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Rob Collie spent 13 years at Microsoft helping lead business intelligence work in Excel. Even with that technical background, he says that AI initially scared him. 

“I wasn’t immune to that,” Collie says in a new interview with 91���� about the technology’s potential effect on his business and career. He says his “fear turned into excitement” after he began studying how companies can actually put AI to work.

Now, as CEO of the 50-person consulting firm , Collie is betting that AI will not simply eliminate work. It will change who gets to create software, solve operational problems and become indispensable inside a company. Collie recently published a book about organizational AI strategy called Fair Game. 

His central advice to workers is straightforward: Do not wait to see what AI does to your job. Help your employer use it.

“You want to be on that train,” Collie says. “The closer you can get to AI, and the more you can embrace it, the more you can give it a hug, that gives you the opportunity to be nimble.”

That matters because, in Collie’s view, no one can reliably predict which roles will change most or how quickly. However, workers can develop practical AI skills that make them agents of change rather than passive observers.

How to have an AI-proof career

Collie says that the best way someone can AI-proof their career is to become an active adopter of AI at work, not waiting to see if the technology replaces them. 

He advises workers to learn to apply AI in a real business context beyond using a general chatbot for one-time tasks. He also tells workers to become part of the company’s “adoption team” by identifying practical workflows AI can improve. 

“You can blow people’s minds with this stuff,” Collie says. “You can be the AI expert at your company in relatively short order, and that’s the approach that I’ve been taking with my company.” 

Collie says that workers can build or help create specialized AI assistants that use company-specific instructions, documents and data. He notes that no one can reliably predict where AI is headed, or how their job will change, so they should focus on developing agility rather than betting on any one job in particular. 

Collie urges workers to use AI to strengthen their judgment and productivity, not replace thinking. His preferred employees are “AI-first,” meaning that their first question when approaching a task is what kind of AI system could help them do it better. 

He also encourages workers to leverage business knowledge when using AI. Collie says that people who understand their company’s processes are more likely to build helpful tools with AI. 

The jobs most at-risk from AI

Collie does not deny that AI will displace some workers. But he believes employers make a mistake when they view the technology only as a headcount-reduction tool.

“You’re missing some of the things that it can do for you,” he says. AI can provide capabilities a company did not previously have, rather than merely replace existing tasks, he adds.

Still, he sees particular risk for entry-level roles where employees can become productive quickly. His test is simple: How much training and organizational knowledge does a worker need before doing the job competently?

“The entry-level jobs that are the most replaceable are the ones where a new hire can be truly productive in the first two weeks,” Collie says.

Call-center work is one clear example. These jobs often involve structured policies, decision trees and predictable response flows, all information that can be documented and supplied to an AI system. In contrast, roles that require years of accumulated judgment, nuanced relationships and substantial company-specific experience may be harder to fully automate.

Collie uses the technical concept of an AI’s “context window” to explain the difference. An AI can absorb only a limited amount of information at once. If the knowledge required to perform a job fits into that practical limit, the role may be more exposed. If it takes a long time and substantial experience to become effective, AI may have more difficulty replacing the worker outright.

For young professionals, however, the threat extends beyond individual jobs. Collie said many companies appear to have become hesitant to hire inexperienced candidates while they try to understand AI’s impact.

“The zero-years-experience job is now turning into, like, a three-years-experience job,” he says. 

He believes some of that pullback may reverse as employers gain confidence about where human talent still adds the most value. But job-seekers should not count on a return to the old entry-level ladder.

AI is a new form of computing

Collie’s career has long centered on making sophisticated technology accessible to people outside large enterprises. At Microsoft, he led business-intelligence features in Excel 2007 before joining the early Power Pivot effort. That eventually became Power BI, a business analytics platform that turns raw data into interactive charts and dashboards. He later founded P3 Adaptive to help smaller companies use tools that had once been largely out of reach.

That experience shapes how he sees the AI moment. He rejects the idea that businesses need to build their own large language models or hire elite AI researchers before getting started.

“AI is just a new form of computing that we haven’t had before,” Collie says. He adds that companies need to learn its strengths, limitations and practical uses, much as they learned to use traditional software.

Off-the-shelf chatbots can boost individual productivity, Collie says, but they cannot automatically understand a company’s specific context. For example, ask a general-purpose AI tool to write a proposal, and it may produce generic, lowest-common-denominator copy. The difference comes when a business gives the system clear instructions and relevant internal knowledge.

Collie compares that process to onboarding a highly educated new hire. An AI may know a vast amount about the world, but it knows nothing about an individual organization.

“If you hired someone who had a Ph.D. in everything but had never met your business, they wouldn’t be useful on day one,” he said. “You would need to give them training.”

Build an AI handbook

Collie calls those written instructions “handbooks.” They explain what the AI’s role is, which information it should use, what standards it should follow and how it should communicate.

A marketing assistant could be supplied with a handbook that captures a company’s brand voice, audience, products and approval rules. Instead of opening a chatbot that must be re-briefed every time, an employee could work with a specialized assistant that already has the relevant context.

“Every time it wakes up, it needs to be handed that manual before it starts working with you,” Collie says. “That is super easy to do.”

He argues that business knowledge, not advanced technical training, is the starting point. A manager who knows how a team handles customer issues, or a marketer who knows the company’s tone, can help create the handbook. Technical employees may still be needed to connect systems and data securely, but they should not be isolated from the people closest to the work.

“Don’t treat AI as a technology problem,” Collie says. “It’s a business problem. It’s about teaching it about your business.”

Collie is applying that philosophy inside P3 Adaptive. The firm has frozen hiring while it retrains its current staff and rethinks roles built around dashboard development. Going forward, he said, workers will need to be “AI-first” in how they approach their work.

That does not mean allowing AI to think for them. It means using it to handle routine work, accelerate drafting and coding, organize context and extend what skilled professionals can accomplish.

“It’s not using AI to replace their brain,” Collie says. “It’s AI to take on lots and lots of stuff for them to make them more effective and more productive.”