Why Big Software Companies Can't Keep Up

"I think that's the difference between developing AI native software and trying to integrate AI into existing software."

This came up during a conversation about why established software companies are struggling to adapt to AI. The insight wasn't technical—it was structural.

Big companies have existing systems. Those systems were designed for humans to interact with. Every button, dropdown, and workflow assumes a person is on the other end clicking things.

When you try to add AI to that, you're fighting your own architecture.

The Dropdown Problem

Chuck described a specific issue he ran into with a popular CRM platform:

"The voice AI cannot make a selection on the dropdown box."

Think about that. The AI can have an entire conversation with a customer. It can understand they want to purchase an investment property. It can capture their timeline, their budget, their preferences.

But it can't click "Purchase" in a dropdown menu.

So after all that intelligent conversation, a human still has to go back and manually select options in the system. The AI did the hard part (the conversation) but couldn't do the easy part (the data entry) because the system was built for human hands, not AI agents.

This isn't a limitation of the AI. It's a limitation of software designed before AI agents existed.

Built for Humans, Broken for AI

Most enterprise software follows the same pattern:

• User logs in
• User navigates to a screen
• User fills in fields
• User clicks dropdowns
• User hits submit
• System processes the input

Every step assumes a human is there, making decisions, clicking things, typing into boxes. The interface is the product.

AI doesn't need interfaces. AI needs APIs—clean ways to read data, write data, and trigger actions. AI doesn't care about screen layouts or button colors. It cares about "can I tell the system to do this thing?"

When a company tries to add AI to software that wasn't built for it, they face a choice:

1. Rebuild the underlying architecture (expensive, slow, risky)
2. Create workarounds that simulate human actions (hacky, fragile)
3. Build AI features that work around the limitations (limited, frustrating)

Most choose option 3, which is why so many "AI features" in established software feel bolted on. They are.

The 15 Layers of Bureaucracy

Even when big companies recognize the need to rebuild, they can't move fast enough.

Josh described the challenge: "They have to go through the 15 layers of bureaucracy that they have to get a change even approved before they even develop it."

In a startup, the conversation goes:

• "This doesn't work"
• "Let's fix it"
• "Done"

In a large company, the conversation goes:

• "This doesn't work"
• "Let me document the issue"
• "Let me get stakeholder input"
• "Let me create a ticket"
• "Let me prioritize against other tickets"
• "Let me get budget approval"
• "Let me find engineering resources"
• "Let me schedule the work"
• "Let me coordinate with other teams"
• "Let me get security review"
• "Let me get legal review"
• "Let me test in staging"
• "Let me schedule the release"
• "Done (in 6 months)"

By the time the fix ships, the market has moved. The startup that could fix it in a day has already captured the customers who couldn't wait.

The Compliance Paradox

Chuck's mortgage AI project illustrates this perfectly. Building AI for regulated industries means compliance has to be foundational, not added later.

"I started with compliance and outcome like simultaneously."

A big company trying to add AI to their mortgage software has to:

• Get the existing compliance team to approve the approach
• Ensure the new features don't break existing compliance
• Coordinate between compliance, legal, engineering, and product
• Document everything for regulators
• Test against all 50 states' requirements
• Get sign-off from multiple stakeholders

Chuck, building from scratch, can:

• Build compliance into the architecture from day one
• Test and iterate rapidly
• Ship updates in hours, not months
• Respond to customer feedback immediately

The big company has more resources. But the small company has more speed. In a fast-moving market, speed often wins.

The Walled Garden Response

Some big companies are recognizing this threat—and responding by locking down access.

"A lot of companies like the big ones are locking it down. They're trying to prevent you because they're trying to stop this flow of people that are like 'we can take your API and make your service better in 10 minutes.'"

If startups can't access the data, they can't build better solutions on top of it. The moat isn't the product anymore—it's the data lock-in.

This might work short-term. But it creates resentment among customers who want better tools. And it creates opportunity for competitors who offer openness.

The companies that try to win by locking customers in often lose to companies that try to win by being better.

What Happens Next

The pattern is predictable:

1. AI-native startups build solutions that actually work
2. Customers compare them to clunky enterprise alternatives
3. Customers switch (or demand that their enterprise vendors improve)
4. Enterprise vendors scramble to catch up
5. By the time they do, the startups have moved further ahead

This has happened before with:

• Cloud computing (AWS vs. legacy data centers)
• Mobile (native apps vs. mobile web wrappers)
• SaaS (Salesforce vs. on-premise CRM)

The companies that were "too big to fail" didn't fail outright. They just slowly lost relevance as faster alternatives captured the growth.

The Startup Advantage

If you're building software today, you have an advantage the incumbents can't buy: a blank slate.

You can design for AI agents from the start. You can build APIs before interfaces. You can make the system work for both humans and machines without awkward compromises.

Chuck's "shadow fields" workaround—using AI to extract information and then trigger workflows that select dropdowns—exists because the underlying system wasn't designed for AI. If you're building new, you don't need workarounds. You can just build it right.

That's not a permanent advantage. Eventually the big companies will rebuild. Eventually the playing field will level.

But "eventually" might be 5-10 years. That's a long time to capture market share from companies that can't move as fast as you can.

The Bottom Line

Big software companies can't keep up because they weren't built for this moment. Their systems assume human interaction. Their organizations assume slow iteration. Their business models assume lock-in over innovation.

AI-native companies start with different assumptions. They build for agents. They iterate in days. They win on being better, not on being entrenched.

If you're a customer, this means evaluating whether your software vendors are actually adapting—or just bolting AI labels onto old architectures.

If you're a builder, this means the window is open. The time to build AI-native solutions is now, while the incumbents are still figuring out how to get their AI to click dropdown boxes.