Ai4 2026: AI Is Changing Fast. Business Isn't.
Ai4 2026: AI Is Changing Fast. Business Isn't.
Four days in Las Vegas reinforced something I've believed for a long time: technology doesn't create value. People do.
I spent four days at Ai4 (www.Ai4.io) in Las Vegas this week, including a leadership session Monday afternoon, conference sessions, the exhibit hall and a lot of conversations. There were 12,000 attendees from over 80 countries.
By Tuesday morning, the badge pickup line was approaching two hours. Fortunately, I had picked mine up Monday. I stood off to the side and took a time-lapse of the line instead which I posted earlier in the week.
It was quite a visual.
The interest in AI is extraordinary.
But after four days immersed in it, my biggest takeaway may be a surprising one:
From a business perspective, not that much has changed.
The technology certainly has.
Agentic AI is moving quickly. Physical AI, world models and spatial intelligence are emerging. Multimodal models continue to expand what's possible. Enterprise data is becoming even more important. Token consumption and AI costs are getting serious attention. Governance, safety and guardrails were everywhere.
But the questions businesses are trying to answer sounded remarkably familiar.
What problem are we solving?
Who is actually using the technology?
Is it changing the way people work?
Is it producing measurable value?
And increasingly: what should we stop paying for if it isn't?
A Few Things I Noticed
There was a lot of gray hair at Ai4.
Including mine.
I actually found that encouraging.
This wasn't simply a conference filled with technologists chasing the newest model. I saw experienced business, technology, data and analytics leaders trying to figure out what all of this means for their companies. This includes: Jim Jacob, Preeti Vaidya, Bill Dwoinen, Momei Qu, Kader Sakkaria, Tali Rosenblum, Nate Campbell, Adam Gunnett, Alex Lustig, Rylan Murry and Joe Kokinda. Plus so many other conversations during networking sessions or meals.
Nobody has this completely figured out yet.
I also noticed something particularly interesting from my perspective: a number of Business Intelligence and analytics leaders are taking on responsibility for AI and the broader Insights function.
That makes sense to me.
These leaders already understand enterprise data, analytics, governance, business workflows and the challenge of getting people to actually use technology. AI isn't replacing those disciplines. It's making them more important. Plus, leaders like Tali Rosenblum are adding a "kill switch" to their local POC's - learning when to stop data product development from getting out of control. All companies should be doing this...
The Conversation Is Moving Toward Value
Across the sessions I attended, a consistent theme emerged.
The conversation is moving beyond Can we build it?
Now it's:
Should we build it? Will people use it? Can we trust it? Can we reuse it? And is it creating enough value to justify the investment?
That's a significant shift.
POCs are easy to create. Production is harder. Enterprise adoption is harder still.
Successful organizations are starting smaller, measuring outcomes, involving actual users, establishing governance earlier, watching costs and creating explicit opportunities to pivot or even kill initiatives that aren't delivering.
And perhaps most importantly, they're recognizing that deployment isn't the finish line.
Adoption is where the value journey begins.
The Ecosystem Is Getting Interesting
I had detailed conversations with a number of great companies approaching pieces of this challenge from very different directions, including: WalkMe, Pendo.io, DTEX, ModelOp, Okta, Dataiku, IBM, Datadog and Workato .
Digital adoption. User behavior. Security. AI governance. Identity. AI platforms. Enterprise technology. Observability. Data reliability. Automation.
Different problems, but increasingly interconnected ones.
At a conference this large and this focused on enterprise AI, their relative absence was noticeable to me. Perhaps that's another indication of where the market is heading. The models themselves are extraordinary, but an enormous ecosystem is forming around what enterprises actually need to put AI to work.
The Last Mile Is Human
This was probably my strongest takeaway from Ai4.
Companies are investing heavily in AI infrastructure, models, data, governance and applications.
All necessary.
But ultimately somebody has to use this stuff.
And usage alone isn't enough.
The next enterprise measurement challenge is understanding:
Which people are adopting which AI capabilities, how deeply and consistently, across which platforms — and is that adoption producing enterprise value?
That's the connection I kept making to what we're building at PieAX (www.PieAX.com).
We started with Business Intelligence because organizations have invested millions in analytics without having a great way to understand the human experience and adoption across those investments.
AI dramatically expands that problem.
The telemetry changes. The technology changes. The economics change. Governance becomes more important.
But the fundamental question remains remarkably similar.
Are people actually using what we've built. Is that usage creating value?
That is the Analytics Experience. And what became even clearer to me at Ai4 is that the concept doesn't stop with Business Intelligence. As BI and AI increasingly become part of the same enterprise Insights ecosystem, understanding how people discover, adopt, use and ultimately create value from both will become increasingly important.
Four days at Ai4 left me more convinced than ever that the human adoption layer is going to matter. It was worth the trip on points and miles by a bootstrapped founder, the jet lag, the Vegas eating and so much more...
Because whether we're talking about BI, AI or whatever comes next:
Technology doesn't create value. People do.
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