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informationweek

2026 tech company layoffs How Sedgwick scaled AI in legacy claims workflows InformationWeek Podcast: CTOs on using AI in regulated spaces How top CIOs are measuring the real ROI of IT automation What AI must learn from Roosevelt, conservation and 1929 Experian's chief innovation officer gleans AI gains with startup collab ETS CIO on competing with AI startups 'running with scissors' Before the next VMware: How CIOs prepare for vendor shocks The strategic alignment powering cyber-resilient organizations The AI infrastructure bottleneck is becoming a CIO problem InformationWeek Podcast: CTOs on reining in rogue AI agents Workplace equity in the age of AI Why and how to implement an AI asset rationalization strategy Why companies are shifting toward private AI models AI agents in automation: When to build, when to buy Navan CTO AI on trial: The Workday case that CIOs can The AI infrastructure boom is coming for enterprise budgets How CIOs can manage LLM costs: A practical guide What CIOs miss when buying vertical SaaS software InformationWeek Podcast: How CTOs balance AI and their teams Whirlpool, Duke Energy, Cleveland Clinic CIOs on scaling AI Where CIOs get stuck rebuilding the enterprise: What 'Rewired' reveals As AI makes projects harder to track, will CIOs need new controls? 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CIOs are renegotiating now The AI spend hangover companies didn't plan for The power of CIO networking in the competitive AI world Why CIOs see AI projects stall: Speed without structure kills scale IT leaders should never let a good crisis go to waste SFO's digital twin maps airport operations from the curb to takeoff CIOs caught in the middle as AI startups disrupt vertical Saas Submit an IT Leadership column to InformationWeek Podcast: Rightsizing AI frameworks to avoid failure modes The invisible labor crisis inside IT: AI work the org chart can't see Why AI teams treat training data like capital Ask the Experts: How CIOs can identify and overcome cultural barriers to innovation Nobody told legal about your RAG pipeline -- why that's a problem Meta's new 'AI Zuckerberg' is a mirror for every C-suite Will the music stop for AI's funding dance? 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Chief AI Officer on course-correcting when AI moves too fast
2026-03-13 · via informationweek

5 Min Read

Rob T. Lee

Rob T. Lee is chief AI officer and chief of research at SANS Institute.(Source: SANS Institute)

In this installment of the IT Leaders Fast-5 -- InformationWeek's column for IT professionals seeking peer insights -- Rob T. Lee, chief AI officer and chief of research at SANS Institute, describes how moving too quickly with AI can cause "everything to tilt," using a recent example from his own team. In Lee’s experience, employees will often "default to older processes" so introducing AI too quickly without organizational buy-in can trigger resistance and stall progress. He also discusses why curiosity and the will to keep learning are essential to working in technology today. 

At SANS Institute -- a cybersecurity training, certification and research organization -- Lee focuses on AI governance and security, advising boards, CISOs and government leaders on how to deploy and defend AI systems. He also serves as an amicus curiae technical advisor to the Foreign Intelligence Surveillance Court through the U.S. Department of Justice.

Related:InformationWeek Podcast: Catching hidden errors in AI-powered code

The Decision That Mattered

What decision -- technical or organizational -- made the biggest difference recently, and why?

I'm really trying to influence not only my organization, but other organizations, by instilling a learning mindset. The biggest influence I've had isn't just on [how to deal with] cybersecurity, but really trying to persuade my friends, neighbors and co-workers to admit how much they're struggling [with AI], and to be okay with that. Just go try -- you're not going to be running the marathon tomorrow. 

You need to work on AI like you exercise -- on a daily basis. You need to spend about 30 minutes daily learning AI. It's also about leading by example, including demonstrating vulnerability by saying "Hey, I'm struggling, 'Don't come to me for all the answers that I might not know.'"

The Hard-Won Lesson

What didn't go as planned recently -- and what did it force you to rethink?

People default to older processes. It's really hard once you become almost an "AI superpower" because you start moving at the speed that AI is able to achieve

A recent use case example is that there was a discussion [at SANS] about creating a microsite. I was told it could take weeks or a month and the team would need to do site design, AB testing -- all those things. 

I used Replit [AI] -- and not being a skilled web designer -- I threw together a microsite in about an hour and a half. I shared it with the team leads to have a discussion on it, and they liked it, but then they shared it with the team that does website design. 

Everything kind of went tilt because you end up challenging old dogma -- at the same time showing the potential velocity of AI when not everyone is caught up yet. There's a palpable fear in how fast you went and what it means for what your team does on a daily basis. There's a balance between the shock and awe of being able to leverage AI and getting the organization to mentally open itself up to using these new technologies. It's something I think most organizations are going to struggle with, even outside cybersecurity.  

Related:Ask the Experts: The red flags that signal an AI project isn't worth pursuing

It causes a disruption amongst your normal workflows … that push/pull is definitely hard. More than just the fear of the AI, it almost flies in the face of the folks that said it would take weeks or months and then you almost say, "Well, you're wrong." And people just don't like being told that they're wrong.

But, I'm definitely sensitive to how that's perceived by current teams. Introducing it, messaging it right -- that needs to be shown by the example of the leaders. I may have messed up by not messaging it right to the other leadership. [The response] was either very positive or very negative -- it was very polar. Ultimately, the organization said, "Let's do it," and everyone was on board with trying to figure it out.

The Talent Trade-Off

Related:Shutterstock CTO's playbook for scaling AI without vendor sprawl

Where are you investing in talent right now -- and what are you consciously not investing in?

It's very easy to say that I would like folks who are AI skilled to come in, but that's way too binary, and it doesn't exist right now. What I'm really looking for is a capacity and will to consistently learn, and a passion to be the expert in your job. 

The desire to learn almost becomes the most important quality in hires. It's that ability to get through those initial humps that are a struggle. It's similar to exercising -- it's hard to motivate yourself to go in there and feel frustrated. I always tell people if you're not frustrated, you're not learning. If you're not frustrated, you're one of two other things -- you already know it, or you don't care. 

The External Signal

What recent external development is most likely to change how your organization operates, even indirectly?

Some of the recent developments [with AI] are really about trying to figure out workflows that can be optimized through the tools that are available to the workforce. Working through a couple workflows with folks to get them going is essential, but that requires mentorship. When I'm starting out with folks [on using AI], we're using ChatGPT for basic analysis, or even writing and consolidating email or Slack messages. You could use AI to potentially make suggestions on how to improve message receptivity. Just basic communication input through AI has been one of the biggest advantages in the company to be able to change our cultural understanding of each other. So I start small. 

The Perspective Shift 

What have you read, watched, or listened to recently that changed how you think about leadership or technology -- even slightly?

I plowed through this book in about a weekend, I listened to it on audio. It's Pablos Holman's book Deep Future: Creating Technology That Matters

It gets to the core of how different technologies need to be viewed, and the concept of hacking -- just figuring things out. It gets you to pause and look at things through a different lens. 

My biggest thing is to never stop learning. And learning sounds very kitschy, but … [learning] is not just for work purposes …It could be learning a new sport, painting or piano. Podcasts that offer that kind of inspiration and learning are also helpful for me.

Get more IT leadership updates and insights three times a week direct to your inbox with the InformationWeek newsletter.

About the Author

Kelsey Ziser

Senior Editor, InformationWeek

Kelsey Ziser is a senior editor at InformationWeek, where she covers C-suite dynamics, data strategies and the evolving cybersecurity threat landscape.

Kelsey also oversees the publication's IT Leaders Fast-5 column, which brings peer insights to IT professionals.  

Before joining InformationWeek, she spent nine years at sister publication Light Reading, reporting on broad range of topics including smartphones and devices, AI, satellite connectivity, and enterprise networking. Outside of work, she enjoys reading four (or 12) books at once, watching movies about space travel, crafting and tending to an ever-growing collection of houseplants. Kelsey is based in Raleigh, N.C. She can be reached at [email protected]