It starts with leading the conversation before the rumor mill does. I've learned that if you're not the first one talking about it, someone else is, and that rarely goes well. I get in front of my teams early and frame automation around them, not the technology.

The "what's in it for me" lens is everything in change management. People don't resist change as much as they resist uncertainty. When staff can see that automation is handling the repetitive, low-value tasks nobody enjoys, they start to see the opportunity. That opens the door to upskilling, more complex work, and a more engaging day-to-day.

Zooming out also helps. AI and automation are reshaping how business gets done everywhere: finance, retail, logistics, across the board. When revenue cycle staff understand this isn't a healthcare-specific disruption but a broader shift in how work operates globally, it normalizes the transition. It becomes less of a threat and more of a wave worth getting ahead of.

Transparency is non-negotiable. People can handle hard news, what they can't handle is feeling like it was hidden from them. So I address the tough topics head on, what's changing, what it means for their roles, and what the hard work ahead actually looks like. No sugarcoating.

When you show up honest, even when the message is uncomfortable, people respect it. That respect builds trust. And once you have that trust, the buy-in follows naturally because the relationship becomes mutual. They know I'm invested in how it lands for them, not just managing a rollout.

Critical thinking and data fluency top the list. As automation handles more transactional work, the staff that thrive are the ones who can look at data, spot patterns, and identify where opportunities are hiding. Analytics has become a baseline expectation, not a specialty.

But the skill I emphasize most is process improvement, and this is where teams often skip a critical step. I've seen firsthand what happens when they do. Before applying any technology to a problem, you have to understand and standardize the process first. Automating a broken process just creates faster failures. The teams that get this right are the ones that get the most out of automation.

Revenue cycle leaders are tactile by nature. We want to get into the work and feel the issue before we can fully understand it. That same instinct should shape how we approach upskilling. Classroom training and slide decks only go so far. Hands-on learning, working through real scenarios and actually using the technology, is what makes it stick.

Pairing that with peer learning accelerates it further. When high performers are actively involved in bringing others along, it creates credibility that top-down training rarely achieves. People learn from people they trust. And when staff feel like they had a hand in it, they defend it. That ownership is everything.

You overcome fear by replacing the unknown with evidence. Share wins early and often, and be intentional about who gets the credit.

The automation didn't get there alone. The staff drove the process improvements, helped with the documentation, and put in the hours to bring everyone along. That success belongs to them and they need to hear it clearly and publicly. When people see tangible results and recognize themselves in them, it stops being something happening to them and starts being something they're a part of. That's usually when things start to click.

Training in a digital environment can't be a one time event. The technology evolves constantly and our approach to education has to match that pace. Staff who are regularly exposed to new tools and updated workflows don't just keep up, they build confidence over time. That confidence is what separates thriving from just surviving.

The other piece I lean on heavily is identifying super users early. Every team has people who naturally gravitate toward the technology and influence those around them. When you elevate them as champions, you extend your reach in a way no formal training program can replicate. Structured education combined with peer influence is what keeps a team adaptive. You've got to have both.

Updates don’t require IT intervention—we can make changes ourselves, just like updating an app on a phone. Teaching others how to manage the content has also strengthened the team, because the best way to learn something is to teach it.

Not formally yet, but it's on the roadmap and something I'm genuinely energized about. As AI adoption expands across the revenue cycle, a more defined learning track is something we're actively building toward. One idea gaining traction is a citizen automation development program, creating a pathway for staff with the aptitude and interest to get more involved in how we build and improve solutions. It accelerates adoption and develops internal talent at the same time. The framework isn't locked in yet, but the appetite is there.  

The one I'm most excited about is something we're in the final stages of building, a payer policy bot.

Staying current with payer policy changes is one of the hardest challenges in revenue cycle. When policies shift and teams don't catch it quickly, it directly impacts billing and reimbursements. It's a problem that has only gotten harder to manage at scale.

The bot targets that problem directly. It monitors payer policies, compares changes across updates, stores historical versions, and highlights the deltas so teams aren't manually hunting for what changed. It notifies the right people when changes occur and allows staff to converse with the bot in plain language to ask questions about specific policy details. All of this happens automatically, no manual uploads, no human monitoring, no manual indexing. The bot handles it end to end.

Beyond the technology, it streamlines workflows across multiple revenue cycle teams, reduces manual burden, and eliminates our dependence on an external vendor maintaining a similar solution. We're building something better internally and owning it. That combination of impact, cost savings, and team empowerment is what makes it stand out.

Most people don't think about who owns the bot after it's built, and that's usually where programs start to break down.

The technical side requires people with the right skillset, ideally those who built the automations or have deep familiarity with the technology. Not every failure is a technical one, and being able to distinguish between a backend issue and a business change driving an unexpected result is a skill that's hard to develop without hands on experience.

Technical monitoring is only half of it though. Production automations are constantly exposed to change, system edits, payer portal updates, shifts in business rules. Catching those early requires the business teams to be part of the conversation and that doesn't happen on its own. It takes ongoing effort to keep operational leaders thinking about automations when discussing workflow changes, not after the fact. We work at it through regular cadence reviews that bring technical and business stakeholders together, creating shared accountability so that when something changes, the right people already have the context to respond.

Both layers are non-negotiable. One without the other leaves you exposed.

Two things:

First, optimize before you automate. Ask honestly whether the process you're attempting to automate is the right one. Is it efficient? Is it something you'd standardize across the enterprise? Automating a broken process doesn't fix it, it accelerates the damage. And I say that having seen it happen. Get the process right first, then build around it.

Second, define what success looks like before you start. This is where a lot of teams fall short, and even experienced leaders get it wrong. If you can't measure it, you can't sell it and you can't sustain it. Hard data is your best change management tool. When people can see the tangible impact automation is making, the skepticism fades and the confidence grows.

Preparing teams for this shift is ultimately about helping them trust the technology. There is no better way to earn that trust than showing them the numbers.