81% of global IT leaders say AI failure puts career “at risk”

As many as 81 percent of IT leaders around the world feel their career progression is at risk if they cannot sustain clear, measurable ROI within the next 12–24 months. 

The findings are published in Freshworks' Cost of Complexity Report, which surveyed 12,021 IT decision-makers. The research also found 78 percent feel significant or moderate pressure from executives to deploy AI within six to 12 months and prove a return on the investment (ROI) in six to eight months. Yet, on average, among mid-market IT teams, 26 percent of time spent on AI-related projects is spent on "troubleshooting, integration firefighting, and complexity management, rather than strategic work". 

The report concluded that often, IT leaders are being judged on timelines shorter than deployment itself, placing "real strain" on the teams doing the work. IT leaders face constant pressure to show progress, shifting focus from building to defending results. Over time, that erodes morale and increases the risk that programs are cut before they can deliver. 

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CX is also under pressure: 52% say ROI burden is increasing 

It isn't just IT leaders feeling the pressure. 

CX Network's annual research into the state of CX has repeatedly confirmed AI capabilities are both top CX trends and top investment priorities. However, the same research has also found that since 2024, the pressure on practitioners to prove ROI has posed the biggest obstacle to CX investment. The 2026 research also found that for 52 percent of practitioners, the pressure to prove ROI is increasing.

Steve Blood, VP of market intelligence at Five9, says the pressure to prove ROI may not be new, but the stakes have never been higher. "A few forces are converging to make this a boardroom-level conversation rather than a contact center one," Blood says.

"AI investment has scaled dramatically. Organizations are no longer running small pilots; they're committing significant budget to AI across the customer journey. That scale demands accountability. When AI moves from experimental to operational, finance teams want to see the numbers, and rightly so," Blood adds. 

Rethinking how AI success is quantified  

Whether it's a direct, quantifiable financial return or the attainment of target metrics, there are many ways to demonstrate whether an AI deployment was successful. 

As set out in the CX Network report, Balancing the books on AI, to calculate returns, practitioners need a means by which to measure the 360-degree value AI generates. This can include margin improvements, new sales, of differentiated experiences that drive customer lifetime value. 

To do this, practitioners must identify metrics that can accurately reflect performance alongside customer outcomes; they must also have a deep understanding of AI pricing structure, and they must know which questions to ask vendors when assessing and procuring solutions. This is explored in detail in the report. 

Before any of that happens, the traditional approach to ROI requires a rethink: the challenge is not proving the returns but correctly framing the application of AI as a tool to drive customer outcomes. 

"Everybody's under pressure to put out an AI roadmap," says MQ Qureshi,  a digital executive who has held senior roles across product, CX and D2C at organizations like Ford Motor, McDonald's, Allstate and others. "It's like asking 10 or 15 years ago, what's your plan for mobile? Or 20 years ago, what's your plan for the internet? AI is a tool to achieve a goal; it isn't the goal. We're putting the cart before the horse by pre-determining a solution." 

Instead, Qureshi says practitioners should remain focused on their customer plan, and how AI can help achieve it. 

He says: "If we know what our customer experience is, what our customer journeys are, how customers interact with us, what the metrics are that matter to us and to them, then AI is simply enabling you to maybe get to those outcomes a little bit faster if it's used correctly."

The foundations of successful AI use can also pose challenges. Strong product knowledge and good data for AI to leverage will drive stronger – and quicker – returns. 

"The legwork in order to get to that result needs to be equally considered," Qureshi says.

"From an ROI standpoint, before signing up for too many goals, we should be looking at all the inputs that we need to have in the right places in order to deliver consistent experience, whether it be through AI first or through direct interactions supported by AI."

The barriers to AI success

According to the research from Freshworks, most mid-market organizations are increasing AI investment, but the majority remain stuck in pilots. "A widening gap between executive ROI expectations and deployment reality is putting IT leaders, careers, and AI programs at risk," it read.

The confidence exists. As many as 90 percent of respondents reported a positive growth outlook for the next 12 months, and 62 percent said they plan to increase headcount, signaling genuine confidence in their business trajectory. AI is central to that momentum: 84 percent  of IT leaders said it's critical or very important to growth, and 89 percent  plan to increase AI investment over the next 12–24 months.

However, progress remains uneven. Most mid-market organizations are not executing AI at scale. Only 15 percent have AI integrated across multiple core business operations, while 36 percent remain in pilots or have yet to deploy AI in a meaningful way. The report concluded that "investment is rising, but execution is stalled" and that "this is the defining challenge of mid-market AI in 2026".