5 metrics to prove your AI strategy’s business value

Originally published in CIO Online

CIOs must have a continuous pulse on AI’s material impact on the business, not just to prove ROI but also to capitalize on opportunities and mitigate risks as they arise.

Credit: Rob Schultz / Shutterstock

Many organizations have licensed language models, tested AI agents, and asked DevOps teams to develop code-generation standards. But production deployments are still a work in progress for CIOs, with only 50% of organizations moving more than half of their AI projects from pilot to production, according to Outsystems’ State of AI Development 2026 report.

Increasing production deployments is a key step for many CIOs, followed by fostering more widespread employee adoption. But usage doesn’t always translate to business outcomes, leaving many IT organizations struggling to deliver business value on their AI initiatives.

Much of that has to do with how they’re measuring AI value.

“Too many organizations are treating AI adoption as the metric when the real question is whether it’s improving business outcomes,” says Rob Scudiere, CTO at Verint. “The metrics that matter are the ones executives already track, and if those numbers aren’t improving, it’s hard to argue that AI is delivering meaningful value.”

Measuring how AI impacts the business strategy and its relevant KPIs is a vital first step, especially when it comes to identifying AI’s business-specific opportunities and risks. After all, AI’s capabilities, practices, and security considerations are changing so quickly that it’s essential for C-level leaders to discuss AI’s strategic business value frequently — and to do so with the right data to back those discussions up.

That, in turn, requires a more collaborative leadership approach.

“Organizations should align with business stakeholders upfront on the ‘so what,’” says Keyur Ajmera, CIO at Boomi. “CIOs should partner with CMOs, CHROs, CFOs, and business practitioners to identify use cases, establish appropriate governance and standardization, prepare employees for new ways of working, and create financial guardrails as usage-based AI costs become more prevalent.”

I heard from more than 30 leaders on their picks for the business value metrics that matter most when tracking your AI strategy’s impact. Below are my top five worth adopting.

Growth driven by organizational expertise

For AI to be transformational and not just reshaping business, executives must show that building greater expertise through AI drives business growth.

“Executives primarily talk about AI ROI in terms of cost savings and efficiency, and that doesn’t adequately measure long-term AI success, because it doesn’t capture whether the organization is actually getting smarter,” says Sarah Edwards, chief product and strategy officer at Kantata.

“I’d point them to what I think of as an organization’s expertise compounding rate, which is its ability to capture, synthesize, scale, and continuously build on institutional and tribal knowledge,” she adds. “Firms also need to look beyond productivity and efficiency and consider how AI drives revenue growth, including new pipelines, better client retention, new pricing models, and the ability to take on more projects without adding headcount.”

Ways to measure: Track how expertise developed via AI capabilities drives revenue. Examples include:

• DevOps teams, newly trained on vibe coding tools, help increase revenue by adding AI capabilities such as shopping concierge agents and genAI search capabilities to customer-facing websites.
• Benchmarking return on ad spend (ROAS) between two campaigns, one developed by a team using AI tools, the other without using AI.
• Contact centers using AI to support service-to-sales or sales-through-service competencies can measure increases in revenue per agent hour or offer conversion rates. In one famous case, Ikea built a new €1.3 billion business line, thanks to a contact center chatbot freeing up associates’ time.

The key pattern is that when AI is introduced into a sales, marketing, or support function, it can be correlated with revenue increases.

Improved customer retention

Improving customer retention is a key metric for products or services, especially those with subscription business models. Customer loyalty and the ability to influence repeat purchases are similar KPIs when the brand influences pricing and customer-perceived value.

How CIOs implement data and AI governance can positively or negatively affect customer retention. According to the State of Digital Trust 2026 from Usercentrics, consumers will now pay 7% more for the brands that have earned their trust around AI. Meanwhile, 24% of consumers canceled a subscription or stopped buying from a brand because they were concerned about how their data was used in AI.

Donna Dror, CEO at Usercentrics, says, “Most CIOs can measure what AI saves them, but the harder number, and the one that shows up in revenue, is what mishandling data in AI costs in lost customers.”

Ways to measure: Measure consent withdrawals, Data Subject Access Requests (DSARs), or AI feature opt-out rates. Businesses with subscription products can track renewal rates in the 90 days following an AI feature launch and add a data-and-AI-use reason code to the cancellation flows.

Employee capabilities beyond productivity

I’ve warned CIOs that focusing on AI’s productivity improvements is a trap, because at some point the CFO will look for cost savings through headcount reductions. Other metrics beyond productivity may matter more, especially for organizations investing in agentic AI workflows.

The first is time-to-decision: AI agents are either capable of taking action autonomously, or they are implemented to include human collaboration in decision-making. CIOs and the business owners of AI agents should determine how to measure the business impact of faster, smarter decision-making.

“Most AI scorecards measure activity, including agents deployed, tasks automated, and prompts processed,” says Vikram Bhandari, chief technology and innovation officer at Riveron. “The metric I push clients toward is time to decision, such as how much faster finance or ops teams can act once AI is in the workflow, and what that speed translates to in revenue or risk avoided. Headcount metrics tell you AI has replaced work, while decision-speed metrics tell you AI changed the business.”

Enterprises can scale beyond individual productivity improvements by re-engineering business processes using AI orchestration platforms. But CIOs have to get AI democratization right by influencing experts to build the organization’s knowledge base and the AI context layer utilized by AI agents.

“Data democratization’s real number is the percentage of institutional knowledge anyone can self-serve, without tracking down one specific person,” says Chris Cope, VP of engineering at CADDi. “Manufacturers map every flow on the factory floor and fixate on single-source supplier risk, yet miss the same risk when it’s a person holding the knowledge.”

Ways to measure: In risk management functions, measure time-to-decision as the difference between mean time to remediate (MTTR) and mean time to detect (MTTD). Measure knowledge management impacts by tracking increased utilization of internal LLMs, while capturing the reduced time spent on answering requests for information (RFIs) by subject matter experts.

Impacts to customer and employee experience

CIOs should hunt for examples of value delivered to customers and for employees that go beyond doing today’s job better. This means exploring areas where AI provides a business capability that is not feasible, or is economically unscalable, if people were performing the function without AI capabilities.

Venkat Ramakrishnan, president and COO at NeuBird, shares this example in IT and security incident management: “The focus has shifted from how fast you resolved an incident to how many users never felt it in the first place. When AI prevents an incident 30 minutes before failure, or resolves it in under 5 minutes instead of 175, it eliminates the experience impact rather than only reducing it,” Ramakrishnan says.

Other examples require exploring game-changing operational transformations in other departments, or finding areas where AI impacts the connected experiences of customers and employees (CX and EX).

“CIOs should measure genAI by whether it changes the economics of the business such as decision velocity, capacity created per employee, exceptions resolved without escalation, revenue leakage recovered, customer and employee effort avoided, and ultimately the rate at which AI benefits reach the P&L,” says Arun Raghavapudi, chief customer officer at R Systems (RSI). “The strongest AI strategy is therefore one where CX, EX, engineering, and business KPIs form a single value chain rather than separate technology scorecards.”

Ways to measure: CIOs should identify AI-native capabilities, those business and operational competencies that are net-new AI use cases. Once identified, business value metrics determine the value delivered, and the AI-native capabilities become marketable innovations to communicate across the company.

Measure AI’s operational impacts

Many CIOs used lift-and-shift cloud migration strategies or overly empowered DevOps teams to procure cloud infrastructure, only to find skyrocketing costs. CIOs applied FinOps best practices and shifted-left financial considerations to reduce costs and instill fiscal discipline.

Even as token costs decline, AI cost debt is very real as consumption of AI models and use of AI agents increases. Tokenomics tracks and manages both costs and the business value delivered from spending on AI tokens. On the cost side, CIOs should factor in the human-effort costs, including the AI hallucination tax when employees have to recognize when AI models and agents are wrong.

“Token spend is becoming a real line item, and if it grows faster than the outcomes it produces, that’s a unit economics problem, not a scaling milestone to be proud of,” says Yasmin Rajabi, COO of CloudBolt. “The metrics that matter include how often AI-generated work is accepted without rework, how many decisions or actions run without a human in the loop, and value per token you get for every token you spend.”

Measuring the value of operational impacts needs to consider both tactical impacts based on individual contributions versus team, department, and business-level outcomes.

“Indicators that AI is creating measurable value rather than simply generating activity include whether critical workflows are completed quicker, decisions are more consistent, and AI-generated recommendations are traceable back to authoritative information,” says Tony Grout, chief product and technology officer at M-Files. “The most successful AI programs will be judged by whether they deliver better business outcomes like faster project completion, higher-quality customer service, improved compliance, and more consistent decision-making.”

Ways to measure: My research on AI agent orchestration platforms identifies several platforms that include tokenomics capabilities, including solutions from Boomi, Cisco, Databricks, Kamiwaze, LangChain, Microsoft, Nutanix, PagerDuty, Salesforce, Snowflake, Tray.ai, and Workato.

Setting realistic expectations

CIOs must lead a progression from AI experiments to production deployments, enterprise adoption, and delivered value. But the organization’s velocity to adopt and deliver value requires a strong change management program that adjusts to business demand, especially if the AI bubble bursts.

“The reality is that enterprise AI follows a productivity J-curve, where companies invest in new skills, operating models, governance, and process redesign long before the full economic benefits become visible,” says Kris Lovejoy, global head of strategy at Kyndryl. “As a result, usage often rises months before measurable business outcomes appear, and this is one reason many executives feel uncertain about the return they’re getting from AI.”

While AI agent deployment and employee adoption are not the end game, CIOs must excel at these before overpromising business value. Two places to start include upskilling IT for agentic AI while avoiding mistakes when deploying AI agents.