Adopting Generative AI in Enterprises: Cautious Approach by CIOs Prevails
Despite the enthusiastic claims from vendors about the widespread adoption of generative AI among enterprise buyers, the reality remains that large corporations typically proceed cautiously with newer technologies. Amidst a flurry of announcements for new generative AI-driven products throughout the year, Chief Information Officers (CIOs) within these companies took notice. This measured approach underscores the deliberate consideration and assessment that precede the incorporation of such cutting-edge technologies into enterprise operations.
Some companies have actually been looking to cut back on spending, or at least stay even, not necessarily looking for new ways to spend money. The big exception is when technology enables companies to operate more efficiently, and do more with less.
Generative AI certainly has the potential to do that, but it also has its own costs associated with it, whether it’s a higher cost for these features in a SaaS product or the price for hitting a large language model API if you’re building your own software internally.
Either way, it’s important for the folks implementing the technology to understand if they are getting a return on their investment. A July Morgan Stanley survey of large company CIOs found that many were proceeding cautiously, with 56% of respondents reporting that generative AI was having an impact on their investment priorities, but only 4% had actually launched significant projects. In fact, most were still in the evaluation or proof of concept phase. This may be a fast moving area, but it fits with what we’re hearing in conversations with CIOs as well.
That said, much like the consumerization of IT a decade ago, CIOs are under pressure to deliver the kind of experiences people are seeing when they play with ChatGPT online, says Jon Turow, a partner at Madrona Ventures.
“I think it’s undeniable that enterprise employees, who are the internal customers of the CIO or CTO, have all tried ChatGPT and they know what amazing looks like. They know where it’s early, and they know where it’s inspiring, and for lack of a better word, where they see greatness. And so CIOs are under pressure to deliver that level,” Turow told.
The drive to cater to internal customers, especially under potential pressure from CEOs, clashes with a CIO’s innate tendency to proceed cautiously, even with the potential transformative impact of generative AI. This tension necessitates the establishment of structured frameworks for its gradual implementation, as highlighted by Jim Rowan, a principal at Deloitte engaged in guiding clients on systematic generative AI integration across enterprises.
Rowan emphasized the importance of devising a comprehensive infrastructure for success. This infrastructure, beyond mere technology, encompasses defining key personnel, processes, governance protocols, and imparting the necessary capabilities for effective setup. “A significant aspect involves discussing practical use cases and strategizing how the technology can effectively tackle specific challenges,” remarked Rowan, shedding light on the pivotal role of application-focused discussions in harnessing the potential of generative AI.
This is in line with how CIOs we spoke to are approaching implementing this in their organizations. Monica Caldas, CIO at insurance company Liberty Mutual, started with a few-thousand-person proof of concept, and is looking for ways to expand that for her 45,000 employee company.
“We know generative AI will continue to play a critical role in virtually every part of our company, so we’re investing in many use cases to further develop and refine them in service of supporting our employees and giving them better internal capabilities,” she said.
Mike Haney, CIO at Battelle, a firm focused on science and technology, has also been exploring generative AI use cases this year. “So we’ve been doing this whole push for AI over the last maybe six or nine months and we’re at the point right now where we’re building specific use cases for each different team and function within the firm.” He cautions that it’s early, and they are still exploring ways in which it can help, but so far the results have been good in terms of offering more efficient ways to do things.
Kathy Kay, executive VP and CIO at Principal Financial Group, a financial services company, says her company started from scratch with a study group. “So any employees who had an interest or passion, we allowed them to join so there’s about 100 people. It’s a combination of engineers and business people, and we are curating probably 25 use cases now that they’ve gone through, and three will be going into production [soon],” she said.
Sharon Mandell, CIO at Juniper Networks, revealed her company’s participation in an initial pilot with Microsoft, focusing on Copilot for Office 365. While she’s encountered varied feedback, ranging from enthusiastic endorsement to reserved reception, gauging increased productivity remains a persistent challenge. Mandell highlighted Microsoft’s initiation of dashboards that exhibit adoption and usage levels, yet quantifying enhanced productivity poses difficulties.
“The crux here lies in the absence of concrete productivity data. We rely somewhat on anecdotal information until we gain proficiency in interpreting Microsoft’s usage dashboards,” explained Mandell, elucidating the current predicament.
As enterprises grasp the potential potency of generative AI, a natural inclination arises to delve deeper into its capabilities for optimizing organizational efficiency. Simultaneously, executives exercise warranted caution, acknowledging the nascent stage of these technologies. Mandell emphasized the need for a learning curve through experimental approaches to discern the true transformative nature of generative AI in these early phases.
