To listen to vendor hype, one might assume that enterprise buyers are fully embracing generative AI. However, like all emerging technologies, large enterprises tend to proceed with caution. Throughout this year, as vendors actively launched new generative AI-driven products, Chief Information Officers (CIOs) have been paying close attention.
In fact, many organizations are looking to either cut costs or maintain their current spending levels rather than seek new investments. The notable exception arises when technology allows these companies to operate more efficiently and maximize their capabilities with fewer resources.
Generative AI holds the promise to enhance efficiency, but it also comes with its own expenses—whether through increased costs for advanced features in a SaaS product or fees associated with accessing large language model (LLM) APIs for in-house software development. Regardless, it is crucial for those implementing these technologies to evaluate their return on investment (ROI). A July survey by Morgan Stanley of large company CIOs revealed a cautious approach: while 56% acknowledged that generative AI was influencing their investment strategies, only 4% had initiated significant projects. Most remain in evaluation phases or are developing proof of concept.
The rapid evolution in this space aligns with insights from CIO discussions as well. Just as consumer IT transformed a decade ago, today CIOs face pressures to deliver experiences similar to those encountered with ChatGPT, according to Jon Turow, a partner at Madrona Ventures.
“Enterprise employees, who are the internal clients of the CIO or CTO, have extensively used tools like ChatGPT,” Turow noted. “They recognize exceptional performance and excellence. Consequently, CIOs are under pressure to meet these elevated expectations.”
This dynamic creates a tension between the desire to satisfy internal stakeholders—often influenced by executives—and the CIO's inherent cautious approach, even in the face of a potentially game-changing technology like generative AI. Implementing such technology requires a structured approach, as Jim Rowan, a principal at Deloitte, suggests. He emphasizes the need to establish the necessary infrastructure for success, which includes more than just technology; it involves identifying the right people, processes, and governance frameworks.
“A significant part of our work with clients revolves around building use cases—applying this technology to address specific organizational challenges,” Rowan explained.
As businesses explore generative AI’s potential, the conversation inevitably turns to regulation on the horizon.
Monica Caldas, CIO at Liberty Mutual, is taking an incremental approach, starting with a proof of concept involving a few thousand employees before scaling up within the company’s 45,000-strong workforce. “We recognize that generative AI will be essential across nearly all facets of our organization. Thus, we are actively investing in diverse use cases to enhance our internal capabilities and support our employees,” she stated.
Mike Haney, CIO at Battelle, a technology and science firm, has been developing specific generative AI use cases this year. “Over the last six to nine months, we've driven a significant push towards AI. Currently, we are at a stage where we are crafting tailored use cases for different teams and functions,” he shared, while cautioning that they are still in the exploratory phase.
Kathy Kay, executive VP and CIO at Principal Financial Group, initiated a fresh start with a study group comprised of about 100 engaged employees, including both engineers and business professionals. “We are currently curating around 25 use cases, with three poised to enter production soon,” she noted.
Sharon Mandell, CIO at Juniper Networks, reported that her company is piloting Microsoft’s Copilot for Office 365. While she has received mixed feedback—ranging from excitement to skepticism—she recognizes the challenge of measuring productivity improvements, despite Microsoft's dashboards that track adoption and usage metrics. “The difficult part is the lack of reliable data on productivity levels. Until we master the insights from Microsoft's dashboards, we are largely reliant on anecdotal evidence,” she explained.
As organizations become increasingly aware of generative AI’s potential, it's natural for them to seek ways to leverage it to improve efficiency. However, it’s equally important to approach this new technology with a measured perspective, understanding that the path to discovering its transformative capabilities requires experimentation.
AI, Enterprise, Generative AI, IT Budgets
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