AI for Customer Success: 7 tools that actually deliver value

“I actually leaned over to colleagues at a recent conference and said, ‘What percentage of these companies talking about AI do you think are actually using it today?’”, says Holly Goodliffe, Sr. Director of Scaled Customer Success at Contentsquare.

Officially, the answer is that 52% of CS teams now incorporate AI.

But most CS leaders are stuck experimenting with basic tools rather than fundamentally rethinking how they deliver customer success. 

"The only true 'agentic' AI that are widely accepted? Chatbots & note-taking," says Gemma Cipriani-Espineira, Global Digital CS leader at Twilio.

What follows is a practical look at how AI is actually reshaping customer success right now, beyond the buzzwords and beyond the basic note-taking apps. The possibilities are far more exciting (and accessible) than most CS leaders realize.

How AI is transforming customer success

CSMs are already informally using AI to proofread emails, automatically take notes during calls, and brainstorm strategies for managing tricky customer situations. (”ChatGPT, here’s a frustrated customer email and a bunch of account data. Help me avoid churn!”)

But this sort of ad hoc experimentation is just the tip of the iceberg.

Here’s what to watch out for:

• Low-touch segments become truly touchless. For mid-tier and SMB customers, Aftab Khanna of Deloitte Consulting envisions "a scenario where there's no human intervention required and pretty much AI is driving automated communications.” As a result, CS teams can manage accounts far more cost-effectively—without a drop in quality—while reserving human communication for issues that need a personal touch.
  
• High-touch segments get intelligent self-service options. Imagine customer portals that proactively suggest value-driving actions based on usage patterns ("a little bit like how on Netflix we get guided recommendations," as Aftab Khanna puts it). Customers can discover more value serendipitously and on their own timeline, boosting product adoption and speeding up time to value—all without CSM involvement.
  
• CSMs transform into strategic advisors. When AI handles routine tasks, your team gains the mental space and capacity to think more strategically. With more time focusing on revenue-generating activities like expansion planning and EBRs, CS naturally shifts to a growth driver.
  
• Customer experiences become truly seamless. AI remembers everything a customer has done and said across all departments. This means customers don't need to repeat themselves when talking to different team members. Whether they're working with sales, customer support, or their CSM, the conversation picks up right where it left off. Customers feel like they're dealing with one helpful company rather than separate teams that don't talk to each other.

Whatever happens, CSMs will continue to be needed. One reason for this is that old habits die hard. This Reddit comment sums up the sentiment nicely: 

"If I'm spending 30k plus a year on a software solution, I want a real person managing my account, not AI. I don’t think that will change anytime soon.”

More importantly, says Cristy Rahman, an enterprise CSM, building valuable customer relationships will always require actual people:

“Good customer success involves knowing your clients at a personal level by disarming them and letting them lean on you to share more than they would with anyone else… all things that cannot be replaced by AI.”

6 key use cases for AI in customer success

AI tools are changing the game for CS teams by handling repetitive tasks and uncovering hidden insights. Here are six ways CS leaders are putting this technology to work.

1. Personalize onboarding without the extra work

58% of CSMs say customer onboarding has the highest potential for AI-driven productivity gains.

There’s a simple reason for this: delivering an unforgettable white-glove onboarding experience makes a tremendous difference to product adoption and customer retention—but it can also take a lot of manual work.

Fortunately, you can sidestep that admin work and launch personalized onboarding hubs at scale using tools like Dock. 

And with Dock AI, you can transform client call transcripts, documents, or Gong recordings into personalized onboarding content—making it easier than ever to deliver a personalized experience with less admin work.

Or with tools like Overhyped.ai, you can even offer voice onboarding via an AI-powered chatbot, letting your customers get real-time advice as they navigate your product for the first time. While voice onboarding still has some kinks to work out, like lag time and robotic voices, it’s only a matter of time before it rolls out on a wider scale.

2. Free your CSMs from meeting busywork

Aftab Khanna of Deloitte Consulting ballparks that about 30%-35% of the CSM workload consists of either prepping for customer meetings or managing the content and action items that come out of those meetings.

Fortunately, AI tools can now manage the full cycle of meeting admin work:

1. Before meetings: “Cheat sheets” for meeting prep. AI can pull together data from multiple sources to give an overview of a customer account in seconds. Instead of poring over CRM notes and product usage stats, CSMs get an instant brief on what’s important for each client.
   
2. During meetings: Automated note-taking and CRM logging. Henrik Hove, a CSM at Brandwatch, uses Gainsight to save time on note-taking and admin: "Whenever I have a meeting with a customer, the notes are logged automatically, and I just need to review them and perhaps edit them. So it's a big time saver for me."
   
   This helps CS leaders, too: AI tools let managers easily review snippets of team calls without watching entire recordings, making coaching and QA more efficient.
   

3. After meetings: Recaps and follow-up tasks. AI can draft personalized emails and action plans after customer calls. Tools like Gong and Chorus.ai use AI to summarize calls and even highlight action items, which CSMs can convert directly into tasks or emails.

Gillian Heltai, Chief Customer Officer at Lattice, points out that CSM job satisfaction is another hidden benefit: customer success managers are eager to outsource what is often the most boring and repetitive part of their work.

“It's great, because it's work that people hated doing: activity logging, meeting notes, recaps to customers. There was some value in it because it forces you to synthesize and summarize and prioritize and all of that. But this work, we can now just totally automate today through AI.”

3. Catch customer frustration before it's too late

What if you could identify a frustrated customer before they even considered canceling? Not just based on declining usage metrics, but by catching subtle signals across their entire experience?

It's no secret that the right technology can predict when customers are likely to churn—customer health scores and predictive churn analysis have been around for years.

What's changed with AI is the sheer volume and variety of signals that can now feed into these models.

While basic tools might track login frequency or feature usage, today's AI systems monitor everything from the emotional tone in support tickets to frustration patterns that would be impossible for humans to spot at scale—all to detect the earliest possible warning signs.

Churnly, which focuses on customer churn reduction for B2B SaaS companies, is a good example of this AI-powered approach.

Each individual seat is its own "sub-customer," giving you granular visibility into which specific users within an account are thriving or struggling—essential when a few disgruntled power users can sink an entire renewal. Machine learning algorithms prioritize the user activities that actually matter for success, helping you see the difference between real product adoption and occasional random clicks.

And crucially, you can see in actual dollars how much revenue is at risk from these warning signs, so your team can focus their attention where it will make the biggest difference.

4. Turn usage patterns into upsell opportunities

The best upsell opportunities aren't random—they're hiding in your customer data. AI is great at uncovering these growth possibilities by analyzing usage patterns, sentiment, and behavior to identify accounts that might be ready to upgrade.

Even basic AI tools offer valuable insights. Many customer success teams already use tools like ChatGPT and Claude to review customer feedback and support conversations.

More advanced approaches use predictive analytics tools to:

• Notice when customers show similar patterns to others who upgraded in the past
• Gauge when customers repeatedly reach their usage limits (like consistently using 95% of their licenses)
• Identify which features create the most value for different customer segments
• Predict future customer needs based on usage trends

Platforms like Journeyz have built entire systems around this concept, automatically surfacing expansion opportunities and even nudging customers directly to take advantage of relevant add-ons or upgrades.

5. Get real-time VoC without waiting for surveys

"Our product team built exactly what customers asked for last quarter, but now they're saying it's not what they wanted.”

Sound familiar? For most CS leaders, keeping a steady pulse on customer sentiment feels impossible. Despite all the conversations your team has with customers each day, the most valuable insights often slip through the cracks—buried in meeting recordings, scattered across account notes, or trapped in your CSMs' heads.

Traditional Voice of Customer (VoC) research provides valuable insights, but let's be honest: running formal surveys takes weeks to organize and analyze. By the time the report lands on your desk, the feedback is already outdated.

Artificial intelligence is changing this dynamic by turning every customer conversation into a VoC opportunity.

For example, UpdateAI, a conversation intelligence tool for CS teams, automatically finds patterns across customer conversations without anyone having to tag or categorize them. You can then filter your data to spot high-level trends. 

For example, you can quickly see if healthcare customers have different concerns than retail customers, or if your larger accounts are experiencing new issues.

6. Use AI agents to solve customer problems 24/7

It’s hard to find a more hype-y area of technology right now than AI agents, which have the potential to act as a “digital workforce” and autonomously handle entire roles rather than just one-off tasks.

But the implications are huge for CS, particularly in low-touch B2C contexts where customers regularly suffer long hold times and receive poor service. For people used to waiting hours to speak with their bank or airline, AI assistants offer a welcome alternative.

Fundrise, a real estate investment platform, automated 50% of their support queries using Intercom’s Fin AI agent—without a drop in quality or accuracy. (The Investor Relations team at Fundrise now focuses more on complex strategy rather than basic account queries.)

Expect AI agents to be used more strategically in B2B customer success—and internally, at least at first.

For example:

• Knowledge base agents that help CSMs quickly find answers in company resources without disrupting customer conversations.
• Compliance agents that help enterprise customers stay compliant by monitoring usage against regulations and automatically creating required documentation.
• Renewal agents that compile account health metrics, usage data, and customer feedback ahead of renewal discussions.
• Churn agents that identify sudden changes in customer behavior patterns and alert CSMs to potential problems before they escalate into support issues.

Still, there’s a strong argument for using customer-facing digital assistants anywhere they can streamline the customer experience. A technical implementation agent, for example, could help customers through tricky integration issues that often stall onboardings.

How should CS organizations think about getting started?

AI is a transformational technology, but in some respects, adding AI into your CS organization is like any other change management effort: it’ll be bumpy at first, and not everyone will like it.

Transforming into an AI-powered CS organization has risks, and one of them is “over-rotation" says Alex Turkovic, Sr. Director, Customer Experience at Belfry:

“I don't want to over-rotate on [AI] to where it becomes a nuisance and it becomes something that the team ends up hating. I don't want to create a burden with any of this stuff. I think one of the things that's key for me as we continue to implement AI strategies and tools is just being eyes wide open in terms of ‘are we actually helping or are we hurting the situation?’”

In other words: take things slow.

Phase I: Experiment internally first

Your first AI use case shouldn’t be letting CSMs copy and paste from ChatGPT into customer emails.

While you’re in the trial-and-error phase of AI implementation, keep your focus on internal efficiency as much as possible. Start with AI meeting assistants, data entry, meeting prep cheat sheets, knowledge base bots, and other tools that make your CSMs' job easier and faster.

Jenny Calvert, Director of Customer Success at Hunt Club, explains that even these small changes can be powerful:

“I used to struggle to truly be present, listen and take notes on calls. Now, my full attention is on our customers- allowing me to really listen, present with better body language and virtual eye to eye contact (which matters!)”

From there, move on to more analytics-focused (but still internal) AI use cases like predictive churn.

🤖 AI tip: Or, just ask AI what to do. Peter Armaly, Vice President of Customer Success at ESG, suggests feeding your current CS workflows into an LLM and asking for a list of low-hanging fruit.

Phase II: Carefully roll AI out to customers

Once you and your team are in full “AI mode,” move on to customer-facing AI applications like personalized messaging, upsell and cross-sell nudges, and self-serve AI solutions.

Just remember that if you deploy AI agents with real responsibility in your CS organization, rules and oversight become critical.

Jonathan Murray, Chief Strategy Officer at Mod Op, explains:

"An agent's doing a task that previously might have been done by somebody in the organization, a real human being who would look at that task and would make a value decision on certain things that need to be done. An agent is not going to do that. An agent is going to run on the data that it has and the rules that have been set, and therefore that requires a higher level of quality in terms of how we think about governance."

One more guiding principle for this phase:

Never pretend bots are actually people. Awkward phrasing or incorrect information can damage trust, especially if those communications are sent under a CSM's name. (75% of organizations think a lack of AI transparency leads to higher churn rates.)

One solution some high-touch CS orgs are experimenting with is to sign emails as “Acme AI agent, supervised by John Smith, Customer Success Manager.”

Best AI tools for customer success

The most successful CS teams are taking an incremental approach to AI tools—starting with specific pain points, measuring results, and expanding from there.

Here's a roadmap of the AI tools making the biggest impact in CS today.

1. Dock

Customer success teams often struggle to keep up with the constant flow of meetings, emails, and calls. Turning these interactions into useful resources for customers takes precious time away from building relationships.

With Dock AI, you can easily convert your customer conversations into useful resources directly in your customer workspace. By uploading a client call transcript, document, or Gong recording, you can instantly generate meeting recaps, customer success plans, project checklists, and anything else you need.

2. ChatGPT and other large language models

Many CS teams know they should be leveraging AI but aren't sure where to begin. The options can feel overwhelming, and it's hard to predict which tools will actually save time versus create more work.

A simple way to start is by trying out easy-to-use generative AI tools like ChatGPT and Claude.

A ChatGPT Team plan gives you extra benefits, letting you build custom ChatGPT versions and share them with your team to keep everyone on the same page. These can help with writing reports, improving emails, or finding simple information quickly.

Don’t underestimate the value of simple AI use cases like voice-to-text, as one CSM on Reddit points out:

“I wanted help putting a value selling slide together for a client and I talked through all the points and what I was trying to convey verbally, and ChatGPT structured it out for me much faster than me writing it."

Alternatives: Copy.ai and similar platforms offer built-in tools specifically designed for keeping your company's writing style consistent.

3. Conversation intelligence

Customer success depends on good conversations, but it's impossible to remember every detail from every call. Without capturing these insights, teams miss early warning signs and opportunities to help customers.

Gong helps by recording and analyzing customer conversations to spot potential problems before they become serious issues. It helps teams tailor their approach for each customer, focus on the most important tasks, and create step-by-step guides for handling common situations.

UpdateAI takes a similar approach, but it’s tailored more specifically for CS teams with functionalities like VoC topic tracking built in.

Alternatives: Avoma, Fathom, Grain, Otter, and Fireflies all help capture and summarize customer conversations.

4. AI customer success platforms

In the past, keeping track of customer health across dozens or hundreds of accounts required endless spreadsheets and manual work. Oftentimes problems weren't spotted until it was too late to fix them.

Tools like ChurnZero are changing this dynamic. Instead of relying on gut feelings, you can use AI to analyze account history, health scores, communications, and customer engagement patterns to build a complete picture of each relationship.

For example, you can get quick answers about accounts, automatically generate summaries, track important topics, review sentiment analysis, and visualize relationship maps—all without the manual work.

Alternatives: Gainsight and Totango offer similar AI-enhanced customer success tools.

5. Knowledge management and documentation

Customer knowledge often gets trapped in individual team members' heads or scattered across different systems like emails, shared drives, and chat threads. This creates frustrating experiences for everyone involved: customers receive inconsistent answers, and new team members struggle to get up to speed.

Guru and similar knowledge management tools solve this by putting all critical information in one central place with smart search capabilities. This means agents can quickly find what they need during customer interactions without putting customers on hold or transferring them around.

Alternatives: Knowmax and Bloomfire each provide their own spin on AI-powered knowledge management.

6. AI agents for customer success

As customer expectations rise, CS teams are being asked to do more with the same resources. This can lead to burnout, inconsistent service, and missed opportunities.

AI agents help by handling routine tasks and providing smart assistance. Agents can research customers, suggest next steps, carry them out with one click, pull insights from conversations, fix contact information, and update records automatically—letting CS managers focus on more important work while helping more customers effectively.

Cust.co provides AI agents with the power to handle low-touch customer outreach and conversations on their own. For high-touch segments, those same AI agents can act as a behind-the-scenes copilot for CSMs.

Alternatives: Conversica and Relevance AI offer their own unique takes on AI assistants for customer success teams.

7. Customer insights & research

Understanding what customers truly think across thousands of interactions is nearly impossible with traditional methods, which often miss important patterns and fail to connect feedback across different channels.

Dovetail, a customer insights hub, addresses this issue by using AI to analyze every customer touchpoint, from support tickets to NPS surveys to recorded calls. It finds important moments in conversations, pulls out useful insights without hours of manual review, and connects related feedback across these different sources to reveal the complete picture.

Alternatives: Insight7 offers similar capabilities for customer interview analysis and market research.

Turn CS insights into action with Dock AI

Most CS teams are just scratching the surface of what’s possible with AI.

AI tools can personalize every customer interaction, predict issues before they arise, and free your CSMs from administrative tasks that drain their strategic potential. Best of all, they're accessible right now, not in some distant future.

Remember, you don't need to completely overhaul your CS team to see results. Start with a single use case that addresses your biggest pain point, then scale as you gain confidence.

Ready to transform your customer success strategy with AI?

Get started with Dock to see how AI-powered workspaces can help you deliver personalized, efficient customer experiences at scale.