Organizations are investing heavily in technology platforms, data, AI capabilities and governance frameworks to drive transformation at scale. Yet when it comes to quantifying value — ROI, operational cost savings, improved customer experience — many leaders struggle to demonstrate meaningful impact.
Research points to a growing challenge: Technology adoption is getting easier; proving its value remains difficult. An IBM CEO survey reports that only 25% of AI initiatives have delivered expected ROI over the last few years, and only 16% have scaled enterprise-wide. Part of the challenge may be that leaders are not aligned on what success looks like. A Gartner survey found an expectations gap between CFOs, who primarily use AI to improve productivity and efficiency, and boards, which expect AI to drive revenue growth, intelligence and competitive advantage. The result is a disconnect between AI activity and perceived business value.
Recent Protiviti research suggests that this misalignment extends across the executive suite. Executive confidence in achieving transformational objectives, operational cost savings and AI ROI — even perceptions of the organization’s technological maturity — varies significantly depending on which leader you ask.
Among those responses, the technology leader — CIO or CTO — emerges as one of the most confident, with priorities centered on AI, data and compliance, and a strong view of their organizations’ technological maturity. But being the most confident is not always an advantage; when other executives do not share the same conviction or see the same results, transformational activity can lose momentum or fall short of its full value.
When CIO confidence outpaces CEO conviction
Technology leaders’ confidence that AI is driving revenue growth and cost savings is more than double that of CEOs and board members, according to the Protiviti survey. Net confidence was 61% among CIOs and CTOs, compared with just 30% among chief executives and directors.
There is a reasonable explanation for the more than 30-point gap. Technology teams tend to focus on metrics they can directly measure and influence — user adoption, model performance, productivity gains, process automation, cycle-time reduction, system utilization, platform stability. CEOs and directors, by contrast, evaluate AI through the lens of broader enterprise outcomes, including revenue growth, profitability, margin expansion, customer experience, competitive differentiation, risk reduction and shareholder value. When they cannot see how AI affects these outcomes directly, confidence declines. Both groups are looking for evidence of success, but through fundamentally different lenses.
The implications are real. If executives cannot agree on whether AI is creating value, it becomes difficult to sustain investment, prioritize initiatives and scale adoption.
In my conversations with technology and business leaders, AI adoption is no longer the primary challenge. Demonstrating value is. It’s one thing to automate X number of tasks to improve employee productivity (activity metrics); it’s another thing entirely to demonstrate how AI is helping retain customers, reduce product defects, accelerate sales conversion rates or improve profitability (business impact metrics). I believe closing the gap between AI adoption and business value realization begins with technology leaders asking a basic question: Why are we doing this and how will we measure it?
Advancing to maturity: the alignment accelerator
The most technologically advanced organizations do more than solve technology challenges; they align leadership around change. Leadership alignment goes beyond AI value to positively affect nearly every aspect of transformation and move the enterprise higher on the technological maturity scale. Mature organizations with closely aligned leaders consistently express higher confidence (more than 70% on average) in areas such as trust in enterprise data, ability to integrate new technologies quickly and securely, achievement of transformation objectives, ability to manage risks and ability of the workforce to adapt to the changes. By contrast, the confidence of their peers in less mature organizations is below 20%, with significant variation in agreement from one leader to the next.
This suggests a different way to think about technological maturity. The MIT CISR’s Enterprise AI Maturity Model already defines maturity as more than technology adoption alone; rather, it’s technology with governance, workforce readiness, decision-making practices and organizational capabilities. The Protiviti survey points to another element that appears to be just as critical: leadership alignment. Organizations make progress more quickly when executives agree on priorities, share a common definition of value and align on the outcome’s transformation is intended to deliver. Executive alignment is not a byproduct of transformation success. It is one of its most powerful accelerators.
The CIO’s role in closing the alignment gap
Consider this:40% of chief operations officers identify AI as the capability with the greatest potential to drive revenue growth. Nearly half of CFOs (49%) consider cost optimization the single biggest driver of transformation initiatives.
These expectations are signals technology leaders cannot afford to ignore. In driving technology forward, CIOs must ensure the connection between their effort and the organization’s business goals is clear, credible and understood across the executive team — from the CEO to the finance, risk and HR executive. To do so, tech leaders should consider several foundational shifts in how they think and operate:
- Reinvent the IT operating model for value-based prioritization — with ROI, value streams and objectives and key results (OKRs) as the primary measures of success. This ensures other business leaders can see and validate technology’s value — driving alignment and accelerating maturity.
- Engage in enterprise prioritization decisions, not just implementation. Position technology investments and their sequencing in the context of long-term enterprise strategy, such that the CEO, board of directors and shareholders can all get behind them.
- Become an arbiter of AI use, ensuring AI investments are tied to ROI, cost discipline and business outcomes. Redefine AI governance to include common language around value, consistent reporting and cross-functional accountability.
In an era of accelerating technology change and growing investment, executive cohesion around what outcomes matter and how to measure them is more than a nice-to-have. It is a business capability as essential as data governance or cybersecurity. It is the discipline that helps determine whether technology investments lead to higher maturity and a competitive edge or dissolve into fragmentation and unrealized potential. The technology leaders who can connect technical activity to strategic outcomes, translate progress into the language of enterprise value and build shared confidence across the executive team will do more than deliver successful transformations. They will help define what leadership looks like in the next generation of digital enterprises.