More AI Investment Is Not Creating Enough AI ROI

As AI adoption expands, many organizations are discovering that traditional technology funding models no longer fit. Unlike conventional software investments, AI costs are decided at run time and granular cost breakdowns are often absent from the bill. This makes it hard to forecast spending, assign ownership and measure returns over time.

This challenge is becoming more urgent as executives face competing pressures. The survey found that 58% of CIOs report increased pressure to deliver cost improvements through AI, while 51% expect AI to increase the total cost of ownership of technology solutions, particularly for run costs.

Vanguard organizations respond by bringing finance into the conversation earlier and more often. Gartner found these organizations spend 50% more time collaborating with CFOs on AI initiatives than their peers. This includes decisions like who is responsible for ongoing token costs? What must go into a business case beyond the vendor price? What are the AI cost drivers and how do they behave at scale?

Creating AI value is a team sport. CIOs cannot manage AI value alone. With the average likelihood of success at one in five, business leaders need to be willing to stop and redirect efforts early and often.  

Vanguard organizations evaluate both potential business impact and organizational readiness before making significant investments. They assess factors such as workflow readiness, leadership commitment, digital ambition and the organization’s willingness to change how work gets done. This approach helps them prioritize initiatives that are most likely to create measurable business outcomes with a strong business partnership.

By contrast, organizations that focus primarily on the technology itself often struggle to move beyond isolated successes. AI can generate impressive outputs, but realizing value typically requires changes in processes, decisions and employee behaviors. The strongest performers recognize that business readiness matters just as much as technical capability.

Business leaders face pressure to embrace AI, which can lead to proliferation in AI use cases. Once a leader has invested time and resources, the sunk cost fallacy mindset can make it hard to walk away even if the value is low. According to Gartner, that mindset can make it harder to achieve meaningful results.

Much of the conversation around AI focuses on efficiency and automation. The Vanguard differentiator lies in their bias to redesign jobs that expand the scope of work possible, not just eliminate inefficiencies.  

Organizations with explicit plans to redesign roles for a human-agent workforce are 2.4 times more likely to be Vanguard organizations. Yet only 4% of organizations currently report having such plans in place.

Rather than maintaining static job definitions, Vanguards recognize that as AI takes on specialized tasks, the boundaries between established technology jobs begin to blur. For example, designers can increasingly perform testing activities, while testers participate in design. Consequently, organizations will require fewer distinct role types, even as the span of responsibility within each role expands.

AI’s impact on the workforce may be more expansive than many leaders expect. While automation remains an important objective, Gartner finds organizations will also need new skills, new roles and new ways of working alongside AI.

Forty percent of respondents expect IT headcount to increase in 2027, particularly in cybersecurity and data and analytics. As AI adoption grows, so does the need for skills that support governance, oversight, security and value realization.

The organizations creating the most value from AI recognize that the future is not AI replacing people. It’s people and AI working together in new ways. Bridging the gap between AI investment and AI value ultimately requires more than deploying technology. It requires rethinking how organizations fund, govern and operate in an AI-enabled future.