A New Chapter: Joining Rad AI

Why I’m excited to build at the intersection of AI, healthcare, enterprise growth, and work that matters.

Next week, I’ll be joining Rad AI as Director of Demand Generation.

I could not be more thrilled—not only because of the role, but because of what the company is building, the moment it is building for, and the kind of work this next chapter will ask of me.

The path here was not entirely linear. My most recent chapter was valuable: it brought me back into close contact with the craft of building campaigns, account programs, content, signal workflows, and Sales activation by hand. It reminded me how much I enjoy working near the details—not as an alternative to strategy, but as the place where strategy proves whether it is real.

An unexpected transition has a way of clarifying what you want next. I knew I was not simply looking for another demand generation title. I wanted consequential work. I wanted a company at a genuine inflection point. And I wanted a role broad enough to combine the two modes that have shaped my career: building enterprise-scale operating systems and staying close enough to customers and execution to understand what is actually working.

That search led me to Rad AI.

A mission you can feel in the workflow

Rad AI builds AI-powered reporting and follow-up solutions for radiology. Its mission is straightforward and deeply human: empower physicians with AI, save them time, reduce burnout, and improve patient care.

That combination matters to me. “AI” can become an abstraction very quickly. At Rad AI, the technology lives inside a specific, demanding clinical workflow. It is designed around radiologists, the way they work, and the judgment that remains theirs. The value is not novelty for its own sake. It is time returned, cognitive burden reduced, communication improved, and important follow-up made more reliable.

I have spent much of my career helping enterprise organizations connect complex products to the people who need them. Healthcare raises the standard for that work. The message has to be credible. The buying process has to respect many stakeholders. The customer experience has to deliver on the promise. And growth has to be grounded in outcomes that matter beyond a dashboard.

That is a challenge worth taking seriously.

The interview process changed the way I saw the role

What began as an application became a series of increasingly substantive conversations about the business Rad AI is becoming.

We talked about more than campaigns, channels, and pipeline. We talked about the long enterprise buying journey; the relationship among Marketing, Sales, Product, implementation, and customer teams; and the importance of turning customer experience into better market understanding. We talked about how a growth system earns trust—internally and externally—and how it keeps learning after a contract is signed.

For the case study, I centered my recommendation on a simple idea: the account is the durable unit of the system. A campaign comes and goes. A lead changes status. But the account carries the history of the problem, the buying group, the Sales conversation, implementation, adoption, outcomes, renewal, and advocacy.

That idea became more important with every conversation. Demand generation should not end at opportunity creation. The promise we make in the market should connect to what Sales discovers, what customers experience, and what the company learns. Customer evidence should shape the next campaign. Adoption and outcomes should sharpen the story. Marketing should help create the conditions for durable growth, not merely produce activity at the top of a funnel.

I left the process convinced that Rad AI wants to build that kind of system.

I was also struck by the quality of the questions. The team tested not only whether I could build a plan, but how I make decisions, respond when a well-built program is not working, earn adoption across functions, and stay accountable to the customer experience. Those conversations gave me a much clearer picture of the culture than a polished values page ever could.

They also made me want the job more.

Bringing the builder and the operator together

My career has moved between scale and craft.

At LiveRamp, I helped build and scale integrated demand across a large enterprise revenue motion. At Cisco, I led marketing operations and analytics for a global organization, aligning teams around measurement, forecasting, and executive decision-making. At Gem, I returned to a more hands-on mode—developing account architecture, integrated programs, creative assets, and AI-enabled workflows close to the point of execution.

Each chapter taught me something different. Scale teaches you that growth depends on operating discipline: shared definitions, clear decision rights, reliable data, and a cadence that helps people act. Hands-on building teaches you that no framework survives contact with the market unchanged. You have to listen, make, test, learn, and sometimes admit that an elegant idea is not producing the outcome you expected.

The opportunity at Rad AI brings those lessons together.

As Director of Demand Generation, I will help build a measurable, account-centered growth system for an enterprise market in motion. That means creating demand, certainly. It also means strengthening the connective tissue among market insight, integrated campaigns, Sales activation, customer evidence, and commercial learning. It means building a team and an operating model that can move quickly without confusing speed with noise.

Most of all, it means staying close to the people behind the work: the radiologists navigating rising volumes and intense cognitive demands, the health systems modernizing critical infrastructure, the teams implementing and supporting the technology, and ultimately the patients whose care depends on clear, timely clinical communication.

What I hope to build

I am joining with a point of view, but also with humility.

Healthcare is a new domain for me. I do not intend to substitute marketing fluency for clinical expertise. My first responsibility is to learn—from clinicians, customers, implementations, support conversations, usage evidence, wins, and losses. The best demand strategy will come from understanding where Rad AI creates real value, where adoption requires trust, and how the lived customer experience should change the story we tell.

Over time, I hope to help build a system that does a few things exceptionally well:

• Recognizes accounts as living relationships, not rows moving through a funnel.
• Turns customer and market signals into coordinated, useful action.
• Gives Sales stronger context and clearer reasons to engage.
• Connects campaign promises to implementation, adoption, and outcomes.
• Makes measurement a tool for better decisions, not retrospective decoration.
• Uses AI to improve judgment and relevance while keeping people accountable for both.

That last point is especially important to me. I am optimistic about AI because I have seen how dramatically it can compress the distance between an idea and something usable. But the goal is not automation everywhere. The goal is better work: more relevant, more informed, more responsive, and more human where being human matters most.

Gratitude and forward motion

Career changes invite a lot of reflection—on the people who taught you, advocated for you, challenged you, opened a door, or simply checked in at the right moment. I am grateful to everyone who did that for me during this transition. I am grateful for the colleagues and experiences in each prior chapter that prepared me for this one. And I am grateful to the Rad AI team for the rigor, candor, and generosity they brought to the process.

The most exciting opportunities often become clear before they become comfortable. This one became clearer with every conversation.

I am ready to learn a new industry, contribute what I know, build with a remarkable team, and help more health systems understand what becomes possible when AI is designed around the people doing the work.

Here’s to the next chapter.