Customer Enablement has always walked a fine line between scalability and personalization. As companies grow, the challenge becomes: How do we give each customer the tools, knowledge, and confidence they need to succeed — without overwhelming our internal teams or drowning in content?
Enter AI.
In the past 18 months, artificial intelligence — particularly generative AI — has gone from a buzzword to a boardroom priority. But when it comes to Customer Enablement, the real question is not whether AI is impressive — it’s whether it actually improves the experience for customers and customer-facing teams alike.
So, is AI a true game-changer for enablement, or is it just more noise in an already crowded tech landscape?
Let’s unpack that.
What Does AI in Customer Enablement Actually Look Like?
At its core, AI in enablement is about helping customers get the right information, in the right moment, with minimal friction. This can show up in a variety of ways:
- A customer asks a chatbot for some guidance on how to set up SSO and receives a personalized step-by-step walkthrough.
- An AI system notices that users in specific industry, such as healthcare, are struggling with data export, and proactively recommends training or sends tailored help content.
- A customer success manager (CSM) is alerted that a key stakeholder hasn’t completed onboarding and is given a suggested outreach message and enablement path.
This is not sci-fi — it’s already happening. Here are some real-world examples of what is possible.
Real-World Examples of AI-Powered Enablement
Gainsight PX + AI Playbooks
Gainsight PX is a product experience platform that helps businesses understand user behavior within their products and improve the overall customer experience. It combines product analytics with engagement tools to drive user adoption, collect feedback, and ultimately increase product usage and customer satisfaction. If a customer isn’t adopting a core feature, the system recommends enablement playbooks — actions like sending a tutorial, launching a walkthrough, or scheduling a check-in. The result is now CS teams spend less time guessing and more time guiding; proactive rather than reactive. Customers get relevant help before frustration sets in and feel that their success matter and they are not just another account in a company’s CRM.
Intercom Fin (AI Support Agent)
Intercom’s Fin uses OpenAI to power its chatbot, which can pull from help docs and support content to answer technical and contextual questions. Think of how many times you are looking to do something like “How do I configure [a feature] for my specific use case?” and just need to find the answer, not pages of content on that feature. With this approach, users can get real answers 24/7, even for nuanced issues. This allows for internal support and customer teams to spend less time on repetitive issues, and more time on meaningful, strategic enablement.
Pendo + In-App Guidance
Pendo is a product experience platform used to help businesses understand how users interact with their software products and improve the user experience. It provides tools for analyzing user behavior, guiding users with recommended targeted in-app messages based on user behavior, and collecting feedback. For example, if a user is struggling with a particular setup step, Pendo can deploy tooltips or videos to provide enablement without human intervention. This results in customers staying in the product and finding success, rather than stopping productivity by searching through help documentation, forum posts, or having to reach out for assistance.
What Makes AI a Potential Game-Changer?
There are a variety of ways we could spin how AI is impactful and possibly change how we approach enabling our customers — everything from reimagining onboarding as a guided, personalized journey, to transforming static help centers into conversational experiences, to empowering CSMs with predictive insights that make customer success feel effortless. While every organization is different, here are (my top) three areas to consider.
Personalization at Scale
Traditional enablement often means casting a wide net: webinars, email campaigns, or one-size-fits-all guides. I constantly hear feedback on how Help documentation, as detailed as it is (for some products), is not simple enough to follow for the typical user or address the nuances and dive into the details enough for other users. AI can be used to flip the script by tailoring those experiences in real time. Using customer and contact information about what training they may have taken already or what their role is within their organization.
Think about the scenario of two customers going through the same onboarding process. However, based on product usage, one receives a basic walkthrough and suggested content to review, while the other gets more advanced configuration tips. All that content is readily available for customers today, but you can surface the right content at the right time to help guide them through the experience.
Proactive vs. Reactive Enablement
One area that I see a lot of, and have for some time, is organizations trying to be more proactive with their customers. This means understanding what roadblocks or issues they may have before they arise. Rather than waiting for a customer to ask for help, AI can flag friction points before they escalate.
A predictive model might detect that customers are skipping key setup steps. This can then trigger a guided tutorial for the customer to follow and get insight into how to avoid common pitfalls and ensure success. Simultaneously, this can be flagged to a CSM to step in and intervene if needed. At the very least, the CSM is now aware of what is happening within the customer, and can take action. Understanding how the customer is using your product(s) is critical for any CSM to be effective.
Content Discovery & Optimization
Everything that is being discussed around enablement has content at the center of the conversation. Customer has a question; we search for content to get an answer. We have a new feature; we make content for customers to consume in mass. How do we surface that content to the right customer at the right time though? I believe that AI is not just about serving up content — it can help optimize it as well. Systems can be leveraged to analyze which articles drive success and which ones fall flat. This leads to faster improvements in your knowledge base and training materials so you can focus on what works and resonates with your customers.
What Are the Challenges?
While the upside is real, AI is not a magic bullet. The human element within Customer Enablement needs to remain, but AL can be used to enhance how we approach these topics. There are, of course, risks and drawbacks that teams need to address head-on. Below is a snapshot of what I see commonly come up with organizations along with possible ways to mitigate them.
| Challenge | Risk | Mitigation |
| Hallucinations | AI may confidently give wrong or outdated answers | Use retrieval-augmented generation (RAG) tied to current, verified docs |
| Loss of Human Touch | Over-relying on bots can alienate customers | Blend AI with strategic human interaction — hybrid enablement is key |
| Model Drift | AI becomes less accurate over time | Build feedback loops from CSMs, customers, and usage data to retrain models |
| Content Readiness | If your docs are scattered or inconsistent, AI will reflect that | Invest in content hygiene and structure before deploying AI |
Should You Invest in AI for Enablement?
Short answer: Yes. Your goal with AI should be to improve time-to-value, reduce onboarding friction, and scale support for long-tail customers.
One piece of advice I would provide is to approach with intention. Start with use cases that matter most and will continue to provide benefits in the future:
- Are you training 100s of customers a month?
- Is your support team bogged down with how-to questions?
- Do customers consistently struggle with feature adoption?
Then identify where AI can augment, not replace, your enablement strategy. This is key to being successful as your focus should be on enhancing your current offerings, making them stronger and more useful. Organizations, departments, and teams should be leveraging tools that can help the here and now and focus on the constants. Try not to get caught up in the hype of trying to predict what new thing will come in the next 5 years and how to prepare for it. Focus on what will remain the same and build towards that.
Final Thoughts
So, is AI in Customer Enablement a hype train or a real transformation?
It’s both.
The hype is real — and so is the opportunity. The game-changer is not AI itself, but how you integrate it into your customer journeys, content workflows, and feedback loops.
The future of enablement is not just digital. It’s intelligent, adaptive, and proactive.
And it’s already here.































