Download the AI in Post-Sale Benchmark Report
A benchmark study on how AI is showing up in post-sale teams today, where productivity gains are outpacing measurable outcomes, and what leaders need to do next.
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Executive Summary
Based on research from 191 post-sale leaders and practitioners, this study shows that AI is already delivering real value inside post-sale teams, particularly in productivity, preparation, and internal efficiency. These gains are meaningful and visible at the team level.
At the same time, post-sale leaders are being held accountable for outcomes such as retention, expansion, customer experience improvements, and cost efficiency that AI has not yet consistently influenced. As a result, many leaders describe AI as helpful, but not yet material to the business results they are responsible for delivering.
This research explores what post-sale leaders are learning about AI today, where adoption is stalling, and what needs to change for impact to follow.
Why This Matters
Post-Sale leaders are increasingly asked to explain how AI experimentation translates into measurable results. When AI improves how teams work, but not the outcomes leaders are accountable for, progress can feel real but difficult to defend.
This research helps post-sale leaders:
- Understand where AI is delivering value today
- Explain the growing gap between AI activity and business outcomes
- Identify why experimentation often stalls despite access to capable tools and strong intent
- Recognize that the challenge is not technology alone, but how AI is embedded into workflows, decisions, and operating models
- See what must change to move from experimentation to impact
4 Key Takeaways from the Report
AI Adoption is Real, but Most Teams are Still Early
Post-Sale organizations are actively experimenting with AI, but few have embedded it into core workflows or customer-facing processes where it can directly influence outcomes.
KEY FINDING
THE OPPORTUNITY
Most organizations are still early in their journey, creating a significant opportunity to move from pilots to scalable, outcome-driven use cases.
Current Stage of AI Adoption in Post-Sale Teams
AI is Improving Productivity Faster Than Business Outcomes
Most post-sale teams are seeing real gains in internal efficiency and productivity from AI. However, these improvements are difficult to tie directly to retention, expansion, and measurable ROI.
KEY FINDING
AI is creating clear productivity gains, but adoption drops sharply when use cases are tied to renetion, expansion or forecasting outcomes.
WHAT THIS REVEALS
WHY THIS MATTERS
Where AI is Being Used Today
Productivity Tasks
Summarizing customer meetings and notes
Drafting customer emails
Conducting company or stakeholder research
Preparing QBR content
Activities that Improve Business Outcomes
Health scoring or next-best action
Identifying churn risks and drivers
Account segmentation / prioritization
Identifying expansion opportunites
Renewal forcasting
The Gap Between AI Activity and Outcomes is Creating a Leadership Credibility Issue
As expectations for AI rise, leaders are increasingly asked to explain how experimentation leads to measurable results. When AI’s impact shows up primarily as internal efficiency (often invisible at the executive level), leaders struggle to demonstrate measurable business value. This creates a growing credibility gap with senior stakeholders.
KEY FINDING
WHAT THIS REVEALS
WHY THIS MATTERS
AI Benefits: Experienced Today vs. Expected Potential
Experienced (actual)
Expected (belief)
Improved team productivity
Improved customer experience
39 point gap
Improved customer retention
Improved expansion / upsell
57 point gap
Shorten time-to-value for customers
Closing the Gap Requires Leadership and Operating Model Change
The research suggests that closing the gap between AI activity and outcomes is not primarily a tooling problem. The challenge lies in how AI is embedded into workflows, decisions, ownership models, and success metrics. Without intentional leadership and changes to how post-sale work is designed and measured, continued experimentation is unlikely to produce materially different results.
From Static Post-Sale Models to Adaptive Customer Success
Traditional Customer Success
Static Segmentation
Fixed Plays & Cadence
Periodic Reviews
Manual Prioritization
Reactive Response
Adaptive Customer Success
Dynamic Prioritization
Adaptive, Signal Driven Actions
Continuous Adjustment
AI-Informed Focus
Proactive Response
What You’ll Get From This Report
Benchmark where your team’s AI adoption sits relative to peers.
Identify which AI use cases are delivering real value today (and which are mostly noise).
Explain why productivity gains haven’t translated into retention or expansion outcomes yet.
Clarify what needs to be owned, operationalized and measured to move from pilots to impact.
Make the case internally for the next set of decisions (and avoid vague “AI theater”).
Prioritize where to focus next based on where teams are seeing defensible results.
About Rod Cherkas
Rod Cherkas is a well-respected consultant, advisor, author, and speaker. He has been a post-sale executive at several of the world’s most customer-centric companies including Intuit, RingCentral, Marketo and Gainsight. He is the founder and CEO of HelloCCO, a strategy consulting firm that partners with innovative companies across diverse industries to develop, execute and scale strategies for their customer-facing functions. His clients rave about their “repeatable results with Rod”.
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