This shows up clearly in the 2026 Q1 AI & Data Product Awards Guide, which brings together insights from the 72 products selected by our Awards Advisory Council. These winners reveal where product strategy is heading next, and three signals stand out.

First, intelligence is moving into infrastructure. Winning AI products are embedded directly into operational workflows and decision environments.

Second, AI outcomes now matter more than AI novelty. Leaders want to see improvements in cycle time, quality, cost, and reach, and the winning products deliver measurable results.

Third, we’re seeing the move from insight to agentic execution. AI is increasingly completing work inside enterprise systems, changing expectations for what software should do.

Taken together, these signals point to something important: AI is becoming operational infrastructure.

That’s exactly what the 2026 Q1 AI & Data Product Awards Guide explores. If you’re building products in the AI era, these are patterns worth studying closely. Download your free copy below.

Explore the Q1 Product Guide

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What’s changing with AI is how these show up in the products we build. Beauty used to mean usability. Today, products are becoming increasingly invisible. Instead of interfaces, we’re designing behaviors. That creates a new responsibility to decide when users need friction instead of seamlessness.

Learning used to depend on iteration speed. Now, iteration is almost instant. The opportunity shifts toward helping teams and users step back and choose what should be optimized.

Meaning used to live mostly in what people created with products, but AI participates directly in creation itself. Questions of ownership and consent are moving from policy conversations into product decisions. Designing for agency is becoming a defining leadership challenge.

If you’re seeing similar shifts in how your teams are building products, continue the conversation with me on LinkedIn.

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The conversation was sparked by an article Akriti recently published on how to prompt AI models the product manager way, an idea that reframes the current AI conversation. While much of the industry focuses on what AI models can do, Akriti argues that the real variable is the person using them: how the problem is framed, how the conversation is structured, and whether the user knows what kind of help they are actually looking for.

Together, they go deep on practical frameworks product builders can use immediately, the mindset shifts that separate those who get real value from AI from those who stay in the shallow end, and what Akriti’s experience across engineering, product management, and venture investing reveals about the difference between product builders who are truly AI-native and those who are simply using AI.

This quarter’s winners made it clear that we’re entering a new phase of product leadership. The strongest products were the most embedded. They’re becoming an essential part of how companies operate: shaping decisions, accelerating workflows, and turning insight into action in ways that would have sounded ambitious even two years ago.

That matters for all of us, because the Product Awards are a snapshot of what “great” looks like right now. They help us see where product excellence is moving before the rest of the market catches up.

Over the next few weeks, we’ll be sharing more of what we learned from this quarter’s submissions and what these signals mean for product leaders building in this environment.

For now, I’m excited to introduce this year’s Product Awards winners. Take a look at the full list here. And as always, I’d love to hear what signals you’re seeing in your own product work right now.

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Check out some of our recent content from top voices in the product space.

• Automating Every PM Task: What We Learned (Descript CEO Laura Burkhauser)

• Product Sense Explained: How Great Product Managers Think and Decide

• AI Is Turning Product Managers Into Builders Again: Laurel CPO

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But AI often validates the perspective of the person asking the question. In personal situations, that can escalate conflict instead of resolving it. I’ve seen cases where two people each go to AI separately for advice, and suddenly the interaction becomes AI-to-AI reasoning instead of human-to-human conversation.

That same dynamic is beginning to appear in the way customers interact with companies. As more experiences become mediated by AI, some of the trust people used to associate with brands starts to weaken.

This creates a new challenge for product leaders. For years, the goal was to remove friction wherever possible. Now the question is different: where should friction remain because it signals human presence? The companies that succeed will be the ones that decide carefully where automation helps and where human connection still matters.

Join the conversation on LinkedIn and share the moment in your product journey where human presence matters most.

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Check out some of our recent content from top voices in the product space.

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• AI Isn’t Breaking Work, We Are

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The 3-Part Framework That Made Grammarly a Great Product | Grammarly & Superhuman CPO
Grammarly CPO Noam Lovinsky shares the product frameworks and leadership decisions behind Grammarly’s shift from a writing assistant to a multi-product AI ecosystem, and what it takes to build AI products that become part of a user’s daily workflow. Listen to the full episode.

The AI PMs Guide to Building Guardrails for Responsible AI
Okta Engineering Security Director Arun Kumar Elengovan explores how product teams can identify hidden AI vulnerabilities early and build scalable guardrails that balance innovation, usability, and security. Watch the recording here.

How Product Managers Can Master Regulatory-Ready AI
Former Zensar Director of Product Management Anurag Sharma explains how product leaders can turn emerging AI regulations into a strategic advantage by embedding responsible AI practices directly into the product development process. Tune into the conversation.

Let me know which of these conversations is resonating most with you. Reply to this email or join the discussion in the comments on Substack.

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A quick note for our New York City community before we dive in. We’re hosting a Speaker Series event on March 19 focused on AI adoption and building in high-stakes industries. Click the banner above to request your invite.

I recently attended a conference where the main topic was not AI, it was demographics. For the last two hundred years, economic growth has followed population growth. More people meant more workers and more consumers, and that assumption shaped the way companies were built. But now that model is breaking.

Many developed economies are aging rapidly. Countries like Japan and South Korea are already dealing with shrinking workforces, and even China is beginning to face population decline after decades of expansion.

Economic growth can no longer rely on adding more people, it has to come from productivity. That helps explain why investment in AI and automation is accelerating so quickly. And this is where product leaders come in. If productivity becomes the primary driver of economic growth, the products that win will dramatically increase the output of the people using them.

I’m curious how others see it. As product leaders, is the biggest opportunity of the AI era building entirely new products, or dramatically increasing the productivity of existing roles? Tag me on LinkedIn and share your perspective.

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On March 24 in San Francisco: San Francisco Executive Salon: Accelerating the AI Flywheel. Apply for your invitation.

Check out some of our recent content from top voices in the product space.

• How Early Customer Buy-In Shapes Successful Product Development

• From AI Pilot to Enterprise Impact: How to Scale AI the Right Way

• How to Build AI-Powered Fraud Detection Products | Amazon Product Leader Explains

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Two product leaders can look at the same data and reach very different conclusions. The gap is now operational edge, meaning the ability to consistently convert information into sound decisions and aligned execution. That’s much harder to commoditize.

If access is no longer a moat, then proximity to information won’t be enough to stand out. The differentiation moves to judgment, clarity, and the ability to build systems that scale decision quality across a team.

I’m curious how you see this. Is access still a real advantage in product leadership? Or has it become table stakes while something else does the real separating? Reply to this email or forward to a product leader who this might resonate with and let's get a good conversation going.

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On March 24 in San Francisco: San Francisco Executive Salon: Accelerating the AI Flywheel. Apply for your invitation.

Check out some of our recent content from top voices in the product space.

• How to Build AI-Powered Fraud Detection Products | Amazon Product Leader Explains

• Building AI Agents for Security: Patterns, Guardrails and Real-World Impact

• Building an Ownership Mindset: How PMs Drive Business Impact

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