The concept that velocity is different from speed is one of the core ideas I draw upon when thinking about strategy, leadership, and organizational management. Lately, I've been using this concept to think about how to deploy emerging tech, like Generative AI, within enterprises.

The difference between speed and velocity is crucial. Speed is about how fast we're moving, for example, 55 miles per hour. Velocity, however, describes moving at 55 mph towards a specific direction, like heading East. This distinction has helped me see some nuance when discussing generative AI with colleagues and peers. For example, a computer software engineer can debug code faster using a large language model as a coding partner. While generative AI certainly helps with speed, merely focusing on productivity through speed probably misses the larger opportunity generative AI provides to managers of teams and enterprises.

In this example, improving speed might actually reduce overall productivity and impact, if the software being improved isn't solving a valuable problem in the first place. Here, generative AI would be more useful in helping the software engineer determine which feature would be most relevant and impact for the user. Going faster is only helpful if you're going in the right direction, the most valuable direction, to begin with. Using generative AI to increase speed in the wrong direction would be a missed opportunity.

It might be tempting to think of generative AI as a tool to "make our employees more efficient." However, it would probably be more transformative to use generative AI as a tool to "help our colleagues spend their time on the most valuable problems." This logic doesn't just apply to IT departments. For example, generative AI can help marketing teams draft copy faster, but it's probably more valuable to ensure they're targeting the best possible consumer segment. For operations teams, Gen AI might help to spot and improve manufacturing inefficiencies, but it might be more useful to help spot which product lines aren’t worth producing in the first place.

As an enterprise leader scrambling to deploy Gen AI, it’s easy to assume that the job to be done is to make everyone else more efficient. While this is partly true, business and technology leaders, especially those deploying powerful, emerging, tech like AI, should also contemplate use cases that improve the quality of leadership and strategy in enterprises - even though doing so might indicate that those leaders had it wrong in the first place.

Employing generative AI in a self-aware manner will require a significant degree of humility. But I believe it's worth it. After all, what's the point of heading east faster if we should be going northwest to begin with?

Consider the lesson learned from my own experience at work, which vividly underscores the crucial difference between speed and velocity in the application of generative AI. As a product owner for data, I've seen my engineering colleagues leverage tools like ChatGPT to streamline coding SQL queries, boosting our operational speed. However, a pivotal moment came when I discovered that a dataset we had meticulously prepared and delivered was left untouched by our business customer for months. Which, by the way, indicated that I had made a poor decision on what was worth spending time on.

Despite our efficiency in producing the dataset, it lacked the essential element of value to the customer. This incident revealed a stark truth: our focus on making engineering tasks faster, though beneficial, paled in comparison to the importance of selecting the right targets from the outset. There have been instances where the right datasets, aligned with clear and compelling use cases, saved our customers millions of dollars. The real win, therefore, isn't just in enhancing our engineers' efficiency but in ensuring that our efforts are directed towards creating datasets so valuable and relevant that our customers are eager to utilize them for significant impact from the moment of delivery.

To truly leverage the potential of generative AI within our enterprises, we must go beyond the pursuit of efficiency. The most obvious path is often the least disruptive—enhancing what already exists. However, the opportunity to create significant, long-lasting value lies in our willingness to question the fundamentals of our strategies and leadership approaches. It's about asking ourselves:

• Where are we merely maintaining the status quo when we could be exceeding it?

• In what areas are we failing as leaders and strategists to anticipate and shape the future?

• How can we redefine our objectives to not just improve but transform our enterprise?

This journey requires a substantial dose of humility and a willingness to embrace change, characteristics not often associated with leadership but absolutely critical in this context. Challenging our 'sacred cows' and reevaluating our core assumptions about what our enterprises do can reveal the most impactful opportunities for applying emerging technologies. Let's commit to this introspective and transformative approach, aiming not just to enhance but to innovate and redefine our enterprises for the better.