AI has the potential to boost productivity within organizations, but only once business models have been transformed, according to Ana Paula Assis, Senior Vice President and Chair, EMEA and Growth Markets, IBM.
Assis told the audience at Fortune’s Global Forum in Riyadh that exploring the full potential of AI means adopting “a systemic approach” to the technology: “One that embraces pervasive implementation across the organization, one that integrates silos, one that moves from just experimentation and isolated pilots to corporate-wide adoption,” she said.
According to IBM research cited by Assis, two-thirds of EMEA leaders are already seeing a positive impact from AI in productivity initiatives. Saudi leaders claimed they were seeing the largest positive impact, with 84% of leaders in the region citing AI’s positive impact.
However while the majority of companies are investing rapidly in AI, some have struggled to get the technology past the pilot phase. In the past year, the number of companies running entire workflows with AI has almost doubled, while overall workplace use of the tech has also doubled since 2023. However, a recent MIT Media Lab study found that 95% of the organizations using AI weren’t seeing a clear return on those investments. This was partly attributed to a so-called “learning gap”—people and organizations not understanding how to use the AI tools properly, rather than an problem with the core technology itself.
Integrating new technology into complicated corporate structures can take time. According to Assis, adopting the technology in a “systemic” way means considering data readiness, openness, and trust.
“AI is only as good as the data you use to train it and to augment it,” she said. “We project, or we assess, that only 1% of the data that is today residing in corporate applications in their own data centers is being touched by artificial intelligence. So imagine the opportunities that are going to be created when we expand this reach.”
Assis also noted that corporations often operate within complex technological environments that require integration and coordination across various systems and teams. She emphasized that openness, interoperability, and flexibility in where AI workloads are deployed are essential for successful enterprise adoption of the technology.
“Companies, more and more, are looking for partners, companies, and solution providers that can demonstrate that they’re going to scale this technology in a trusted, responsible way — and that requires orchestration capabilities, security at the core, and governance approaches that allow them to encode in these workflows their guidelines and principles,” she said.
