How AI can make sales managers your biggest force multiplier

The rush to adopt revenue AI has been exhilarating, and defined largely by the arrival of productivity boosts. We've embraced features like AI-generated email drafts, automated call summaries, and note-taking because they save time for reps. And while rep productivity is important, I wonder if we miss opportunities for more transformative ROI when it’s the sole focus of our AI investments.

Revenue AI is primed to enable frontline managers and turn them into force multipliers. That’s where I believe revenue leaders should focus their AI investments over the next 12 months. (Some of my fellow Gong Customer Advisory Board members may have strong opinions on this too!)

Below are the primary reasons I believe revenue leaders should invest in manager enablement to drive superior, measurable business outcomes with revenue AI.

Historically, sales coaching has been plagued by two problems: It's been manual and generic. Managers are often stretched thin, relying on gut instinct, or basing feedback on easily gamed activity metrics like talk minutes or dial counts, which don't always correlate with winning. This results in "spray and pray" coaching — a lot of effort with minimal focused improvement.

AI changes this by introducing quality assurance at scale.

This means focusing less on how to coach faster and listen to fewer calls, and more on finding the one, two, or three trends revenue AI can spot at scale that your reps are missing. This approach moves you past measuring activities and into replicating winning behaviors, like including certain discovery questions or getting to the heart of a conversation quickly.

Having this information delivered on a silver platter frees managers to focus on directive, intentional skill building that’s based on data. They can now approach a coaching session with data, addressing specific skill gaps their revenue AI operating system identified, rather than generalized advice. This is how you scale a manager's expertise across an entire team.

This shift in focus can change the most sacred ritual in sales, the weekly performance review, turning it into a more strategic event that’s founded on deep insights into customer interactions.

Forecast meetings are no longer a recitation of lagging indicators from static spreadsheets, but rather, a dynamic, cross-functional, “so what?” conversation, driven by AI-informed predictions. This newfound visibility is critical to achieving the forecast accuracy boards crave.

You’ll find in these meetings that AI can abolish the traps that historically polluted forecasts and pipelines:

1. Eliminating the "hero commit"- AI helps expose pipeline sandbagging early on so no one hits a massive "hero commit" in the third month to trigger an accelerator. By continuously tracking activity against pipeline health over time, AI smooths out artificial peaks and valleys, driving predictability into the process.
2. Cleansing the pipeline - Don’t rely on guesswork to decide whether an aging deal is worth pursuing. Revenue AI analyzes your conversations and engagement history to prioritize what should be actively worked and what you should let go. This allows leaders to enforce a clean pipeline, so reps can focus their finite time on deals that are likely to close.

For managers, The Gong Revenue AI Operating System’s impact doesn’t stop here. It also provides the deep market intelligence they need throughout the customer lifecycle, notably in the post-sale phase.

Once you move into retention and expansion, revenue AI solves another critical risk that’s not centered around productivity metrics: undetected churn signals. To surface these, you need to understand key customer behaviors at scale.

Revenue AI like the Gong Engage solution we use, can surface churn signals by looking at usage patterns, like how customers use a product, what's driving value, and what’s not. With it, we can use this behavioral data to create predictive, color-coded account statuses in red, yellow, or green.

This is a stark way to reveal the moment an account begins to stall, and it allows managers to make sure a solution is in the works. For example, if an account hasn't logged into your platform for a long time, it’s coded as red, and you can build a playbook that maps that specific red signal to the next best action.

This ensures that human outreach, like checking in on a forgotten password or a proactive feature tutorial, is timely, relevant, and based on the customer’s actual needs. Addressing these churn signals makes forecasts and pipelines far more predictable.

The rush to AI productivity was fast, but deeper ROI is possible in your revenue org if you equip your managers to spend their time coaching reps and managing their pipeline. You've seen the evidence: AI scales coaching quality, cleanses the pipeline, drives forecast predictability, and identifies churn risk before it costs you revenue.

This is the path to moving beyond measuring activity to measuring winning behaviors and predictable growth. It’s time to stop settling for productivity gains alone. The key to driving superior, measurable business outcomes in the AI era is the decision to further enable your managers.