AI Won’t Replace Humans—But It Will Replace Old Ways of Working
The past year has been brutal for tech. Layoffs, downsizing, and the relentless march of AI are reshaping how we design, build, and interact with digital products. As someone who’s spent years working in product design—from food delivery apps to fintech—I’ve seen shifts before, but nothing like this.
The industry is in full panic mode, with companies slashing teams left and right, hoping AI will magically pick up the slack. But here’s the thing: AI isn’t some all-knowing oracle—it’s a power tool, and right now, most people are trying to build a house with a chainsaw. Loud, messy, and not exactly precise.
Is AI really coming for our jobs? Not exactly. At least, not in the way people fear. The real disruption isn’t about losing jobs—it’s about losing the comfort of doing things the way we’ve always done them. The product teams who’ll thrive aren’t the ones hiding from AI; they’re the ones figuring out how to wield it.
Most Companies Are Doing AI Wrong – Here’s Why
Let’s be real – corporate AI “strategies” today are mostly just checking boxes. Companies are slapping ChatGPT APIs into their products like it’s 2015 and they just discovered “Uber for X” was a thing. They give employees access to an AI chatbot, call it innovation, and call it a day. But this isn’t implementation – it’s decoration.
Most AI outputs are pure garbage because the inputs are garbage. It’s like giving a toddler a flamethrower and being shocked when everything burns down. Companies think buying an AI license equals having an AI strategy. Newsflash: giving your team ChatGPT access without workflow integration is like giving a construction crew a single hammer and telling them to build a skyscraper.
Here’s what actually works: AI needs to be baked into your actual workflows, not just tacked on as a shiny new feature. The magic happens when humans who understand the problem space direct AI to solve specific challenges.
A designer prompting Midjourney for app icons? Powerful. A marketing team using AI to analyze customer sentiment? Game-changing. Random employees pasting half-formed thoughts into ChatGPT and calling it work? That’s just expensive autocorrect.
The companies winning at AI aren’t just using the tools – they’re redesigning processes around them. They train teams on prompt engineering, build custom models for their specific needs, and most importantly, they understand that AI is an amplifier, not a replacement for human expertise. Until more organizations get this, we’ll keep seeing “AI-powered” products that are about as intelligent as a toaster.
Killing Old Ways of Working
AI won’t replace humans—but it will replace outdated ways of work. Design research, product delivery, prototyping—everything’s up for reinvention. Teams that adapt fastest will win, but they’ll also pay the “early adopter tax”: the learning curve, the skepticism, the trial-and-error phase where nobody knows what works yet. And let’s not forget the biggest hurdle—human resistance. People hate change until they see it working, and right now, we’re in the messy middle where everything feels unstable.
Take user testing. Figma made high-fidelity prototyping effortless. Now, AI lets us spin up real MVPs in hours, test with real users, and iterate at lightning speed. But adoption is hard because most teams fear failure—even though failing fast is the only way forward. The irony? The companies clinging to “safe” old methods are the ones most at risk. The future belongs to teams willing to experiment, iterate, and embrace the chaos of this transition.
What I’m Doing Differently
I stepped away from coding years ago to focus on design and management. But lately, I’ve been diving back into tools like Next.js and Flutter, “vibe-coding” my way through side projects. Why? Because understanding these tools lets me help teams move faster—whether it’s turning Figma files into deployable prototypes with Bolt or using AI to accelerate MVP testing. It’s not about becoming a full-stack dev again; it’s about speaking the language so I can bridge gaps between design, engineering, and AI.
And honestly? It’s been liberating. There’s something refreshing about getting your hands dirty experimenting with different tools, especially when you realize how much AI can offload the grunt work. The key isn’t mastery—it’s fluency. You don’t need to build the whole car, but you should know enough to explain how the engine works. That’s how you spot opportunities to automate, optimize, and cut through the noise.
The Real Gold: AI + Battle-Tested Frameworks
The big question for product teams isn’t “Will AI take our jobs?” but “How can AI help us move faster?” The winners will be the ones who crack this early—creating new frameworks that merge AI with proven methodologies. Right now, everyone’s fumbling in the dark, throwing spaghetti at the wall to see what sticks.
That’s why I’ve spent the last year stress-testing a system that combines the best of both worlds: design/dev best practices supercharged with AI.
After 15+ years in product design, I’ve seen what works (and what doesn’t). This framework isn’t theoretical—it’s built on real-world wins and faceplants. It’s about launching faster, validating smarter, and scaling without rewriting everything from scratch. And the best part? Nobody’s nailed this yet—not even Meta or OpenAI. That’s why I’m opening it up to a small group of product people to test with their own projects. No fluff, no vague promises—just a battle-tested playbook for the AI era.
If you’re tired of guessing and ready to build with confidence, sign up for the newsletter below. I’ll share the full framework in upcoming webinars—for free.
