360Learning Leadership & Management

What does effective management look like on your team, and how does it impact your day-to-day work?

For me, effective management starts with really understanding the role of the team you’re leading. I came up through the 360Learning customer success team myself, so I have a very clear picture of the day-to-day demands that CSMs face. 

Because of that, I try to lead in a very practical way. Clear communication and accessibility are really important to me. My team knows they can come to me for guidance, whether it’s navigating a difficult client situation or thinking through a strategic account plan. I also believe strongly in leading by example. If a newer CSM is preparing for a more advanced conversation with a client, I’m always happy to hop on the call to help present or support them. It’s a great way to model the approach while building confidence in our team. 

At the same time, I try to keep a long-term lens on development. I believe that an essential part of the manager role is helping people grow in their careers. Whether that’s developing stronger consultative skills, taking on more strategic accounts or preparing for the next step in their role, my goal is to make sure my team feels supported in the moment while helping them hone their CS skills for the future.

How do leaders at 360Learning create clarity around priorities and expectations?

One thing I really appreciate about working at 360Learning is the level of open communication across the organization. There has always been a strong culture of transparency that helps ensure people understand not just what the priorities are, but why they matter.

360Learning leaders are very intentional about clearly articulating goals and expectations, whether that’s through company-wide updates, leadership communication or team-level conversations. This clarity makes it easier for teams like customer success to align our work with broader company initiatives.

This level of transparency has created a culture of trust that I have not experienced elsewhere. When priorities shift, leadership does a good job of communicating the reasoning behind those changes. Their transparency helps our teams stay focused on what will have the biggest impact for customers and the business.

Can you share an example of when leadership followed through on a commitment or navigated a challenge successfully?

One example that stands out to me is how leadership has continued to invest in providing a strong customer experience. When I joined 360Learning five years ago, our North American team was very lean as we broke into the market. Our global leadership committed to ensuring the team had the structure and support required to provide the level of service expected in the North American market. 

They followed through on their commitment by evolving regional team leadership, investing in processes that help us better support customers, and encouraging strong cross-team collaboration to ensure our product met shifting needs. The 360Learning leadership team was actively working with the team to build solutions.

What stood out to me most was how leadership remained engaged with the team and our customers as we evolved. There was ongoing communication, opportunities for feedback and a genuine effort to make sure the changes were helping both employees and customers succeed. The level of follow-through expected of 360Learning leaders continuously reinforces the culture of trust and collaboration here.

What’s a hallmark of good management here — and how is it reinforced weekly?

The line I repeat most is ‘bet small, learn fast, let the upside run.’ Good management here isn’t about being right upfront, it’s designing decisions so the cost of being wrong stays cheap while the payoff of being right compounds. I coach four group product managers, who each coach three product managers. To protect that asymmetry, we kill an idea within a week if the signal is bad, but let a squad run hard once the data says yes.

We reinforce it weekly through rituals: each squad holds a weekly review of what shipped, what was learned and what’s being tested next and no manager pre-approves the call, the squad owns it and defends it to peers. That’s our low-authority feedback culture. Group product managers discuss trade-offs in weekly one-on-ones and don’t dictate them. In my own product review, the question I ask most is ‘what’s the cheapest version of this we could test this week?’ Repeated enough, that question becomes the culture. Teams stop asking permission to experiment and start asking how fast they can find out.

What keeps priorities obvious — and what signal shows it’s working?

Two artifacts and they have to agree with each other. The high-level roadmap is anchored to the strategic objectives we set at the start of the year, it doesn’t move quarter to quarter on a whim, so nobody has to guess what ’priority’ means. Underneath it, a Trello board carries the actual work, open to the entire company, not just the product org. Anyone — sales, support, engineering outside a squad — can see exactly what’s being built and why, with no gatekeeping between what leadership discusses and what the teams actually do.

We reinforce it weekly. Squads update their cards as commitments change and the GPMs check that what’s moving on the board still ties back to the roadmap’s objectives.

The signal works. Anyone in the company, not just product people, can open that board and answer ’why are we doing this’ themselves. When people stop asking me or their GPM to explain priorities and start pointing to the board instead, transparency has actually replaced authority as the source of clarity.

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

Three things energize people at once. First, we get to reinvent how people learn, not just digitize it: AI-simulations, new formats that make learning feel less like content and more like practice. Second, it’s not only for learners. The same AI is making people’s day-to-day easier, automating the busywork around learning so time goes back to the work itself. Third and what excites people most is thinking past learning altogether to a wider ecosystem where AI agents talk to each other, hand off context and quietly assemble a learner experience that feels seamless, without anyone stitching it together by hand.

One of the metrics that shows progress is self-service usage. How much learning people seek out on their own, unprompted by a mandatory assignment. It matters because it proves two things at once. People actually enjoy learning and see its value and the content reaching them fits what they need, when they need it, rather than being pushed regardless of relevance.”

What’s the leadership like on the AI and machine learning team at 360Learning?

At 360Learning, leadership is about creating the conditions for people to own decisions. We operate with low authority and high accountability: Teams decide how to achieve an outcome and remain accountable for its impact.

This is especially important in AI engineering, where it’s impossible to know upfront which ideas will work. Leadership isn’t about having all the answers; it’s about providing strategic context, defining measurable outcomes through objectives and key results, and giving teams space to test assumptions and learn quickly through a focused proof of concept, a small client test or a low-cost experiment.

We actively encourage innovation: Teams are empowered to challenge the status quo, explore new approaches, experiment and learn from failure, while staying focused on customer value and business impact. We also challenge decisions with facts. Teams bring data, explain their reasoning, track impact and consider alternative perspectives, especially when results don’t meet expectations.

Ultimately, leaders set the context, priorities, guardrails and ambition; the people closest to the problem own the decision and have the freedom to innovate their way toward the outcome.

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

Leadership helps the team prioritize efforts through our company objectives and roadmap, while balancing customer impact, business impact and innovation.

Our high-level roadmap is aligned with our strategic objectives for the year. This gives us a clear direction and shared understanding of where to invest our effort, without rethinking priorities every quarter. When evaluating an AI or ML opportunity, we look at the value for our clients, the impact on the business and the effort required. We want to focus our resources where the expected impact is highest, whether that means improving the product, solving a customer problem or creating business value.

At the same time, AI evolves too quickly for everything to be planned upfront. We leave room to test new technologies and ideas through small experiments, without immediately committing significant resources. Then, we can see if it can lead to a further investment and a place in the roadmap.

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

Candidates should know that we expect both strategic thinking and a strong appetite for experimentation. The strategic direction gives teams a clear understanding of what we’re trying to achieve, but it doesn’t prescribe every solution. We want engineers to challenge assumptions, explore new approaches and test what new AI capabilities can actually bring to our product/business.

The key is how we experiment. We favor small, focused bets that allow us to get a signal quickly rather than investing heavily before we know whether an idea works. A POC, a limited feature or a test with a small group of clients can be enough to learn whether something is worth pursuing.

When the signal is weak, we should be comfortable stopping or changing direction. When it’s strong, we give the team room to move quickly and invest further.

For candidates, this means a high degree of ownership: You’re not expected to wait for permission to test an idea, but you are expected to connect your experiments to a meaningful problem, measure the impact and learn from the outcome.