When customer advocate/reference programs came into being, they were typically tasked with one or two objectives: create case studies, and/or find customer references to help Sales move forward and close opportunities. As such, the function existed more or less disconnected from most of the enterprise. They weren’t viewed as an enterprise-wide resource, but offered benefits to just one or two departments.
Somewhere along the way executives, CMOs in particular, recognized that customer advocates could help a myriad of scenarios, and by the way, seemed to have more credibility with buyers, analysts, the media, and investors, than conventional advertising and marketing efforts that did not feature customers and their real-world successes. This was the beginning of legitimate customer advocate programs. While there was general head nodding around the concept, what didn’t always happen was the cultural shift that was necessary to integrate the program across the enterprise in terms of planning, collaboration, and two-way SLAs.
Customer advocate programs are about as cross-functional as it gets. What they offer is valuable to nearly every department in an organization. In that way, they inherently have strategic potential. But only with skilled, competent program leaders and meaningful buy-in from executives and team managers.
Many years ago we worked with a client at the early stage of building its inaugural customer advocate program. After defining the scope of their program, there was a deep conversation about how they would interact with each stakeholder team; and where the synergies lay. The give and take, the reciprocity, between a program and its stakeholder teams is typically informal and assumed. But we felt—and this client organization was fertile ground for a test—that formalizing these relationship commitments would be a way to ensure accountability and success.
The process begins with taking inventory of how each stakeholder group (executives, Sales, events, PR, demand gen, etc.) will benefit from the program, and how the program will benefit from the stakeholder group.
Here are a few examples:
Customer Advocate Program ⇒ Executive Team
Executive Team ⇒ Customer Advocate Program
Customer Advocate Program ⇒ Sales
Sales ⇒ Customer Advocate Program
Customer Advocate Program ⇒ Events Team
Events Team ⇒ Customer Advocate Program
This benefits statement then defines stakeholder agreements: commitments each party needs to make to realize the benefits. Your list of commitments, by stakeholder group, should be the result of a collaborative effort with each team, perhaps starting with team managers. This is an opportunity to educate and to explain how an effective working relationship will help them achieve their goals, and ultimately, success. This is a fundamental tenant of change management: Awareness.
Once the commitment list is finalized, it can be dropped into a simple agreement, specific to each team, to make it official. Following is an example for Sales:
This becomes a touchstone document, which should be revisited over time as a means for keeping the parties accountable, and ensuring it’s still accurate and complete. You may think the signatures seem excessive, but there is something about putting ink to the agreement that gives it weight.
Transforming customer advocacy from a side project into a core strategic function is essential for corporate growth. That transformation demands a unified commitment and collaboration across all departments—because customer advocacy is, after all, a team sport. By doing so, you not only elevate the voice of your customers, but also amplify the collective success of your organization. Most programs don’t begin with this premise, but it’s never too late to reinvent and create a center of excellence in your organization. Learn how we can help you grow your customer advocacy program to make a real impact on your organization’s bottom line.
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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