For all the excitement surrounding AI, the question that continues to haunt CIOs and IT leaders is simple: where’s the return? While global spending on AI systems is expected to exceed $300 billion by 2026, according to the IDC, the return on investment remains frustratingly elusive for many enterprises. This isn’t surprising when you consider that Gartner estimates 48% of AI projects never make it to production. The reason for this disconnect is a widespread focus on innovation and buzzwords over measurable impact.
It’s time to move past experimentation and look at AI as a strategic business investment, not just a cool new technology. For CIOs under pressure to demonstrate tangible value, the focus must shift from “What can AI do?” to “What is AI measurably doing for the business?” A recent study by IT consultancy Crayon highlighted that 54% of IT decision-makers already see IT cost optimization as their top challenge, even before they add the complexities of AI initiatives.
ROI Starts with the Problem, Not the Tech
Too many AI initiatives begin with a shiny tool and a vague sense of potential. But real AI ROI doesn’t emerge from the capabilities of the model, it starts with the clarity of the problem being solved. AI should be deployed to address a defined business challenge, one that stakeholders already recognize and agree on as a priority.
Whether it’s reducing customer churn, automating routine claims processing, or personalizing digital experiences at scale, successful AI initiatives begin with a measurable outcome in mind. Without that, even the most advanced models risk becoming elegant solutions in search of a problem. A lack of knowledge about how to optimize spending is a common obstacle, with Crayon’s report noting this as a key challenge for 28% of organizations.
Why So Many AI Pilots Fail to Scale
There’s a growing graveyard of AI pilot projects that showed early promise but never went anywhere. This happens because they weren’t built to scale or, worse, were never tied to business KPIs in the first place. This disconnect between experimentation and enterprise value is a common pitfall.
A team might train a generative AI model to summarize internal documents, but if no one defines success metrics, aligns with business owners, or plans for adoption, the project stalls. A prototype in a sandbox is not a win. Instead, it’s a cost. CIOs must demand more than a demo; pilots should be designed with a clear path to production, a metric-based business case, and alignment with the stakeholders who own the outcomes.
Operationalization Is the Key to ROI
Even well-targeted AI initiatives can fail to generate ROI if they aren’t embedded into operations. AI models don’t deliver value in isolation; they depend on high-quality, accessible data, change-ready processes, and integration into daily workflows. As Crayon’s research has found, a significant 95% of businesses feel their IT budgets are not fully optimized, often due to a lack of visibility into costs.
Success requires asking hard questions early:
- Is your data infrastructure ready to support this use case?
- Who will maintain and monitor the model over time?
- How will users interact with the outputs—and what will they do differently because of them?
The real work happens after the model is trained, and that’s where transformation and measurable value live. This focus on operationalizing AI is crucial for preventing a lack of ROI. For this, you must focus on the people aspect—training, creating AI champions who support new users, and a change management plan that explains why the new tool matters.
Make ROI Everyone’s Job
One of the biggest myths about AI is that it’s an IT initiative. In reality, AI ROI lives at the intersection of technology, business strategy, and organizational behavior. IT can’t deliver ROI in a vacuum. Business leaders must help define use cases, finance teams must validate value assumptions, and end users must be part of adoption planning from day one.
Cross-functional collaboration isn’t a “nice to have” , it’s a requirement. Crayon’s study found that 20% of global businesses leave IT cost decisions to CFOs and finance teams, but true optimization requires a broader, more collaborative approach. When all stakeholders are aligned on the why, the how becomes much easier to execute, and the potential for a positive ROI increases exponentially.
Building Frameworks for Sustainable AI Value
To sustain long-term AI adoption and value, organizations need repeatable frameworks that tie new projects to business outcomes, user impact, and long-term scalability. This means moving from isolated wins to an AI operating model that includes a structured intake process for new use cases, consistent evaluation criteria (ROI, risk, readiness), and ongoing measurement post-deployment.
When AI initiatives are approached this way, the conversation shifts from “cool demo” to “compounding business value.” As such, 90% of businesses list IT cost optimization as a high priority, underscoring the need for a strategic, framework-based approach.
ROI Is Not Just a Report
For CIOs navigating boardroom scrutiny and tight budgets, AI ROI must become a living conversation, not just a post-mortem metric. It’s about creating clarity from the start, aligning across teams, and holding projects accountable to the same standards as any other investment. Done right, AI can be transformative. But it won’t happen by accident and it won’t happen in the lab. Real AI ROI doesn’t start with a model. It starts with a mindset.
