Cutting through the BS of CMA | BTTBS webinar

What's Next?

Now that the user has three advocates, what should happen?

• Should they email the customer directly?
• Should they contact the Customer Success Manager first?
• The account executive for one of the accounts was about to make a request. Was that considered?
• Has anyone noticed that this customer has already participated in three activities in the last 60 days?
• Are they currently navigating a difficult support issue?
• Did they recently decline another invitation?
• Would someone else actually be a better choice?

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.

Haven't We Seen This Movie Before?

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:

• A spreadsheet might tell you that Sarah from ABC Company has spoken at a conference. It couldn't tell you that she'd spoken three times already this quarter.
• Custom CRM fields could tell you a customer was referenceable. They alone couldn't coordinate approvals, notify relationship owners, recognize participation, measure outcomes, or attribute revenue.

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.

When Search Replaces Process

Let's imagine two different worlds.

In the first, AI recommends an advocate for a sales call.

1. A request is automatically created.
2. The Customer Success Manager approves participation.
3. The customer receives preparation materials.
4. The call takes place.
5. The activity is recorded.
6. Recognition is issued.
7. The opportunity is linked to the advocacy activity.
8. If the deal closes, revenue attribution updates automatically.
9. Executive dashboards reflect the contribution.

Months later, AI knows this customer recently participated and may deserve a break before being asked again.

Now imagine the second world.

1. AI recommends the same advocate.
2. The salesperson sends an email.
3. The customer agrees.
4. The meeting happens.
5. Everyone moves on.

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.

Invisible Work Stays Invisible

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.

• It loses context, attribution, and recognition.
• It loses another piece of history that could have helped improve the next decision.

The most valuable advocacy data isn't simply who your customers are.

It's everything they've done.

• Every request, acceptance/decline, event presentation, analyst interview, product beta, reference call, press interview, reward, closed-won opportunity revenue influenced by their participation.

That's the story AI actually wants to read.

AI Needs Memory, Not Just Data

It's often said that AI needs good data.

That's true.

But operational history is far more valuable than static customer information.

• Advocate profiles answer questions about who someone is.
• Operational history answers questions about what consistently works.
• That's where AI begins uncovering insights that no spreadsheet could ever reveal.
• Perhaps healthcare advocates participate twice as often as financial services advocates.
• Perhaps customers who join advisory boards are twice as likely to become conference speakers.
• Maybe advocates who receive recognition within a week participate significantly more often than those who don't.

Those aren't search results.Those are patterns.

• Patterns emerge from history.
• History emerges from process.
• Process emerges from systems.

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.

Don't Stop at "Who?"

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.

• Every request becomes institutional memory.
• Every activity measured.
• Every contribution attributable.
• Every outcome becomes another lesson AI can learn from.

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.

• "Where are we running short of advocates?"
• "When is the most effective time to use advocates?"
• "What types of advocacy generate the greatest business impact?"
• "What patterns have we been missing?"

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.