We’re excited to announce that Bonney Pelley has joined Distributional as Chief Operating Officer. She was previously COO at mParticle and Senior Vice President of Strategy and Operations at New Relic, where she helped the company double both its revenue and its employee base. She is also an LP in the Operator Collective, an early investor in Distributional. Bonney brings over 25 years of senior management experience to her role at Distributional, where she will focus on unifying strategy across the company.

I sat down with Bonney for a brief Q&A about how she approaches the role of COO, what she did as “Chief Summer Vacation Officer” this summer, and what’s next for her at Distributional.

How are you approaching the role of COO at Distributional?

There is no standard definition for the COO role. Sometimes it can cover GTM functions and look more like a CRO role—sometimes it can look more like a Chief of Staff or be primarily administrative. We already have a great CRO at Distributional (learn more about Nick through his blog series on AI/ML lessons), and personally, CoS roles have never appealed to me. Instead, I’ve always been most drawn to roles where I can both help the company develop a clear strategy and a plan to execute against that strategy.

As Distributional’s Chief Operating Officer, my job is to help the executive team define our strategy and then work with the entire company to develop our execution plans. I have a core remit that covers BizOps, Finance, IT, and People, but I’m always looking across the company at our people, processes, and systems to make sure we have what we need in place to execute. My priorities and how I spend my time and my team’s time need to shift to reflect Distributional’s priorities. It’s never static. I always tell my teams that I can’t tell you exactly what you will be working on in six months, but I can promise that you will be working on something important to the business.

What was your background prior to joining Distributional?

I’ve led strategy and operations for SaaS companies for a long time. I moved to Silicon Valley right after college, and despite many booms and busts, I have always loved the industry’s ability to innovate and solve hard problems. 

More recently at both New Relic and mParticle, I gained a lot of experience with technical enterprise software buyers and users. You need to be set up to support both of these personas throughout their customer journeys. Working for later-stage companies, my teams and I had to tackle lots of problems that were created because the company didn’t put the right foundations in place early on to handle scale and growth. These were great experiences that taught me so much that I’m excited to bring with me to my work at Distributional. In a way, I get to be a time traveler who goes back and fixes problems before they become problems in the first place.

You took some time off as “Chief Summer Vacation Officer” to travel with your family between jobs. What was it like joining the Distributional team remotely while traveling the world? 

I started working with the Distributional team in July as a consultant after being introduced to Scott through Operative Collective, a venture fund of badass (if I’m allowed to say that here!) technical operators that I’m super proud to be part of. I left my previous job in July, and I wanted to take the summer to spend time traveling with my family.

My husband and I are currently worldschooling our two teenage sons and exploring ancient cities and sites all around the Mediterranean. I’m a huge history fan and being able to see all the sites I’ve been dreaming of visiting since I was a kid (the Dolomites, Pompeii, the Roman Forum and the Acropolis, to name a few) has been a dream come true. It has been so good to recharge and let my mind wander a bit. Now I feel ready to rumble! One of the pieces of advice I give to colleagues and friends is to take time between gigs to refresh, recharge, and take the time to do things you have always wanted to do.

I joined Distributional full-time on October 1st, and I was very fortunate to celebrate my first day at the company at our all-company fall offsite in Brooklyn. It was so great to meet everyone face-to-face and get a better sense of what to prioritize. You always have to be flexible and make adjustments based on new data and feedback from the team. I always say that 30/60/90 plans are like birth plans. It’s good to have a plan, but things don’t ever quite go the way you expect.

What appealed to you about Distributional?

What is the opposite of a creature of habit? A creature of change? That’s me. I enjoy challenging myself to learn something new and maybe getting a little uncomfortable. The opportunity to work for a startup and gain more experience with early-stage strategy and the hard work it takes to nail product market fit excites me.

And then there’s AI. There is no question at this point that we’re witnessing a technology paradigm shift of a colossal magnitude. Like the Copernican Revolution or the Industrial Revolution magnitude. I want to be part of that! And while there are tons of companies out there trying to figure out how to make money leveraging LLMs, I think Scott and the team at Distributional are solving a real problem that’s quickly developing for many companies as they try to figure out how to harness the power of AI without creating operational risk.

Plus, addressing the operational risks that production software can create is a career theme for me. At New Relic, I focused on helping software teams move faster with confidence by providing them the ability to observe how their software behaved in production and quickly triage problems. Distributional helps software teams deploy and maintain production applications with confidence, despite the complexity AI has added to the SDLC.

What appealed to you about the team at Distributional?

The origin story of a company can tell you a great deal about what drives its founders. I’ve talked to many founders who decided they wanted to start a company and then backed into a problem they could solve. One of my favorites is the founding story of Shopify starting as a snowboarding e-commerce company—the team developed their own commerce platform because they weren’t satisfied with what was available on the market.

The problem that Distributional solves is one that Scott and the team have lived in their previous work, and they’re passionate about solving it for other companies. And from the top down, this is a humble, kind, and super smart team.

Why does AI testing matter? 

If software deployed to production is going to be making decisions—whether it’s how to respond to a customer service chat question, drive a vehicle, make a stock trade, or dispatch emergency assistance—we need to have confidence that the decisions are made within acceptable parameters. And to test if the software will behave within acceptable parameters despite model changes, we need to approach testing these applications in a fundamentally different way. That’s where Distributional comes in.

What’s a misconception around AI that you wish could change?

I would change the misconception people have that AI is “bad” and will destroy jobs and probably the world, often without understanding what the technology is doing. When the printing press was invented, many people had the same end-of-the-world-as-we-know-it thoughts. And I think the printing press has turned out to be a pretty useful technology. As with all new technological advancements, it comes down to how humans use it. AI will certainly change our world, but as a society we have some say in how any given technology will be used and governed.

When you think about the potential impacts of AI, what’s the future you want to see become real?

I hope that AI will help free more people from having to do repetitive physical jobs that can impact their health. My mother was a bus driver before she retired. She has had shoulder, arm, and wrist issues for years because of all the driving she did to support our family. Wouldn’t it be great if you didn’t need to trade your health for a paycheck?

Favorite AI model or algorithm?

I’m still learning and can’t answer this one just yet. And I won’t let ChatGPT answer for me. Come back and ask me again in a couple of months. 😉