AI Impact: What’s Slowing AI Down Right Now?
Editor’s note: This is AI Impact, a weekly newsletter exploring how business leaders unlock real value through artificial intelligence.
Core Intelligence
When Governance Enables Speed
As artificial intelligence matures, many organizations discover that the biggest obstacle to scale isn’t capability—it’s confidence. In a preview of an upcoming AI webinar titled “AI Governance: Balancing Innovation and Risk,” Suraj Srinivasan, a professor at Harvard Business School and the webinar’s host, framed the current slowdown as a rational response to unresolved risk rather than a failure of ambition.
“Organizations are quite rightfully concerned about risk and safety—privacy, customer harm, mistakes,” Srinivasan said. “But that means they can’t get the most value from what the technology is capable of doing.”
This tension explains why many AI initiatives remain confined to narrow pilots. The technology is powerful, but the systems needed to oversee it—clear accountability, escalation paths, and shared risk frameworks—are still catching up. Without those guardrails, leaders default to caution, even when the upside is obvious.
Srinivasan’s most useful reframing is that governance is not a brake on innovation, but the condition that makes acceleration possible. “The engine is powerful,” he said, “but we are deliberately unable to use the maximum potential…because we are still behind in figuring out the risk, safety, and governance aspects of it.” His analogy was blunt: “If you can’t create good brakes, you can’t put in powerful engines.”
Implications for Leaders
The implication for leaders is structural. AI maturity will not be defined by who experiments first, but by who builds the institutional muscle to manage uncertainty at scale. Companies that treat governance as an afterthought are likely to keep AI boxed into low-risk use cases. Those that invest early in oversight, ownership, and clarity position themselves to unlock far more of the technology’s value.
As Srinivasan put it, the point of good governance isn’t restraint—it’s control. “The idea of the brakes is not to go slow,” he said. “It’s to help you steer—and actually go fast.”
Upcoming Webinar
AI Governance: Balancing Innovation and Risk
Join Prof. Suraj Srinivasan of Harvard Business School and Keith Enright, partner at Gibson Dunn, co-chair of both the firm’s Tech and Innovation Industry Group and the Artificial Intelligence Practice Group, and the former chief privacy officer at Google, for an essential webinar exploring the high-stakes intersection of AI innovation and risk. The live webinar will take place on Tuesday, February 24, at 1 p.m. Eastern. Register for free now.
AI Use Case of the Week
For Bonutti Technologies, the challenge wasn’t proving that UV-C works; the science behind ultraviolet disinfection is well established. The harder problem was execution: applying UV-C correctly, safely, and consistently across real-world clinical environments.
Dr. Peter Bonutti, who leads the company, said the gap between laboratory efficacy and day-to-day use became increasingly clear. “When UV-C is applied correctly, it can reduce targeted pathogens by up to 99.9 percent, but manual processes vary, environments differ, and protocols are often compressed.”
To close that gap, Bonutti Technologies built AI directly into its UVCeed system to manage both safety and dosage. Using integrated cameras and sensors, the system evaluates spatial and operational factors in real time, adjusting UV-C delivery based on the specific environment.
The result has been greater confidence and repeatability. Treatment times have been optimized, and facilities have gained a more standardized disinfection process aligned with high pathogen-reduction targets.
Context Window
Super Bowl advertisers made AI a blockbuster marketing theme, with companies like Databricks and Cisco rolling out major updates to their AI offerings.
These developments signal a shift: spending on AI infrastructure and data centers is set to drive global IT budgets in 2026, with AI technologies growing far faster than other categories.
Transfer Protocol
Tracking executive moves across the AI landscape reveals a dynamic shift in leadership, with key appointments at companies focusing on AI-driven solutions for various sectors.
Magic Moment
One unexpected and rewarding use of AI has been in modeling real-world motorcycle riding behavior to help prevent incidents. By combining radar, cloud analytics, and mobile systems, the platform identifies patterns in how riders respond to traffic and obstacles, enhancing safety measures.
In conclusion, the current landscape of AI is shaped by governance, execution, and innovative applications that promise to unlock significant value across industries.
