Why Most People Are Doing AI Wrong

We're launching a series on AI over the next two months to help you navigate your own thinking about how you want to use AI and what you may want to expect from your suppliers and partners.

We've spent time with many different people across industries, listening to how they're thinking about AI. There is a lot of movement. There is far less clarity.

The more confident the voices get, the less clarity there seems to be. Leaders are making significant decisions about technology, people, and the future of their organizations based on information that is loud, fast, and often wrong.

When it's hard to tell credible from confident, we’re seeing that organizations do what people do under uncertainty. They follow.

• They move because others are moving.
• They adopt because others are adopting.
• And they call it strategy.

But following is not leading. And, motion is not progress.

History doesn't reward the early. It rewards the right. Sony didn't win the MP3 category. Apple did. Netscape didn't define the browser. Chrome did. The companies that defined these categories weren't first. They understood something the early movers missed.

Being early is not the same as being right.

It Starts With Why

To use AI well, you have to understand where it breaks.

Over the past couple of years, we've spent time with leading researchers and operators at the frontier of this work, and applied what we learned in real work, with real stakes.

• Avi Goldfarb’s work shaped our understanding of AI as prediction, which clarifies where it's powerful and where organizations are at risk of overreliance.
• Michael Littman reframed how we think about outputs. AI learns through interaction, not just observation, and that distinction changes how you build.
• Ayelet Israeli and Donald Ngwe forced a harder look at where synthetic survey data shines and where it breaks down.
• Robb Willer reminded us that AI doesn't transcend the systems it's trained on. It inherits them.
• Sameer Maskey and Kiva Kolstein showed us what it actually takes to embed AI into real workflows, not as a feature, but as infrastructure.

We'll build on these ideas throughout the series.

What We're Here to Do

Over the next seven weeks, we'll share our takeaways from these experts and from our own work, including:

• Prompting as a discipline, not a step — and why framing drives output quality more than anything else.
• How the same data can lead to very different conclusions — depending on who's asking and how.
• Why pattern recognition is not the same as decision-making — and what gets lost when we confuse the two.
• Where AI platforms add genuine value — and where they don't (yet).
• Why structured output gets mistaken for insight — and what real insight actually requires.
• Why one model is rarely enough — and what a smarter stack looks like.
• What other industries are already doing differently — and what market research can learn from them.
• The growing risk of overconfidence — when fluency outpaces understanding, the mistakes get harder to see.

That's what this series is built around. The thinking that actually changes outcomes.

Where This Road Leads

At Bovitz, we are Obsessed With Humanity®. Not casually interested. Not politely attentive. Our edge comes from understanding people deeply, uncovering how they feel, what they value, and what they don’t always say out loud, and turning that into clearer strategy and better outcomes.

That's how we approach AI. AI as augmented intelligence. Something that expands thinking, requires human judgment, and becomes more powerful the more deeply you understand the people behind the data.

Speed and cost only matter if they lead to better decisions and real advantage. If AI delivers that, it's invaluable. If it doesn't, efficiency gains are beside the point.

The organizations that will look back on this moment with confidence won't be the ones who moved fastest. They'll be the ones who:

• Stayed curious when everyone else got certain.
• Kept asking why when the conversation moved to how.
• Kept people at the center when the technology pulled attention everywhere else.

That's the kind of thinking we're built for. It’s also the conversation we're inviting you into.

We'll share what's working, what isn't, and where we're still learning.

If that resonates, stay with us. This is just the beginning.