I've been talking with founders lately who share a common anxiety: what happens when your large competitors start adopting AI in their products? After all, they have massive distribution and deep pockets. If building products becomes nearly free, and every product adds AI, doesn't that just hand the advantage to whoever already owns the customer relationship?
It's a reasonable concern. But I think it misses something important.
Who Cares If Your Competitors Added AI to Their Products?
One founder despondently pointed to every one of their competitor's homepages. The word AI sprouted from every headline like mushrooms after a warm spring rain.
Don't let it intimidate you. You can't differentiate your business by adding AI features—not anymore. AI is ubiquitous and available for pennies per token. The market for anything plus AI has been saturated. In the few niches still untouched by AI, the cost of adoption is dramatically lowered because AI can help write the code to integrate itself. AI is not a moat; it's not even a speed bump.
To be clear, I'm not discouraging you from adding AI to your product. There's plenty of space for better AI-powered products. But the "now with AI!" badge isn't going to sell them. Positioning requires a target audience, a point of view, and a differentiated value proposition.
The Advantage That Actually Matters
Here's the central point: AI embedded in your business operations often matters more than AI embedded in your product.
Despite AI's visibility in product marketing, many customers don't actually care how you deliver value. Some—especially in the enterprise—are even wary of AI-powered tools, making "we've been doing this reliably for years" a viable counter-position against newer AI-native competitors.
What does operational AI look like in practice? AI drafting and personalizing outbound emails. AI qualifying inbound leads. AI generating first drafts of proposals, SOWs, and customer documentation. AI summarizing call transcripts, extracting action items, and suggesting improved sales techniques. AI offering tough feedback from your target audience's perspective.
Claude Pro can assist with most of these tasks for $20 a month. None of this touches your product—but all of it lets a scrappy five-person team operate like a ten or twenty-person team. And here's the paradox: it won't make a 100-person team work like a 200-person team. The larger the organization, the more difficult and expensive change becomes. The monthly cost of AI will look like a rounding error compared to the cost of training and integration. And this is why your competitors will struggle.
Why Incumbents Won't Catch Up
Large companies appear well-positioned to dominate the AI era, given their resources and distribution advantages. But this assumption deserves scrutiny. Just because an organization can do something doesn't mean they will; the larger the organization, the more friction they have.
The resistance plays out in boardrooms daily. Somewhere right now, an employee gives a heartfelt pitch to lower marketing costs with AI, only to hear: "My daughter used AI for her math homework, and it couldn't correctly add two numbers. AI is too risky."
And it's not just executives. In engineering departments everywhere, software developers resist pressure to use tools like Cursor or Claude Code, even as they add AI features to their products. Designers don't want to brainstorm with image generation. Writers don't want their writing critiqued.
Organizations resist change. I remember working with a software developer in the early 2000s who was an absolute DOS wizard. He could crank out C code at an incredible rate to perform sophisticated tasks. However, the writing was on the wall. Linux was deemed our organization's future, and he wasn't happy. His identity and his skills were tied to DOS. He saw Linux as a threat that he resisted at every opportunity, and it proved a huge problem for management and his career. I hear stories like this every day, only instead of hearing about Linux in 2001 or cloud computing in 2008, I'm hearing about teams fighting AI adoption in 2026.
The AI-specific version of this story is still being written. But the pattern holds: Figma challenged Adobe XD. Linear challenged Jira. The next wave of disruption will come from teams that use AI to operate faster—not just ship AI features.
This is Clayton Christensen's Innovator's Dilemma playing out again: incumbents dismiss emerging technologies as toys, or adopt them superficially, while those technologies mature into forces that reshape markets.
The difference with AI is that it can transform both what companies sell and how they operate internally. Incumbents will add AI to their products. But changing how thousands of employees work and think day to day? That's a change management problem that takes years.
The Opportunity
Stop worrying about whether your competitors will copy your AI features. They will. Start asking how AI can make your sales process, your marketing, your product team, and your ops run twice as fast at half the cost. This won't be trivial work or risk-free; AI does need guardrails and quality control. For a practical framework on implementing this, see Adopting AI Internally.
That's the advantage incumbents will be slowest to capture—and it's available to you today. But don't mistake a head start for a moat. Like email, cloud computing, and group messaging, AI will eventually become standard practice even in the largest enterprises. The clock is ticking. Go take on your competitors while they still debate!
Thanks to Jason Cohen for his generous feedback.