Quit Flaunting AI. Fix Real Problems.

Opinions expressed by Entrepreneur contributors are their own.

Key Takeaways

Focus on solving real customer problems, not just showing off AI.
Keep humans in control while AI handles repetitive or noisy work.

When I first started writing this a couple of weeks ago, the AI conversation looked completely different. Just a few weeks later, everything has shifted. There are new agents, new frameworks and new hype posts flooding my feed every morning.

That’s the reality of AI right now: anything you say feels outdated almost immediately. I’m not offering a definitive playbook. This is simply my perspective — as an entrepreneur, not a data scientist — on what’s actually useful in the B2B world and where I see the real opportunities emerging.

The view from here

What I’m seeing right now is a wild mix. Some brands are forcing AI into every corner of the business. Others are actively trying to keep it out. In my space, there’s plenty of AI theater — shiny features slapped on top of existing products just to tick a box.

They look good in a press release, but they don’t actually make anyone’s life easier. If you spend any time on LinkedIn, you’ll know what I mean: endless posts from suppliers that are “blown away” or “beyond thrilled” to announce some new feature their team cranked out in a week.

It’s fun to watch the hype cycle, but it isn’t the same as solving the real jobs customers need done. The real win is figuring out where AI can actually speed things up, help you make better calls and not bog people down in complexity.

While we’ve been building AI into our own tools, we’ve stuck to a clear principle: humans own the loop. Our clients want to know: Can I trust this? Will it help me move faster? Will it make better decisions easier, not harder?

That trust piece matters because if the AI gets it wrong, even once, it can send you down the wrong path. Nobody wants to make a decision based on something that turns out to be off. Hallucinations are real, and there’s no shortage of synthetic-data charlatans promising shortcuts, so you need ways to adjudicate what the system produces and keep control.

From features to agents

Most AI adoption starts small: a summarizer here, a sentiment scorer there. Helpful, but siloed. I see a real shift coming when you start connecting those pieces into agents, which are semi-autonomous tools that understand your goal and help you get there. They take on the repetitive, noisy work so people can focus on thinking.

In my industry, one of the biggest opportunities for AI is unstructured data: open ends, transcripts, reviews, chat logs. Every quantitative study has them, and they’re often where the real stories live. When you line those open-ends up with the hard numbers from quantitative data, you get context, nuance and the kind of anecdotes that make stakeholders lean in.

Over the past couple of years, we’ve started using AI to go much deeper: scoring response quality, triggering contextual follow-ups, summarizing huge datasets, even stitching together highlight reels from video clips.

This year, we pulled all of that into a single unstructured data agent that makes those voices digestible and links them directly to the hard data. It turns what used to be time-consuming into decision-ready outputs, helping our clients capture both the evidence and the stories that drive action, without forcing them to learn a whole new system.

The next step? Connecting agents so they can actually talk to each other and cover all the phases of research: planning, collecting data, running the analysis and sharing results. Plus, every time you ask a question in research, you almost always uncover more.

AI can now probe in the moment, ask follow-ups, and keep that loop going with participants instead of leaving insights on the table. The beauty is you don’t have to go all-in right away; these connected agents can run the whole process, or just handle the pieces you need. And through it all, people still own the loop, making sure the system stays pointed in the right direction.

Call it a super-agent or just a smarter system, but the goal’s the same: cut the friction, keep people in charge, and make the whole thing flow end to end.

Make space to experiment

You don’t “accidentally” build a good AI workflow between back-to-back Zoom calls. You need space. One thing that’s worked for us is “AI Day,” where once a month, the team steps away from their regular work to test tools, try new workflows and share what they’ve found, learned and experienced. This innovation space provides a sandbox for prototyping ideas before they’re roadmap-ready.

In fact, I feel so strongly about this concept that we recently launched the Innovation Insider Program, a year-long cohort for up to a dozen brands that want to go deeper. This program focuses on real leadership skills, experimenting safely, and figuring out how to operationalize AI across an organization.

Every month, participants get hands-on with custom GPT agents, parallel test them against their existing workflows and collaborate directly with our team to see what sticks. By the end, they’re not just keeping up with AI, they’re shipping working solutions, saving time and building an internal playbook for how to do this responsibly.

That experimentation is also how you figure out fit. Some clients want to move fast and self-serve. Others want a partner in the trenches. AI slots in differently for each model, and the only way to adapt is to keep playing with it. Curiosity and play are the two most important ingredients right now. If you can bring those, you’ll stay ahead of the curve.

My take (for now)

This is all changing fast. By the time you read this, Amazon, OpenAI, Anthropic or some startup you’ve never heard of might have dropped something that reshuffles the deck. That’s fine. For me, the takeaway right now is simple:

Don’t pretend you’re further along than you are.
Start with where AI can add real value to customers, even if it’s small.
Be ready to adapt, especially if AI starts to eat into what you do today.
Make space for learning, not as a side project, but as a habit.
Lean on your domain expertise: pick the areas where you already have a strong body of knowledge, but the work takes too long, and let AI help you deliver faster.

Today, AI is more than simply a feature. It can be (and should be) part of the infrastructure and mindset of your business. And if you keep the focus on solving real problems, not just selling the AI story, you’ll be in a better position when the next big change drops (which, let’s be honest, could be tomorrow).