Enterprises were quick to begin piloting AI tools, but when it came time to scale those projects, a litany of obstacles stood in the way. Perhaps the most prickly of all were governance gaps.
“It’s amazing how fast things are moving, and what’s happened is governance processes also have to evolve,” Joe Depa, global chief innovation officer at EY, told CIO Dive. Traditional governance models aren’t a perfect fit for today’s AI applications.
CIOs have worked to help make adjustments where necessary as pressure builds to show the benefits of a sustained AI focus.
At EY, for example, Depa said executives have created three governance protocols for varying levels of risk per use case. “It’s about creating risk profiles so that you can put the right guardrails in place, so that organizations can innovate responsibly around it,” Depa said.
CIOs whose enterprises are experimenting with the latest AI technologies are wary of the risks associated when moving forward without laying the right foundation. Improper AI access controls can lead to broader data compromise and operational disruption, according to an IBM survey. Governance gaps can also exacerbate the high cost of data breaches and the financial damage tied to poor risk management.
Governance is no longer a term that should scare executives, but rather the safest way to speed up time to value. After all, poor risk management and mitigation are counterproductive and rarely lead to success.
“If you’re providing clarity and guardrails, then letting your team innovate within those lines [is] actually a sweet way to speed up innovation,” Depa said. “Governance really should be the way you get to ‘yes’ responsibly.”
Next year is likely to bring an even sharper focus on AI governance and adequate risk management, especially as more organizations try to scale agentic AI projects.
Here are six stories illustrating the amplified attention on AI governance this year:
