June 23, 2025If you spend any time… at all… on LinkedIn, you’ve seen it: AI everything, everywhere, all at once. Every industry. Every niche. Everyone trying to position themselves as “leading the charge” or “getting ahead of the curve.” But when it comes to AI-powered software development, while the conversation is cacophonous… it’s often missing something (and it’s a big something).
There’s a good reason for how prevalent the conversation is, to be clear — the technology is moving fast, and the winners are going to reap maybe the most outsized rewards we’ve ever seen. But one of the things that bothers me about most of the AI conversation right now is how generalized it all feels. Everyone’s talking about ‘AI’ in broad strokes… but very few people are actually talking about the real, on-the-ground impact it’s having on digital product development today. That’s where I want to spend some time.
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While nobody really knows exactly where this is all going, we are already seeing real, material shifts in how product teams operate… and how companies like ours are integrating AI-powered software development into live client projects right now.
When I think about how AI-powered custom software development is progressing, I tend to split it into two big buckets. First is what a lot of folks are calling “vibe coding” tools. These are platforms like Replit or Lovable that promise prompt-to-code functionality (where you describe what you want and the system spits out something close to a working prototype).
Now, I’ll be very clear: this stuff isn’t ready for prime-time, mission-critical production software yet. You’re not going to run your core business processes on something fully built by vibe coding. But that doesn’t mean it isn’t valuable. In fact, it’s already a huge accelerator for early concepting, prototyping, and stakeholder alignment. Instead of building static design prototypes, you can now have something clickable — even somewhat functional — ready in hours.
I was recently at a conference where one product team member actually built a live demo during a brainstorming session, while the conversation was still happening. That would have been unthinkable just a few years ago.
But as powerful as that is, the second bucket is where I think we’re going to see even more transformative change: the rise of what I’d call “agentic-assisted development.” This is where tools like GitHub Copilot and other coding agents become active collaborators for senior developers and tech leads. Not replacing them… but massively amplifying their output.
The metaphor I keep coming back to is a tech lead assigning JIRA tickets. You’re not handing the entire software build off to an AI agent. But you are reaching the point where agents can execute well-scoped tasks that might previously have gone to a junior or mid-level developer. That changes team composition. That changes velocity. That changes how we think about staffing and resource allocation. The benefits of AI in software development are already graspable if you know where and how to look.
Of course, it also raises some really interesting questions around the long-term pipeline of talent. If AI is doing more of the entry-level work, how do we develop the next generation of senior engineers and architects? I don’t think we fully know the answer yet. It’s possible that younger engineers will gain exposure to more complex challenges sooner, guided by strong leads… or it could introduce entirely new gaps in hands-on experience we’ll have to figure out down the line.
And that uncertainty brings me to one of the biggest themes I keep coming back to: nobody actually knows exactly how this plays out.
Anyone out there making bold proclamations about exactly what AI product development will look like 6 months or 5 years from now? Approach those people with skepticism. The tools are evolving too quickly. What was mediocre three months ago is suddenly much more useful today. Entirely new capabilities are coming online monthly. Even those of us working inside this world every single day are still experimenting, testing, adapting.
For me, that’s actually part of the point. The best way to “get ahead of AI” isn’t to try to predict where it’s all going… it’s to get hands-on with it now. Use it. Experiment with it. See what’s useful today, what isn’t quite ready, and where the leverage actually shows up in your own workflows. That hands-on experience creates a kind of pattern recognition that makes it much easier to spot valuable breakthroughs as the tools mature.
For example, we’re already using AI-powered software development heavily inside our Innovation Lab work at ENO8. The benefits of AI in software development already show up in the deep research that used to take us multiple weeks of manual effort; it can now happen in a fraction of that time. We can analyze markets, identify personas, stress test business models… not just faster, but often more comprehensively, because the tools let us cover more ground than even a very good junior analyst could have tackled manually. And that allows us to get to the kind of real-world validation that de-risks product development earlier and with far less sunk cost.
Because at the end of the day, the hard part has never been the code. The hard part is knowing what to build… and why. What problem are you solving? Is it valuable enough? Will customers care? Will they actually pay you to solve it this way? Is this the best way to solve it?
Those strategic questions aren’t going away. AI might help us get answers faster, but it doesn’t eliminate the need for sharp thinking, clear alignment, and taste.
That’s another word that keeps coming up for me: taste.
In a world flooded with AI-generated output, having the ability to separate signal from noise becomes a real superpower. It’s not about who knows every tool inside and out. It’s about who can recognize excellent output when they see it… and who can guide these systems to get there. This holds true for content, it holds true for software, it holds true for dang near everything. If you have the taste to recognize excellence, you’re far more dangerous with AI. It might not deliver every time… but when it does, you can refine and systematize that excellence going forward.
If you’re leading product teams right now, a big piece of advice is this: curiosity and adaptability are becoming career-defining skill sets. The people on our team who are leaning into AI — tinkering, experimenting, exploring — are already producing results that surprise me. Because they’re not waiting for someone to hand them a perfectly defined playbook. They’re getting in the weeds and figuring out where the leverage really is.
Does that mean there aren’t risks? Of course not. Poorly implemented AI can absolutely backfire. I think about customer support chatbots that promise efficiency but end up frustrating real customers who can’t get answers to basic questions. The promise is huge… but you still have to do the hard work of training, validating, and contextually integrating these tools before you can trust them in live, mission-critical roles.
But when applied thoughtfully, the upside is enormous. Faster iteration cycles. Cheaper experimentation. Higher-velocity innovation. The ability to explore more ideas with less risk. If you’re disciplined about how you integrate these tools (and clear-eyed about where they actually add value versus where human oversight remains critical), AI-powered software development becomes a serious force multiplier.
And that’s really where my head is right now: how do we leverage AI to make ENO8 even more valuable to our clients? How do we deliver better product outcomes, faster, with more confidence? How do we take what we already do well — product strategy, user research, alignment, architecture — and layer these tools on top to amplify that? How do we reap the benefits of AI in software development without losing sight of our (and our clients’) key differentiators?
Because while the tools will keep evolving, the fundamentals remain the same: code isn’t the hard part. Knowing what to build is.
Jeff Francis is a veteran entrepreneur and founder of Dallas-based digital product studio ENO8. Jeff founded ENO8 to empower companies of all sizes to design, develop and deliver innovative, impactful digital products. With more than 18 years working with early-stage startups, Jeff has a passion for creating and growing new businesses from the ground up, and has honed a unique ability to assist companies with aligning their technology product initiatives with real business outcomes.
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