As companies race to adopt generative AI, agents, and new data tools, leaders face a more demanding question than whether to use the technology: How can they turn it into measurable value without creating new costs, risks, and organizational confusion?
At Fortune’s AIQ Summit at the New York Stock Exchange, executives from the technology, finance, industrial operations, travel, and human resources sectors explored what separates meaningful AI transformation from scattered pilots. The discussion made clear that the strongest companies are not simply distributing AI tools. They are redesigning workflows, developing talent, strengthening governance, and building the data and operating discipline needed to scale responsibly.
Together, we explored these key trends:
- Moving from experimentation to enterprise transformation
- Building an AI-ready workforce
- Managing governance, trust, and risk
- Applying AI in physical and regulated environments
- Measuring ROI, controlling costs, and creating growth
From AI adoption to AI transformation
THE MOST IMPORTANT AI QUESTION IS NOT WHETHER A COMPANY HAS DEPLOYED TOOLS, BUT WHETHER IT HAS CHANGED HOW WORK GETS DONE.
Speakers warned that widespread access to AI does not necessarily create productivity, business value, or organizational maturity.
Diana David, Director of Futures at ServiceNow, said leading companies distinguish themselves through their commitment to people and operational discipline, not technology alone. In ServiceNow’s Enterprise AI Maturity Index, 57% of “pacesetters” invest in ongoing AI upskilling, compared with 4% of other organizations. The widest gap involves attracting, hiring, and retaining AI talent: 68% of pacesetters do so, compared with 10% of other companies. “Talent investment. That was the biggest gap,” David said.
David cautioned against treating broad employee access to AI as proof of 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.” The work, she argued, begins with leadership, a clear vision, transparent systems, the right data foundation, and iterative redesign of specific workflows.
Paul Fipps, President of Global Customer Operations at ServiceNow, made a related point: the greatest gains will not come from automating old processes faster, but from rebuilding them around new capabilities. “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.”
Fipps said companies should deploy AI horizontally, across employee-facing functions such as HR, legal, and technology, rather than isolating it in one department. He cited Standard Chartered, where AI has helped deflect 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.”
Amy Webb, Founder and CEO of Future Today Strategy Group, was more blunt about the gap between ambition and readiness. “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.” Webb warned that companies are pouring significant capital into pilots without sufficient strategy, leadership, or planning—creating isolated projects that fail to produce durable value.
Building the AI-ready workforce
AI TRANSFORMATION IS A WORKFORCE TRANSFORMATION.
The Summit underscored that organizations need more than new software: they need employees and leaders willing to rethink established processes, learn new skills, and make room for new ways of working.
Stacy Dillow, Executive Vice President and Chief People Officer at HPE, said companies must resist simply inserting AI into existing systems. “We’ve got to force ourselves to think differently, because 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. For senior executives, she added, AI adoption and transformation have become central management responsibilities.
Danielle Gonzalez, Chief People Officer at Palo Alto Networks, described how AI is already changing the employee experience. At Palo Alto Networks, a custom AI agent has reduced IT tickets by 83% and supports workflows across HR, finance, and legal. Instead of turning to a human HR partner or business contact, employees can increasingly ask an agent for answers tied to their role and location.
Gonzalez said companies should hire and develop people who can adapt rapidly—not only learning new skills but questioning assumptions that no longer hold. “We’re looking for curiosity,” she 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.”
Drew Holler, Chief Human Resources Officer at Lennar, stressed that the responsibility is shared. “We’re going to give you all the tools, but it’s your responsibility to upskill yourself as well,” he said. “We’re going to give you trainings, but you, as an individual, have to upskill.”
The workforce implications extend beyond reskilling. Francis Shanahan, Chief Technology Officer at Peloton, warned that AI-enabled creation can create its own challenge. Employees are “inundated now because they can literally build anything,” he said, and “burnout” is becoming more of an issue.
Michelle Kwon, Chief Operating Officer at Runway, urged leaders not to mistake volume for productivity. “Just because you are using AI, that is not a proxy for your productivity or for doing a good job at work,” she said. AI should improve how people work, she argued, without generating additional work for colleagues or overwhelming them with output that has not been properly assessed and synthesized.
Governance, trust and control
THE CASE FOR AI MUST BE MATCHED BY A CASE FOR OVERSIGHT.
In regulated industries and high-stakes applications, leaders emphasized that successful deployment depends on selecting the right technology, understanding the risk, and keeping human judgment where it matters most.
Hari Gopalkrishnan, Chief Technology and Information Officer at Bank of America, cautioned companies not to reach for AI reflexively. “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. Bank of America begins with client needs and a detailed inventory of the relevant process; sometimes a conventional mobile feature or real-time decision rule is the better tool.
The bank evaluates projects through 16 risk pillars, including privacy, bias, workforce impact, and intellectual property. It is also proceeding carefully with autonomous systems. “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, Chief Client Officer and Co-Head of Market Intelligence, Kensho Data & Platforms, at S&P Global, emphasized that AI’s usefulness depends on trustworthy data. “Data is the currency within AI,” she said, noting that clients in regulated workflows require accuracy, citations, auditability, and traceability back to the source. Her larger point was that companies should begin with the problem to be solved, rather than the technology they want to deploy. “What am I trying to solve for here? And where can this technology take me?” she said.
Fipps argued that governance should not be framed as an obstacle to innovation. “I see governance and trust driving speed,” he said. His example—of a regulated bank that had built dozens of agents but could not deploy them because leaders lacked confidence in their controls—illustrated the cost of inadequate oversight.
Webb likewise urged executives to take a practical approach to liability and risk. She said leaders need to “stop anthropomorphizing” AI: failures often arise not because technology has become independently sentient, but because people have deployed poorly designed systems without sufficient safeguards.
AI in the real world
PHYSICAL AND REGULATED ENVIRONMENTS REQUIRE A DIFFERENT STANDARD OF AI DEPLOYMENT.
In buildings, factories, water systems, and financial services, reliability, safety, cost, latency, and data sovereignty shape what companies can responsibly put into production.
Suresh Venkatarayalu, Chief Technology Officer at Honeywell Technologies, said industrial customers “really demand 99.9999%” reliability, while frontier models “could be at 85%.” That gap makes deployment in safety- and mission-critical settings fundamentally different from putting an AI assistant into a desktop workflow.
Venkatarayalu described the near-term direction as “a semi-autonomous world,” rather than a fully autonomous one. “Autonomy is also not about removing people,” he said. In commercial buildings and industrial facilities, operators must remain involved because downtime and system errors can have immediate operational consequences. “You cannot afford to have a building shut down for one and a half hours,” he said.
AJ Wijesinghe, Chief AI Officer at Ecolab, made the economic case for a more selective approach. When the company used “the best model that’s out there” for high-volume tasks, “the tokenomics go off the roof,” he said. “Sometimes it’s more expensive than having humans.” Ecolab responded by optimizing its use of models and cutting token costs by roughly 70% to 80%.
Wijesinghe said companies do not always need the most advanced available model: “Sometimes you don’t have to have the fastest car.” He advocated a “human in the lead” approach for agentic AI and argued that results depend on keeping data foundations, process readiness, and cost discipline in balance. “If one is heavier than the other, then you don’t get the value,” he said.
Value, cost and growth
THE NEXT TEST FOR AI WILL BE WHETHER IT DELIVERS MEASURABLE RETURNS.
The Summit’s finance and operating leaders agreed that companies must pursue productivity and growth while maintaining discipline on model costs, data infrastructure, and outcome quality.
Ewout Steenbergen, Executive Vice President and CFO of Booking Holdings, said uncertainty over AI returns is widespread, even among the largest technology investors. “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.”
For Booking, the path to value is not indiscriminate spending but redesigning processes “from a white sheet.” Steenbergen said the company’s customer-service costs are slightly down as bookings grow at a high-single-digit rate, while customer satisfaction has risen. He also said Booking’s roughly 9,000 engineers are getting about 30% more code into production, counting merge requests that pass its testing and quality-control standards.
“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.” Booking manages this through “effective model cost routing,” using lower-cost models for simpler tasks and reserving more expensive models for work that requires greater reasoning capability.
Steenbergen said AI’s larger potential may lie in customer growth. The technology will not change the basic nature of travel—“The hotel is still the hotel, and the airline is still the airline, and the rental car is still the rental car”—but it can make planning, booking, and responding to disruptions easier for travelers. By linking parts of a trip and anticipating a customer’s needs, he said, AI can help create “more loyalty, more trust, and more brand value.”
The AIQ Summit demonstrated that AI leadership is not defined by the number of tools a company has purchased, the size of its pilot program, or the volume of content it can generate. The organizations moving ahead are pairing technical ambition with human development, process redesign, financial discipline, trustworthy data, and accountable governance. As AI moves deeper into the enterprise, the leaders who succeed will be those who use it not simply to automate existing work, but to build more resilient, productive, and responsive businesses.
Generative AI was used for research assistance and/or transcription of this article. The reporter independently reported and verified the factual claims in this article, and a human editor reviewed it before publication.
This story was originally featured on Fortune.com
