AI Can Give Frontline Managers the Leverage They’ve Always Needed

Frontline sales managers have always been asked to do a lot.

They’re expected to coach their sellers, manage performance, inspect pipeline, forecast accurately, reinforce sales methodology, develop people, and often jump into deals themselves. At the same time, they’re expected to sift through dashboards, Salesforce reports, spreadsheets, and an ever-growing collection of data to figure out what’s actually happening on their teams.

It’s no wonder managers spend so much time switching between roles instead of doing the thing they’re uniquely positioned to do: lead and develop their people.

That was the starting point for a conversation I recently had with Tim Riesterer on The Emblazers Show. We talked about the changing role of the frontline manager, what AI can realistically do to help, and why the conversations happening between managers and sellers may be one of the most valuable sources of performance data organizations have yet to tap.

The frontline manager has become a leverage point—but we haven't equipped them for the job

There’s a growing recognition that frontline managers are one of the biggest leverage points in a sales organization. They sit closest to sellers and have the ability to influence behavior, develop skills, and intervene when performance starts to drift.

But there’s a disconnect between how important we say managers are and how much we actually invest in them.

For the past several years, most of the tooling and training investment has gone toward the individual seller. Then we effectively hand the manager the responsibility for making sure all of it gets adopted, reinforced, and translated into behavior.

At the same time, we’ve given managers an enormous set of responsibilities. They need to understand sales, coach effectively, analyze performance data, spot trends, remember the history of each seller, and know what intervention will actually help.

That creates an incredible amount of context switching that AI can potentially alleviate.

Instead of asking managers to become expert data analysts on top of being expert people leaders, AI can take on some of the analytical work: unpacking performance trends, identifying where someone is pacing toward goal, highlighting potential issues, and preparing a manager for an upcoming conversation. In other words, making managers a better, more efficient version of themselves.

The best AI prepares the manager

Imagine a manager walking into a forecast or coaching conversation with a seller.

Historically, preparing for that conversation might mean digging through Salesforce, looking at dashboards, reviewing historical performance, and trying to remember what happened in previous conversations. If the manager doesn't have the time, the meeting can easily turn into, “What do you want to talk about?”

Instead, a manager could have an AI workflow prepare a concise view of what's happened: performance trends, relevant KPIs, areas that need attention, and potential topics for discussion. Instead of spending hours figuring out what to talk about, the manager can spend that time actually having the conversation.

That distinction matters because the real value lies in giving the manager enough context to show up informed and ready to coach.

As organizations increase manager spans of control, that leverage becomes even more important. If managers eventually have 10, 12, or 15 people reporting to them, there simply isn't enough time for them to manually perform every piece of analysis for every seller.

We may be sitting on an enormous, untapped source of performance data

One of the ideas Tim and I discussed that I find particularly exciting is the manager-seller conversation itself.

Organizations have spent years figuring out how to capture and analyze seller-customer conversations. We know there's valuable signal in those interactions, like what buyers are saying, what sellers are doing, and what behaviors correlate with better outcomes.

But what about the conversations happening internally? Every week, managers and sellers are talking about forecasts, pipeline, deal health, performance, development, and goals. Those conversations contain an enormous amount of information about how a sales organization actually operates.

And increasingly, we can capture it. Recording and transcribing these conversations means we can move beyond simply asking whether a coaching conversation happened and start asking better questions: 

Did the manager cover the topics the organization considers important? Did the manager and seller agree on next steps? Were those actions followed through? What do the highest-performing managers consistently do in these conversations?

Let's zoom in on that last question. Organizations have long tried to identify what their best sellers do differently, and we can now apply that same thinking to management. If certain managers consistently produce strong outcomes, we should be able to examine their coaching rituals and identify patterns—not just what they believe they’re doing, but what they actually do.

AI is strongest when it has a point of view, not just data

There's an important distinction between using AI and using AI well. Any manager can take some Salesforce data, drop it into Claude or ChatGPT, and ask for a coaching plan. And sure, that might be better than showing up completely unprepared. But if every manager is doing that independently, you end up with a system that's decentralized and inconsistent.

One manager gets one answer. Another gets something completely different. And neither necessarily knows whether the recommendations they're receiving actually work.

The real opportunity is to combine AI with the organization's existing knowledge: its management processes, sales methodology, guidelines, standards, and—most importantly—performance outcomes.

I like the way Tim described this during our conversation: methodology should govern AI, not just inform it.

If an organization knows what good management looks like, AI should be able to incorporate that knowledge into the workflow. It should understand the company's standards, look at the relevant performance data, and help the manager determine the right action. Then, over time, the organization can learn from the results.

What did the manager do? What happened afterward? Which interventions actually improved performance?

That creates a flywheel where AI isn't just generating plausible advice. It's helping organizations build a more consistent management system based on what actually works.

The manager is still at the center

For me, that's the most exciting part of where this is heading. There’s plenty of conversation about AI replacing people or automating entire jobs. But I think one of the more valuable applications is much more straightforward: give good people more leverage.

The frontline manager isn't going away. If anything, the manager becomes even more important as organizations ask them to lead larger teams and navigate increasingly complex sales environments. The opportunity is to remove some of the work that prevents managers from being great managers.

Let AI analyze the data, surface patterns, prepare the conversation, and capture what happened and whether there was follow-up.

Then let the manager do what only the manager can do: understand the person in front of them, build trust, ask the right questions, coach, motivate, and help that seller get better.

We're still early in figuring out exactly what this looks like at scale, but I believe we're entering a period where organizations can finally give frontline managers the tools, context, and leverage they've needed all along.

Watch or listen to the full conversation on The Emblazers Show to hear Tim and me dig deeper into AI, frontline management, coaching rituals, and the future of sales performance.