Conal Gallagher is the CIO at both Flexera and its division Revenera, where he manages IT and information security programs.
Just a few years ago, CIOs were challenged to answer the big enterprise question: how quickly can we adopt AI? Now many organizations are adopting AI faster than they can fully see, govern and financially manage.
CIOs today are at the forefront of assessing all the AI their organizations have adopted. What I see increasingly is that AI adoption is no longer concentrated in a few technology teams. It’s appearing across business functions. What started as a race to experiment has quickly evolved into a challenge of visibility and accountability.
At the same time, leaders are facing increased pressure to demonstrate tangible business value from their AI investments, being evaluated through the lens of cost, accountability and measurable outcomes.
To succeed in 2026 and beyond, organizations must rethink what success looks like in the next phase of AI maturity and how to build visibility so they can scale AI with confidence. Here’s how you can do that:
Step 1: Gain Visibility Into AI Usage
Unlike traditional technology deployments that typically move through centralized IT processes, many AI tools are being adopted directly by employees and business teams. As AI becomes embedded across applications, workflows and business processes, organizations often have limited visibility into where it is being used or how quickly that usage is expanding. Interestingly, it is not just junior employees driving this trend. Nearly two-thirds of senior decision-makers admit to using unapproved AI tools.
Visibility is no longer simply about discovering unauthorized tools. It is about understanding how AI is being embedded into the enterprise, who is using it, what data it can access and what role it plays in day-to-day operations. Without that understanding, organizations may struggle to enforce policies consistently, protect sensitive information or assess the risks associated with AI usage.
Organizations that establish visibility first are better positioned to manage risk and scale AI responsibly. Visibility creates the foundation for accountability, helping organizations make more informed decisions about governance, investment and long-term AI strategy.
Step 2: Make Sense Of Fragmented AI Spending
AI adoption is moving fast and not just showing up in one budget or technology category. Unlike many earlier technology initiatives, AI is being adopted simultaneously across technical teams, business functions and employee workflows, so it becomes harder for organizations to understand the true scope of their investments.
This distributed consumption model creates a new accountability challenge. Different teams may be purchasing, deploying and using AI in different ways, often without a shared view of costs, usage or business outcomes.
According to Flexera’s AI Pulse Report, 36% of leaders say they have spent too much on AI applications. As AI adoption accelerates, organizations need greater visibility into how AI is being consumed across the enterprise and how those investments align with business priorities.
To address this, leaders need a comprehensive view of how AI is being consumed across the enterprise. That means understanding where AI is being used, who is using it, what it costs, what data it can access and whether it is delivering measurable business value.
Accountability: The Next Phase Of AI
Organizations cannot govern what they cannot see. As adoption expands across applications, workflows and business processes, the challenge is creating the visibility, governance and operational discipline needed to manage it responsibly at scale.
Succeeding will mean establishing clear accountability for how AI is used, what it costs, the risks it introduces and the value it delivers. By balancing innovation with accountability, leaders can create the confidence needed to scale AI responsibly and ensure every AI investment delivers measurable business value.
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