Enterprises are finally moving beyond fear of missing out (FOMO) as the driver for their AI initiatives. They’re looking for measurable productivity and efficiency gains — for real-world metrics that can demonstrate ROI. Yet many are finding this a challenge. In a recent Gartner survey of infrastructure and operations leaders, only 28% reported that their AI projects were meeting ROI expectations and one in five said they’d failed outright. EY research published in May found that the genAI initiatives of 16% of the surveyed enterprises were creating zero ROI and fewer than half had seen any substantial returns.
So why are enterprises still experiencing this disconnect? A 2026 PwC AI performance study points to one possible explanation: most businesses “still concentrate AI in pockets, consisting of a few use cases scattered across a few functions.” Deloitte’s 2026 State of AI in the Enterprise report backs this up, suggesting that only 30% of organisations are redesigning key processes around AI and 37% are using it at a surface level, without any deeper change. Recent HP research indicates that although nearly one in two U.S. workers could use AI at work, the majority don’t. Even among those that do, 39% use it just occasionally for specific tasks.
As Faisal Masud, President of HP Digital Services and Workforce Experience, put it in a recent blog, “AI is on nearly every desktop, but it’s in almost no workflows. The gap is becoming a structural divide.”
The solution, as Masud pointed out, is integration, transforming AI into infrastructure deliberately, with governance designed in. This means not just embedding AI into core workflows — although this is crucial — but also reshaping the whole operating model.
Out go manual workflows overlaid with fragmented AI tools. Dashboards become irrelevant when AI is already tracking the important metrics, spotting trends and taking or recommending actions in response. Employees are no longer copying and pasting from enterprise systems into AI chatbot windows and then copying and pasting the results back again. Instead, they’re using AI within those core systems or working through multiple AI agents that operate inside them.
This AI activity is visible and governed at the enterprise level, so that organisations can trace which model acted on what data and why each decision was made. It’s standardised across the business to avoid fragmentation. These aren’t constraints but a pathway to developing trust in AI and scaling mature, consistent operations.
Enterprises can prepare by looking at their systems and their workflows, working out where they can weave intelligence in to create tangible business value. However, they also need to dig deeper, transforming how their teams coordinate, how and where tasks begin and end and how key decisions are made. As Masud put it, “Processes that were once driven by calendar-based checkpoints and manual escalation begin to respond to real-time signals. Tasks that required human routing and follow-up become orchestrated across systems.”
Rethinking operations in this way isn’t easy, particularly when it involves both reimagining workflows and adjusting roles. As AI adds new capabilities, enterprises will need to reevaluate where human employees add the most value, where human decision-making is crucial — and where their business structures need to evolve. This may be the most fundamental shift of all, and it’s key to going beyond AI as somewhat ad hoc augmentation, towards achieving significant and lasting ROI.
To learn more about how HP can help you review and rethink your operations, click here.