For decades, Customer Marketing & Advocacy (CMA) has been the unsung hero of B2B growth. It’s the engine behind those success stories that inspire prospects, the whispered endorsement that tips a deal in your favor, and the subtle force that makes your demand gen campaigns land with credibility.
Yet, for most organizations, CMA has been chronically underfunded, short-staffed, and—let’s be honest—often underappreciated. To too many executives, CMA looks like a cost center. Something “nice to have.” Something expendable when budgets get tight.
For those of us who have lived and breathed CMA, this is maddening. We’ve seen the magic: customers who speak at your events, advise buyers based on real-world experience, and turn your sales cycle into a trust-building exercise rather than a cold pitch. When an executive *gets it*, the possibilities are endless. Unfortunately, there just aren’t enough of those executives.
If CMA has struggled to get attention from the c-suite, the current AI frenzy may just change the game. Boards are pressuring leadership teams to turn AI into ROI—fast. Executives are leaning on managers to show tangible progress—if not results. Suddenly, there’s appetite for experimentation, and the once-overlooked CMA function has a shot at the spotlight.
This is our moment. But it comes with a twist.
Agentic AI—the emerging class of AI tools that can act autonomously, interact conversationally, and adapt to situations—is unlike the automation of the past. Automation was predictable, procedural, and safe. Agentic AI is…well, a little “schizophrenic.” It hallucinates. It can produce five different answers to the same question. In domains like CMA, where trust and relationships are currency, that’s not a small risk.
Still, in this rare season of executive curiosity (and tolerance for AI’s warts), CMA leaders can step into a new era—if they balance ambition with caution.
Let’s imagine how the role of a CMA program manager will evolve as agents become capable teammates rather than just tools. Take for example, the ongoing chore of discovering customers ripe for advocacy.
Recruiting advocates today is notoriously time-consuming. It depends on your relationships with sales reps, CSMs, product marketers, and executives—and that’s before the first outreach email goes out.
In a mature agent-driven environment, much of that manual work could be offloaded. Imagine an agent that:
It sounds magical—and in some ways, it will be. But designing this magic requires deep CMA expertise, in addition to competent interaction design. Without it, the agent might create social faux pas that damage relationships instead of nurturing them. AI providers acknowledge that humans are often better suited for dealing with customers in marketing, sales, and customer success. Human employees are essential for managing and resolving sensitive matters with customers, and what’s more sensitive than asking a customer to put her reputation on the line for your company?
As agents handle more operational tasks—candidate identification, outreach, data collection—the CMA program manager’s role shifts. Tomorrow’s CMA leader will:
Think of it as moving from an assembly-line role to a command-center role. Tools like Salesforce’s emerging Agentforce Command Center will make it easier to track performance and refine agents. Because agents need direction, it will be incumbent on program managers to invest more in strategic planning and program design than in the pre-AI world. The consequences of not doing so will be dramatic, visible and fast within an organization.
No matter how smart the agent, there will always be gaps in the data. For example:
These situational nuances require human judgment. A savvy program manager will design workflows where agents flag these scenarios for human intervention, preserving the buyer experience—and the deal.
For the first time, CMA program managers have a shot at scaling their programs without scaling their headcount. Agents can become true virtual team members, freeing program managers to do what they’ve always wanted to do:
But this will only work if we combine deep CMA expertise with smart agent design. Without that foundation, organizations risk over-rotating on AI, frustrating customers, and undermining trust—the very currency of advocacy.
At Point of Reference, we’ve poured over 20 years of CMA experience into our agent design principles. Technology may power the future, but empathy and context will be our north star.
The CMA program manager of the future won’t just manage relationships—they’ll manage the agents who manage the relationships, elevating both the human and digital sides of advocacy. And for those ready to embrace this shift, the sky’s finally the limit. For more on the role of AI in Customer Marketing, check out this podcast with Sunny Manivannan.
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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