Chief ecommerce officers, once crucial members of the C-suite at many organizations, have all but disappeared as online sales emerged as a main revenue driver instead of a sideshow. Some experts believe chief AI officers and chief data officers could face the same fate.
AI use and good data governance will eventually become so central to most organizations that all top executives, even beyond CIOs and CTOs, will need to understand the impact and deliver results, some IT leaders say.
When all parts of the business, including the CFO and CMO organizations, are executing AI and data strategies, CAIOs and CDOs may no longer be necessary, says Glen McCracken, head of data, analytics, and automation at ION Analytics, a data modeling firm focused on capital markets.
“We used to have chief ecommerce officers, and it made sense in the short term, but then longer term, every part of the business needed to understand how to use the internet to be more effective in their roles,” he says. “We’re at the same stage and saying, ‘Wow, you need this separate chief AI officer role.’”
In recent conversations about the role of the CAIO, McCracken pushed back on the reason for the position. “My throwaway comment was, ‘Shouldn’t that be the CIO?’” he says. “Isn’t that their role?”
In a recent LinkedIn post, McCracken predicted that the CAIO and CDO roles will eventually disappear. The debate in the comments was lively, with several posters agreeing with him, and others questioning his logic.
In Foundry’s AI Priorities Study 2025, businesses reveal they’re allocating more money to AI projects than ever before, with nearly half of organizations now dedicating budget for AI projects, up from 36% in 2023. Plus, they’re allocating almost a quarter of IT spend to AI initiatives.
While some are taking a more measured approach than others, the consensus among South African tech leaders in particular is that AI investments must deliver tangible results, and that AI budgets have to be channelled strategically across the business. At financial services group Sanlam, it’s less about upping AI budgets, says group CIO Theo Mabaso, and more about allocating more of the budget toward high-impact areas. “After a period of experimentation, launching multiple AI outcomes and learning by doing,” he says, “we’ve figured out where the objective AI value is concentrated, and have thus narrowed our focus to delivering fewer but higher impact AI outcomes that deliver ROI for the group.”
For example, it’s understood that gen AI assists productivity and enables employees to focus on high-value tasks. And Sanlam has seen time saving and benefits through the likes of GitHub CoPilot, and using LLMs to review lengthy contracts against standard terms and conditions. “However, we recognize that productivity doesn’t always drive objective metrics, so we’re critical of where we deploy such technologies,” he says. “We look for an area where a variable can be affected to create an objective downstream effect, as opposed to large-scale deployments of productivity tools that deliver soft benefits tied to multi-year contracts.”
Werner Leithgöb, Lactalis Southern Africa
Lactalis
Werner Leithgöb, IT director of dairy manufacturer Lactalis Southern Africa, agrees. He believes the elevated hype around AI only scratches the surface of what these tools can do. “When the Amazon Alexa first came out, everybody thought it was so awesome because you could interact with the devices in your home ecosystem using voice commands. But I guarantee that 99% of consumers who have one use it purely to stream music. So, essentially it’s a glorified speaker. For me, AI is very similar. In order to utilize this technology properly, you have to be very deliberate and define clear use cases for it so people can see what value it adds.” As part of being deliberate, he says, you must regularly use the technology so it becomes part of how you work.
Maximizing potential
As use cases evolve, just experimenting with AI for the sake of innovation doesn’t cut it anymore, says Jenny Mohanlall, senior director for IT at DHL South Africa. “It’s about leveraging it to solve real problems, drive efficiency, and create meaningful impact,” she says. For most organizations, AI priorities are focused on enhancing the customer experience and revolutionizing operations and ways of working.
Jenny Mohanlall, DHL South Africa
DHL
The findings of Foundry’s AI Priorities Study support this, highlighting that IT departments are more readily partnering with other business units, like customer service and marketing, to ensure AI adoption aligns with broader business goals. By leveraging AI tools to streamline and improve something like customer support, it’s possible to free up human agents to tackle more complex issues. And by using sentiment analysis tools to dig into customer feedback, businesses can identify areas for improvement and handle issues before they escalate. “Some even take it a step further with predictive support, where AI anticipates customer needs and proactively addresses issues before they escalate,” says Mohanlall. “Imagine a scenario where a chatbot not only answers questions but also predicts what a customer might need next based on their behavior. This is the kind of innovation that’s transforming customer service.”
IBM Research is making a significant push for industry-wide standardization of AI evaluation metrics through the SaaS release of ITBench, the company’s benchmarking platform for enterprise IT automation. The move elevates what began in February as a limited academic beta into a bid to establish an industry standard for measuring AI effectiveness in IT operations.
With this public release, IBM is officially collaborating with the AI Alliance — a coalition of over 150 organizations, including tech companies, academic institutions, and research labs — to drive broader adoption of standardized AI evaluation methods within the enterprise space.
“We aim to leverage our collaboration with open source communities like the AI Alliance to expand ITBench into new domains and real-world scenarios across complex IT environments,” Daby Sow, Director of AI for IT Automation at IBM Research, told CIO. “By open-sourcing the tool, we are inviting partners to help shape benchmarks and build responsible, standards-based evaluation practices.”
Platform enhancements in public release
ITBench now functions as a complete SaaS implementation with automated environment deployment and scenario execution. “ITBench handles both the setup and execution of enterprise-relevant scenarios, removing the need for manual configuration,” Sow explained.
IBM has also launched a public GitHub-hosted leaderboard that transparently tracks performance metrics across vendors and solutions. “Hosted on GitHub, ITBench leaderboard provides transparent performance tracking, fostering competition and innovation in IT automation,” Sow said.
The framework has also expanded to include more comprehensive scenarios based on feedback from the beta period. The platform now encompasses 94 realistic scenarios across three critical enterprise domains: Site Reliability Engineering (SRE), Financial Operations (FinOps), and Compliance and Security Operations (CISO).