A staggering $30 to $40 billion is being invested annually into enterprise generative AI, a figure that signals a seismic shift in corporate strategy. Yet, for all the capital deployed and bullish predictions made, a stark reality is emerging from the balance sheets: many organizations struggle to achieve substantial or measurable ROI from their AI initiatives. This disconnect between expenditure and outcome is becoming the central challenge for executives navigating the AI revolution. The recent launch of specialized tools like Portal26's Agent Adoption Platform (AMP), designed specifically to measure and secure returns, underscores a new market imperative—transforming AI's potential into demonstrable profit.
Across industries, accelerated AI adoption is colliding with delayed or diminished financial returns, forcing a strategic reassessment.
Measuring the ROI of AI in EAM Solutions
Since late 2022, corporate posture toward artificial intelligence has decisively shifted from consideration to commitment. Most large organizations have moved beyond pilot programs to allocate significant budgets for enterprise-wide AI integration, according to a Harvard Business Review report. This transformation, however, is proving more complex and less immediately profitable than many leaders anticipated.
The chasm between investment and impact is significant. A 2025 analysis from MIT, ‘The GenAI Divide,’ found that a startling 95% of generative AI pilots fail to deliver measurable profit-and-loss impact. This finding is corroborated by broader market observations. According to Forbes, which cited McKinsey’s 2025 report, 'Superagency in the workplace,' only 19% of C-level executives report revenue increases greater than 5% from their enterprise AI investments. The data paints a picture of widespread experimentation where only a fraction of initiatives successfully cross the threshold into scalable, value-generating operations.
Further analysis reveals a tiered structure of success. At scale, data suggests only about 5% of companies achieve substantial AI ROI, while a more significant 35% report partial or localized returns. For those who do see a payoff, the results can be meaningful. A report from Master of Code notes that the average AI return on investment reaches approximately 1.7 times the initial outlay, with cost savings of 26–31% registered in functions like supply chain management, finance, and client operations—all core components of effective enterprise asset management (EAM). The challenge, therefore, is not that AI is incapable of generating value, but that the conditions for achieving that value are exceptionally difficult to create and sustain. This difficulty is reflected in the fact that over half of finance executives admit they cannot clearly demonstrate ROI from their AI initiatives, and a concerning 42% of companies reportedly abandoned most of their intelligentization projects in 2025.
