AI is reshaping customer education from the ground up, changing not just how content is created but how, when, and where customers encounter it. Truth or hyperbole? Just how much of the AI talk is hype, and how much has the potential to make a genuine impact on the customer experience? Below, we take a look at five customer education “jobs” that AI makes easier. From how learning is designed and delivered, to how the internal teams better align themselves to customer outcomes, these are the AI capabilities you’ll want to prioritize.
Use AI to deliver learning in context
Since digitizing learning, customer education teams have provided opportunities for customers to build their product knowledge and skills in context with interactive labs, simulations, and in-app walkthroughs.
The next step in contextual learning requires moving beyond the confines of the software application or customer academy to reach customers in the moment. This means making content currently contained in support articles, community discussions, tutorial videos, and formal courses available to be crawled by LLMs.
When properly structured, learning content can be delivered to customers wherever they happen to be searching: in the ChatGPT app, in a Google search, in Slack, on LinkedIn, or via the company’s website chatbot. The question customer education leaders must ask themselves is “Where are my customers, and where would a learning experience be most effective?” Omnichannel delivery is the next frontier in contextual learning.
Use AI to personalize learning
Personalization has long been a goal of both learning and customer experience teams. But as anyone who has received an AI-scripted sales email can attest, personalization does not equal relevance. In order to be effective, personalization must transcend attributes that, while specific to the user, may not pertain to the issue. A customer might at first appreciate being addressed by name during hours convenient to their time zone, but those personalized elements are useless if the assistance provided is not relevant to their role, product configuration, use case, or lifecycle stage.
Demographic and account-level data from a CRM can be used to customize the learning experience via APIs and a “headless” or extensible learning management system. When connected to data on user behavior and progress, machine learning can recommend learning that accelerates product adoption by providing the right next step toward the customer’s desired outcome.
Offering customers a conversational AI interface more closely matches the experience of their favorite AI assistant, as well as empowering them to take control of the learning journey. Conversational search helps customers find the right answer or next step in natural language, without manually navigating filters or guessing at keywords.
Use AI to make learning adjust dynamically
To remain personalized and effective, learning must adapt based on customer behavior, knowledge, and preferences in the moment. AI makes it possible to scale dynamic learning through coaching, adaptive pathways, and real-time video translation.
Coaching offers learners the spaced repetition needed to build competency, along with feedback to guide subsequent practice. AI coaching can include practice and demonstration, as well as individual assignments, video Q&A, interactive prompts, and personalized feedback.
Similarly, when the various layers of the customer intelligence tech stack are integrated, learning pathways can adapt based on user behavior. Instead of progressing through customer education content sequentially, as in a traditional course, customers receive recommendations based on signals from product analytics, learning interactions, CRM, customer success, and customer health systems.
When customers do choose to take a video-based course, they can adjust the delivery in real time by altering the playback speed, adding captions, or translating the audio into their language of choice. In addition to meeting customer expectations, dynamic learning is more likely to influence the behaviors correlated with product adoption, retention, and growth.
Use AI to keep pace with product enhancements
Odds are, you’re already using AI tools to create content more efficiently, whether it’s your LLM of choice or tools integrated in the admin experience of your LMS. Given the speed with which product teams are shipping new features, it can feel difficult to keep up.
Plug-ins like Thought Industries for Claude allow you to author, update, and manage your learning catalog from within Claude. This allows you to browse your full catalog and reference existing content before you build something new. Every change is staged as a draft for you to review before publishing in the Thought Industries platform.
Use AI to unify product training, documentation, and support
The long-cherished goal of customer success and education teams has been to create a frictionless customer experience. AI makes it easier to unify learning and content across in-app tutorials, customer academy, customer community, and help center. The customer success hub model helps teams align product training, documentation, and support to customer outcomes with a centralized repository or “content lake” that serves up learning wherever the customer needs it.
Where to take AI next
AI empowers customer education teams to meet customers where they are, with content that reflects who they are and what they’re trying to do right now. By making learning more contextual, personalized, and dynamic; by accelerating content production and updates; and by unifying learning into a single customer experience, AI capabilities help customer education teams drive product adoption, retention, and growth.
As you consider how to incorporate AI into your customer learning strategy, start by asking which capabilities would remove the most friction from your customers today. Download our free AI-Powered Customer Education Action Plan to get started.
Structuring and indexing learning content for LLMs allows it to delivered in multiple ways, beyond the customer academy. This can include wherever customers are searching for answers, whether that’s inside the software product they purchased, in the ChatGPT app, a Google search, Slack, LinkedIn, or a company’s website chatbot.
Demographic and account-level data from a CRM can be used to customize the learning experience via APIs and a “headless” or extensible learning management system. When connected to data on user behavior and progress, machine learning can make personalized recommendations of courses, articles, or events. Conversational search can also make the learning experience more tailored to the individual learner’s needs.
Dynamic learning adapts based on customer behavior, knowledge, and preferences. AI makes it possible to scale dynamic learning through coaching, adaptive pathways, and real-time video translation.
Dynamic learning adapts based on customer behavior, knowledge, and preferences. AI makes it possible to scale dynamic learning through coaching, adaptive pathways, and real-time video translation.
AI makes it easier to unify learning and content across in-app tutorials, customer academy, customer community, and help center.
