The AI Readiness Reality Check: Is enterprise AI still stuck in pilot mode?


Despite years of hype around AI-driven transformation, a recent industry report revealed a sad reality: Most enterprise AI projects remain stuck in pilot mode.

According to Riverbed AI AI Readiness Report for AIOps in 2025 only 12% of AI projects have been fully deployed in organizations, even as enterprise spending on AI has doubled over the past year.

It is a striking discrepancy: unprecedented investments, but limited results.

“What we’re seeing is this motivation and energy around AI – everyone is talking about it, budgets are increasing – but the reality of moving from pilot to production is much slower,” said Nicolas Leszczynski, principal solutions engineer at Riverbed AI. Unified communications today.

“Only about 36% of organizations today feel ready to operationalize their AI initiatives. »

The gap between ambition and execution is growing, and it’s not just a question of technology.

The survey highlighted a complex mix of challenges: fragmented data, uncertain governance, skills shortages and a persistent mismatch between leadership confidence and technical readiness.

While 87% of organizations say their AI projects are meeting ROI expectations, many remain small-scale – more of a proof of concept than an enterprise-wide transformation.

“There is a gap of about sixteen points between what leaders think and what is actually happening on the ground,” Leszczynski added.

“IT leaders tend to overestimate their level of AI readiness compared to those who manage day-to-day operations. It’s this reality check that prevents many projects from growing.”

The Optimism Gap

Executive optimism is one of the most enduring features of today’s AI landscape.

Executives see the promise – improved productivity, a better customer experience, faster decision-making – but underestimate the complexity of deploying at scale.

Although 86% of respondents last year said their organization would be “fully ready to scale AI by 2028,” the same number said the same this year.

This circular optimism shows how difficult it is to transform strategic intent into operational capability.

In other words, preparation does not increase as quickly as investment.

According to Leszczynski, this is not due to a lack of enthusiasm, but to a lack of fundamental maturity.

Organizations know where they want to go. They understand the value and have confidence in the return on investment. But gaps in data quality, integration, and alignment across teams slow the process.

The data dilemma

Data has always been the fuel of AI, but for many companies it is tainted fuel.

Less than half of respondents say they are confident in the completeness and accuracy of their data. Without high-quality, connected data, AI models struggle to provide meaningful and reliable insights.

“Eighty-eight percent of organizations recognize that they need high-quality data to drive automation and AI,” added Leszczynski.

“But when you look at the metrics, they’re just not there yet. The data they’re collecting is not consistent enough or granular enough to support large-scale AI initiatives.”

The problem is particularly visible in unified communications (UC), where poor data visibility directly impacts employee experience.

Riverbed research has shown that employees spend approximately 42% of their week using unified communications tools like Teams or Zoom, and that 15% of all IT support tickets are related to UC performance issues. For one in five tickets, resolution takes more than an hour.

This represents a huge drag on productivity – and a real opportunity for AI to add value.

Is AIOps a way forward?

If there is a positive point in research, it is AIOps (Artificial Intelligence for IT Operations).

Even though much of the AI ​​ecosystem remains experimental, Riverbed says AIOps is delivering tangible, measurable results.

According to the report, 87% of organizations are already reporting positive ROI from AIOps initiatives, and many see it as the most mature entry point for operationalizing AI into their IT stack.

In the case of Riverbed, the approach focuses on collecting high-resolution, real-time data from across the entire IT landscape, from endpoints and network infrastructure to applications and UC platforms. This data then feeds an intelligence layer that can identify patterns, diagnose performance issues, and accelerate root cause analysis.

“The reality of UC performance issues is that they can come from anywhere: the endpoint, the network, the SaaS layer or the configuration itself,” Leszczynski said. “With AIOps, you get a complete and correlated view of all these elements. This is what allows you to act in real time and improve the employee experience.”

The road ahead

If 2024 was the year of AI experimentation, 2025 appears to be the year businesses will start demanding results.

Investment is no longer the problem, but execution. And while the AI ​​readiness gap is real, progress is being made: basic work on data, monitoring, and alignment is paving the way for broader adoption over the coming years.

“I think we are seeing real progress,” concludes Leszczynski. “The enthusiasm is there and the return on investment is clear. The next challenge is scale: taking what works in the pockets and integrating it into the fabric of the business. That’s where the real transformation will happen.”

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