AI is all the rage. Executive teams are all-in. Marketing wants to automate everything. Product wants smarter recommendations. Everyone’s buzzing with ideas.
And who gets handed the whiteboard marker to make it all happen? Engineering and IT.
While enthusiasm for AI is high across the board, the teams tasked with bringing it to life are navigating a messier reality—one full of disconnected data, overhyped tools, and tech stacks held together with digital duct tape.
We dug into the data from Fullstory’s latest industry survey to understand what’s really standing in the way of AI progress for IT and engineering teams.
Download the full Reality Check report for a deeper look at the trends shaping digital experience strategies in 2025.
You can’t automate chaos
If there’s one thing engineers and IT pros want the rest of the company to know, it’s this: AI won’t save you from messy data.
According to the survey, 51% of engineering and IT teams cite disconnected data as their top challenge when it comes to AI and automation. Not budget. Not staffing. Just straight-up chaos in the data layer.
Without a strong foundation, even the most powerful AI models are essentially guessing. And while AI can do a lot, it can’t untangle a spiderweb of inconsistent inputs, partial records, and siloed sources on its own.
Translation: Before anyone gets excited about generative anything, the data has to be clean, connected, and ready to move.
Budget isn’t the barrier—it’s belief
Good news: budget isn’t the main blocker for most teams. Only 17% of engineering and IT leaders say lack of funding is their biggest barrier to AI adoption.
But here’s the twist: only 35% of those same leaders say they’re confident in their organization’s ability to turn data into actionable decisions.
That gap tells us a lot. It’s not that teams can’t afford to invest in AI. It’s that they don’t fully trust their systems—or their data—to support it. And when confidence is low, it’s no surprise that rollout is slow.
Integration fatigue is real
AI doesn’t just plug into your stack like a phone charger. Behind every new tool or workflow is a technical team figuring out how to connect it, scale it, and keep it secure. And those teams are tired.
Survey data shows that across industries, IT and engineering teams are constantly battling disjointed systems and repetitive tasks. Adding AI into the mix without addressing underlying inefficiencies just creates more complexity.
One team’s dream automation is another team’s seventh integration this quarter.
And while everyone’s chasing innovation, technical teams are often stuck cleaning up the side effects of last year’s tools.
What’s actually working?
Despite the roadblocks, there’s real progress happening—especially among teams that have laid the right foundation.
Engineering and IT leaders who are confident in their ability to use AI effectively tend to focus on use cases like:
Identifying inefficiencies across systems and processes
Supporting real-time decision-making
Powering personalization at scale
These same leaders are also more likely to report strong data cultures, better access to behavioral data, and clearer alignment across departments.
That’s not a coincidence. AI outcomes improve dramatically when you start with connected data and cross-functional collaboration.
Final thought: It’s not about more tools
The path to smarter, more efficient systems doesn’t begin with another vendor pitch—it begins with a hard look at your data, your workflows, and your ability to turn insight into action.
For IT and engineering teams, AI isn’t a magic button. It’s a strategic investment. And like any good investment, it depends on a solid foundation.
Let’s fix the pipes before we try to flood the system with innovation.
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