Super frustrating, right!? They clamored for it, and you delivered the equivalent of the best thing since sliced bread. What’s going on?
So, what’s going on. The short answer is, there are gaps in your change management planning. Not intentional, by any means. It’s really common to underestimate how much humans resist change. As much of an improvement as the change promises, it’s change. Change management specialists, Prosci, developed the ADKAR® model (Awareness, Desire, Knowledge, Ability and Reinforcement).
When you don’t get the adoption you expect, you need to determine where the problem is, and at this early stage the places to look are Awareness, Desire, Knowledge and Ability.
There are two primary ways to get feedback:
Advisory boards provide a forum for discussion and an opportunity to dive a bit deeper into various subjects. But it is a sample data set of the entire population. As a result, the feedback may be a bit skewed. Keep this in mind when making consequential decisions based on the input. Advisory boards provide an ongoing forum for end-user feedback far beyond adoption. They can uncover insights that help you plan your program strategy. Surveys provide a way to gain a broader perspective, but it may be more of a challenge to gain granular information without building an unreasonably long survey. That makes the formulation of the survey’s goals and related questions critical, striking a balance between a tolerable length, and not spawning more questions than conclusive answers.
Surveys are the ideal way to understand stakeholder behavior, or lack thereof, at scale. We know that sometimes it’s a nontrivial exercise to get the necessary approvals to run a survey that includes salespeople in particular (Don’t distract them from selling!). You have to practice the ADKAR change management practices even at this stage. Make the appropriate parties aware of the need for the survey, share the questions and justify the length, collaborate on timing and gain support from front line managers to cajole (or reward) participation. How can an argument be made for squandering an investment in people (you and others involved) and technology? Well, stranger things have happened, so approach the ask in full ADKAR mode.
Let’s talk about the survey itself.
If you aren’t getting customer advocate candidates from Sales, Customer Success, or other customer relationship owners, you should ask:
Have you nominated any of your customers as advocates?
If not, why not?
If there’s an indication that stakeholders aren’t finding the advocates they need (i.e., going somewhere other than your database), you should ask:
When you search for an advocate, you find what you need:
When you don’t find what you need, is there a combination of criteria that’s consistent?
Here you’d offer an open text box because you want to allow maximum flexibility and specificity. It could, for instance, be product A+B, financial services, and Central America.
In summary, when in doubt don’t just guess, or worse, give up. Ask! And to ensure you get a sufficient response from your very busy and distracted stakeholders, enlist the managers of the stakeholders to communicate the importance of their timely participation. You can’t help your stakeholders if you don’t give them a chance to help you. And they won’t help you help them if they don’t understand the benefits of making the leap from the old way of doing things, to the new, future state. When you find you have an adoption problem, immediately think about change management and the ADKAR components. Adoption is the outcome of effective change management, plain and simple. For more resources on how to increase user adoption through effective change management, check out this podcast.
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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