AI investment is entering a new phase.
For the last several years, organizations have been rewarded for AI experimentation. Pilots, proofs of concept, and early deployments were enough to secure budget and executive attention. That phase is ending.
As organizations race to deploy AI at scale, boards and executive teams are no longer satisfied with promising pilots or anecdotal success. They want clear evidence that AI delivers measurable value and scales responsibly. They're asking a more pointed question: What return are we actually seeing, and can it scale?
Return on AI investment (ROAI) is increasingly becoming the factor that determines which initiatives move forward and which stall. The difference comes down to whether organizations can demonstrate that return in ways leadership can trust.
Leaders are struggling to prove ROAI
This pressure to prove value is already visible in the data. A recent global Ponemon Institute survey of CIOs, CISOs, and senior IT leaders found that 57% say AI adoption is a top priority for their organization. Only 54%, however, are confident they can demonstrate ROI from their AI initiatives.
That gap matters. It reflects a growing disconnect between AI ambition and the ability to show measurable return. Organizations are investing aggressively in AI, but many are not yet prepared to prove its value. Teams that can't demonstrate ROAI struggle to justify the next round of investment, regardless of how innovative or sophisticated their models may appear.
Proving ROAI starts with measuring the right things
One of the clearest patterns across AI programs today is the gap between mature and early-stage adopters. Teams that see strong returns tend to focus less on surface-level metrics and more on whether AI is solving meaningful problems and reducing operational risk. Newer adopters often track activity. Mature teams track impact.
That shift matters because many of the most important AI gains don't show up immediately on the bottom line. Early value often appears as faster release cycles, fewer incidents, quicker recovery, improved decision quality, and reduced risk. These are the signals that AI is becoming a trusted operational advantage, and they are far easier to defend to boards than abstract productivity claims.
Proving ROAI also requires visibility and trust. Leaders need confidence in both the data feeding AI systems and the outputs those systems produce. When inputs and outcomes can be understood, validated, and measured over time, AI value becomes something organizations can explain and stand behind.
As organizations enter a prove-it phase for AI investment, demonstrating these outcomes is what separates pilots that scale from those that stall. ROAI is proven when AI results translate into outcomes that leadership can recognize, measure, and invest in.
4 things leaders should look for when proving ROAI
1. Make ROAI a gating criterion, not a reporting metric
While usage metrics or activity outputs show promise, they're not enough to show true value. Leaders who expect continued investment need to be explicit about what qualifies as proof. ROAI should function as a threshold for funding decisions, not a dashboard statistic to optimize after the fact.
2. Look for evidence of improvement over time
ROAI can't be proven in isolation. Insist on measuring baselines and trendlines that show whether AI is actually improving performance, resilience, or decision quality as programs expand. Without that context, even successful initiatives struggle to survive scrutiny when budgets are reviewed.
3. Broaden the definition of value beyond immediate revenue gain
Some of the most important AI gains show up first as reduced risk, greater reliability, faster recovery, or better decisions. These are benefits that protect and strengthen the business before they move the bottom line. Leaders who recognize these signals are better positioned to evaluate AI programs realistically and invest with confidence.
4. Treat readiness as part of the investment decision, not a follow-on cost
Organizations that wait to address readiness until after pilots succeed often find themselves unable to prove ROAI when it matters most. Leaders who expect AI programs to scale need to build the foundations for measurement, trust, and accountability alongside experimentation, rather than adding them later. ROAI begins with information readiness, which requires controlling and contextualizing data well enough for AI to deliver outcomes that are trusted, compliant, and repeatable.
How to move from pilot success to sustained AI investment
As AI moves from experimentation into core operations, the bar for continued investment is rising. Boards and executive teams are no longer funding — and frankly, shouldn't fund — on potential alone. They are looking for clear signals that AI is improving performance, reducing risk, and strengthening decision-making.
That makes ROAI a leadership responsibility. Leaders decide what counts as proof, how value is measured, and whether AI readiness is treated as part of the investment or an afterthought. Those choices determine which AI initiatives grow into durable programs, and which remain stuck in pilot mode.
The question facing leaders now is straightforward. It's not whether to invest in AI. It's whether their organization is ready and how they can show the return required to keep investing.
ROAI is the metric that matters in 2026. Learn how OpenText solutions can help.
This post was created by OpenText with Insider Studios.
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