What’s the catalyst for most customer marketing initiatives? From our experience it’s usually something like this from a sales or marketing executive:
“Sales can’t find references because they’re in people’s heads, or spreadsheets.”
“Sales doesn’t know where to find customer videos or content. It’s on our corporate site, Dropbox folders, GoogleDocs or who knows where.”
“As a company we do a terrible job with references. We need to fix it!”
When the marketing or sales executives get behind the idea of improving reference selling it’s typically viewed as a transactional, matchmaking function: a seller needs a reference for a call, reference program sources the reference and fulfills the request. Rinse and repeat.
It’s understandable. Most executives worked at other companies where this matchmaking service existed. The idea of this function having strategic value didn’t occur unless a seasoned program manager came into the picture and did some evangelizing.
Why bother establishing a strategic program?
What isn’t often understood is the caliber of individual needed to build and maintain a program that delivers measurable results that CxOs can appreciate.
Would an intern or newly graduated college student be asked to inhabit a title like that? Would a company hire someone to lead a critical function like the demand generation team with no demand gen experience? Of course not. That would be unrealistic and unfair to the employee. And the program would fail.
Many skills are needed to balance the short and long-term demands of a customer reference program: relationship management, creativity, business analysis, project management, data management, diplomacy & tact, persistence, training; all with an emphasis on continuous improvement. Sounds like a consultant skill set, right?
We routinely help our client contacts hone their program management practices by coaching and sharing a variety of best practice resources, facilitating peer networking, and encouraging the attendance of relevant conferences. Our most recent resource is a diagram that illustrates and describes the many facets of the program manager role. Beyond our own clients, we thought this would be helpful to share with any manager tasked with owning a program (new or established).
If you read our Getting UnStuck! eBook you’ll know that a successful program manager must allocate time to the right activities, some daily, some weekly, monthly or quarterly. It’s all about balance.
If you are the program manager today and not getting a lot of executive mindshare, it’s likely because your manager has a) no idea what running a program entails, and b) you’ve been unable to quantify program contribution to company growth. We hope this helps you explain part (a). This post addresses part (b) and performance metrics.
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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