The AI measurement framework: How to track the impact of investments

AI has officially arrived in revenue orgs. From forecasting and deal inspection to automated briefs and next-best-action nudges, AI is rapidly becoming part of every revenue workflow.

But with 87% of revenue leaders under pressure to implement AI, one big question still looms:

How do we measure the impact of our AI investments?

It’s no longer enough to say your reps are “saving time” or your teams are “more effective.” To prove the value of AI and scale it effectively, revenue leaders need to measure their return on investments (ROI) through a combination of leading and lagging indicators tied directly to business outcomes.

Here’s how to do it right.

Before we get into the framework, it’s critical to understand the difference between leading and lagging indicators.

Leading indicators predict success. They clarify whether your organization is ready to use AI, how well your team adopts AI, and whether AI is starting to shift your targeted activities and behaviors in the right direction.

Lagging indicators, on the other hand, confirm success. These are the outputs — and ultimately, the hard business results (e.g., revenue, win rates, cycle length) — that validate whether your AI investment is paying off.

While it’s tempting to focus solely on outcomes, to truly measure revenue AI’s full impact, you need both leading and lagging indicators.

This measurement framework tracks four categories of metrics, all of which will help you consistently monitor the success of your revenue AI initiatives from start to finish.

These metrics help determine whether your organization is ready to leverage AI effectively in its revenue operations. For example, an organization that struggles with data hygiene is almost guaranteed to get lackluster results with a new AI tool. Here are examples of metrics that indicate your team is ready for AI:

Example metric

Description

Integrations

# of go-to-market (GTM) systems integrated with AI or machine learning platforms

Data preparedness

% of clean and structured customer or revenue data available

Skill readiness

% of sales and marketing team members trained on new AI tools or workflows

Process alignment

% of AI-related GTM workflows that are documented and standardized

Governance

% of team certified on or compliant with AI guidelines and policies

Tasks that eat up your team’s time usually point to where AI can pay off. If you see that the adoption of a particular feature is high, chances are it’s getting results in one of those areas. Below are examples of leading indicators you can use to track how often and how effectively reps use AI in their daily revenue activities:

Example metric

Description

Usage frequency

# of times an AI application is opened and used, per user, per week

Automation actions

# of tasks automated via AI, per user, per week (e.g., meeting summaries, lead scoring)

Prompting volume

# of generative AI prompts written

Feature adoption

% adoption of new AI-powered features/functions

These are the near-term outputs tied directly to your AI-enabled activities. These could be efficiency metrics like time savings or increased capacity, or effectiveness metrics like improved lead quality or forecast accuracy. They emerge as the first category of lagging indicators:

Example metric

Description

Seller capacity

# of new opportunities worked by each rep

Lead quality

% improvement in qualified lead prediction accuracy

Forecast precision

% improvement in pipeline forecast accuracy

Initiative adherence

% of the team adhering to new initiatives, whether that’s for messaging, sales process, etc.

Seller response time

% reduction in time it takes reps to respond to buyer inquiries, follow up, etc.

Time savings

# of hours saved by using AI

These data points are what you usually see highlighted on AI providers’ websites and in their customer stories. They reflect the high-level, longer-term value AI can bring to your business. As a revenue leader, these are ultimately the metrics you’ll be on the hook for:

Example metric

Description

Average deal size increase

$ increase in deals where AI identifies upsell or cross-sell opportunities

Win rate increase

% change in win rates for deals where AI recommendations were used

Cost efficiency

Reduction in cost per lead, CAC, cost to support, etc.

Cycle time reduction

Decrease in average sales cycle duration due to AI insights

Customer retention

% increase in renewals or upsells driven by AI-powered insights

Not every AI benefit will show up in this framework or on a dashboard. That’s why it’s important to pay attention to qualitative wins too, like these:

• Reps feeling more confident and prepared for calls
• Increased time savings that reduce burnout across the team
• Managers spending less time gathering context and more time coaching, leading to a better culture of team development
• Selling teams feeling more aligned, and execs jumping into deals without scrambling for info

Ultimately, these are all signs that AI is creating a higher-functioning, more connected revenue team.

AI isn’t magic. It only delivers ROI when it’s properly configured, adopted, embedded into workflows, and tied to measurable outcomes.

For CROs, RevOps leaders, and GTM teams, the path to unlocking the value of revenue AI is clear:

• Start with a solid foundation of AI-ready data and integrated systems
• Monitor adoption and workflow engagement across teams
• Track time savings and activity execution
• Report on lagging outcomes like win rate, cycle time, and revenue

When you deploy AI strategically and measure it rigorously, it not only speeds up your team but also makes your entire revenue engine smarter.

Ready to start your journey with the Gong Revenue AI Platform? Let’s talk.