IT Tech Pulse Exclusive Interview with Syed Ali Founder and Chief Executive Officer of EZO

Syed Ali, Founder and CEO of EZO, discusses how connected asset data and contextual AI are transforming IT, maintenance, and operations management.

As Founder and CEO, you’ve grown EZO into a suite spanning EZOfficeInventory, AssetSonar, EZRentOut, and EZO CMMS what core expertise has let you keep expanding into new categories without losing focus?

Every product we’ve built, whether it’s tracking a laptop, an IT asset, rental equipment, or a piece of industrial machinery, comes back to the same core discipline: giving teams one reliable, connected record of what they own and what’s happening to it. That’s a narrow expertise, not a broad one, and it’s what’s let us expand into new categories without diluting focus. Today that shows up as three products: AssetSonar for IT asset management, EZRentOut for rental operations, and EZO EAM for enterprise asset management and maintenance. We’ve actually simplified the lineup as we’ve grown, EZO CMMS is now built into EZO EAM, because asset visibility and maintenance are really one problem, not two separate ones.

EZO launched Zoe, an AI layer built across reports, tickets, and asset data rather than a single bolted-on assistant what made you build it that way rather than starting with one narrow use case?

A narrow use case would have meant picking one screen, say tickets, and building a smart assistant just for that. But the real value only shows up when the AI can see across reports, assets, tickets, and procurement at once, because that’s how these problems actually show up for a team. We built Zoe across the whole platform from day one because a contextual assistant that only sees a fraction of the picture isn’t actually contextual. It’s just a chatbot with a narrower blind spot.

You’ve said most IT and operations teams don’t have an information problem, they have a context problem how does grounding Zoe in one connected picture of a team’s asset estate close that gap?

Most teams already have the data; it’s sitting in reports, tickets, and asset records. What’s missing is getting the right piece of it in front of the right person at the right moment. Because Zoe is grounded in one connected picture of a team’s asset estate, it doesn’t have to go looking for context, it already has it. Our ITSM Copilot is a good example: because AssetSonar unifies IT asset and service data, Zoe already knows the requester’s device, OS, patch status, installed software, and warranty history before an agent even opens the ticket. That’s the difference between answering a question and actually closing the context gap.

EZO recently landed on the 2026 Capterra Shortlist and Software Advice FrontRunners lists, building on earlier G2 Leader recognition in four categories what do these rankings tell you about product-market fit?

Rankings like these are a signal, not a strategy, but they matter because they’re built from real customer reviews, not our own claims. Being recognized as a G2 Leader across four categories and now landing on the Capterra Shortlist and Software Advice FrontRunners tells us the fit isn’t limited to one product or one type of buyer — it’s holding up across IT asset management, equipment management, and rental operations. That consistency is what gives us confidence to keep investing in the platform, and in Zoe, rather than chasing a single category win.

As a Forbes Business Council member whose company now serves 3,000+ customers across six continents, how has that scale shaped what you want EZO’s next chapter, built around Zoe and AI, to look like?

At this scale, the question isn’t whether we have enough data, it’s whether we’re using it well. Serving customers across six continents means we’re seeing an enormous range of how organizations actually manage assets, and the patterns repeat: the data exists, but turning it into a decision takes too long. That’s exactly what I want EZO’s next chapter to solve — not more dashboards, but a platform that acts more like a team member, one that already knows the context and can tell you what to do next.

Independent research from Deloitte shows manufacturers using predictive maintenance see 19% less unplanned downtime  how does Zoe’s predictive maintenance feature turn that kind of industry data into practical, day-to-day guidance for maintenance teams?

Independent research from Deloitte shows manufacturers using predictive maintenance see 19% less unplanned downtime — how does Zoe’s predictive maintenance feature turn that kind of industry data into practical, day-to-day guidance for maintenance teams? That 19% gap is exactly what happens when the context, asset history, usage patterns, service records, doesn’t reach the people who need it in time. Zoe’s predictive maintenance feature generates structured maintenance checklists and flags issues before they become downtime, so a team isn’t starting from a blank page or a buried manual. It turns a statistic like Deloitte’s into something concrete: a specific asset, a specific recommendation, on a specific day.

Looking toward 2027, as more IT platforms bolt on generic AI chat features, where do you see contextual, workflow-embedded AI like Zoe pulling ahead for teams managing physical and IT assets?

Generic AI chat features can answer questions about your product. They can’t answer questions about your assets, because they were never given that context in the first place. As more platforms add a chatbot on top of the same shallow data, the gap between that and an AI layer that’s actually grounded in your reports, tickets, and asset history is only going to get more visible. By 2027, I think teams managing physical and IT assets will judge AI tools less by whether they can chat, and more by whether they already know what’s true about their environment before being asked.

What advice would you give IT and operations leaders who are sitting on years of asset and maintenance data but haven’t yet found a way to turn it into every day, actionable decisions?

Start with where the data already lives — don’t wait to build a perfect data warehouse first. Most of what you need is already sitting in your reports, tickets, and asset records; the opportunity is connecting it, not collecting more of it. And look for AI tools that can act on that context directly, generating a checklist, drafting a purchase order, flagging a risk, rather than ones that just summarize what you already knew. That’s the difference between data you have and data you use.

Thank you, Syed Ali, for taking the time to share your insights with us.

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