redesign, talent development, governance, and cost discipline.

What separates AI transformation from scattered pilots?

David cautioned against treating tool distribution as success. "95% of everybody has X, Y, Z," she said. "But it is not translating into productivity, and it's often not with training, and that doesn't equal maturity." She put attracting, hiring, and retaining AI talent at the center: 68% of pacesetters do it, against 10% of other companies.

Paul Fipps, President of Global Customer Operations at ServiceNow, said the largest gains come from rebuilding processes, not accelerating old ones. "If I just automate or drive autonomous agents to drive the current processes I have, that's a choice that might get some productivity bump," Fipps said. "But the ones that are actually rethinking and reimagining the business processes … are really making the difference."

He cited Standard Chartered, where AI now deflects 77% of employee requests. "If you get 25% to 30% deflection in those use cases, it's game-changing," he said. "If you get 75% to 80%, it is life-changing." Fipps urged horizontal deployment across HR, legal, and technology rather than isolating AI in one department.

Amy Webb, Founder and CEO of Future Today Strategy Group, called the readiness gap bluntly. "Let's be fair, Fortune 500 companies are pretty late to the party on this," she said. "Artificial intelligence didn't just show up a couple of years ago." She warned that companies are pouring capital into pilots without strategy, leadership, or planning.

How is AI reshaping the workforce?

At Palo Alto Networks, a custom AI agent has cut IT tickets by 83% and supports workflows across HR, finance, and legal, said Chief People Officer Danielle Gonzalez. She looks for adaptability in hiring. "We're looking for curiosity," Gonzalez said. "We're looking for agency, and we're looking for those people who can not only learn something at a fast rate … but also unlearn what they thought to be true, so that they can make space to move forward."

Stacy Dillow, EVP and Chief People Officer at HPE, said retrofitting AI into unchanged processes fails. "If we're not trying to change how we work, we're just trying to retrofit and put AI in a process that isn't going to give us a different answer," she said.

Drew Holler, Chief Human Resources Officer at Lennar, framed responsibility as shared: "We're going to give you all the tools, but it's your responsibility to upskill yourself as well." Francis Shanahan, CTO at Peloton, flagged a new risk: employees are "inundated now because they can literally build anything," and burnout is rising. Michelle Kwon, COO at Runway, added that AI use is "not a proxy for your productivity or for doing a good job at work."

Why does governance determine deployment speed?

Hari Gopalkrishnan, Chief Technology and Information Officer at Bank of America, warned against reflexive adoption. "One of the biggest mistakes we see ourselves and others doing is rushing to AI as a solution, when deterministic models do a plenty good job," he said. The bank evaluates projects through 16 risk pillars, including privacy, bias, workforce impact, and intellectual property, and is keeping humans in the loop. "There is so much juice to be squeezed right now with assistive agents that are actually working with humans in the loop," Gopalkrishnan said.

Sally Moore of S&P Global's Kensho Data & Platforms stressed data quality. "Data is the currency within AI," she said, noting regulated clients require accuracy, citations, auditability, and traceability. Fipps argued oversight accelerates rather than blocks innovation. "I see governance and trust driving speed," he said, citing a regulated bank that built dozens of agents it could not deploy because leaders lacked confidence in controls.

What does AI cost in physical and regulated environments?

Suresh Venkatarayalu, CTO at Honeywell Technologies, said industrial customers "really demand 99.9999%" reliability while frontier models "could be at 85%." He described the near term as "a semi-autonomous world." "Autonomy is also not about removing people," he said, noting operators must stay involved because "you cannot afford to have a building shut down for one and a half hours."

AJ Wijesinghe, Chief AI Officer at Ecolab, made the cost case. Using "the best model that's out there" for high-volume tasks meant "the tokenomics go off the roof" — "sometimes it's more expensive than having humans." Ecolab optimized model selection and cut token costs by roughly 70% to 80%. "Sometimes you don't have to have the fastest car," he said.

Where is the measurable ROI?

Ewout Steenbergen, EVP and CFO of Booking Holdings, said even the largest spenders lack clarity. "I even think that the hyperscalers that spend hundreds of billions on the development of their large language models, they don't really know what is going to be the ROI," he said. "They have maybe some assumptions and some hypotheses, but the whole point is, you don't want to fall behind."

At Booking, redesigning processes "from a white sheet" has pushed customer-service costs slightly down while bookings grow at a high-single-digit rate and customer satisfaction rises. The company's roughly 9,000 engineers are getting about 30% more code into production, counting merge requests that pass testing and quality control. Booking manages costs through "effective model cost routing," reserving expensive models for work requiring greater reasoning.

"The returns will be there for those processes that are being redesigned end to end," Steenbergen said, "but you also have to make sure that those costs on the other hand are not going out of control." He sees the larger prize in customer growth: by linking trip components and anticipating needs, AI can build "more loyalty, more trust, and more brand value" — while "the hotel is still the hotel, and the airline is still the airline."

By Amara Osei