Stop flaunting your AI. Fix the actual problem.

When I first started writing this a few weeks ago, AI conversations looked very different. Just a few weeks later, everything changed. New agents, new frameworks, and new hype posts flood my feed every morning.

That’s the reality of AI today. What you say quickly feels outdated. I’m not offering a definitive playbook. This is just my perspective as an entrepreneur, not a data scientist, on what actually works in the B2B world and where the real opportunities appear.

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view from here

What I’m seeing now is a wild mix. Some brands are forcing AI into every corner of their business. Some are actively trying to stop it. My space is full of AI theater. In other words, shiny features are layered on top of existing products just to tick a box.

It looks good in a press release, but in reality it doesn’t make anyone’s life easier. If you’ve spent any time on LinkedIn, you know what I mean. Suppliers keep posting that they’re “surprised” or “beyond excited” about the new features their teams have created in a week.

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

Even as we incorporate AI into our own tools, we have stuck to a clear human principle. own loop. Our clients want to know: Can I trust this? Will it help me move faster? Will it make making better decisions easier, rather than harder?

This trust piece is important because if the AI ​​makes one mistake, it could lead you down the wrong path. No one wants to make a decision based on something that turns out to be wrong. Illusions are real, and synthetic data scammers promise shortcuts, so we need a way to determine and maintain control over what our systems produce.

Related: How to make AI a core part of your business strategy

From features to agents

Most AI deployments start small. A summarizer here, an emotion scorer there. Useful, but siled. I think real change will happen when we start connecting these elements to agents. Agents are semi-autonomous tools that understand your goals and help you get there. They take on repetitive and noisy tasks so that people can focus on thinking.

In my industry, one of the biggest opportunities for AI is unstructured data such as open-ends, transcripts, reviews, and chat logs. All quantitative studies have them, and often there’s a real story to it. Juxtaposing these open ends with hard numbers from quantitative data provides context, nuance, and engaging anecdotes.

In recent years, we’ve started using AI for deeper tasks such as scoring response quality, triggering contextual follow-ups, summarizing large data sets, and even piecing together highlight reels from video clips.

This year, we brought it all into a single unstructured data agent, making those voices easier to understand and linking directly to your hard data. We transform what was previously time-consuming into decision-ready outputs, allowing clients to capture both the evidence and the story that drives action, without forcing them to learn an entirely new system.

What’s next? Connecting agents allows them to actually talk to each other and cover all stages of an investigation: planning, collecting data, performing analysis, and sharing results. Moreover, every time you ask a question in research, more is almost always revealed.

Rather than leaving insights on the table, AI can now investigate in the moment, ask for follow-up, and stay in the loop with participants. The good thing is you don’t have to go all in right away. These connected agents can run the entire process or handle just the parts they need. And through it all, people still own the loop and make sure the system points in the right direction.

You might call it a super agent, you might call it a smarter system, but the goal is the same. It’s all about reducing friction, keeping people in charge, and making the whole flow end-to-end.

Create space to experiment

You don’t build great AI workflows “by accident” between back-to-back Zoom calls. Space is required. One thing that has worked for us is once a month, we have an “AI Day” where our team takes a break from their regular work to test tools, try new workflows, and share their discoveries, learnings, and experiences. This innovation space provides a sandbox for prototyping ideas before a roadmap is ready.

In fact, I feel so strongly about this concept that I recently Innovation Insider Programa one-year cohort of up to 12 brands looking to dig deeper. This program focuses on real-world leadership skills, safe experimentation, and understanding how to operationalize AI across your organization.

Each month, participants will get hands-on with a custom GPT agent, test it alongside their existing workflows, and work directly with our team to see what sticks. Ultimately, we’re not just catching up with AI, we’re shipping working solutions, saving time, and building an internal strategy on how to do this responsibly.

This experiment is also a way to determine your fit. Some clients want to be responsive and self-service. Some people are looking for partners in the trenches. AI slots are different for each model, so the only way to adapt is to stick with it. Curiosity and play are the most important factors right now. If you can bring these things with you, you can stay ahead of the curve.

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My opinion (so far)

Everything is changing rapidly. By the time you read this, Amazon, OpenAI, Anthropic, or some startup you’ve never heard of may have dropped something that reshuffles the deck. That’s fine. The takeaway for me at this point is simple.

• Don’t pretend you’re more advanced than you actually are.
• Start where AI can add real value to your customers, even on a small scale.
• Be prepared to adapt, especially if AI starts to encroach on what you do today.
• Make space for learning as a habit, not as a side project.
• Rely on domain expertise to select areas where you already have strong knowledge but are taking too long to work on, and leverage AI to help you deliver faster.

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

When I first started writing this a few weeks ago, AI conversations looked very different. Just a few weeks later, everything changed. New agents, new frameworks, and new hype posts flood my feed every morning.

That’s the reality of AI today. What you say quickly feels outdated. I’m not offering a definitive playbook. This is just my perspective as an entrepreneur, not a data scientist, on what actually works in the B2B world and where the real opportunities appear.