How to Embrace AI to Scale White-Glove Customer Success

While AI dominates news headlines about transforming Customer Success, the data indicates there is still significant room for forward-thinking CS teams to turn AI into a competitive advantage. Based on a Department of Commerce survey, of 1.2 million U.S. businesses, only 6% plan to use AI. Even among the 52% of CS teams claiming to use AI, most are running basic automation. This gap creates an opportunity to leverage AI in ways that streamline operational efficiencies and deepen customer relationships.

So, how do we move past basic automation to truly valuable AI?

This is where Chris Regester, Chief Customer Officer at Planhat, weighs in. He has more than 20 years of experience building CS strategies, teams and processes around the globe, and, as a leader who ensures customers are at the center of every business, his perspective cuts through the usual AI hype.

In our recent webinar, Chris joined Mainsail’s VP of Customer Experience, Jess Bicknell, to discuss the AI strategies that can help drive efficiencies within Customer Success (CS) teams and scale white-glove customer service.

Watch the full webinar here.

Inside the AI Toolbox

Effectively using AI in CS starts with understanding the difference between two technologies: generative AI (genAI) and machine learning (ML).

GenAI serves as an intelligent assistant that handles content creation and processing. It excels at turning information into action, such as converting meeting notes into next steps, support insights into tickets, and customer data into strategic recommendations.
Machine learning works behind the scenes to spot patterns. It analyzes user behavior, identifies trends, and helps predict where attention is needed, such as an early warning system for customer health and opportunities.

This combination of technologies can impact your entire organization beyond CS. Pattern recognition, for example, can help marketing teams identify perfect case study candidates and sales teams find relevant customer references. Because CS sits at the heart of the business, it’s the perfect launchpad for cross-functional AI initiatives.

Lay the Groundwork for AI

Three elements should be in place before implementing any GenAI or ML tool in your Customer Success organization. First, your Help Center must be comprehensive and current – AI is only as smart as your documentation.

Second, your customer data requires careful organization and consistent tracking. As your company scales, you’ll acquire more data and, if it’s organized, you can easily map data points to outcomes to learn how to adjust your CS strategy.

Finally, your CS strategy (e.g., how do you think about your post-sales process? How do you onboard customers? What do you do when a decision-maker leaves your customer org?) must be documented even as it evolves.

Building these foundational elements will help you get the most value from AI.

Build Organizational Buy-in

Getting your organization behind AI initiatives requires more than choosing the right tools. CS leaders often struggle to drive AI adoption from their seats because AI can seem “magical,” enabling everyone to develop their own vision of what it could do. (If you’re a CEO reading this, your support and encouragement of AI adoption within the org is essential to your department leaders in obtaining further buy-in.)

Start by being extremely clear about what you want to achieve with AI and how you’ll measure it. Build curiosity by identifying sticky problems that frustrate your team: repetitive tasks, time-consuming analysis, or processes that don’t scale. Then, frame AI as a solution to these specific challenges rather than a magical transformation.

Keep in mind that this is a fast-moving space, so always be aware of legal risks and your organization’s AI policies. Ensure you work with Finance, IT, and Legal to ensure your use of AI adheres to legal requirements.

How to Put AI to Work

If you don’t know where to start, you’re not alone. While there’s no one-size-fits-all approach to AI, Chris recommends starting internally with what you know best: your team’s daily work.

Many CS teams begin by creating their own AI assistant, trained on their knowledge base. Let your Customer Success Managers (CSMs) experiment with it for internal tasks first – many find this becomes an invaluable tool for onboarding new team members and creating shared resources.

Here are some areas to focus on:

Daily operations: Train AI to convert customer calls into structured notes, create reusable communication templates and build a searchable knowledge assistant. Start with simple formulas and templates your team uses daily.
Trend analysis: Set up your tools to analyze support tickets, NPS responses, and product usage patterns. Look for ways to automate regular reporting and surface emerging issues before they become problems.
Strategic planning: Help CSMs prepare for customer interactions by generating SWOT analyses and flagging accounts showing expansion potential or risk. The goal is shifting time from constant research to strategic customer conversations.

Remember: your AI strategy should align with and enhance your CS processes. What works for a high-touch enterprise team doesn’t suit a product-led growth model. Start with the workflows your team already knows, then expand based on your specific needs and your customer base.

Measuring the Impact of AI

The metrics that matter in CS are different from other departments. While Sales teams can show AI impact through immediate pipeline growth (like using AI for prospecting emails), CS requires patience and different measurements:

Revenue metrics: Track how AI initiatives affect retention and expansion
Operational metrics: Monitor what percentage of customers have defined, measurable outcomes
Portfolio efficiency: Measure how AI helps CSMs manage larger customer loads
Profit & Loss impact: Document how your efforts affect customer profitability

Document specific customer objectives and categorize them systematically. For example, start with your top six customer outcomes, then break these into 12 more specific categories. This helps track customer journeys more precisely and proves AI’s impact on achieving these outcomes.

Expect to wait six to nine months to prove real results in Customer Success. Think of it like steering a cargo ship – changes take time to show impact.

What Not to Do with AI

Avoid the temptation to use AI for direct customer communication. Today’s AI lacks the authenticity needed for meaningful customer interaction. Focus instead on making your CSMs more efficient at handling larger portfolios while maintaining personal connections.

Don’t waste resources chasing AI-powered churn prediction. Without years of detailed customer history, these models struggle to deliver actionable insights. Start simpler: use basic rules to flag behavior changes that correlate with churn risk.

The Future of AI in Customer Success

The future isn’t AI replacing your CS team – it’s helping them deliver better results at scale. Get your foundation solid first. Start small with internal tools that save your team time. Expand only when you can prove impact through revenue metrics and customer outcomes.

In Customer Success, meaningful change takes time, so plan realistic budgets and timelines and continuously document any changes in your processes. And, if you start now, you can build an AI strategy that delivers the outcomes your customers want.

To learn more about Planhat’s Customer Success platform, reach out to Planhat here.

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