MURTEC — the Multi-Unit Restaurant Technology Conference — draws more than 800 executives, operators, and solution providers to Las Vegas each March. We were there as sponsors of the inaugural AI Summit, a full day of dedicated programming designed to help enterprise leaders move from AI curiosity to AI execution. We went because a significant portion of our client base intersects with that world. We came away with something more useful than vertical-specific intelligence: a clear view of an organizational pattern that has nothing to do with restaurants.
The pattern is this. In every room we sat in, every conversation we had on the floor, the same gap appeared. Leaders who are genuinely excited about AI — who have board pressure, competitive anxiety, and real strategic intent — are hitting the same wall. Not a technology wall. A readiness wall. Their data is fragmented across systems that don’t communicate. Their tech stacks haven’t been modernized to support the kind of infrastructure AI actually requires. Their governance frameworks aren’t built for the accountability questions AI deployments raise. They know where they want to go. They don’t yet have the foundation to get there. We’ve seen this in financial services. We’ve seen it in healthcare. We’ve seen it in logistics and manufacturing. MURTEC just gave us another angle on the same problem — and the chance to hear someone articulate it better than we usually do.
The keynote that cut through the noise
The standout moment was Sol Rashidi’s keynote. She’s had 200-plus enterprise AI deployments behind her, which means she’s also watched 200-plus of them struggle. That track record gave her something most conference keynotes lack: earned skepticism. Her central argument was that companies are impatient about AI ROI in ways that are going to cost them — that the glamorous deployments are the hardest to measure, and the ones that actually move the needle are in procurement, scheduling, and demand forecasting. She drew a sharp line between using AI and doing AI — plugging in a tool versus building the infrastructure and judgment to know if it’s working. The warning she kept returning to: don’t let convenience become dependency.
“Don’t drive faster off a cliff. Outsource your tasks, not your critical thinking.”
Her “Executive AI Compass” framework stages AI adoption across four modes — and argues most organizations are skipping straight to the hardest one before the foundational work is in place.
Micro-Tasker — AI handles specific, bounded, repeatable tasks. Low risk, fast ROI. The right place to start — and often undervalued.
Co-Pilot — AI assists human decision-making. The human remains in the loop and retains accountability. High value when done well.
Pilot — AI leads the process with human oversight. Requires mature data infrastructure and governance to be trustworthy.
Coworker — AI operates autonomously within defined boundaries. Most organizations aren’t ready for this — and that’s okay.
What the floor confirmed
As sponsors of the AI Summit, we had back-to-back conversations with executives and operators who were there because they knew they needed to act on AI — and had no clear path to doing it. Industry was different. The problem was identical. The most common question we fielded wasn’t about tools or vendors or which LLM to standardize on. It was: where do we start? Asked genuinely, not rhetorically. What these organizations lacked wasn’t awareness — it was a viable first move that their existing infrastructure could actually support.
Data readiness surfaced in nearly every substantive conversation. Not “do we have data” — every organization has data — but “is our data in a state where AI can actually use it?” Years of operational records sitting in siloed systems, in formats that predate the APIs that would make them useful, governed by structures that weren’t designed with AI in mind — that’s the reality for most enterprises right now, regardless of industry. The AI strategy conversation is actually a data recovery conversation. Most organizations need to hear that before they sign another contract.
The other pattern was one we’ve seen in our own client work for years: tech stack modernization and AI strategy are not sequential. You cannot bolt a sophisticated AI layer onto a fragmented, twenty-year-old architecture and expect it to perform at the level the business is imagining. The organizations seeing real returns from AI are the ones that did the modernization work first — or are doing it in parallel. That’s not a detour. That’s the road.
Our takeaways from MURTEC 2026
1. Start with non-glamour use cases. Procurement, scheduling, demand forecasting, and inventory management return faster, more measurable ROI than consumer-facing AI. Build your track record there before you aim for the spotlight.
2. Data readiness is the real conversation. Every company we talked to had data. Very few had data that was clean, accessible, and structured for AI use. Solve that problem first — the tools come after.
3. Measure effectiveness, not just efficiency. Going faster in the wrong direction is still going in the wrong direction. The right question isn’t “are we doing things faster?” It’s “are we doing the right things?”
4. Not all AI is the same. Vision, speech, predictive analytics, and generative AI solve different problems and require different infrastructure. Treating “AI” as a single category is how you end up buying the wrong tool for the problem you actually have.
5. Outsource tasks, not critical thinking. The most repeatable line from the keynote — and the one most worth writing on your whiteboard. AI should handle the tasks that consume your team’s time without requiring their judgment. The judgment is the part you keep.
6. Tech stack modernization and AI strategy are the same conversation. You cannot layer sophisticated AI on top of fragmented, legacy infrastructure and expect it to perform. Modernization isn’t a precursor to AI strategy — it is AI strategy.
The pattern doesn’t care what industry you’re in
If your team is fielding board questions about AI while privately knowing your data architecture isn’t ready — this is your situation. If you’re sitting on years of operational data in systems built before the cloud was a serious option — this is your situation. If your modernization initiative keeps getting deprioritized in favor of AI pilots that won’t scale until the modernization is done — this is your situation. The gap between AI ambition and AI readiness is a cross-industry problem, and it won’t close by buying better tools.
That’s the work Sirrus7 was built for — closing the gap between where organizations are and where they need to be before AI can deliver on its promise. If any of this sounds familiar, we’d like to talk. The context window is always open.