AI TRAINING
AI for Customer Success Leaders
Equip CS leaders to drive net retention using AI-powered health scoring, churn prediction, and intelligent playbooks.
What it covers
This programme teaches Customer Success leaders how to design and operationalise AI-driven workflows that improve retention and expansion revenue. Participants learn to build predictive health scores, interpret churn signals, leverage conversation intelligence, and embed AI-assisted playbooks into their team's daily motions. The programme blends strategic frameworks with hands-on tool evaluation and live case studies from B2B SaaS CS organisations. By the end, leaders can make a credible business case for AI investment and run a data-informed CS organisation.
What you'll be able to do
- Design a multi-signal customer health score using product, support, and commercial data inputs
- Interpret churn prediction model outputs and translate risk tiers into concrete playbook actions
- Evaluate and shortlist conversation intelligence tools against defined CS use cases
- Build a business case quantifying the NRR impact of AI-assisted CS workflows
- Define the data and integration requirements needed to deploy AI tooling in a CS tech stack
Topics covered
- Designing predictive customer health scores using product usage, support, and engagement signals
- Churn prediction models: interpreting outputs and acting on risk tiers
- Conversation intelligence platforms (Gong, Chorus, Clari) for CS insights
- AI-assisted playbook design: triggers, recommended actions, and automation thresholds
- Net Revenue Retention levers and where AI creates measurable lift
- Data requirements and CRM/CS platform integration (Gainsight, Totango, ChurnZero)
- Evaluating and selecting AI tools for CS teams
- Change management: getting CSMs to adopt AI-assisted workflows
Delivery
Delivered as a blended programme over three to four weeks: two live virtual workshops (half-day each) bookending three self-paced modules covering tooling, data fundamentals, and playbook design. Optional in-person capstone day available for cohorts of 10+. Participants work on a real health-score redesign project throughout the programme. Hands-on exercises make up roughly 50% of total learning time.
What makes it work
- Starting with a clearly defined retention metric (GRR or NRR) as the north star before designing any AI model
- Involving frontline CSMs in playbook design so AI-generated recommendations map to real customer conversations
- Running a 90-day pilot on a single segment before scaling AI tooling across the full book of business
- Establishing a data feedback loop where CSM actions are captured and used to retrain health score models over time
Common mistakes
- Building health scores from a single data source (e.g. NPS only) rather than multi-signal composite models
- Deploying churn prediction without a clear escalation playbook, leaving CSMs unsure how to act on risk flags
- Selecting AI tools based on vendor demos rather than evaluating against the team's actual data maturity and CRM integration requirements
- Treating AI adoption as a technology rollout rather than a change management effort, resulting in low CSM adherence
When NOT to take this
This training is not the right fit for early-stage CS teams (under 5 CSMs) that lack a CRM or CS platform and have no structured health scoring in place — they need foundational CS operations training before tackling AI tooling.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.