AI isn’t hype anymore—it’s real. IDC predicts that by 2028 AI spending could hit $623 billion by 2028. That kind of investment doesn’t come from buzz. It comes from companies seeing real value.
AI tools are already cutting costs, speeding up work, and – let’s be honest – making jobs more enjoyable. Nobody misses the repetitive stuff. Instead, we’re doing more of what we’re actually good at: strategy, creativity, and problem-solving.
So now that companies have tasted that value, many want to go further. Not just use AI—but build entire internal AI-powered solutions themselves. Stitch together some models, build an app, launch it to their teams. The thinking goes: if off-the-shelf tools work, imagine how great it’ll be if we control the whole thing.
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Here’s the reality: for most companies, especially non-tech companies, building in-house AI solutions is a bad bet. They take too long, cost too much, and rarely deliver what the business actually needs.
Let’s talk about why.
Jean-Marc Chanoine
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SVP of Business Development, Templafy.
It’s not about the model. It’s about the missing link Between tech and impact.
Companies are already experimenting with models. They’re using GPTs, building copilots, testing agents. That’s not the problem. The problem is believing the solution is just about picking a model or wiring one together. That’s not where most projects fail.
They fail because the solution—how it fits into your workflows, your systems, your people—isn’t well thought out. It’s fragmented. It’s not scalable. It doesn’t stick. The model might be powerful, but the experience around it doesn’t work. And without that, the value never materializes. This is why the connective layer matters.
The interface. The orchestration. The automation. The safeguards. It’s what turns “we have a model” into “we’re driving results.” And most companies don’t have the internal expertise to build that layer right.
Going solo comes with hidden costs
Trying to build your own AI-powered solution might feel brave. But unless your company is a product and engineering company, the odds are stacked against you.
Here’s where most organizations get it wrong:
1. You Don’t Have the UX Muscle
AI only delivers value when people actually use it. That means seamless, intuitive, trustworthy interfaces. Most enterprises don’t have the product design and UX software and development capabilities to build interfaces that users actually want to engage with. Internal tools often look—and perform—like science experiments.
2. You’re Flying Blind
Vendors bring learning from hundreds of deployments. You don’t. If you’re rolling out a custom AI solution based on a few internal tests and gut instinct, you’re guessing. You don’t have enough data to know what “good” looks like—or what real adoption takes.
3. You’re Not Budgeting for What Comes Next
AI isn’t static. Models evolve. Interfaces break. User needs change. If you’re not committing budget and headcount for constant iteration, retraining, and support, that in-house solution will be outdated in under a year. And it will sit unused, no matter how promising it looked at launch.
4. Security Concerns Are Overblown
Yes, protecting data is critical. But assuming vendor AI tools are inherently less secure? That’s a flawed take. The best AI providers build with security and compliance at the core. If you trust cloud infrastructure, you can trust enterprise-grade AI vendors.
5. “Only We Know Our Business” Misses the Point
Your internal team knows your business better. That’s not in question. But they likely don’t know how to build scalable, production-ready AI. Vendors do. They’ve already solved the engineering challenges, the data problems, the deployment mess. Why start from scratch?
If you’re not a tech company, stop trying to be one. There’s no shame in partnering with experts—it’s how the winners win faster.
Agentic AI is coming—and it’s even harder to build right
The next phase is agentic AI. These systems don’t just generate—they act. They make decisions. They learn. They execute. It’s already revolutionizing workstreams like customer service, reporting, and document creation.
But these aren’t lightweight features. They’re full systems—requiring real orchestration, context awareness, governance, and maintenance. Trying to build them internally without the right foundation? That’s not just inefficient. It’s risky.
You don’t need to build these things. You need to leverage the companies that already have.
AI is a team sport, play with the pros
AI feels like it’s getting easier. And in some ways, it is. Open-source models. No-code platforms. Accessible APIs.
But building an AI solution that actually moves the needle? That’s still hard. Really hard. And if you think your internal team can replicate what vendors have spent years perfecting, you’re wasting time—and likely money.
The smartest companies aren’t trying to do it all themselves. They’re focusing on what they do best and partnering for the rest.
AI is a team sport. Play with the pros.
That’s how you win.
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