By Cory Watson, Director of Strategic Sales & Consulting
After a week in Nashville for OpenText World 2025, one thing stood out to me across nearly every conversation: while the spotlight was firmly on Aviator and AI-driven innovation, the most meaningful discussions kept circling back to something more foundational: how content actually enters the OpenText ecosystem in the first place. The excitement around AI made it clear that organizations are rethinking capture, architecture, and data quality with fresh urgency.
That theme showed up in who attended, too. I met a large number of people who were newer to OpenText products or even to content management as a discipline. They were highly engaged and genuinely curious about how AI fits into their business processes, but many also shared that they didn’t have deep experience with traditional ECM concepts. Instead, they tended to view “content” through the lens of their operational systems like CRM or ERP (rather than documents, records, and the information lifecycle), which shaped the kinds of questions they asked throughout the week.
That shift in background changed the conversations in a good way. Instead of debating niche ECM terminology, people wanted to understand something more fundamental:
“How do I get my data into OpenText in a way that makes AI actually useful?”
And that became the through-line of the week.
AI only creates value if your data enters the system cleanly
OpenText spent much of the event highlighting the power of Aviator and the opportunity to enrich, classify, and reason over enterprise content. Attendees were excited but also honest. Many told me: “We want AI, but we’re still struggling with intake.”
That gap matters. If content arrives incomplete, poorly structured, or tied up in a legacy distributed capture architecture, AI has less to work with. The insights, automation, and visibility we all want from Aviator depend on what happens before the content hits OpenText.
That’s the part of the journey organizations often overlook, and it’s the part that quietly determines whether an AI initiative succeeds.
This is where conversations got the most energized
The most meaningful discussions at our booth centered on how to modernize capture without disrupting what’s already in place. Attendees wanted practical answers to questions like:
– How do we fix distributed capture bottlenecks?
– How do we bring AI-powered extraction into our existing OpenText environment?
– How do we improve SLAs, reduce remediation, and increase throughput—not just “add AI”?
People weren’t asking for a break fix solution.
They were asking for a better foundation.
And that’s where ImageTrust resonated: not as a replacement for OpenText Capture, but as a modern, browser-based layer that strengthens what organizations already use. AI-powered extraction, consistent validation, and better architecture give Aviator (and any AI hyperscaler) cleaner, more complete data from the start.
When that happens, everything downstream performs better, from records management to customer applications to analytics.
My takeaway: AI may be the headline, but capture is still the lever
If there’s one insight I’m taking home from OpenText World this year, it’s this:
AI’s value is determined at the moment content enters the system.
Modernize that point of entry, and your entire OpenText ecosystem gets better.
Aviator can surface insights.
AI can enrich and classify content.
But none of that matters if the data isn’t captured accurately, consistently, and at scale.
For organizations looking to get more from their OpenText investment, the capture layer isn’t just a technical component; it’s the strategic multiplier that determines what AI can actually do.
And based on the conversations in Nashville this week, more teams are starting to realize it.
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