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When the COO of Nike, the chief of operations at an $84 billion food distributor, and the CEO of a major tech media company walked into the same room at the Fortune COO Summit, they came ready to talk about what AI was doing\u00a0for\u00a0them. Speed. Scale. Revenue unlocked. The future arriving ahead of schedule.<\/p>\n
What they described instead, during a lunch roundtable hosted by Thomson Reuters, was something closer to organized chaos.<\/p>\n
\u201cThe biggest challenge I could see is speed without clarity,\u201d said Venkatesh Alagirisamy, EVP and COO of Nike. \u201cI see a lot of hype around AI that drives a lot of energy within organizations in wanting to adopt AI, but without that clarity, without that sense of purpose, that speed could get us in the wrong direction.\u201d<\/p>\n
Welcome to what panelists called the \u201cautomation illusion\u201d \u2014 the dangerous gap between what AI promises operations leaders and what it actually delivers.<\/p>\n
The promise was simple<\/p>\n
The way the COOs described it to Fortune Editorial Director Diane Brady the AI pitch was almost too good. Automate the routine. Free up the workforce. Let the machines handle forecasting, logistics, compliance, customer service. Let humans handle strategy.<\/p>\n
Aayush Bhatnagar, global head of customer service at Sysco \u2014 which moves food to restaurants across North America, generating nearly $84 billion in annual revenue \u2014 put it plainly: the goal was to take tribal knowledge baked into decades of human relationships and institutionalize it at scale. \u201cEvery piece of broccoli you\u2019re eating has moved an average of 2,000 miles,\u201d he said. The supply chain that makes that happen runs on judgment calls made by people who\u2019ve been doing it for years. AI was supposed to absorb that expertise and multiply it.<\/p>\n
And in some ways, it has. Nike launched an internal learning platform 12 months ago \u2014 peer-curated, bottoms-up, not mandated from above \u2014 and logged 20,000 digital courses taken, with 3,000 live training sessions conducted. Sysco is using AI to rethink how it forecasts and buys. Thomson Reuters is deploying it to help lawyers, tax accountants, and trade professionals work faster.<\/p>\n
But this has all come with a big reality check.<\/p>\n
The illusion kicks in<\/p>\n
Laura Clayton McDonnell, president of corporates at Thomson Reuters, expanded on the automation illusion. \u201cWe\u2019re going to move fast, we\u2019re going to get these answers really quickly,\u201d she said. \u201cBut what about making sure that output is reliable, it\u2019s accurate, it\u2019s something that you can drive your business on?\u201d That, she added, is where companies really need to pause instead of give in to the need for speed.<\/p>\n
For the professionals Thomson Reuters serves \u2014 lawyers walking into courtrooms, accountants navigating tariffs, trade teams dealing with sanctions \u2014 there is no margin for error. \u201cYou cannot be wrong,\u201d McDonnell said. \u201cYou just can\u2019t be wrong.\u201d A large language model that confidently produces a plausible-but-wrong answer isn\u2019t a productivity tool in that context, but a liability.<\/p>\n
The illusion runs deeper than accuracy, though. The bigger problem is that AI has made the operating environment fundamentally less predictable \u2014 precisely the environment COOs are paid to manage.<\/p>\n
Olivia Nottebohm, COO of Box, said she has watched it play out inside her own company. Box sells AI products. It runs Box AI internally. It talks about AI constantly. And when Nottebohm looked at the adoption numbers, they were low. \u201cHere we are, an AI company selling AI,\u201d she said, \u201cand I wasn\u2019t even seeing the adoption I was expecting.\u201d When she dug in, she found the answer wasn\u2019t resistance \u2014 it was confusion. People didn\u2019t know how. The tools were available. The skills weren\u2019t.<\/p>\n
She shared that the company impemented a program called \u201cNo Boxer Left Behind.\u201d It worked, but it also revealed a harder truth: even at a tech-forward company, the gap between deploying AI and operationalizing it is enormous. \u201cReally making sure that people don\u2019t feel disenfranchised, I think that has been the thing that took me the longest to figure out,\u201d she shared, adding that she \u201cshould have figured it out sooner.\u201d The company\u2019s mandatory trainings are clear about what Boxers have to learn, \u201cand if you choose to opt out of being on the AI transformation, that\u2019s up to you. But we, as an employer, are not going to let you do that.\u201d<\/p>\n
The management problem no one has solved<\/p>\n
Nothing illustrated that gap more starkly than Bhatnagar\u2019s admission about his team. Four weeks ago, he told the room, he added seven AI agents to his direct reports. They have names. They have defined roles \u2014 an escalation agent, a delivery agent, a communications agent. Their performance is reviewed alongside the humans at his weekly business review.<\/p>\n
\u201cI lost some sleep that night,\u201d he said, \u201cthinking that our traditional laws of leadership, principles of leadership, do not apply to these agentic agents.\u201d To his point, there is no management literature for that, no HR policy or performance improvement plan you can put an agent on. And yet COOs like him are already accountable for their output \u2014 output that can scale instantly and go wrong just as fast.<\/p>\n
\u201cHow do I train my managers now?\u201d he asked the room. It may have been the most honest summary of where enterprise AI actually stands.<\/p>\n
The deeper stakes<\/p>\n
Near the end of the discussion, the question hanging over the room became explicit: what happens to the entry-level workers who traditionally built their judgment doing the exact tasks AI is now absorbing?<\/p>\n
McDonnell kept returning to the same guardrail: the human in the loop isn\u2019t optional, it\u2019s structural. \u201cI don\u2019t think we\u2019ve found a tool yet that actually can exercise business judgment,\u201d she said. \u201cThat\u2019s the difference maker.\u201d<\/p>\n
Alagirisamy framed it as the central leadership capability of the moment: learning agility. Not AI fluency, not technical depth, but the organizational muscle to keep adapting as the ground keeps shifting. \u201cDoes your team have the learning agility to adapt to this new environment?\u201d he said. <\/p>\n
For COOs, the automation illusion isn\u2019t just about bad AI outputs. It\u2019s about the widening gap between the speed at which the technology is moving and illusion of how much work can be automated, and the reality that it\u2019s much easier said than done.<\/p>\n
They came in talking about what AI was doing for them. They left still trying to figure out what to do about it.<\/p>\n
For this story,\u00a0Fortune\u00a0journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.<\/p>\n
#automation #illusion #making #COOs #jobs #harder #easier<\/p>\n","protected":false},"excerpt":{"rendered":"
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