AI Isn’t Software. It’s a Coworker That Needs Training.
For years, rolling out new technology followed a familiar script:
Then along comes AI, especially agents, and that approach doesn’t quite apply.
Because AI doesn’t behave like traditional software. It behaves more like a new hire. A fast one. A tireless one. One that can draft, analyze, summarize, and recommend at scale. But also one that can confidently hand you something that looks right and isn’t.
So the question isn’t just how do we train people on AI?
It’s, How do we train people to work with it?
Before we throw out the old playbook, it’s worth noting: a few fundamentals still make sense.
Your sales team doesn’t care about marketing use cases. Your CS team doesn’t care about pipeline generation. Relevance is still oxygen.
“Here’s how to cut content generation time by 90%” will always land harder than “here are 12 general capabilities.”
Executive sponsorship, clear expectations, reinforcement loops; none of that goes away. If leadership expects everyone to just “be productive!”, sub-optimal adoption follows.
Office hours, quick wins, internal champions; same story, new tool. So far, nothing shocking. Now for the parts that are quite different.
Traditional tools are procedural.
Do this → then that → get result.
AI is interpretive. It requires users to constantly evaluate:
That’s not a workflow. That’s a mindset. That’s not a typical part of training.
This is the part people underestimate.
AI is only as good as the instructions it’s given. A lot of people aren’t well-versed with AI prompting, at least at first. Same thing with the early days of using Google.
Think of it like managing a very capable intern with zero context:
Training needs to include:
Otherwise, users hit friction early and don’t reach the promised productivity..
Most software behaves predictably (deterministically). AI doesn’t always.
Agents can evolve:
Agent behavior isn’t frozen in time. The expectation should be:.
“This is how it works now. It will learn and evolve every day.”
With traditional software, misuse is limited. With AI, misuse can scale…fast.
Training needs to clearly define:
This isn’t a feature conversation. It’s a boundaries conversation.
Most users fall into one of two camps:
Both are risky. The goal is a middle ground:
AI is powerful. And I’m still responsible/in charge.
That balance doesn’t happen by accident. It has to be taught, reinforced, and modeled.
This is the quiet shift that changes everything.
Software helps you do tasks.
AI helps you think through tasks.
That means training needs to cover:
In other words, you’re not just teaching usage. You’re teaching partnership.
They train AI like it’s Salesforce, a marketing platform, or a project tool.
Feature walkthroughs. Navigation demos. Maybe a few canned examples.
Then they wonder why adoption stalls.
Because none of that teaches people how to work with AI in the flow of real decisions.
If you want this to stick, flip the model. Start here:
What slows each role down today? Start there.
Give people the language to get useful outputs.
Teach them how to spot strong vs. weak responses.
Be explicit about risk, data, and boundaries.
Not sandbox examples. Actual tasks they care about.
When AI adoption lags, it’s because people don’t know how to use it well, not its lack of capabilities.
And in a world where everyone claims AI will make them faster, cheaper, and smarter, the companies that win won’t just have the best tools.
They’ll have the best-trained humans working in partnership with AI.
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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