Artificial intelligence is moving faster than any technology wave I’ve seen in my career. But speed alone isn’t the real story. The deeper challenge is how organizations adapt – how leaders think, decide, and build capacity when tools, vendors, and expectations are changing in real time.

That’s why I wanted the very first acceligence videocast to focus less on AI tools and more on leadership. In the conversation accompanying this article, I sat down with Brad Wheeler – James H. Rudy Professor of Information Systems at Indiana University’s Kelley School of Business and former Vice President for IT and CIO at Indiana University – to explore what feels familiar about this moment, what is genuinely different, and what leaders are getting right and wrong as they respond.

Brad brings a rare combination of deep research and real executive experience. Over decades, he has lived through multiple waves of technology-driven change, not just studying them but leading organizations through them. In our discussion, he reflects on why organizational choices matter more than technology itself, how the role of the CIO is evolving, and why building sustainable capacity is far harder – and far more important – than chasing the next new capability.

I hope you enjoy this format for our first videocast. For those who enjoy reading, I have summarized our conversation below the videos, though I encourage you to check out the full videos for deeper insights, stories, and reflections from a leading executive and scholar of the industry. We will be posting more videos, interviews, insights, and events on our YouTube and Vimeo channels in the future, so be sure to subscribe, like, and follow!

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Organizational change always matters more than the technology itself

Justin: Brad, you’ve spent years as both a CIO and an information systems scholar. Looking back, what experiences most shaped how you see technology-driven change today?

Brad: I’ve always been drawn first to organizations rather than to technology as an object of fascination. Technology has been the constant companion throughout my career, but it has never been the primary determinant of success or failure. What matters is how organizations behave when new capabilities disrupt how work is coordinated, how decisions are made, and how power is exercised.

Even in the late 1980s and early 1990s, there was serious thinking about what post-industrial organizations might look like – how information flows, networks, and digital coordination would reshape authority, decision rights, and structure. I had the benefit of studying those ideas academically and then watching them collide with reality as organizations attempted to implement them.

What became clear very quickly is that technology initiatives almost never fail because the technology doesn’t work. They fail because organizations don’t change. Leaders underestimate culture, incentives, political dynamics, and the difficulty of sustained transformation. They assume that installing a system is equivalent to changing behavior.

I saw this repeatedly during the rise of the internet, with enterprise systems, and later with cloud computing. Organizations spent enormous sums of money trying to bolt new capabilities onto old structures, hoping the technology would do the hard work for them. It never did. AI doesn’t change that dynamic. If anything, it amplifies it. The organizations that succeed will be the ones that confront organizational change head-on rather than hiding behind technical sophistication.

AI feels familiar in many ways – except for the pace

Justin: As you look at AI today, what feels familiar from earlier waves of change, and what feels genuinely different this time?

Brad: From an organizational behavior standpoint, much of this feels very familiar. Leaders are once again tempted to reuse old playbooks – creating isolated innovation units, outsourcing transformation, or letting dozens of disconnected initiatives bloom without coherence. Those instincts were visible in earlier digital transformations, and they’re visible again now.

What is genuinely different is the pace. I often say that ninety days in AI feels like a year in earlier technology cycles, and I mean that quite literally. The speed at which capabilities improve, diffuse, and become normalized is unprecedented. Models evolve quickly, expectations shift rapidly, and what felt cutting-edge six months ago can feel outdated today.

This compresses planning horizons dramatically. Organizations don’t have the luxury of multi-year deliberation cycles anymore. In higher education, for example, we’re preparing for students who will arrive having lived with generative AI throughout their entire high-school experience. That reality forces rapid rethinking of curriculum, pedagogy, assessment, and credentialing.

So leaders are dealing with familiar organizational challenges under extreme time pressure. That combination is unforgiving. It exposes weak governance, unclear leadership roles, and brittle organizational structures very quickly.

Higher education is under pressure because AI creates personal “jaw-dropping” moments

Justin: You’ve described higher education as being in a “hair-on-fire” moment. What are you seeing that makes it feel so intense?

Brad: What makes this moment different is that AI is not arriving as an abstract institutional capability. It’s arriving as a deeply personal experience. People aren’t just reading about it or hearing about it in leadership briefings – they’re encountering it directly in their own work, often in ways that fundamentally challenge their assumptions about expertise, effort, and value.

Faculty members are seeing AI systems generate feedback, analysis, and coaching at a level that forces immediate reconsideration of assignments they’ve relied on for years. In some cases, AI can produce work that looks indistinguishable from what a strong student might submit, which immediately raises uncomfortable questions about assessment, learning outcomes, and academic integrity.

Students are having similar experiences. They’re realizing that tools can generate polished outputs almost instantly, sometimes without a corresponding depth of understanding. That creates both opportunity and risk. On one hand, it lowers barriers to expression and exploration. On the other hand, it exposes gaps in comprehension that traditional assignments were never designed to reveal.

Staff and administrators are experiencing this as well. Workflows that once took weeks or months can now be compressed into days. That changes expectations almost overnight. Once someone has a “jaw-dropping” moment – whether it’s AI assisting with research synthesis, medical reasoning, legal analysis, or curriculum design – it becomes impossible to pretend nothing has changed.

That accumulation of personal experiences is what creates the sense of urgency. This isn’t hype-driven anxiety. It’s lived reality colliding with institutional structures that were designed for a much slower world.

Teaching has to assume AI is present, not pretend it isn’t

Justin: How are you thinking about AI entering the classroom and changing how learning actually happens?

Brad: I’ve fundamentally redesigned my teaching to start from the assumption that AI is present. Trying to ban it or pretend it doesn’t exist is not only futile, it’s pedagogically misguided. It distracts from the real educational challenge, which is helping students develop judgment, reasoning, and the ability to explain and defend their thinking.

The question is no longer whether students are using AI. The question is whether they understand what they’re producing, why they’re producing it, and what tradeoffs are embedded in their choices. That requires a shift away from static artifacts and toward demonstrations of thinking.

In practice, that means assignments that emphasize framing, synthesis, and explanation rather than output alone. It means asking students to justify their assumptions, walk through their logic, and reflect on where AI helped them and where it fell short. In some cases, it means using AI itself to probe understanding through adaptive questioning – effectively simulating oral examinations at scale.

This approach changes the role of the instructor as well. Faculty become coaches and evaluators of thinking rather than gatekeepers of information. That’s uncomfortable for some, but it’s far more aligned with how work actually happens now.

Ultimately, education has to prepare students for a world where AI is part of the environment, not an exception. Pretending otherwise does a disservice to everyone involved.

“Good enough” answers matter more when time is scarce

Justin: You’ve written about certitude and answers that are “good enough for context.” How does that idea apply to AI?

Brad: Organizations rarely operate with perfect information. Decisions are made under time pressure, with incomplete data, and often with competing objectives. That reality hasn’t changed with AI – if anything, it’s become more pronounced.

In that context, a perfect answer delivered too late is often far less valuable than a timely answer with reasonable confidence. What AI does extraordinarily well is accelerate access to those “good enough” insights. It helps frame problems faster, surface options more broadly, and reduce the cognitive effort required to get started.

The danger isn’t that AI produces imperfect answers. Humans have always done that. The danger is when people lack the experience or expertise to know when an answer is wrong, misleading, or incomplete. That’s where judgment still matters enormously.

What I see emerging is a widening gap between people who know how to supervise AI thoughtfully and those who either overtrust it or reject it outright. Over time, that gap becomes a source of competitive advantage or disadvantage at both the individual and organizational level.

AI doesn’t eliminate the need for judgment. It makes judgment more important.

The CIO role matters more than ever – if it’s defined correctly

Justin: How do you see the CIO role evolving in the age of AI?

Brad: The CIO role matters more now than it has in a long time – but only if it’s defined and positioned correctly. Organizations that treat the CIO as a technical operator or a service provider rather than as a business leader create structural problems that compound over time.

An effective CIO operates at the executive table. They understand the business deeply enough to translate strategy into technology decisions and technology capabilities back into business outcomes. They manage cost, risk, and agility as an integrated portfolio rather than as isolated concerns.

AI raises the stakes dramatically. Decisions about architecture, data, governance, and vendor relationships have long-term consequences that are difficult to unwind. Without strong executive technology leadership, organizations either move too slowly or fragment themselves through uncoordinated experimentation.

Titles matter less than positioning, trust, and influence. But when the CIO role is poorly defined, organizations pay for it repeatedly – in missed opportunities, increased risk, and brittle systems.

Innovation requires capacity, not just enthusiasm

Justin: How should leaders balance experimentation with sustaining and running their business?

Brad: Experimentation is essential, especially in a fast-moving environment like AI. But unchecked experimentation leads to fragmentation, duplicated effort, and technical debt that’s very difficult to unwind later.

The solution isn’t heavy governance or shutting experimentation down. It’s intentional capacity. Organizations need small, focused teams that are explicitly chartered to explore ideas, build prototypes, assess time-to-value, and make disciplined decisions about what to scale and what to stop.

Crucially, that capacity has to be carved out. You cannot expect already-overloaded teams to innovate on top of their existing responsibilities. When leaders do that, they’re not encouraging innovation – they’re creating burnout and chaos.

Disciplined experimentation is a leadership choice. It requires clarity about priorities, willingness to say no, and the courage to stop initiatives that aren’t delivering value.

Durable AI value comes from flexibility, not lock-in

Justin: Where should organizations invest to build AI capabilities that last?

Brad: I’m cautious about deep, early lock-in to vertically integrated vendor stacks. While embedded AI features can deliver real value in the short term, they often reduce flexibility and increase long-term switching costs.

Organizations benefit from maintaining architectural independence, especially when they need to span vendors, integrate diverse data sources, or adapt as models evolve. Flexibility becomes a strategic asset in an environment where capabilities change so quickly.

There are also economic considerations. At scale, perpetual renting can become inefficient, particularly when proprietary data and sustained usage are involved. Leaders need to think carefully about build-versus-buy decisions and the long-term implications of each.

The goal isn’t to avoid vendors. It’s to avoid dependency that limits future choices.

Human skills become more important, not less

Justin: What human capabilities matter most in an AI-shaped future?

Brad: Despite all the technological change, human skills become more important, not less. Critical thinking, ethical judgment, collaboration, persuasion, and problem framing are exactly the capabilities that AI cannot replace.

AI amplifies intent. In the hands of capable, ethical leaders, it can be transformative. In the hands of poorly prepared or unethical actors, it can be destructive. That places a premium on judgment and responsibility.

Education and leadership development need to reflect this reality. We’re not just teaching people how to use tools. We’re shaping decision-makers who will determine how those tools are deployed and what consequences follow.

In hindsight, we may realize we were both too slow and too naive

Justin: When we look back in ten years, what do you think we’ll say we misunderstood?

Brad: I suspect we’ll realize we were slower than necessary to hand off certain tasks machines were already capable of doing well. At the same time, we may regret not establishing clearer guardrails earlier, particularly at geopolitical and societal levels.

As with every major technology wave, outcomes won’t be inevitable. They’ll be shaped by the choices we make – or fail to make – along the way. That’s both sobering and empowering.

Closing reflection: leadership sets the ceiling

This conversation reinforced something I see repeatedly in my work with executives and boards: AI is not primarily a technology problem. It’s a leadership problem.

Tools will evolve. Models will improve. Vendors will come and go. What endures is how organizations choose to lead – how they define roles, build capacity, invest intentionally, and keep humans firmly in the loop.

If there’s one takeaway from this discussion with Brad, it’s this: pace is real, but panic isn’t required. Leadership, judgment, and organizational design will determine who keeps up – and who falls behind.