Gradient AI Leadership & Management

What’s the leadership like on the AI and machine learning team at Gradient AI?

Leadership is split between developing AI/ML as part of our core product offering, which I lead, and the more general use of AI for everyday productivity across all our employees, which is a function that falls under our CTO. As AI becomes increasingly commoditized, these lines have started to blur. For example, I have led R&D to summarize customer call transcripts in the past, and now that is a standard offering in nearly every application managing text data. Good leadership must work together to define what is a core competency of our company requiring deep investment in R&D, and what is a commoditized or generally available feature which is better sourced externally, whether to enable our core products or our employees. Doing that well at a leadership level requires deep expertise in AI science and technology to be able to build what you need, broad experience across markets and product leadership to know how to buy what you can, and the wisdom to know the difference.

How does leadership help your team decide where to focus AI or ML efforts?

The best leaders explicitly do not decide where to focus AI or ML efforts. The best leaders decide where to focus overall investment to solve the problems that need to be solved. At the end of the day, our job is to create a product that creates value for our customers, and the most important part of that job is to focus on the value proposition and the customer need. This is what we call “customer-obsessed.” Exactly how we solve these problems — with AI, with traditional data science/machine learning techniques, with human-in-the-loop or even rote automation — is an implementation detail. 

My role as an AI/ML leader is to help envision the “art of the possible” with leadership and create possibilities for our customers, but an equally important part of my role is to reframe the problems as “non-ML” for leadership so they can solve them in more appropriate ways, with more appropriate teams. Leadership decides what to build, not how to build it, and my role in collaboration with other technical leads is to enable that with the best possible outcome and ROI.

What should candidates know about how your team balances strategic direction with experimentation?

I like to think about “strategic experimentation.” Experimentation serves many purposes: It sometimes unlocks revolutionary tech, but sometimes, it’s needed for team bonding or learning opportunities. Many teams “experiment” without being strategic about what they want to accomplish. This can lead to disappointment when there is a mismatch between what the employees and the company want to get from the time. Like everything, better results come when we act strategically and with purpose. 

To help my team grow as leaders, I set a blanket five to 10 percent time allowance for any experimentation that is clearly justified by our goals. Want to try a new technology, read a few long-shot papers or try a new code design? It’s your time to invest, and your leadership is demonstrated by how well you manage these bets and if they consistently pay off or not. Other techniques, like hackathons, can also be successful if there is strategic alignment; a hackathon intended for team bonding will be very different from one intended to try five moonshot techniques and decide which are worth pursuing. Being clear about what we are trying to accomplish makes a huge difference in the impact we drive.