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Imagine someone upstream in your company just deployed an AI agent. Their throughput doubles overnight. Work starts flying to you at twice the speed. But you\u2019re still in Excel. You still don\u2019t have access to the company\u2019s data lake. Overnight, you\u2019ve become the bottleneck \u2014 the weak link in a chain that\u2019s suddenly moving faster than ever.<\/p>\n
\u201cThis will expose the weakest link in an organization,\u201d said Eric Bradlow, chair of the marketing department and vice chair of AI and analytics at the Wharton School of the University of Pennsylvania, who uses that exact scenario to describe what he fears is coming. \u201cIf efficiency gains are happening here but not here,\u201d he said, gesticulating with his hands, \u201cit will be exacerbated and you will see it quickly.\u201d<\/p>\n
That bottleneck problem is materializing across corporate America \u2014 and the root cause isn\u2019t technology. It\u2019s that companies aren\u2019t doing the hard, unglamorous work of preparing the people who are supposed to be working alongside it.<\/p>\n
The 7% problem<\/p>\n
The numbers are stark. Across the corporate sector, consultants and analysts see similar, troubling patterns. According to Deloitte\u2019s most recent Tech Trends report (covered by Fortune when it was released), IT accounts for roughly 93% of AI adoption budgets. Only 7% of companies are making meaningful progress designing how humans and AI actually work together. <\/p>\n
The deliberate, structural work of figuring out what happens to the people whose jobs are being transformed is an afterthought, said Lara Abrash, chair of Deloitte U.S.. \u201cNinety-three to seven is not the right level of effort in both places,\u201d she said. \u201cCompanies should be spending as much time on the workforce right now as they are on the technology. And we\u2019re seeing most companies focus much more on the technology.\u201d<\/p>\n
courtesy of Deloitte<\/p>\n
The same imbalance shows up in Wharton\u2019s AI adoption research. Bradlow said Wharton and GBK Collective found in a prior research report what he calls a \u201cdonut hole\u201d at the center of most large organizations: the C-suite is investing heavily in AI, younger workers have grown up using it natively, but the middle managers who actually have to orchestrate workflow change are the ones resisting \u2014 or being left behind. It was unclear from the data whether this took the form of passive or active resistance.<\/p>\n
\u201cYou have the C-suite making massive investments in AI,\u201d he said, and \u201cobviously the young people, they\u2019re trained using AI and it typically is the middle, the middle managers where the, if you like, the reluctancy is.\u201d<\/p>\n
Why companies keep getting this wrong<\/p>\n
The reasons for the imbalance are not mysterious. Technology investments are legible: you can point to a use case, benchmark a result, or show a board a number. Workforce transformation is messier, slower, and harder to quantify.<\/p>\n
\u201cIt\u2019s a little bit easier to get your hands around what you would need to do with technology,\u201d Abrash said. \u201cIt\u2019s a lot harder to deal with the workforce.\u201d This isn\u2019t just an \u201cAI-specific thing,\u201d she added, noting, for example, how companies have grown fond of reorganizations, seemingly for their own sake, and managers looking at various mechanisms to cut headcount instead of doing the hard work of optimizing their workforce. \u201cThis behavior is not because of AI. It\u2019s just the way it generally is.\u201d<\/p>\n
Linda Hill, a professor at Harvard Business School and head\u00a0faculty chair of its Leadership Initiative, put it in a broader leadership context in a recent conversation with Fortune. In her new book\u00a0Genius at Scale, co-authored with Jason Wild and Emily Tedards, she argued that the entire model of what makes a great leader is shifting \u2014 and many executives are still operating on the old playbook.<\/p>\n
\u201cTraditional leadership has been: be decisive, stick out the chest, show confidence. This is the destination. Get in the car and follow me, it\u2019ll be okay,\u201d said Wild, a 25-year innovation veteran who led teams at Microsoft, IBM, and Salesforce. The problem with that approach now, he added, is that \u201cthe world is literally shifting underneath our feet by three or four feet every week.\u201d<\/p>\n
Jason Wild.<\/p>\n
courtesy of Jason Wild<\/p>\n
Hill and Wild call the new required skill \u201cwayfinding\u201d \u2014 a deliberate contrast to the old chest-sticking-out method of \u201cpathfinding.\u201d Pathfinders set a destination and drive toward it. Wayfinders navigate fog. It\u2019s suddenly an era, Hill added, when org chart whispers include \u201cI don\u2019t even know what team I\u2019m going to need in a year, let alone three,\u201d arguing that the wayfinder way of leadership will matter enormously. Hill explained it this way: pathfinding isn\u2019t an inherently old-fashioned way of leading, but it is one orientated around a clear destination in sight; we aren\u2019t in that kind of circumstance now. The destination is ahead of us, but it\u2019s unclear. <\/p>\n
\u201cWhen we finally realized what we were studying was wayfinding and not pathfinding,\u201d Hill said, \u201cwe also realized how emotionally and intellectually challenging innovating and being agile really are.\u201d<\/p>\n
What happens when you skip the human work<\/p>\n
The consequences of neglecting the workforce side of AI aren\u2019t hypothetical. Abrash described them in vivid terms.<\/p>\n
\u201cWorkforces are like antigens in your body,\u201d she said. \u201cThey can fight things they want to fight pretty hard \u2026 If they don\u2019t see how it makes their jobs better and how they can show up and bring what makes them special, they\u2019re going to be that antigen and they\u2019re going to fight it.\u201d<\/p>\n
That resistance leads directly to failed adoption \u2014 companies spend heavily on AI tools that employees quietly route around, ignore, or undermine. But there\u2019s a subtler and potentially more dangerous risk: when a human is removed from the loop without a deliberate design for what they\u2019re supposed to be doing instead, the AI operates unchecked.<\/p>\n
\u201cYou could end up having hallucinations and bad outcomes because you don\u2019t have a human in the loop,\u201d Abrash warned. \u201cIt\u2019s a brand and reputation issue. It has to be done at the same time.\u201d<\/p>\n
Bradlow added a precision dimension that is often overlooked in popular coverage. In high-stakes industries \u2014 aerospace, life sciences, financial regulation \u2014 \u201c90% accuracy is not okay. 95% is not okay. Maybe even 99% accuracy is not okay. You might need to be 99.999% accurate.\u201d Training AI agents to reach those thresholds requires active human supervision, correction, and feedback loops that most companies haven\u2019t built.<\/p>\n
courtesy of the Wharton School<\/p>\n
Nearly the same point was made by Wild, who noted that enterprise systems are deterministic \u2014\u00a0\u201cyou do a search on the internet, you want the same freaking answer every time,\u201d but now we\u2019re in different territory. \u201cAI is a\u00a0probabilistic\u00a0system, right? You ask the same question, word it the same way, in ChatGPT five times, you get five different answers.\u201d Time for a whole new style of leadership, in other words.<\/p>\n
The real skills that will matter<\/p>\n
What does the human bring that the machine can\u2019t? Abrash cited Deloitte\u2019s survey of high-performing teams produced a consistent answer of six consistently critical human capabilities, with three key ones to note. The first is curiosity \u2014 the drive to generate novel questions, not just process existing ones. \u201cA machine is not tuned to create curiosity,\u201d she said. \u201cAnd when teams come together, designed to create new ideas and solutions, that\u2019ll drive innovation and it\u2019ll optimize what the machines do.\u201d<\/p>\n
The second is emotional and social intelligence. Machines can simulate empathy, but can\u2019t feel the actual stakes of a team under pressure, a client in distress, or a workforce absorbing a major change. \u201cWe need EQ in the workforce,\u201d Abrash said flatly.<\/p>\n
The third is divergent thinking \u2014 the uniquely human capacity to generate multiple solutions rather than converge on one. \u201cThe technology is going to be intelligent and drive you down to one solution. That\u2019s how it\u2019s built. A human is not tuned that way.\u201d<\/p>\n
Linda Hill of Harvard Business School.<\/p>\n
courtesy of Harvard<\/p>\n
Hill echoed that idea in the context of leadership. She studied Kathy Fish at Procter & Gamble, the former Chief R&D and Innovation Officer who told her team bluntly: \u201cWe\u2019re going to have to innovate on how we innovate.\u201d Facing an activist investor and a product-centric legacy, Fish redesigned not just what P&G made but who was responsible for making it \u2014 expanding the definition of \u201cinnovator\u201d to include virtually everyone in the organization. The lesson, Hill said, is that human creativity can\u2019t be siloed. \u201cYou need everybody to be able to innovate.\u201d<\/p>\n
Bradlow talked about his college-age son, who is sorting through what to do with his career. \u201cEvery one of his friends are thinking, \u2018So what is that job that\u2019s going to be out there for me in two years? What actually are firms going to be hiring for it?’\u201d He acknowledged that Wharton, the top business school in the world, has followed a certain model where finance and consulting majors go into certain tracks, but \u201cI\u2019m not sure those tracks and career paths exist anymore.\u201d<\/p>\n
Looking at the problem from an enterprise level, he said, \u201cthere\u2019s a big human resources \u2014 I\u2019ll just call it a mental health challenge that we\u2019re going to face, which is people having to think about like, \u2018Do I have a job future? What is it?’\u201d Bradlow said he would be proud if his son chose to be an electrician, but he thinks it\u2019s shortsighted to rush into supposedly AI-proof careers. Maybe consulting firms, banks and private equity won\u2019t need as many highly educated workers due to AI adoption, but more \u201cantiquated\u201d members of the Fortune 500 surely will. <\/p>\n
By the way, Bradlow added, this same concern applies to his job at the University of Pennsylvania itself. \u201cWe\u2019re going to find out very quickly whether something that was founded by Benjamin Franklin can pivot quickly enough to really educate people on the skills that are needed today.\u201d At the end of the day, the Accentures of the world are going to evaluate who has AI skills and doesn\u2019t, regardless of their training, and \u201cif we\u2019re not adding value and if we don\u2019t totally redo our curriculum around the kind of skills that are needed, we\u2019re going to have a problem as an institution.\u201d For instance, Wharton has now offers an entire AI major at both the undergrad and MBA level, in addition to its Business Analytics major, which is a decade old. Bradlow\u2019s Wharton AI and Analytics department also offers experiential projects and short courses on AI.<\/p>\n
Leadership roles no one is hiring for<\/p>\n
Hill and Wild\u2019s research identifies a specific kind of leader who is increasingly critical and increasingly rare: what they call the \u201cbridger.\u201d These are the people who translate across organizational boundaries \u2014 between IT and operations, between startups and legacy systems, between technology teams and business units.<\/p>\n
She said she hears a constant refrain from executives: \u201cWe don\u2019t have people who know how to bridge.\u201d Leaders admit they can\u2019t do all the work by themselves and need partners within their business, she added, but it\u2019s a rare skillset.<\/p>\n
At Delta, for example, a leader trying to build a biometric boarding-pass system with startup Clear had to navigate the airline\u2019s own IT department, federal regulators at TSA, and the startup\u2019s risk tolerance \u2014 simultaneously. That work is invisible, rarely credited, and too often structurally undervalued. Metrics and siloed organizational structures can get in the way of breakthroughs like a whole new system for boarding a plane.<\/p>\n
\u201cThere are no bridger titles,\u201d he said. \u201cBut Chief of Staff, RevOps, Forward Deployed Engineer \u2014 those are all bridger roles.\u201d Wild said he can almost draw a line between companies investing in bridger roles and \u201claying off those people,\u201d he argued, \u201cthey\u2019re going to regret it later.\u201d<\/p>\n
Bradlow, meanwhile, said he\u2019s watching something similar play out in talent markets. The AI skills gap is real, but the solution isn\u2019t to flood into trades that seem \u201crobot-proof\u201d \u2014 a temptation he sees in students and workers everywhere.<\/p>\n
\u201cI\u2019m concerned there\u2019ll be a wide-level redeployment of people towards things they think are protected from artificial intelligence,\u201d he said. \u201cMaybe there\u2019s a short-run version of that. But I\u2019m not convinced there\u2019s a long-run version.\u201d<\/p>\n
His preferred metric for talent in the AI era: \u201cYou don\u2019t invest in someone who\u2019s got a high intercept. You invest in someone who\u2019s got a high slope. I don\u2019t care what you know now. I care how quickly you can learn.\u201d<\/p>\n
The upside no one is pricing in<\/p>\n
For all the doomsday narratives, there\u2019s a revenue story hiding behind the efficiency story \u2014 and it may be the bigger one.<\/p>\n
Accenture\u2019s James Crowley, Bradlow\u2019s research partner, said the dominant productivity framing of AI misses the point. \u201cWe\u2019re trying to pivot from just the productivity conversation to the revenue and upside conversation.\u201d In modeling a hypothetical $60 billion company for their most recent in-depth report, \u201cthe age of co-intelligence,\u201d the researchers estimated approximately $6 billion in potential annual revenue growth from well deployed-AI, meaning that higher productivity among redeployed workers will lead to greater revenue, rather than a shrinking workforce. Among executives surveyed, 78% said they see more benefit on the revenue growth side than the cost-cutting side.<\/p>\n
\u201cThe gains on the revenue side are going to eventually dwarf the gains on the efficiency and productivity side,\u201d Bradlow said. \u201cIt\u2019s corporations doing things they just could not do before.\u201d<\/p>\n
Abrash offered a concrete illustration. Knee replacement surgery used to require a surgeon to manually saw bone \u2014 an inherently imprecise process. Today, a robotic system handles the cutting with precision born of thousands of prior procedures, while the human surgeon focuses entirely on judgment, risk assessment, and the decisions that require a human mind. \u201cThere\u2019s a set of work that someone no longer needs to do,\u201d she said. \u201cAnd it positions them to do something that\u2019s higher value.\u201d<\/p>\n
The companies most likely to struggle aren\u2019t the ones that failed to buy the right AI tools. They\u2019re the ones who treated the workforce as an afterthought \u2014 spending 94% of their budget on technology and 6% on the people who have to use it.<\/p>\n
\u201cYou have better tools than the explorers did,\u201d Hill said. \u201cYou actually do have data. You do have all these emerging technologies to help us figure things out faster. But the emotional task, because we\u2019re human, of working through that \u2014 given the amount of anxiety that exists in the world today \u2014 those are incredibly complicated challenges for leaders.\u201d<\/p>\n
#Top #leadership #experts #sound #alarm #doomsday #bosses #choosing #tech #people<\/p>\n","protected":false},"excerpt":{"rendered":"
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