A CIO’s 5-point checklist to drive positive AI ROI

AI initiatives require executive sponsorship and a clear vision for how they improve the business. “Strong leadership is essential to translate AI investments into results,” says Adam Lopez, president and lead vCIO at managed IT support provider CMIT Solutions. “Executive sponsorship and oversight of AI programs, ideally at the CEO or board level, correlates with higher ROI.”

For example, at IT services and consulting company Xebia, a subgroup of executives steers its internal AI efforts. Chaired by global CIO Smit Shanker, the team includes the global CFO, head of AI and automation, head of IT infrastructure and security, and head of business operations.

Organizations must be open to shift their talent framework and redesign roles. “CIOs should adapt their talent and management strategies to ensure successful AI adoption and ROI for the organization,” says Ramasamy. “This could involve creating new roles and career paths for AI-focused professionals, such as data scientists and prompt engineers, while upskilling existing employees.”

CIOs should also view talent as a cornerstone of any AI strategy, adds CMIT’s Lopez. “By investing in people through training, communication, and new specialist roles, CIOs can be assured that employees will embrace AI tools and drive success.” He adds that internal hackathons and training sessions often yield noticeable boosts in skills and confidence.

Upskilling, for instance, should meet employees where they are, so Asana’s Srivastava recommends tiered paths: all staff need basic prompt literacy and safety training, while power users require deeper workflow design and agent-building knowledge. “We took the approach of surveying the workforce, targeting enablement, and remeasuring to confirm that maturity moved in the right direction,” he says.

But assessing today’s talent framework goes beyond human skillsets. It also means reassessing your work to be done, and who or what performs what tasks. “It’s essential to review business processes for opportunities to refactor them, given the new capabilities that AI brings,” says Scott Wheeler, cloud practice lead at cloud consulting firm Asperitas Consulting.

Shifting the talent framework is only the beginning. Organizations must also reengineer core processes. “Fully unlocking AI’s value often requires reengineering how the organization works,” says CMIT’s Lopez, who urges embedding AI into day-to-day operations and supporting it with continual experimentation rather than treating it as a static add-on.

To this end, one necessary adaptation is toward treating internal AI-driven workflows like products and codifying patterns across the organization, says Srivastava. “Establish product‑management rigor for intake, prioritization, and roadmapping of AI use cases, with clear owners, problem statements, and value hypotheses,” he says.

At Xebia, a governance board oversees this rigor through a three-stage tollgate process of identifying and assessing value, securing business acceptance, and then handing off to IT for monitoring and support. “A core group is responsible for organizational and functional simplification with each use case,” says Shanker. “That encourages cross-functional processes and helps break down silos.”

Similarly for Ramasamy, the biggest hurdle is organizational resistance. “Many companies underestimate the change management required for successful adoption,” he says. “The most critical shift is moving from siloed decision-making to a data-centric approach. Business processes should integrate AI outputs seamlessly, automating tasks and empowering employees with data-driven insights.”

Identifying the right areas to automate also depends on visibility. “This is where most companies fall down because they don’t have good, documented processes,” says Skillsoft’s Daly. She recommends enlisting subject-matter experts across business lines to examine workflows for optimization. “It’s important to nominate individuals within the business to ask how to drive AI into your flow of work,” she says.

Once you identify units of work common across functions that AI can streamline, the next step is to make them visible and standardize their application. Skillsoft is doing this through an agent registry that documents agentic capabilities, guardrails, and data management processes. “We’re formalizing an enterprise AI framework in which ethics and governance are part of how we manage the portfolio of use cases,” she adds.

Organizations should then anticipate roadblocks and create support structures to help users. “One strategy to achieve this is to have AI SWAT teams whose purpose is to facilitate adoption and remove obstacles,” says Asperitas’ Wheeler.

To evaluate ROI, CIOs must establish a pre-AI baseline and set benchmarks upfront. Leaders recommend assigning ownership around metrics such as time to value, cost savings, time savings, work handled by human agents, and new revenue opportunities generated.

“Baseline measurements should be established before initiating AI projects,” says Wheeler, who advises integrating predictive indicators from individual business units into leadership’s regular performance reviews. A common fault, he says, is only measuring technical KPIs like model accuracy, latency, or precision, and failing to link these to business outcomes, such as savings, revenue, or risk reduction.

Therefore, the next step is to define clear, measurable goals that demonstrate tangible value. “Build measurement into projects from day one,” says CMIT’s Lopez. “CIOs should define a set of relevant KPIs for each AI initiative. For example, 20% faster processing time or a 15% boost in customer satisfaction.” Start with small pilots that yield quick, quantifiable results, he adds.

One clear measurement is time savings. For instance, Eamonn O’Neill, CTO at Lemongrass, a software-enabled services provider, shares how he’s witnessed clients documenting SAP development manually, which can be an extremely time-intensive process. “Leveraging generative AI to create this documentation provides a clear reduction in human effort, which can be measured and translated to a dollar ROI quite simply,” he says.

Reduction of human labor per task is another key signal. “If the goal is to reduce the number of support desk calls handled by human agents, leaders should establish a clear metric and track it in real time,” says Ram Palaniappan, CTO at full-stack tech services provider TEKsystems. He adds that new revenue opportunities may also surface through AI adoption.

Some CIOs are monitoring multiple granular KPIs across individual use cases and adjusting strategies based on results. Asana’s Srivastava, for instance, tracks engineering efficiency by monitoring cycle time, throughput, quality, cost per transaction, and risk events. He also measures the percentage of agent-assisted runs, active users, human-in-the-loop acceptance, and exception escalations. Reviewing this data, he says, helps tune prompts and guardrails in real time.

The resounding point is to set metrics early on, and not fall into the anti-patterns of not tracking signals or value gained. “Measurement is often bolted on late, so leaders can’t prove value or decide what to scale,” says Srivastava. “The remedy is to begin with a specific mission metric, baseline it, and embed AI directly in the flow of work so people can focus on higher-value judgment.”