I remember the exact moment I realized AI was going to change everything. I was sitting in a board meeting for one of my portfolio companies, and the CTO started talking about a new large language model. He wasn’t just talking about chatbots; he was talking about rewriting our entire customer service playbook. The room was buzzing, but I saw a lot of blank stares from the non-technical execs. They were lost. Maybe you’ve felt that way too.
The hype around AI is deafening. It feels like if you aren’t a machine learning PhD, you’re already behind. That’s just not true. I’ve led two companies to successful exits and invested in over 200 startups, including some of the biggest names in AI like Anthropic and OpenAI. My secret? I’m not a coder. I’m a founder. I focus on people, problems, and market opportunities. You can lead a successful AI transformation by doing the same.
Stop Chasing Ghosts and Start with a Real Problem
Too many companies get infected with what I call "AI fever." They hear about a new model and immediately start brainstorming ways to use it, whether it makes sense or not. It’s a solution looking for a problem. I’ve seen companies spend millions on AI projects that go nowhere because they started with the technology, not the business need.
At RemoteTeam, which was later acquired by Gusto, we didn’t start by asking, "How can we use AI?" We started by asking, "What are the biggest pain points for remote teams?" One of the top answers was the headache of managing international payroll and compliance. It was a messy, manual process that was costing us time and money. Only then did we look at how AI could help. We ended up building a system that automated 90% of the compliance checks, saving our customers hundreds of hours a month. We didn’t set out to build an "AI company." We set out to solve a real problem, and AI was the best tool for the job.
Before you write a single line of code or hire a single data scientist, answer these questions:
- What is the most inefficient process in your company?
- What is the biggest complaint you hear from your customers?
- Where are your employees getting bogged down in repetitive tasks?
Find a problem that is costing you real money or hurting your customer experience. That’s your starting point.
You Don’t Need to Be a Mechanic to Drive a Car
I can’t build a transformer model from scratch. I’d bet most of your executive team can’t either. And that’s okay. You don’t need to understand the intricacies of neural networks to lead an AI strategy, just like you don’t need to be a mechanic to drive a car. You just need to know what the car can do, where you want to go, and how to read the dashboard.
Your job as a leader is to translate business goals into technical requirements. You need to be the bridge between the business side and the tech side. This means getting comfortable with the language of AI, but not necessarily the code. I make it a point to understand the difference between a large language model and a predictive analytics model. I know what "training data" is and why it’s important. I don’t need to know the math behind it, but I need to know enough to ask smart questions.
Here’s a cheat sheet of what you actually need to know:
- What are the different types of AI? (e.g., generative, predictive, classification)
- What are the limitations of AI? (e.g., bias, hallucinations)
- What kind of data does this type of AI need?
- How will we measure success? (e.g., cost savings, revenue increase, customer satisfaction)
I once sat in a pitch where a founder told me their AI could predict stock prices with 99% accuracy. My first question wasn’t about their algorithm. It was, "What’s your training data?" They got defensive. It turned out they were training their model on historical data in a way that created massive lookahead bias. The model wasn’t predicting the future; it was just remembering the past. You don’t need a PhD to spot that kind of red flag.
Your People Are Your Most Important Asset
An AI transformation isn’t a technology project; it’s a change management project. You can have the best algorithm in the world, but if your team is scared of it or doesn’t know how to use it, it’s worthless. I’ve seen this happen over and over. A company rolls out a new AI tool, but nobody uses it because they’re afraid it will make their job obsolete.
When we started integrating AI at MovieLaLa (acquired by Gfycat), we didn’t just hand the new tools to our marketing team and say, "Good luck." We held workshops. We brought in experts. We created a "safe zone" where people could experiment and ask dumb questions without fear of judgment. We were transparent about our goals. We explained that the AI was there to help them do their jobs better, not to replace them. We showed them how it could free them up from tedious tasks like data entry so they could focus on more creative work.
Your first AI hire shouldn’t be a data scientist. It should be a "translator": someone who can speak both business and tech. This person can be a product manager, a business analyst, or even just a curious employee who is passionate about AI. Their job is to work with the different departments to identify opportunities and build a roadmap. They are your AI champion.
Start Small, Win Big
The biggest mistake I see leaders make is trying to boil the ocean. They get so excited about the potential of AI that they launch a massive, company-wide initiative that is doomed to fail. It’s too big, too complex, and too expensive. When it inevitably hits a roadblock, the whole company loses faith in AI.
Instead of a moonshot, aim for a series of small wins. Find a single, well-defined problem and solve it with AI. At one of my portfolio companies, they started with an internal tool to automate the summarization of customer support tickets. It wasn’t glamorous, but it worked. It saved each support agent about an hour a day. That small win built momentum. The support team became advocates for AI. Other departments saw the success and started asking how they could use AI too. Within a year, they had a dozen AI projects running, all driven by real business needs.
Your first project should be:
- Low-risk: Don’t bet the farm on your first try.
- High-impact: Choose a problem that, if solved, will be a visible win.
- Measurable: Make sure you can track the results and show a clear ROI.
Leading an AI transformation is not about being the smartest person in the room. It’s about having a clear vision, asking the right questions, and empowering your team. You don’t need to be a tech expert to do that. You just need to be a leader. So, what problem are you going to solve first?
Build a Culture of Curiosity, Not Fear
Let’s be honest. When people hear “AI,” a little part of them hears “job replacement.” It’s a natural fear. I’ve seen it in every company I’ve worked with. If you don’t address this fear head-on, it will fester and kill your AI initiatives from the inside. Your employees will resist the change, they’ll sabotage the new tools (often unintentionally), and they’ll never truly embrace the potential.
At one of my angel investments, a fintech company, they were rolling out an AI-powered fraud detection system. The system was brilliant, capable of spotting complex patterns that were invisible to the human eye. But the fraud analysis team was terrified. They saw the new system as a threat to their jobs. The project was on the verge of failure before it even started.
The CEO, a very sharp leader, did something brilliant. She didn’t just send out a memo. She held a series of town halls where she personally walked the team through the new system. She showed them how it would handle the thousands of false positives they had to sift through every day, freeing them up to focus on the truly sophisticated fraud cases that required human intuition and expertise. She announced a new training program to upskill the entire team, turning them from manual analysts into “AI-assisted investigators.” She reframed the narrative from one of replacement to one of augmentation.
That’s how you build a culture of curiosity. You have to show your team that AI is a tool that will make them better at their jobs, not a replacement for them. Here are a few things that have worked for me:
- Invest in training: Don’t just throw new tools at your team. Invest in proper training and give them the time and space to learn.
- Celebrate experimentation: Create a culture where it’s safe to try new things and fail. Not every AI project will be a home run. That’s okay. The goal is to learn and iterate.
- Be transparent: Be open and honest about your AI strategy. Explain the “why” behind your decisions. If there are going to be changes to roles, be upfront about it and provide a path for your employees to transition.
The Road Ahead is Paved with Data
Here’s a hard truth that many leaders learn too late: your AI is only as good as your data. You can have the most advanced algorithm in the world, but if you feed it garbage data, you’ll get garbage results. I’ve seen companies spend millions on AI projects only to realize that their data is a mess—siloed in different departments, full of errors, and poorly organized.
Before you even think about building an AI model, you need to get your data house in order. This is often the least glamorous part of an AI transformation, but it’s the most critical. It means investing in data infrastructure, creating a single source of truth for your key metrics, and establishing clear data governance policies.
At RemoteTeam, we spent the first six months of our AI initiative just cleaning and organizing our data. It was a painful process, but it was the foundation for everything that came after. We created a centralized data warehouse and built a set of APIs that allowed us to access the data in a clean and consistent way. It wasn’t sexy, but it was the work that made our AI-powered compliance engine possible.
Don’t underestimate the importance of data. It’s the fuel that will power your AI transformation. Start by asking your team:
- Where does our most important data live?
- How clean is that data?
- Who owns the data?
- How can we make it easier to access and use?
Leading an AI transformation can feel like a daunting task, but it doesn’t have to be. By focusing on real business problems, building a culture of curiosity, and getting your data house in order, you can lead your company into the age of AI, no matter your technical background. The future isn’t about being a coder; it’s about being a leader who can see the opportunity and guide your team through the change. The journey of a thousand miles begins with a single step. What’s yours?
Frequently Asked Questions
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.