Enterprise AI spend is up. Value is not. The gap isn't the technology — it's what happens after the contract is signed.
Futurum Group just surveyed 830 enterprise software decision-makers. The headline buried in the data: 73.8% of organizations are considering switching vendors between now and 2028. GenAI and agentic AI top the purchase criteria. And yet, the same organizations can't close the gap between what they're spending and what they're getting back.
This is not a technology problem. It's a deployment problem — and in regulated industries, it compounds fast.
Sixty-six percent of enterprise buyers now favor unified platform suites over best-of-breed. That's a rational instinct. Fewer vendors, fewer integrations, fewer points of failure. But 41% of those same buyers are simultaneously planning to reduce or consolidate their application stacks. They bought the platform. Now they're drowning in it.
Here's what that cycle actually looks like inside a mid-market medical device company or a regional financial services firm: a senior leader approves a platform purchase based on a compelling demo and a competitive vendor pitch. IT gets it deployed. Ops gets a rollout date. And then the frontline workers — the QA managers, the loan officers, the compliance analysts — get handed a new tool with a two-hour onboarding session and a PDF guide nobody reads.
Six months later, adoption is low. The productivity gains never materialized. And the vendor is already pitching the next module.
The Futurum data is clear: workers are being handed increasingly powerful AI-driven tools without the training or contextual guidance required to use them effectively. In an unregulated environment, that's a productivity problem. In a regulated environment, it's a liability problem.
Think about what it means when a QA analyst at a medical device manufacturer misuses an AI-assisted document review tool because nobody trained her on what the model is actually doing, what it can miss, and how to escalate edge cases. Or when a loan underwriter at a mid-market bank uses an AI scoring tool as a black box because the training materials never explained what inputs drive the output.
This isn't hypothetical. We see it regularly. The tool gets blamed. The vendor relationship sours. And the organization starts shopping for the next platform — repeating the same cycle.
When nearly three-quarters of enterprise buyers are considering switching vendors within three years, they're not telling you the technology failed. They're telling you the implementation failed. The ROI promise didn't land because the human infrastructure to support it was never built.
Platform consolidation won't fix this. Switching vendors won't fix this. Both moves just restart the clock on the same underlying problem.
In regulated industries, the stakes are higher because the compliance overhead follows every transition. Every vendor switch triggers a re-validation cycle, new SOPs, updated audit trails, and a change management burden your team probably isn't staffed for. The switching cost isn't just financial — it's operational, and it's regulatory.
The companies that convert AI spend into durable operational value do four things that most don't.
They treat workflow design as a compliance artifact. The question isn't just whether your team can use the tool — it's whether you can document, audit, and defend how your team uses it. That framing changes the training requirements entirely.
They define the human decision points before go-live. AI doesn't replace judgment in regulated workflows. It informs it. Every deployment needs a clear map of where the model's output hands off to a human, and what that human is responsible for verifying.
They run structured friction reviews at 30, 60, and 90 days. Not satisfaction surveys — structured reviews that surface where the workflow is breaking down, where people are working around the tool, and where the gap between what the model does and what the job requires is widest.
They separate vendor success from operational success. Your vendor's customer success team will tell you the deployment is going well. Their metrics are logins and seat utilization. Your metrics are cycle time, error rate, audit outcomes, and margin. Those are different scorecards.
If you're evaluating a platform purchase right now, or if you're six months post-go-live and quietly wondering why the ROI isn't showing up: stop looking at the technology first.
Map the workflow. Find the human handoffs. Identify what your compliance team needs to see in order to trust the output. Then build the training and escalation protocols around that map — not around the vendor's feature list.
The platform doesn't create value. The people using it correctly, inside a workflow designed to hold up under regulatory scrutiny, do.
That's the part most vendors won't tell you. It's also the part that determines whether your AI investment shows up in your next audit as a strength — or a finding.
Dealing with a similar challenge?
We work with mid-market companies in regulated industries to build AI workflows that actually hold up.
Let's TalkSean Cummings
Founder of Laminar Flow Analytics. Specializes in AI workflow automation for regulated industries — medical device, financial services, and complex logistics operations.