Energy CX Leadership & Management

What’s a quotable hallmark of good management on your team — and how is it reinforced?

Praise in public, criticize in private. In the words of Dale Carnegie, “Any fool can criticize.” Abraham Lincoln had a rule that he never criticized anyone, ever. The greatest president in the history of our country never criticized anyone, so why should I? Throughout the week, there are a lot of opportunities to publicly praise team members for the great work they are doing. As a manager, you have to take advantage of those opportunities. If you have the whole team, department or company together in one place, take advantage of it, and praise someone’s good work. The outcome is two-fold. The individual feels empowered, and the audience is inspired by their work and given insight into how to mirror that success. 

On the other hand, there are absolutely moments where people can do better. Being radically honest about that is crucial, and it’s equally as important that it happens during a one-on-one or in private conversation. You never want people to think that they may be “called out” in front of a large group. It results in scared work, not confident work.

Which forum or ritual keeps priorities and expectations clear?

On a weekly basis, we meet with the team twice: once during our Monday Morning Meeting (MMM), and again during our Wednesday Round Table. MMM is more related to housekeeping and getting everyone on the same page to start the week, while Round Table is my favorite meeting of the week. We usually choose a couple main topics that our head of market intelligence and president of strategy educate the team on, followed by open discussion on areas the team has questions on, ending with a brief venting session, highlighting areas where our team or another team could be doing better. The combination of these two meetings allows management a platform to set expectations and clearly identify weekly priorities, while also providing a platform for team members to shift the conversation toward areas they feel need attention.

What part of the strategy excites people — and what metric shows progress?

I believe that an underlying assumption that everyone is doing their absolute best is essential to managing people. With that, I trust that members of my team are able and excited to get their work done at a high standard. I have found that this creates a space where people are not afraid to ask questions, which ultimately leads to team members striving to be better without any negative reinforcement or “pressure.” 

Ultimately, there are always instances where I have to step in and expedite the educational process, but if at all possible, I prefer to take a hands-off approach with the knowledge that any gaps will be brought to my attention for the sake of their continued success in the role. Beyond tangible metrics and team goals, success is measured through an individual’s eagerness and ability to learn about their customer, their pipeline and the industry as a whole. The ones that have a genuine burning desire to improve are not only the easiest to trust, but the quickest to meet and exceed expectations.

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

Our approach is simple: empower, experiment, measure. 

Empowerment comes first because the best AI use cases don’t originate with me — they originate with whoever feels the friction. One salesperson built himself a sales dashboard to streamline his workflow, and another built an automation that sends quotes directly to customers. Neither was assigned; both were people closest to a problem who were given the tools and the permission to solve it. To help encourage innovating with AI, we ran an internal artifact challenge. We wanted to make that behavior visible and reward it. 

Experimentation is the default posture rather than a stage we schedule. I believe it’s better to ship 10 small things and learn from the three than spend a quarter specifying one.

Then, we measure. Enthusiasm isn’t evidence. If a workflow didn’t save time, improve accuracy or unlock something we couldn’t do before, we say so and kill it. That discipline is what keeps the empowerment from turning into a pile of half-used prototypes — and it’s what gives the team room to keep experimenting.

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

Mostly by lowering the cost of finding out. I run weekly AI office hours. It’s part news about what changed in the AI world in the last seven days and what’s becoming possible, and part clinic, where anyone can bring a piece of their day-to-day work, and we work on it live. Recent sessions have covered what skills are and how to build them, automating recurring work with scheduled tasks and routines, and using artifacts to manage work in the databases we use. 

The effect is that prioritization happens bottom-up with guidance rather than top-down with a mandate. I’m not guessing at which workflows are painful; people tell me, weekly, with specifics. Where I do set direction explicitly is on unstructured data and intelligence. In energy procurement, most of the signal lives in contracts, bills, call transcripts and supplier correspondence, text that was economically unreadable at scale until recently. That’s the highest-leverage frontier for us, so leadership has prioritized deliberate investment rather than opportunistic attention.

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

First, you’ll have real latitude. We don’t route AI ideas through a committee. If you see something worth trying, you’re expected to try it, not to write a proposal about trying it.

Second, you’ll be asked to measure it, and the bar is specific. Before you build, define what improvement looks like. Then, answer the question I ask about nearly everything: Is the declining cost of intelligence applicable here? If a task was too expensive to do well a year ago and is now cheap, that’s where we should be spending our attention. And if the answer is no, this doesn’t get better as models get cheaper and stronger, I want to hear why not. That’s usually the more interesting conversation because it tells you the constraint is data, process or trust rather than capability.

Candidates who thrive here are comfortable being given a direction instead of a spec, and comfortable having their own work evaluated honestly. If you want your ideas taken seriously, this is a good place to bring them.