I’m going to say something that might sound crazy. Most of what we’re all obsessing over in AI today won’t matter in three years. The models, the benchmarks, the latest fundraising rounds… it’s mostly noise. I’ve seen this movie before. Twice, with my own companies. And about 200 times as an angel investor.
When the dust settles, the tech itself becomes a commodity. What’s left? Leadership. The ability to take this powerful, chaotic new thing and actually build something valuable with it. And the way we lead through this transition is about to get turned on its head.
I spend my days talking to founders, executives at massive companies, and the smartest engineers I know. Forget the current hype. I'm looking ahead to 2027, and I see three seismic shifts in AI leadership that are going to catch most people completely by surprise.
1. The Chief AI Officer Becomes the New COO
For the past few years, the “Head of AI” or “Chief AI Officer” has been a glorified technical advisor. They’re the person in the room you point to for an explanation of how a large language model works. They might run a small, isolated data science team. That’s about to change, drastically.
By 2027, the Chief AI Officer (CAIO) will be the most important C-suite executive next to the CEO. Why? Because AI stops being a department and starts being the very fabric of the company. It’s not a feature; it’s the new operating system for your entire business.
I remember a board meeting for one of my portfolio companies back in 2023. The CEO presented a beautiful roadmap, and then the Head of AI presented a completely separate “AI roadmap.” It was a disaster. The two weren’t connected. The AI strategy was a science project, totally divorced from the business goals. The company is struggling today because they never figured out how to merge the two.
In 2027, the CAIO won’t just manage data scientists. They will be responsible for operational efficiency, go-to-market strategy, product innovation, and human capital. Their core job will be to answer one question: how do we apply intelligence to every single process in this company to drive growth and profitability?
- Sales? The CAIO will oversee the systems that predict which customers will churn and which are ready for an upsell.
- Marketing? They’ll be in charge of the entire content generation and personalization engine.
- Engineering? They will be responsible for the AI-powered tools that double developer productivity.
- HR? They’ll own the platform that helps find the best talent and make sure they’re happy.
This isn’t a technical role anymore. It’s a strategic, operational, and deeply commercial one. The best CAIOs won’t have a PhD in machine learning. They’ll be business leaders who happen to be deeply fluent in the language of AI. They’ll be the ones who can connect a new model architecture to a P&L line item. If you’re not thinking about who this person is in your organization, you’re already behind.
2. The Most Important Skill: Decisiveness Under Ambiguity
The pace of change right now feels fast. It’s about to get a lot faster. The temptation for most leaders will be to wait. Wait for the perfect model. Wait for the technology to mature. Wait to see what competitors do. Waiting will be a death sentence.
In the era of AI, leadership is about making the right bets with 70% of the information. The winners will be the ones who can act, learn, and pivot faster than anyone else.
I learned this the hard way at RemoteTeam. We were building a platform for managing remote employees. A new API was released by a major cloud provider that could, in theory, automate a huge chunk of our onboarding workflow. It was buggy. It was poorly documented. My engineering team was split. Half wanted to build our own solution, which would take nine months. The other half wanted to take a bet on this new, unproven API.
We had to make a call. I remember sitting with my co-founder, staring at a whiteboard. We could play it safe and build it ourselves, and probably miss the market window. Or we could take a risk, knowing it might blow up in our faces. We chose the risk. We decided to bet on the API.
For the first three months, it was a nightmare. Things broke. Customers were frustrated. But we learned. We built wrappers around the API, we figured out its quirks, and we got it to work. By the time our competitors had finished building their own clunky, in-house solutions, we were six months ahead of them, with a more powerful and scalable system. That decision was a huge part of why Gusto acquired us.
Leading in the AI era is not about having all the answers. It’s about your OODA loop: Observe, Orient, Decide, Act. How fast can you cycle through that? When a new open-source model drops that is 10% better than what you’re using, do you have a process to evaluate and deploy it in two weeks, or does it take six months of committee meetings?
Leaders who need certainty will be paralyzed. The ones who thrive on ambiguity and can make quick, calculated bets will build the next generation of great companies.
3. The Real Work is Change Management, Not Technology
We can talk all day about AI models and infrastructure. But the hardest part of this transition has nothing to do with technology. It’s about people. It’s about taking a team of smart, capable individuals who have been doing their jobs a certain way for a decade and telling them that everything is about to change.
I’ve seen this go wrong so many times. A company spends millions on a new AI-powered CRM. They roll it out with a one-hour training session. A year later, nobody is using it. The sales team has gone back to their old spreadsheets because it’s what they know. The project is a failure. The money is gone.
The problem wasn’t the technology. The problem was that the leadership treated it as a technology problem, not a human one.
By 2027, the smartest leaders will spend 10% of their time on the AI strategy and 90% of their time on the change management strategy. They will be obsessive about answering the human questions:
- How do we bring our team along on this journey? Not with a single email, but with constant communication, open forums, and real-world examples of how this makes their lives better.
- What’s in it for them? How does this new AI tool free up a salesperson from tedious data entry so they can spend more time building relationships and closing deals? You have to sell the benefit to the individual, not just the company.
- How do we create psychological safety? People are scared. They’re worried about their jobs. You have to create a culture where people can admit they don’t understand something, where they can experiment and fail without being punished. If you don’t, they will just resist the change quietly until it goes away.
This is the unglamorous, difficult work of leadership. It’s not about giving a big speech on a stage. It’s about the one-on-one conversations. It’s about identifying your internal champions and empowering them. It’s about over-communicating until you’re sick of hearing your own voice.
I’m an investor in companies like Anthropic, OpenAI, and Scale AI. I see the cutting edge of this technology every single day. And I can tell you with absolute certainty that the companies that win will not be the ones with the slightly better algorithm. They will be the ones with the leaders who master the human side of this transformation.
So, as you think about the next few years, stop asking “What is our AI strategy?” and start asking “Who is our AI leader?” and “How are we preparing our people for what’s coming?” The answers to those questions will define your future.
Frequently Asked Questions
Can I implement all of these at once?
I'd strongly recommend against it. Pick the 2-3 items that resonate most with your current situation and focus there. Trying to do everything simultaneously is a recipe for doing nothing well.
Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
Which item on this list has the highest impact?
It depends on your stage and context, but in my experience, the items near the top of the list tend to have the broadest applicability. That said, sometimes the less obvious items create the biggest breakthroughs for specific situations.