Imagine this: someone upstream deploys an AI agent. Their throughput doubles overnight. Work starts flying to you at twice the speed. But you're still in Excel. You still don't have access to the company's data lake.
Congratulations — you've just become the bottleneck in a chain that's suddenly moving faster than everyone expected.
This isn't a hypothetical future problem. According to Deloitte's latest Tech Trends report, 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.
Let me translate that: companies are spending fourteen times more on the technology than on preparing the people who have to use it.
The Math Doesn't Work
"Ninety-three to seven is not the right level of effort," said Lara Abrash, chair of Deloitte U.S. "Companies should be spending as much time on the workforce right now as they are on the technology. And we're seeing most companies focus much more on the technology."
Why? Because technology investments are legible. You can point to a use case, benchmark a result, show a board a number. Workforce transformation is messy, slower, and harder to quantify.
"It's a little bit easier to get your hands around what you would need to do with technology," Abrash noted. "It's a lot harder to deal with the workforce."
But skipping the human work doesn't make it go away — it just defers the problem until it becomes a crisis.
The Resistance You're Not Pricing In
Wharton research found what Eric Bradlow, chair of the marketing department and vice chair of AI and analytics at the Wharton School, calls a "donut hole" 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 — or being left behind.
"You have the C-suite making massive investments in AI," Bradlow said, "and obviously the young people, they're trained using AI, and it typically is the middle, the middle managers where the reluctancy is."
That resistance isn't just friction — it's active sabotage to your ROI.
"Workforces are like antigens in your body," Abrash said. "They can fight things they want to fight pretty hard. If they don't see how it makes their jobs better and how they can show up and bring what makes them special, they're going to be that antigen and they're going to fight it."
The result: companies spend heavily on AI tools that employees quietly route around, ignore, or undermine. Your $10M AI deployment becomes shelfware because the people who need to use it never bought in.
The High-Stakes Risk Nobody's Talking About
There's a subtler and potentially more dangerous failure mode: when a human is removed from the loop without a deliberate design for what they're supposed to be doing instead, the AI operates unchecked.
"You could end up having hallucinations and bad outcomes because you don't have a human in the loop," Abrash warned. "It's a brand and reputation issue. It has to be done at the same time."
This matters especially in high-stakes industries. In aerospace, life sciences, financial regulation, 90% accuracy isn't okay. 95% isn't okay. Maybe even 99% accuracy isn't okay — you might need 99.999% accurate.
Training AI agents to reach those thresholds requires active human supervision, correction, and feedback loops that most companies haven't built. The deterministic systems we've relied on for decades ("you do a search on the internet, you want the same freaking answer every time") are being replaced by probabilistic systems where you ask the same question five times and get five different answers.
That shift requires a whole new approach to oversight — and most companies aren't funding it.
The Skills That Actually Matter
So what does the human bring that the machine can't?
Deloitte's survey of high-performing teams identified six consistently critical human capabilities. Three stand out:
Curiosity — the drive to generate novel questions, not just process existing ones. "A machine is not tuned to create curiosity," Abrash said. "When teams come together, designed to create new ideas and solutions, that'll drive innovation and it'll optimize what the machines do."
Emotional and social intelligence — machines can simulate empathy, but can't feel the actual stakes of a team under pressure, a client in distress, or a workforce absorbing a major change. "We need EQ in the workforce," Abrash said flatly.
Divergent thinking — the uniquely human capacity to generate multiple solutions rather than converge on one. "The technology is going to be intelligent and drive you down to one solution. That's how it's built. A human is not tuned that way."
These aren't nice-to-haves. They're the core value proposition for why you keep humans in the loop at all.
The Revenue Story You're Missing
For all the productivity hype, there's a revenue story hiding behind the efficiency story — and it may be the bigger one.
James Crowley at Accenture modeled a hypothetical $60 billion company and estimated approximately $6 billion in potential annual revenue growth from well-deployed AI. That's 10% top-line growth — not from cutting headcount, but from higher productivity among redeployed workers leading to greater revenue.
Among executives surveyed, 78% said they see more benefit on the revenue growth side than the cost-cutting side.
"The gains on the revenue side are going to eventually dwarf the gains on the efficiency and productivity side," Bradlow said. "It's corporations doing things they just could not do before."
The companies most likely to miss that upside aren't the ones that failed to buy the right AI tools. They're the ones who treated the workforce as an afterthought — spending 93% of their budget on technology and 7% on the people who have to use it.
The Roles Nobody's Hiring For
There's a specific kind of leader who is increasingly critical and increasingly rare: what Harvard Business School's Linda Hill and innovation veteran Jason Wild call the "bridger."
These are the people who translate across organizational boundaries — between IT and operations, between startups and legacy systems, between technology teams and business units. At Delta, for example, a leader trying to build a biometric boarding-pass system with startup Clear had to navigate the airline's own IT department, federal regulators at TSA, and the startup's risk tolerance — simultaneously.
"There are no bridger titles," Wild said. "But Chief of Staff, RevOps, Forward Deployed Engineer — those are all bridger roles."
The companies investing in bridger roles are the ones who will actually get ROI from their AI deployments. The ones laying off those people, Wild argued, "they're going to regret it later."
What To Do About It
If you're a CIO, CTO, or CFO watching this unfold, here's what matters:
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Rebalance your budget. If you're spending 93% on tech and 7% on people, you're building a bottleneck.
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Invest in bridgers. The people who can translate between systems, teams, and stakeholders are the ones who will unlock ROI.
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Design the human loop deliberately. Don't remove humans from workflows and hope for the best. Define what they're supposed to be doing instead.
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Measure resistance as a leading indicator. If middle managers are fighting your AI deployment, that's not a people problem — it's a design problem.
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Price in the revenue upside. The efficiency story sells AI to the board. The revenue story funds the workforce transformation you actually need.
The companies that figure this out will see 10% top-line growth. The ones that don't will see $10M in shelfware and a workforce that's actively routing around your AI investments.
Your choice.
What do you think? Are you seeing the 93/7 split in your organization? Hit reply — I read every response.
Sources:
- Fortune: Top leadership experts sound the alarm on the AI doomsday
- Deloitte Tech Trends 2025: AI Adoption Report
- Wharton-GBK AI Adoption Report
- Accenture: The Age of Co-Intelligence
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