Most CIOs can easily describe their AI initiatives, but struggle to demonstrate and report on AI value and ROI. This is evidenced by Gartner’s AI ROI Vendor Sentiment survey, which found that 27% of respondents strongly agree that ROI from AI initiatives is difficult to quantify. In addition, only one in four had a clear process to evaluate success of AI initiatives.
In the digital transformation era, compute costs were relatively cheap, and the focus was less on FinOps and more on user experience, and the classic software attributes of availability, reliability, scalability, performance, and security.
AI-native and AI-enabled applications still bring these considerations, along with a host of new ones, such as accuracy, validity, explainability, safety, and all the other trust attributes like those in the NIST AI RMF. But as AI scales, it’s quicky becoming a FinOps exercise, too.
I’ve discussed techniques CIOs can apply to improve AI value and ROI such as architectural decisions related to controlling efficiency at the data layer, fine-tuning the application layer, and closing the gap between AI strategy and governance.
Here, we’ll look at three best practices for CIOs looking to formalize their process to evaluate the effectiveness of their AI initiatives through approaches, frameworks, and techniques that support measurement and ROI reporting across the entire AI portfolio.
1. Take a portfolio approach in line with AI ambition
The first step to AI value is to take a portfolio approach and look across your entire AI estate and how it supports your AI strategy and broader organizational strategy. This means thinking about your AI innovation ambition and how you’d like to move the needle year-over-year by adjusting your percentage of spend across quick wins versus must haves, internal (employee-facing) versus external (customer-facing) applications, near-term versus long-term initiatives, and cost savings and productivity versus revenue generation.
This AI innovation ambition approach is a best practice in innovation management as organizations work to gain quick wins early on to prove value, and then get more ambitious in subsequent years and allocate a larger percentage of their spend on more strategic, longer-term, and externally focused initiatives for revenue generation.
For example, an early maturity organization might focus 80% of its AI investment on internal-facing initiatives and 20% on external-facing ones in the first year. Then, it moves to a 70/30 split in year two, and so on. While many organizations may not track these spend allocations, it’s a valuable discipline and one which often differentiates AI leaders from the rest of the pack. In PwC’s 2026 AI Performance Study, the top 20% of companies capture 74% of all AI-driven economic value, delivering a 7.2 performance multiplier over peers.
Deploying focused pilots alongside quick wins is a great way to capture early insights into AI value and ROI before scaling applications. According to Joe Vaccaro, SVP and GM of Network Platform and Assurance at Cisco, networks have become distributed, continuously changing systems that produce far more signal than any human-led operating model was designed to interpret.
“Our survey of 1,000 IT and NetOps leaders found more than half believe a failure to automate will open critical security gaps and elevate enterprise risk,” he says. “For CIOs seeking to demonstrate ROI from AI investments, a credible path is to start with targeted, measurable, and reversible workflows. Focused pilots can validate outcomes and strengthen governance before broader deployment.”
At this level where organizations look at their holistic AI spend, it’s also important to have a well-crafted set of AI value metrics, which can be used to assess the current health and productivity of the portfolio. Borrowing from best practices in innovation management, AI value metrics, such as those that look at input and mix, health and efficiency, and outcomes, help to look across the entire innovation pipeline, from idea to value.
- AI innovation input and mix — What’s going into your pipeline in terms of the types of innovations in the queue.
- AI innovation health and efficiency — The flowrate through your pipeline and the amount of funding being applied.
- AI innovation outcomes — The measure of return on innovation in terms of commercialized innovations that have captured revenue or other objectives.
2. Develop an assessment framework focused on full cost benefit
While many organizations are laser-focused on the cost side of their AI initiatives, your assessment framework needs to incorporate a full cost benefit analysis. “While the current focus for many organizations is squarely on AI tokenomics, it’s important to look at both sides of the coin when it comes to AI value, and look at the business value that’s being delivered by each initiative to give a clear picture of actual return,” says David Evans, AI and growth strategist at thought leader networking platform Thinkers360.
Ben Schein, VP of AI and analytics at AI infrastructure software company Progress Software, adds that CIOs should stop measuring AI activity and start measuring outcomes where possible. “Usage tells you the meter is spinning, not that value came out,” he says. “Instrument the cost side completely, since every AI action can carry its workload, model, and cost. Then pair costs with benefits where the work allows, so cost per workflow run against value per workflow run, never enterprise-wide spend against anecdotal wins.”
However, Dan Priest, PwC US CAIO, recommends getting back to basics, starting with the investment side but adding a few modern updates. “AI ROI has been illusive for many, in part because organizations are still learning how to achieve it, and because AI sometimes gets a pass on the typical disciplines required to measure it,” he says. “For major AI initiatives, starting with investment might include new infrastructure, labor for the build, models for the intelligence layer, and variable expenses like token costs. All of these should be contemplated in ROI calculation, some of which will require new tracking and controls.”
His benchmarking also points to a threshold effect: ROI stays insignificant until AI investment crosses roughly 1.6% of revenue, and above that line, EBITDA is up 9.5%, revenue is up 3.5%, and total shareholder return is up 20.2%.
3. Include benefits and measure what matters
Another best practice from innovation management is to look at both the quantitative and qualitative benefits of AI initiatives, not solely the direct financials.
“It sounds obvious, but CIOs should measure the business value created like productivity, growth, and other outcomes rather than activity completed, such as building an app, creating a dashboard, or collecting customer feedback,” says Priest. “We recommend looking across five dimensions of value: financial, operating, functional, trust, and workforce. That gives leaders a more consistent way to measure and compare value across an AI portfolio, even when individual investments are designed to produce different outcomes.”
Calculating projected as well as actual ROI can also give CIOs insight into how their AI portfolio is living up to expectations. This is where it’s important to factor in governance costs early in the lifecycle before initiatives get funded, particularly for high-risk applications where downstream governance requirements are high.
“Ultimately, measure what matters, keep an eye on variable costs, and monitor actuals against expected costs and benefits,” adds Priest. “That gives CIOs the information they need to determine where investments are delivering, where changes are required, and where to lead, lag, or exit.”
Quantifying AI value and ROI doesn’t require a completely new methodology or new approach. It simply takes the same financial rigor applied to many other previous investments. The variable cost dynamics may be more complex, but these are solvable if you align with your AI innovation ambition, apply your assessment frameworks consistently, define your AI value metrics, and measure what matters.