CIO insights: What’s next for AI in the enterprise?

CIOs are under increasing pressure to deliver AI across their enterprises – a new reality that, despite the hype, requires pragmatic approaches to testing, deploying, and managing the technologies responsibly to help their organizations work faster and smarter. 

The top brass is paying close attention. Seventy-one percent of business leaders expect AI and ML to have a worldwide impact, according to the Workday in the AI Indicator study, the No. 1 concern is that IT leaders will face pressure to make difficult decisions about where to apply AI and ML. 

Those decisions will have far-reaching impact across the organization. IT leaders expect AI and ML to drive a host of benefits, led by increased productivity, improved collaboration, increased revenue and profits, and talent development and upskilling. As AI and ML evolve, so will the skills of the humans supporting these initiatives. 

“A lot of new roles are going to emerge in the next couple of years as some of the existing roles become less important,” says Prashant Nema, global CIO at Arch Capital Services, in the report. “There has to be an ongoing focus on making sure that your talent is continuously learning and developing.”

A data-driven foundation

Of course, a dose of caution is in order, particularly with newer AI offshoots such as generative AI. IT leaders understand that the models are only as good as the information on which they are educated. Outrageously inaccurate ChatGPT musings are just an opener for what could later be catastrophic mistakes predicated on bad data. 

In the AI Indicator Report, almost 60% of IT leaders conceded that their company’s data is somewhat or completely siloed, making it difficult for AI and ML to leverage fully.

“Our biggest blocker to unleashing the power of AI is uncertainty over the integrity of the dataset it’s working from,” Dan Cohen, CIO and director of operations at The Amenity Collective, says in the report. “Our internal data and adherence to process is where our focus is, and we don’t necessarily want to leap ahead until we feel like we have a stable footing there.”

Ensuring data integrity is part of a broader governance approach organizations will require to deploy and manage AI responsibly. The . “The thing that’s dawning on everyone is that it’s tough to see any sector in the economy that isn’t going to be adopting these tools.” 

In the same podcast, Ajay Agrawal, professor at the University of Toronto’s Rotman School of Management, recommends that every company pick at least one AI project in a key business area to get started: “Unlike any other tools our human civilization has used before, AI learns, so it gets better with use. The people who sit on the sidelines will miss all that learning time, and those that are building their AI now [will gain] the advantage.” 

For more insights, strategies, and best practices for IT leaders, visit Workday’s CIO Insights.