Will Generative AI be our Rocket Science?

I was reflecting on a phrase someone shared with me recently—one I’m sure you’ve heard before: “Well, it isn’t rocket science.” It made me smile, as I had written about this concept a while ago, asking if nothing is rocket science what complexity is left to solve? Then, with everything happening around AI, I began wondering: how close are we to seeing Generative AI as our rocket science?

Some of you might be rolling your eyes or wondering how I could compare Generative AI to rocket science. Let me explain.

Rocket science is often regarded as the pinnacle of human ingenuity. The sheer number of variables that must be managed is staggering: propulsion systems, guidance and navigation, structural integrity, thermal protection, and data communication, just to name a few. Each element requires specialized expertise, and they all need to work harmoniously for a mission to succeed.

This endeavor is not a solitary one. Rocket science demands the efforts of cross-functional teams—engineers, computer scientists, materials experts, and project managers. These teams must communicate, collaborate, and adapt swiftly to emerging challenges. A single miscalculation can lead to catastrophic outcomes in terms of cost or even loss of life.

So why would I compare this to Generative AI?

The adoption of Generative AI specifically within businesses mirrors the complexity and collaborative nature of rocket science. When we see Generative AI as a business capability first and a technology second, it becomes clear that in its integration to the business requires a robust framework, not just an IT implementation. Generative AI, like rocket science, brings together multiple components—IT architecture, data management, business processes, ethical considerations, HR change management, governance, security, and strategy. Each of these alone can affect the success of a Generative AI initiative, but combined, they represent a level of complexity most businesses haven’t confronted since the dawn of the digital age.

Just as with rocket science, all these elements must be carefully calibrated to function as intended and drive business outcomes.

Cross-Functional Collaboration is Key

Cross-functional collaboration is just as critical in Generative AI. Data scientists, machine learning engineers, domain experts, business strategists, process owners, ethicists (a role that will soon become commonplace), vendor managers, change managers, and security officers must work in unison to ensure Generative AI solutions are successfully deployed and integrated. The interdisciplinary nature of AI demands diverse perspectives and expertise, all working toward a common goal—realizing persistent business value from AI.

You might be thinking, “But we don’t have all these roles working together in my organization and it seems fine today.” If you're experimenting with Generative AI or using it to provide basic knowledge management, then you don’t need such broad collaboration. However, when your goal is to realize long-term value from AI use cases, a different approach is required. Simply implementing a use case won't suffice.

Most business are placing a significant focus on data, there is a clear appreciation on the importance of leveraging real time, consistent and quality data as a key success lever for AI— which is true, but data alone isn’t enough. Every Generative AI use case impacts processes, people, security, ethical monitoring, and even vendor contracts. Imagine a common business process like “Book to Bill.” In most organizations, you’d find that various teams have developed manual workarounds for process deviations. Now imagine how an AI use case would function if more employees followed these exceptions than the standard process. The AI agent supporting the use case would likely deliver flawed outcomes, and the use case would be perceived as failing to generate value.

Now scale this challenge. Think about what happens when you manage 30 AI use cases, or, as some businesses are working on, over 100 across multiple workflows, all with the aim of reducing costs or driving revenue growth. At this scale, the comparison to rocket science starts to feel real.

Leadership Must Drive AI Programs

Why am I making this connection? Because launching a rocket into space requires a detailed program framework, strong governance, and leadership oversight. There’s a clear balance of empowerment, cross-functional collaboration, and program guidance aimed at specific goals. The same principles apply to Generative AI.

I would encourage business leaders to reflect on their own organization’s AI journey. Who is the most senior person driving AI full-time? In many cases, it’s someone three layers down from the CEO is this high enough to have leadership visibility? Is there a programmatic approach with business leaders taking ownership of outcomes? Are vendors being held accountable for results? Are your AI service providers and system integrators committing to delivering large-scale benefits? If reading this article makes you pause and reflect on your own AI strategy, that’s a good start.

Complexity is Okay—It’s About Coordination

It’s okay for things to be complicated. Running a business is complicated. But just like a complex machine, all parts must work together seamlessly. Learning to integrate AI effectively is a process, and it’s one we must undertake together. This creates a culture of ownership, innovation, and collaboration—something we can draw valuable lessons from in the meticulous efforts of rocket science.

Both endeavors, when executed with precision and synergy, have the power to propel us toward remarkable advancements.

As always, this is just my perspective. I’d love to hear your insights. If you have a different view, please share. I don’t presume to have all the answers, and this article is based on my experiences and discussions with various executives I’ve had the privilege to engage with.