AI Call Coaching for Lean Revenue Teams: How to Use AI Agents to Better Coach GTM Teams
The Problem: Your Revenue Team Is Growing Faster Than Your Enablement Function
Early-stage revenue teams are often asked to do two things at the same time: grow quickly and build the systems that make that growth repeatable. The issue is that there may not be sufficient bandwidth or capacity to build the required systems. A startup may have 10 - 50 sales reps, three customer success managers, and a handful of revenue leaders, the team is growing, deals are getting more complex, and managers are spending more time coaching. Or at least trying to, but the demands of revenue-driving activity are taking priority. This is the exact moment AI can and should be used to scale the coaching rhythm of the Front Line Managers.
Managers want to know:
Are reps asking enough discovery questions?
Are they identifying the actual business problem?
Are they talking too much?
Are they effectively handling objections?
Are they creating clear next steps?
Are CSMs driving adoption and value conversations?
Are managers seeing the same patterns across multiple calls?
Traditionally, this is where an enablement team comes in, an enablement manager builds the framework, defines what good looks like, reviews calls, identifies patterns, creates training, and helps managers turn those insights into better rep behavior. But there is a gap between needing enablement and being ready to hire an enablement team. For many early-stage companies, that hire isn't necessarily the best use of limited capital. The same dollars may be needed for another salesperson, marketing programs, customer acquisition, travel, or product development and yet on the opposite side of the coin, if we were doing those things better, maybe we could hire a full-time enablement professional. It is a real chicken-and-egg situation.
This predicament doesn't mean the team can afford to ignore enablement and coaching of the front-line employees. It means the company needs to think differently about how enablement gets done. This is also where AI agents can become particularly valuable, not as a replacement for managers and not as an excuse to automate coaching without a clear methodology. Instead, AI agents can give sales and customer success managers a way to systematically review real customer conversations, identify coaching opportunities, and turn those insights into team-level action. The most important part in my experience is getting the system right.
Don't Start With the AI Agent. Start With the Rubric.
One of the biggest mistakes teams make with AI call coaching is starting with the technology. They build an agent off of a few good call examples or a robust prompt and tell it something like, "Review this sales call and tell me how well the rep did." The result will probably sound impressive, it may even be useful. But it won't necessarily be consistent. The problem is that you've given the AI the same problem you gave the manager: subjectivity. You are asking the agent to determine the benchmark without giving it the answers to the questions below.
What does "good discovery" actually mean? What does bad discovery mean?
How many questions constitute good discovery?
What makes a question valuable?
What constitutes a strong objection response?
When should a rep push back on a customer?
What does a good next step sound like?
If those things aren't clearly defined, an AI agent is going to make its own interpretation, and if every manager builds their own version of "good," your organization can quickly end up with five different definitions of what good looks like. The first opportunity is not building an AI agent that listens to calls, it is to build an AI call-scoring system based on a rigorous, standardized rubric. The rubric becomes the source of truth, and the agent becomes the mechanism for applying it consistently and at scale. Something we will cover in a later post is how to use these agents at scale to identify where your pipeline may be at risk or what parts of the sales cycle your teams need more guidance.
What Makes a Strong AI Call-Scoring Rubric?
A useful rubric should translate your organization's expectations into observable behaviors. For example, instead of telling an AI agent, "The rep should demonstrate good discovery." Define what good discovery sounds like with actual quoted examples and, more importantly, tell it what bad sounds like. This has been a secret to the success of eval agents I have built. When you define both ends of the spectrum with multiple examples, the agent is then able to remove the subjectivity that they so often
A discovery competency might include:
Competency: Business Impact
What good sounds like:
"Tell me about what happens to the business if this problem isn't solved this quarter?"
"How is this issue affecting your team's ability to hit its goals?"
What weak discovery sounds like:
"So you guys are having trouble with reporting?"
"Is that something you're looking to fix?"
When you supply the agent with observable evidence that it can look for in an actual conversation. That leads to consistent grading measurements, which will ultimately lead to clear and data-driven indications of where reps are succeeding and where they are not. If you only provide examples of successful behavior, the agent has to infer the boundaries of unacceptable behavior. What we see most often when this happens is the agents are not trusted because they are quality-checked once and it showed a sub par example as good. Then the trust in the agent to help grade goes down and the agent it ultimately abandoned.
For every competency, consider defining:
What the competency means
Why it matters
What strong behavior looks like
What weak behavior looks like
Examples of strong language
Examples of weak language
What evidence the agent should look for
What should not count toward the score
That last point is often overlooked. For example, a rep asking 15 questions doesn't necessarily mean they demonstrated excellent discovery. The questions may have been superficial. A strong rubric might explicitly state that the agent should evaluate the quality and progression of discovery, rather than simply counting questions. I also suggest a 0, 1, 2, 3 scoring mechanism for each competency. This ensures you are not just bucketing reps into Pass/Fail, which is often too dramatic to track great from just fine. This also allows for the nuance of what is just fine versus an absolute rock star example of what good looks like.
One of my favorite ways to use this scoring mechanism is to create a low threshold that does require manager feedback.
Set a Threshold for Human Review
Here's where AI call scoring becomes particularly powerful for a lean revenue team. Your manager probably doesn't have time to listen to every call, and they shouldn't have to. Imagine a sales manager has three hours each week to dedicate to manual call review, if we are lucky. If they have 12 reps and each rep generates several calls per week, reviewing everything is impossible. Establishing a low threshold and high threshold ensures that the manager can prioritize the lowest-performing reps first, as those are the reps that need coaching immediately. Then, the high threshold ensures that the manager can source and QC (Quality check) what great examples are coming through. Having both serves two purposes. First, coaching and recognition for both low and high scores, respectively. But, more importantly, live examples to feed back to the agent for what good and bad actually sound like in a call to make the agent even more powerful.
If you want to see an example of a fully built-out rubric, please feel free to reach out through the contact us link, and I can share that with you.
The biggest benefit here is that the manager no longer spends 3 hours randomly picking calls to review and maybe picking good ones and maybe picking bad ones. Instead, the agent is doing the first pass, the manager is spending their limited time where it is most valuable in coaching and giving the field an actual example of what good looks like. This is a much more realistic model for lean revenue teams than expecting an AI agent to completely replace human coaching, it just isn’t there yet.
How Does My Team Do This?
The hardest part of all of this is the time required to set it up right. This is one of those things that a lot of folks try to cut corners with by asking an agent to create. The issue with this is you are starting with an agentic bias not rooted in the nuance of human-defined best practice. And anyone who has been in sales for a while understands that there is a lot of human-defined nuance in what defines a great call from a just fine one.
Your team can meet and hash out what the parameters for what good is and is not, you can QA/ QC the agent test calls, roll out the process with the managers and follow up to ensure that it is being followed, but very few teams have the time for that in a scaling startup. I have built countless agents like this over the last couple of years, and it is usually a 10 - 20 hour process for each stage of the sales cycle, with even more nuance if you have separate segments. But you don’t need to hire a full-time employee just to do this work; you need someone with a proven process, template, and the experience to build it, train on it, and make it work. This is where teams like Enablement Advantage come in. We do the research, build the rubric, build the agents, quality-check performance, and leave the team with clear instructions on how to do it again if changes or iterations happen in the future.
If you are looking for something like this in your Go-to-Market org today, book a consultation or reach out we are happy to have a conversation about how to do this, or take on the project for you.