TL;DR
- The panic is real, but it’s aimed wrong. Most companies aren’t losing to AI. They’re failing to make it work: MIT found 95% of organizations getting zero return on GenAI.
- The gap has a name. A Forward Deployed Operator understands the tech, the process, and how the business makes money, then builds the thing that connects them.
- Build on what you already know. Your experience is the foundation. Stay current, add AI skills, and become the person who shows AI working, not the person who talks about it.
Last week I sat down with my coffee and pressed play on Marina Mogilko’s Silicon Valley Girl podcast. Her guest was James Manyika, Senior Vice President of Research, Labs, Technology and Society at Google.
The comments under episodes like that one all sound the same. Is my job next? Should I go back to school? Is it already too late?
Then Manyika said something that stopped me. AI adoption, he said, is “very wide but very shallow.”
Wide. But shallow.
That’s what I see every week inside operations. Everyone has a ChatGPT tab open. Almost nobody has changed how the work actually runs.
And that gap? That’s where your next job is.
The headlines vs. the data
Let’s not pretend the fear is made up. Jensen Huang told the Milken Global Conference in May 2025: “Every job will be affected, and immediately. It is unquestionable.” Andy Jassy told Amazon employees: “We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs.”
That second half matters. More people doing other types of jobs.
The World Economic Forum’s Future of Jobs Report 2025 projects 170 million jobs created and 92 million displaced by 2030, a net gain of 78 million. It also says that if the global workforce were 100 people, “59 are projected to require reskilling or upskilling by 2030.”
So the question isn’t “will AI take my job?” The question is: which new job are you going to be ready for?
Most companies aren’t AI ready
Here’s what I see when I walk into a company.
Some of them never invested in testing. They bought a tool because a competitor did, or because someone saw a demo. Nobody sat down and asked which platforms and technologies could actually work for this business.
Others have the tools but never looked deeply at their processes. They layered AI on top of a messy workflow and got a faster mess.
The research says the same thing, louder.
MIT’s NANDA initiative, in The GenAI Divide: State of AI in Business 2025, found that despite $30 to 40 billion in enterprise investment, 95% of organizations are getting zero return. Their explanation: “Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.”
BCG’s The Widening AI Value Gap (September 2025): “Only 5% of companies in our 2025 study of more than 1,250 firms worldwide are achieving AI value at scale.” And: “Fully 60% of companies are not achieving material value at all.”
McKinsey’s The state of AI in 2025: “Nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise.” The high performers are “nearly three times as likely as others are to say their organizations have fundamentally redesigned individual workflows.”
Read that again. The winners didn’t buy better AI. They redesigned the work.
That’s not a technology problem. That’s an operator problem.
So what does a Forward Deployed Operator actually do?
A Forward Deployed Operator works inside a business on a business problem, at the intersection of people, process, technology, and execution.
They understand how the work really runs. They diagnose what’s holding it back. They redesign the workflow, build what’s needed, deploy it with the team, and own the outcome.
FDEs deploy technology. FDOs deploy change.
A consultant is measured by the deck they leave behind. An FDO is measured by whether the business works better.
To do that well, you need two foundations.
1. You understand the technical side.
Not just “I use ChatGPT.” You understand the business’s own technology: the CRM, the spreadsheets holding everything together, the systems nobody wants to touch. And you know the tools out there, what each one is good at, and where it breaks.
Stay as up to date as you can. New tools ship every week. But build on a strong foundation: how data moves, how a workflow is structured, how to test before you commit. The tools change. The foundation doesn’t.
2. You understand how the business makes money.
This is the part most AI conversations skip.
Is this a product business or a service business? Where does the revenue actually come from? What slows it down: quoting, onboarding, fulfillment, follow-up?
Because the question isn’t “where can we use AI?” The question is “how can AI speed up the way this business earns?”
A product company might need faster order handling or smarter inventory decisions. A service company might need its people spending less time on admin and more time with clients. Same AI. Completely different answer.
When you can connect the tech to the money, you stop being “the AI person.” You become the person leadership calls first.
Build on your experience
This is the part I want you to hear.
Your experience is not the thing AI replaces. It’s the thing AI needs.
You know where the work gets stuck. You know which customers call twice, which handoff always breaks, which report nobody trusts. AI doesn’t know any of that. You do.
What changes is what you build on top of it. Stay current. Add AI skills. Then show you can make AI work inside the work you already understand.
Brian Chesky said it well on Silicon Valley Girl: “AI is like an amplifier. It makes the good people better.” He’s a designer by training, and he talked about how prompting let him start to code. He didn’t throw away his design background. He added to it.
And he was clear about what matters even more now: “great thinking, great taste, great understanding, understanding user problems.” In his words: “It’s no longer can you build it, it’s what do you build? Should you build it? And does it matter?”
Those are experience questions. You can only answer them well if you know the business.
Manyika made a similar point on the same podcast: we “still bring the judgment, the expertise, the ability to check and the ability to frame the right questions.” And he’s already seeing new kinds of work show up: “So are there people now overseeing agents? Are there people now watching agent orchestration? Are there people now doing new kinds of evaluations?”
Somebody with deep knowledge of the business has to do that work. Why not you?
Jassy’s advice to his own employees was simple: “be curious about AI, educate yourself, attend workshops and take trainings, use and experiment with AI whenever you can.”
That’s how I think about being AI forward and AI first. AI does the repetitive 80%. You do the 20% that needs your judgment and experience, and you keep the final say.
What to do this week
Start with what you already know.
- Pick one real process. Something you or your team does every week. Map it, step by step, the way it actually runs.
- Find where the money is. Which step slows down revenue or eats the most hours? Start there.
- Test one tool on it, and document it. Before, after, what broke, what you’d change. That document is your proof.
Do that three times and your experience stops being something you describe. It becomes something you can show.
OPS INTEL
- Anthropic on its own workforce (Oct 16): roles are broadening “to include building products, managing agents and tackling the strategic and interpersonal work that remain beyond the scope of the best LLMs.” Worth reading in full.
- James Manyika on Silicon Valley Girl: “My expectation is that the growth in jobs coming from both new jobs and existing ones expanding will be bigger than the declines we see.” His point on standing out: make your contribution “unique and distinctive, particularly in a way that complements what the technology does.”
- Brian Chesky on Silicon Valley Girl: the episode title says it: “People Who Know What to Build With AI Will Pull Ahead.”
- McKinsey on what separates high performers: “this intentional redesigning of workflows has one of the strongest contributions to achieving meaningful business impact of all the factors tested.”
Want to hear what this looks like inside a real business? In Episode 13, I sat down with Susie Bulloch to talk about building, launching, and what it takes when the moment hits.
AI won’t replace the people who make it work. Be the one who makes it work.
Know someone who needs this translated? Forward it to the executive about to make this decision.
If your operation has AI tools but no results, let’s talk.
P.S. If you want to learn this by doing it, FDO School is an 8-week live cohort where you go inside an actual company, work on a real problem, and build a working AI system you can show.