Using AI to Scale and Personalize Customer Training

The New Imperative in Customer Education

In today’s fast-paced business landscape, products and services are evolving rapidly, and customers need ongoing education to keep up. Effective customer training has become a strategic priority for companies across industries, as it directly impacts product adoption, satisfaction, and loyalty. Research shows that when customers are well-trained on a product, 68% use the product more frequently and 56% tap into more features, leading to higher engagement. Crucially, trained customers are far more likely to stay – one study found a 92% subscription renewal rate for trained customers, compared to 80% for untrained ones. These numbers underscore that educating customers isn’t just a nice-to-have – it’s a key driver of retention and revenue.

Yet, traditional customer training methods are struggling to meet modern demands. Many programs still rely on one-size-fits-all tutorials, static online courses, or occasional webinars. Such approaches often fail to engage today’s learners or accommodate their diverse needs. Busy customers may skip generic training, and those who do participate often face content that is either too basic or too advanced. It’s no surprise that completion rates for conventional self-paced courses can be dismal – often under 10% in many cases. Moreover, scaling up in-person workshops or individualized coaching for thousands of users is impractical for most organizations due to resource constraints. The result is a gap: companies need to train large customer bases effectively and personally, but traditional methods can’t easily scale or adapt to each learner.

This is where artificial intelligence (AI) is stepping in as a game-changer. AI technologies are enabling a new era of scalable, personalized customer education. In fact, experts predict that AI tools (like advanced chatbots and content generators) will soon become part of virtually every customer education program. As customers increasingly expect companies to understand and cater to their individual needs (with 76% of customers expecting businesses to recognize their needs and provide tailored experiences), leveraging AI in training is quickly moving from experimental to essential. The following sections explore how AI can help organizations overcome traditional training challenges by delivering learning experiences that are both massively scalable and highly personalized.

Effective customer training is vital because it drives successful product usage and builds long-term loyalty. When users truly understand a product, they derive more value from it – which translates into higher customer satisfaction and business outcomes. As noted, companies that invest in robust customer education see measurable benefits like increased feature adoption and improved renewal rates. Well-trained customers are more self-sufficient (87% of customers in one study said they can work more independently after training), which in turn reduces support burdens on the company. They’re also more likely to explore advanced functionalities and upgrade to higher service tiers, fueling growth.

However, the value of customer education can only be realized if training programs reach all the right users and actually help each individual succeed. This is a tall order. Many enterprises serve thousands (or even millions) of customers, each with different roles, experience levels, and learning preferences. A generic tutorial or a one-time seminar will inevitably miss the mark for a large portion of this audience. For example, imagine a complex software platform: a brand-new user needs fundamental onboarding, while a veteran user may seek tips on advanced features. If both get the same cookie-cutter training, one will be lost and the other will be bored. Inconsistent knowledge can also hamper product adoption – if customers don’t fully grasp how to use key features, they might never realize the product’s full value.

Moreover, scaling traditional training is resource-intensive. Hiring more trainers or customer success managers to personally guide every user is not feasible for most companies. Live training sessions and workshops don’t easily accommodate global customers in different time zones. And producing high-quality learning content (videos, manuals, FAQs) for every new product update or use case can overwhelm training teams. The fast pace of innovation exacerbates this challenge – as one learning executive observed, product details “that may have been true a couple of months ago feel antiquated” now. Keeping educational materials up-to-date and relevant for all users, at all times, is extremely difficult with manual efforts alone.

In short, businesses today face a dual mandate in customer education: scale up to train large audiences efficiently, while personalizing the experience to ensure each customer gets exactly the guidance they need. Meeting this mandate is crucial to drive customer success, but traditional methods fall short. This is why organizations are turning to AI-powered solutions – to achieve the scale and personalization that human-centric approaches struggle to deliver.

Traditional customer training approaches come with several inherent limitations when it comes to scale and personalization. One major challenge is the “one-size-fits-all” content problem. Most conventional training content is designed for the “average” user. In reality, customer audiences are very heterogeneous – they include beginners and power-users, technical and non-technical personas, and people with varying objectives. With static training modules, everyone gets the same material regardless of relevance. This often leads to disengagement: segments of users either tune out material that’s too simple for them or give up on material that’s too complex. As a result, engagement metrics suffer. (Recall that many generic e-learning courses see under 10% completion rates, which means the vast majority of customers aren’t finishing the training.)

Another challenge is the scalability of human-led training. Instructor-led sessions, live onboarding webinars, or 1:1 coaching calls can be excellent for small groups, but they don’t scale well to a large customer base. As your number of customers grows, it becomes impossible to offer high-touch training to everyone without a proportional increase in headcount. Many companies face a situation where their training and customer success teams are stretched thin, only able to onboard the largest clients or reactively address issues for others. This leaves gaps in education for many users. It also introduces inconsistency – one trainer might emphasize certain best practices while another focuses elsewhere, leading to uneven experiences across the customer base.

Geographical and time constraints further complicate scaling. Customers may be spread worldwide, but live training typically happens at fixed times that may not suit all regions. Supporting multiple languages and local contexts adds more complexity if relying on human trainers or manually translated materials.

There’s also the issue of content upkeep and responsiveness. Products can change with frequent releases and new features. Traditional training content (like PDF manuals or recorded videos) can quickly become outdated, yet updating and re-recording content is laborious. Without constant maintenance, training programs risk teaching obsolete information or missing new capabilities entirely. This lag means customers might not learn about features that could benefit them, harming adoption.

Finally, cost is a non-trivial factor. Personalized attention doesn’t come cheap – whether it’s hiring more staff or flying trainers to client sites. Many organizations simply cannot afford to deliver personalized training to every customer through manual means. Thus, they face a trade-off between offering high-quality education to a few or low-touch basics to many.

These challenges collectively create a perfect storm: the need for broad and tailored customer education is rising, but traditional methods can’t economically rise to meet that need. The consequence is often subpar customer onboarding, low engagement with training resources, and underutilization of the product – all of which undermine the customer’s success and the company’s goals. This sets the stage for AI-driven solutions, which promise to break through the constraints of traditional training approaches.

AI technologies are unlocking the long-sought ability to personalize learning at scale. An AI-driven training platform can dynamically tailor the content, pace, and style of instruction to each customer – something that was impractical to do manually for thousands of users. Here’s how AI enables a more personalized customer training experience:

• Adaptive Learning Paths: AI can analyze a learner’s profile and behavior to present the most relevant content for them. For example, an AI system might consider a user’s role, industry, or past activity in the product to recommend what training modules they should start with. As the user progresses, the system can adjust the path in real time. If the AI detects that a customer is struggling with a particular topic (taking longer on quizzes or repeating a lesson), it can offer supplemental resources or simpler explanations to reinforce that concept. Conversely, if a user demonstrates mastery, the AI might skip ahead or suggest more advanced topics. This ensures each learner isn’t bored or overwhelmed, but appropriately challenged. Over time, the platform essentially builds a custom curriculum for every customer.
  
• Tailored Content and Examples: AI enables content personalization down to the level of examples and use cases. For instance, an AI-powered training module for a software product could present different examples to a marketing user versus an IT user, making the material more relevant to each. Modern customer education platforms use AI to automatically curate micro-learning content suited to each user’s needs. They can break training into bite-sized modules and then recommend the right “bite” at the right time. For example, AI-driven microlearning systems can deliver short lessons targeted to a user’s specific job role and skill level. A sales employee at a client company might receive quick tutorial videos focused on sales-related product features, whereas an administrator user gets training on configuration and analytics features. This granular personalization keeps learners engaged because the content “speaks” to their context.
  
• Intelligent Tutoring and Feedback: One of AI’s most powerful capabilities is to mimic a personal tutor’s responsiveness. AI-powered virtual coaches or chatbots can be available within the training interface to answer questions on demand. If a customer is confused about a step in a tutorial, they can ask the AI assistant for clarification and get an instant explanation, rather than waiting for a support ticket response. These virtual assistants can even walk users through complex procedures step-by-step interactively. Furthermore, AI systems can provide real-time feedback on exercises or simulations. For instance, if a customer is practicing using a software feature in a sandbox environment, an AI tutor can observe their actions and give pointers or corrections immediately, much like a human instructor would. This kind of 24/7 individualized support makes learning more effective and builds the user’s confidence. It also ensures that no customer is left stuck on a problem just because a human trainer isn’t available at that moment.
  
• Recommendation Engines for Ongoing Learning: AI personalization doesn’t stop after initial onboarding. As customers continue to use the product, AI can analyze their usage patterns and periodically recommend new training materials that might interest them. If the system notices, for example, that a user has started using a new module of the software, it could suggest a short course on advanced tips for that module. These tailored recommendations create a continuous learning journey that encourages customers to expand their skills. They also help users discover features they might not be aware of. For example, if a customer completes a basic lesson on product setup, the AI might suggest an advanced tutorial or a webinar on optimizing the product, creating a natural progression for the learner. This proactive approach keeps customers engaged over the long term and maximizes their proficiency with the product.
  
• Personalization by Language and Locale: For global customer bases, AI can automatically personalize training content to the user’s language and region. AI-driven translation tools can convert training materials into multiple languages swiftly, and even adjust cultural references or units of measure. Additionally, AI can take into account local compliance or industry regulations in training content if applicable. This means each customer gets the training in a form that’s linguistically and contextually appropriate, improving comprehension and comfort.
  

The cumulative impact of these AI-driven personalization techniques is a training experience that feels highly relevant and custom-fit for each learner. This level of individualization was traditionally only achievable via one-on-one tutoring, which doesn’t scale. AI now makes it possible to approximate a personal tutor for every customer simultaneously. As one industry leader put it, the “holy grail” of education technology has been to achieve tutor-level outcomes at scale, and with AI, we’re finally getting there. Companies using AI in this way report significantly higher engagement in their training programs. Users are more likely to complete courses and retain knowledge because the training meets them where they are. For instance, Databricks, an enterprise software company, found that by using an AI-enhanced learning platform with personalized content, course completion rates jumped to around 80%, whereas traditional video-based courses often saw completion below 10%. Personalization keeps customers motivated to learn, which ultimately translates into deeper product usage and success.

Beyond personalization, AI is invaluable for scaling up the delivery of customer training to large audiences. Automation and intelligent algorithms allow training programs to reach more people at once, without sacrificing quality or incurring prohibitive costs. Key ways AI enables scalable training include:

• Always-On, Unlimited Availability: AI-powered training tools (such as chatbots, virtual coaches, and e-learning platforms) can operate 24/7 and handle virtually unlimited users concurrently. This means your customer education can be “open for business” at all times, across all time zones, without requiring an army of trainers on call. For example, an AI-based chatbot can simultaneously guide thousands of customers through an onboarding process or answer their questions in real time, something no human team could manage. The experience remains consistent and high-quality for every user. In practice, AI systems can interact with an unlimited number of customers at once while providing the same level of support and instruction to all. This effectively removes the cap on how many customers you can train at the same time. Whether you have 100 or 100,000 users taking a course, an AI-driven platform can accommodate them all together by dynamically scaling computing resources – all without users experiencing delays or reduced attention.
  
• Rapid Content Creation and Updates: AI, especially generative AI technologies, can dramatically speed up the production of training content. Instead of having instructional designers spend weeks creating a new course or writing documentation, AI tools can help generate first drafts of training materials, tutorials, or even interactive simulations in a fraction of the time. For instance, if there’s a new product feature release, an AI content assistant could quickly pull together the relevant information (from release notes or knowledge bases) and create an outline or slides for a training update. Human experts can then review and refine it, but the heavy lifting is done by the AI. This ability to update learning content on the fly means training keeps pace with product changes. As noted earlier, in rapidly changing domains like technology, continuous learning is crucial – AI enables that continuous refresh of educational content with much less effort. Some advanced platforms even let you input raw data (like product manuals or past support tickets), and the AI will output a draft course or FAQ automatically. By accelerating content development, AI ensures your customer training is always current and can be scaled to cover more topics without proportional headcount growth.
  
• Automation of Administrative Tasks: Scaling training isn’t just about the content delivery; it also involves a lot of coordination and administration. AI excels at automating these repetitive tasks, which frees up human trainers and program managers to focus on higher-level work. For example, AI can handle enrollment by automatically inviting new customers to appropriate training modules once they sign up for your product. It can send reminders and nudges to users who enrolled but haven’t completed a course, improving completion rates. AI can also grade quizzes or assignments instantly, provide performance analytics, and even generate reports on training outcomes for stakeholders – all tasks that would be tedious manually. As one expert described, “AI can streamline the process, from enrolling customers and tracking their progress to generating reports and sending reminders, allowing the organization to focus on enhancing content quality”. By automating the logistics of training delivery, AI enables your program to efficiently handle far more learners. A small customer education team can manage a huge user community because the AI takes care of the heavy operational load.
  
• Scalable Support and Coaching: In traditional models, if a customer had a question during training or needed extra help, they might have to wait for a response via email or schedule time with a trainer – a bottleneck when scaled to many users. AI removes that bottleneck by providing instant, on-demand support. For example, a conversational AI assistant embedded in the training platform can answer frequently asked questions instantly. It can also monitor learner progress and proactively reach out: if a user is lagging or stuck on a module, the AI might suggest scheduling a live help session or provide additional resources. This kind of proactive support at scale ensures that no learner falls through the cracks just because of volume. Some companies use AI-driven “virtual tutors” that check in with users regularly (“I see you haven’t completed Module 3, do you need help?”) and guide them along. Such capabilities mean you can effectively mentor thousands of customers through their learning journey simultaneously, something impossible with a limited human staff.
  
• Consistent Quality at Scale: Another benefit of AI in scaling is consistency. Human trainers have varying styles and may deliver content differently, but an AI-driven system will provide a reliably uniform experience to everyone. You can codify best practices into the AI – for example, ensure it always uses the most effective explanation for a concept – and that will be replicated for all users. This consistency is important when scaling globally; every customer gets the same accurate information and level of attention. Of course, it can be augmented with human personalization when needed, but the baseline quality remains high everywhere. One education leader highlighted that with AI assistance, an individual instructor can now serve thousands of learners instead of just a few dozen, because the AI handles the repetitive interactions without dropping quality. The human experts supervise and step in for edge cases, but they are no longer a scarcity limiting the program’s reach.
  

By leveraging AI for these scaling advantages, companies can dramatically expand the scope of their customer training initiatives. What might have required a large department of trainers can now be achieved with a lean team plus intelligent automation. Importantly, this scale doesn’t come at the cost of personalization – as the previous section described, AI simultaneously can tailor the experience for each user. Scalability and personalization go hand-in-hand with AI. The end result is a training program that can grow with your customer base, maintaining effectiveness whether you’re training 100 people or 100,000 people. This ensures that as your business scales, your customers’ knowledge scales right along with it, powering their success with your product.

AI in customer education is not just theoretical – many organizations are already reaping the benefits of these approaches. Let’s look at a couple of examples and scenarios that illustrate how AI is transforming customer training in practice.

Databricks – Personalized Learning at Scale: Databricks, a leading data analytics platform, faced the challenge of training over 10,000 enterprise customers on its continually evolving software offerings, including new AI-powered features. Traditional training methods struggled to keep up with the rapid product updates and the diverse roles of their users. Databricks turned to an AI-powered learning platform to solve this problem. By partnering with an AI-driven education provider, they delivered interactive courses that adapt to each learner. The content was personalized by job role, region, and even language for users around the world. AI role-play exercises and open-ended assignments were introduced, in which an AI would simulate scenarios and provide instant feedback as customers practiced using the product. Human instructors remained involved in designing content and monitoring forums, but AI handled a lot of the heavy lifting in providing real-time guidance and support.

The results have been impressive. Databricks saw course completion rates near 80%, a huge leap from the single-digit percentages typical of self-paced online courses. Customers reported uniformly high satisfaction with the training. Perhaps most telling, the company noticed improved product usage among trained customers, validating that the education was truly effective. As Databricks’ VP of Learning noted, by combining AI with expert instructors in a “human-in-the-loop” model, they can now deliver a “supportive, personalized experience to thousands of customers” without sacrificing quality. In her words, “I don’t have a scale problem anymore. Now, we can personalize learning at scale in a way that stretches and develops every learner”. This case demonstrates that AI can dramatically expand the reach of customer training programs while also boosting learner engagement and outcomes.

Immersive Training for Complex Products: AI is also being applied in industries beyond software. Consider a manufacturing company that sells advanced industrial equipment to businesses. Training customers to operate and maintain this equipment safely is critical, but traditional training might involve sending instructors on-site or asking customers to attend centralized training sessions – both are hard to scale. By integrating AI with technologies like Augmented Reality (AR), such a company can create a powerful remote training solution. For example, the equipment manufacturer could provide an AR training app: when a customer points a tablet or smart goggles at the machine, the app overlays interactive guidance (labels, arrows, step-by-step instructions) onto the real-world view. An AI component drives the experience – it recognizes what the user is doing, and can analyze the customer’s performance in real time. If the customer is performing a maintenance procedure incorrectly in the AR simulation, the AI can immediately alert them or show a corrective animation. This approach lets customers learn hands-on, but virtually, with instant AI feedback. It’s safe (no risk of damaging real equipment) and can be done at the customer’s own facility and pace. Such an AI-AR training program could be deployed to thousands of customer sites without sending a single human trainer in person. Early examples of this idea are emerging: for instance, in some customer education settings, AI is used to power AR simulations where customers practice using a product in a risk-free virtual environment, and the AI provides instant feedback on their actions. This greatly scales the reach of practical training, and clients in fields like healthcare, manufacturing, or automotive are beginning to leverage it for complex product education.

Other Use Cases Across Industries: Virtually any sector that requires customer training can find an AI-driven solution to enhance scale or personalization. For example, in financial software, AI chatbots can guide new users through setting up their account and proactively answer common questions about compliance or reporting features. In the SaaS industry, many companies have added AI-based recommendation engines to their customer university portals – these suggest to each user what learning content to take next based on their role or even based on analytics of their product usage. E-commerce or consumer tech companies might use AI to personalize help center content: when a customer visits the support site, an AI system can greet them, ask what they need, and then instantly pull up the most relevant how-to guide or tutorial video (even customizing the answer if the product in question has different versions). All of these examples point to a common theme: AI allows customer education to be more responsive, interactive, and tailored at massive scales.

It’s worth noting that while AI can automate and enhance a lot of training, the human element remains important in many of these examples. The best results often come from a blend of AI efficiency and human expertise. In the Databricks case, instructors still design content and verify AI outputs; in the AR example, subject matter experts likely create the core simulation scenarios, which AI then executes. AI is thus a force multiplier for the training team – extending their reach and capabilities – rather than a complete replacement. Organizations pioneering AI in customer training are finding that this synergy yields the strongest outcomes: customers get the personalized attention and immediate support of AI, backed by the wisdom and oversight of human experts.

Adopting AI for customer training can deliver impressive benefits, but it requires careful planning and execution. Here are some best practices and considerations for enterprise leaders and L&D (Learning & Development) professionals looking to leverage AI in customer education:

• Start Small and Pilot Programs: Rather than overhauling your entire training approach overnight, begin with a pilot project. Identify a specific training area or customer segment where AI could make a big impact (for example, onboarding for a new product line), and trial an AI solution there. Monitor the results closely – track metrics like completion rates, customer feedback, and support ticket reduction. Starting with a contained pilot helps you validate the AI’s effectiveness and work out any kinks on a smaller scale. Databricks followed this approach by testing their AI-driven platform with a key customer group first, refining the experience, and then scaling it up over time. A successful pilot builds internal buy-in and creates a blueprint for broader implementation.
  
• Keep Human Experts in the Loop: AI can automate content delivery and even content creation, but it still needs oversight. Ensure that your training experts (instructional designers, product specialists, customer success managers) remain involved to supervise the AI’s output. For example, have instructors review AI-generated course materials or chatbot answer sets for accuracy and tone. Maintain control over the “what” and “how” of training content – the AI is a powerful assistant, but your team should set the educational strategy. This human-in-the-loop approach is crucial because AI is not infallible. Generative AI can sometimes produce incorrect or irrelevant information with great confidence (a phenomenon known as AI “hallucination”). By having experts curate and approve the AI’s contributions, you ensure the training remains reliable and trusted. Leverage AI for efficiency, but have humans ensure quality.
  
• Focus on High-Value Use Cases and ROI: With the excitement around AI, it’s easy to get caught up in adopting technology for its own sake. Instead, ground your AI initiative in clear business objectives. Identify the pain points in your current customer training – e.g., low engagement, high cost per trainee, slow content updates, or inconsistent results – and target those with AI solutions. Set specific goals and metrics to measure success, such as improving course completion by X%, reducing training support tickets by Y%, or accelerating onboarding time for new customers. This will help you choose the right AI tools (whether it’s a chatbot, an adaptive learning platform, a content generator, etc.) and demonstrate the value of the investment. As one industry expert advised, “It can’t be AI for AI’s sake – there has to be an ROI to it”. Prioritize use cases where AI can clearly drive better outcomes or cost savings, and build your business case around those wins.
  
• Ensure Data Privacy and Ethics: Implementing AI often involves using customer data (such as product usage data, past questions, or personal preferences) to personalize the experience. It’s vital to handle this data responsibly. Make sure you comply with data privacy regulations (like GDPR or other applicable laws) when feeding customer data into AI systems. Anonymize or aggregate data where possible, and secure any sensitive information. Be transparent in your privacy policy about how customer data might be used to improve training. Additionally, be mindful of biases – AI models can inadvertently perpetuate biases present in their training data. Monitor AI-driven content or recommendations for any biased or unfair outcomes, especially if your customer base is diverse. Establish guidelines for ethical AI use in line with your company’s values. By proactively addressing privacy and ethics, you build trust with your customers that the new AI-powered training is designed to help them, not to exploit their data.
  
• Train and Prepare Your Team: Introduce your training and customer success teams to the AI tools and provide them with adequate training on how to use them. The effectiveness of AI in customer education also depends on the people operating it. Equip your staff with the skills to interpret AI analytics dashboards, update AI-driven content, and handle escalations from AI (e.g., when the AI flags a learner who needs human intervention). Encourage a culture of continuous learning within your team – just as you are improving customer learning, your team should also learn to work alongside AI. This might involve new roles, like an “AI training curator” who manages the AI content or an analyst who fine-tunes the personalization algorithms. When your team is comfortable with the technology, they can more fully exploit its capabilities and innovate new ways to delight your customers.
  

By following these best practices, organizations can increase the likelihood of a smooth and successful integration of AI into their customer training programs. Ultimately, the goal is to combine the scalability of AI with the judgment of human experts to deliver training that is effective, efficient, and empathetic.

AI is ushering in a transformative era for customer training, one where companies are no longer forced to choose between scale and personalization – they can achieve both. For HR professionals, training managers, and business leaders, this presents an enormous opportunity. By intelligently deploying AI, you can provide each customer with a high-touch, customized learning experience without straining your resources. The payoff is a more educated and empowered customer base that fully leverages your product or service, leading to higher satisfaction and stronger customer relationships.

Embracing AI in customer education is becoming increasingly essential in an environment where customer expectations continue to rise. Modern customers expect timely, relevant answers and learning tailored to their goals. AI allows organizations to meet these expectations by delivering “always-on” training and support that feels personal and responsive to each user’s needs. Companies that have begun integrating AI into their customer training are already seeing benefits like faster onboarding, reduced support loads, and improved adoption of features. They are turning training from a one-off activity into a continuous, adaptive journey that grows with the customer.

That said, success with AI doesn’t happen automatically – it requires thoughtful implementation, clear objectives, and a commitment to quality content. But as we’ve explored, the tools and strategies are now available to do this effectively. And importantly, adopting AI in training doesn’t eliminate the human touch; instead, it amplifies your team’s ability to reach more people in a meaningful way. It enables your experts to focus on what they do best – crafting strategy, building relationships, and tackling complex problems – while the AI handles the scale and routine interactions.

In conclusion, using AI to scale and personalize customer training is a powerful approach to maximize the impact of your customer education efforts. It ensures each customer – no matter how many you have – can get the knowledge they need in the way that helps them most. This leads to more confident users, deeper product usage, and ultimately, a more successful partnership between you and your customers. As AI technology continues to advance, the organizations that leverage it wisely in their training programs will be well-positioned to enhance customer experience and drive sustainable growth in the years ahead.

FAQ

How does AI personalize customer training at scale?  

AI analyzes individual profiles and behavior to recommend relevant content, adjust learning paths, and provide tailored feedback and examples, creating a highly personalized learning experience for each customer.

What are the main challenges of traditional customer training methods?  

Traditional methods often rely on static content, are resource-intensive to scale, and struggle with maintaining relevance and engagement across diverse and global customer bases.

How does AI help scale customer education without sacrificing quality?  

AI enables 24/7 on-demand support, automates content creation and updates, and automates administrative tasks, allowing training to reach large audiences efficiently with consistent high quality.

Can AI-driven training improve customer engagement and retention?  

Yes, personalized AI learning experiences increase course completion rates, knowledge retention, and product usage, leading to higher customer satisfaction and renewal rates.

What are best practices for implementing AI in customer training programs?  

Start with pilot programs, keep human oversight, focus on high-value use cases, ensure data privacy, and train your team to effectively leverage AI tools.