Embracing AI in Customer Success: Future Use Cases and Opportunities

A few weeks back, I attended two events with fellow Customer Success professionals. The buzz around AI was everywhere—so much so that if I earned a dime for every mention of "AI," I might have enough to buy shares of OpenAI!

Listening to my colleagues’ insights, it’s clear that AI and customer data are set to reshape our approach to customer success. So, what are the key use cases emerging from this shift?

Key AI-Driven Use Cases in Customer Success

✔️ Summarizing customer calls and action items

✔️ Preparing CSMs and CS leaders for meetings

✔️ Recommending the best follow-up actions based on customer sequences (shoutout to Rupert.AI)

✔️ Building knowledge base items for customer service reps and CSMs

✔️ Conducting multi-variant usage analysis considering customer profile, segment, and size

As I reflected on these conversations, it became clear: the race with, not against, AI has begun.

Now, I want to highlight the next-generation use cases where AI will empower CS teams and individuals to excel even further. You can consider them:

Future AI Use Cases for Customer Success

1. Optimizing Customer Focus: “Am I spending my time effectively?”

This operational use case enables CS professionals to prioritize high-impact activities. AI tools will analyze customer behaviour and the correlation between CS actions and outcomes, helping teams work smarter and more proactively.

Examples:

• Historic effort analysis: AI can review previous CS activities to identify actions that resulted in positive reactions, increased engagement, or successful renewals.

• Prioritizing accounts: Based on available data such as satisfaction levels, usage, and relationship strength, AI can suggest which customers require more attention to meet success plan objectives.

• Impact measurement: AI can recommend which actions (e.g., a call, a training session) will most likely improve customer satisfaction or usage, based on historical data.

2. Which Communication Channel Is Most Effective?

This use case guides CSMs in selecting the optimal communication method for each customer interaction. AI can recommend whether to send an email, schedule a call, or use another channel, based on customer preferences, history, and context.

Examples:

• Recommending the best time and channel for outreach

• Suggesting tailored messaging for different customer segments

• Responding effectively to complaints or queries

3. Upsell Probability Assessment: “What is the likelihood of upselling to this customer?”

This commercial use case may be a “game changer” by enabling CS and sales teams to approach customers with greater confidence. AI provides unbiased probability scores derived from historical data, such as usage patterns, engagement metrics, and previous successful expansion initiatives, rather than relying solely on human judgment.

Examples:

• Usage metrics across similar customer segments

• Engagement levels from emails, chats, and calls.

• Historical success or failure in upselling and cross-selling

• Customer plans and intentions, recorded in CRM or CSP

• Stakeholder seniority and influence

• Company financial health (public info)

• CSM/Sales confidence levels

• Urgency and perceived need for expansion

4. Proactive Expansion Planning: “How can CSMs plan expansion 3, 6, or 9 months ahead?”

This is often referred to as the “goldmine” for Customer Success. AI can analyze onboarding progress, adoption metrics, and historical upsell data to identify expansion opportunities well before any triggers occur.

Data points analyzed may include:

• Customer onboarding status

• Adoption rates and usage patterns

• Customer attributes, such as segment, spend level, and geography.

• Customer communication and expressed needs.

• Publicly available information

• Previous upsell and cross-sell results across regions and segments

• Stakeholder relationship quality

• New features or capabilities delivered by the vendor

  
  

Benefit: By leveraging this data, CS teams can craft targeted strategies for upselling and cross-selling, staying ahead of customer needs and market trends.

5. Cross-Team Collaboration: “How can AI facilitate better collaboration across teams?”

Given the complexity of customer issues that often require cross-functional efforts, AI can guide teams toward the most effective resolution paths.

Potential scenarios:

• Defect classification: AI can assess the operational and customer impact of defects, suggesting whether to prioritize a fix or implement a workaround.

• Impact analysis: Explain how a new feature request might affect customer experience and outcomes.

• Customer advocacy: Based on engagement data, AI can recommend strategies for CS and marketing to turn customers into advocates, including case studies or tailored messaging.

Conclusion

As we navigate this disruptive frontier, I encourage all Customer Success professionals to remain curious and open-minded. Embrace AI’s potential, experiment with these innovative use cases, and share your insights along the way.

Once we master the basics, we’ll move toward a new generation of AI-powered capabilities, enhancing individual performance, team collaboration, and building consistent growth.

AI is poised to revolutionize how we engage with our customers, enabling us to drive significant business outcomes more effectively.