Featured Guest: Kelsey Dobbs, Senior Customer Advocacy Specialist, Illumio
Launching a customer advocacy program is no small feat—especially when you’re navigating competing priorities, internal skepticism, and the ever-present need to prove value early. In this episode of The CustomerX Files, host Alison sits down with Kelsey Dobbs, Senior Customer Advocacy Specialist at Illumio, to unpack what it really takes to build a strong, scalable, and high-impact advocacy program from the ground up.
Kelsey brings a unique perspective shaped by hands-on experience building an advocacy function in a rapidly growing cybersecurity company. Throughout her journey, she’s learned that success doesn’t come from simply introducing a new platform or launching a shiny initiative. It comes from guiding people through change—aligning with stakeholders, activating early champions, and creating structures that enable advocacy to flourish long term.
This episode pulls back the curtain on the operational and cultural strategies that make those outcomes possible. Alison and Kelsey explore how to establish trust across the organization, set aligned expectations, and ensure advocacy becomes a critical part of the customer lifecycle—not just a marketing afterthought.
Listeners will gain valuable insights into how to:
No matter where you are on your advocacy maturity journey—whether you’re launching your first program or optimizing what already exists—this conversation delivers practical advice and relatable inspiration. Kelsey and Alison share both wins and challenges, giving listeners a realistic roadmap for what success looks like and how to get there faster.
If you’re ready to move beyond theory and start driving visible, lasting advocacy impact within your organization, this episode is a must-listen. Tune in to learn how you can spark internal alignment, empower customer champions, and build a program that continues to grow, influence, and deliver measurable business value.
It's only natural that many advocacy leaders have landed on the same objective: make the program easier to use by meeting users where they're already working.
Today, that increasingly means Microsoft Copilot, ChatGPT, Claude, Gemini or whatever generative AI assistant employees happen to have open.
Imagine a salesperson simply asking AI, "Find me three German healthcare customers using product Y, willing to speak with a prospect," instead of navigating to another interface, or waiting for someone from advocacy, or elsewhere, to respond. It's easy to see the appeal. Removing friction has always been one of the fastest ways to increase adoption.
It is exactly the right instinct.
The difficult parts, arguably the reason program managers exist, occur before and after AI says, "Here are your three best matches."
The value advocacy professionals bring is the ability to operationalize and scale customer advocacy for maximum impact. Quality advocate information doesn't just appear, it's the result of a system.
Now that the user has three advocates, what should happen?
Notice what happened. The search was completed.
The next steps are just as manual as ever if AI search is the be all, end all.
Reality Check
AI can tell you who could participate. It can't tell you who should participate unless someone (or something) has been keeping score.
This is where the story starts to feel strangely familiar.
Many companies still operate their program using spreadsheets, scattered CRM fields, shared drives, email folders, and the remarkable memories of a handful of program managers.
Eventually, organizations realize they aren't managing an advocacy program at all. They're managing lists that happen to contain advocates.
But the shortcomings are real:
Purpose-built advocacy platforms emerged because advocacy is much more than a search problem.
Ironically, AI has convinced some organizations to revisit the same shortcut they worked so hard to escape.
Let's imagine two different worlds.
In the first, AI recommends an advocate for a sales call.
Months later, AI knows this customer recently participated and may deserve a break before being asked again.
Now imagine the second world.
Three months later someone asks how many customer reference contributed to the revenue this quarter.
Silence. Nobody really knows.
The advocacy happened...hopefully. The program didn't. Collectively, the organization slowly stopped feeding the very system it depended on to understand its advocacy program.
Reality Check
If AI helps facilitate twenty closed-won opportunities this quarter, but none are recorded, your executive dashboard still says zero.
One of the easiest mistakes to make in an AI-first world is assuming that successful interactions somehow become organizational knowledge on their own.
They don't.
If a customer agrees to speak with a prospect and nobody records it, the organization loses far more than a single activity.
The most valuable advocacy data isn't simply who your customers are.
It's everything they've done.
That's the story AI actually wants to read.
It's often said that AI needs good data.
That's true.
But operational history is far more valuable than static customer information.
Those aren't search results.Those are patterns.
Remove any one of those pieces and AI becomes little more than an exceptionally fast search engine.
Reality Check
Every workflow skipped today is a pattern AI won't discover tomorrow.
The AI revolution has created tremendous excitement, and rightly so. Finding the right advocate is becoming dramatically easier than it was only a few years ago.
That's worth celebrating.
Just don't confuse a better search experience with a better advocacy program. Search is only one chapter in the story.
The organizations that see the greatest return from AI won't necessarily be the ones with the most sophisticated models.
They'll be the ones with the richest operational history.
Those organizations won't use AI merely to answer the question, "Who should we ask?"
They'll use AI to answer far more valuable questions.
That's when AI stops behaving like a better Google search.
That's when it starts behaving like a strategic partner.
Finding the right advocate has always been the opening scene.
If your AI can find advocates but your program can't learn from using them, you've built a remarkable search engine instead of a remarkable advocacy program.
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