5 Key Use Cases for AI in Learning and Development
AI is reshaping learning and development, helping teams create content faster, personalize learning experiences, and align skills with business goals. Learn how L&D leaders are using AI today and what to consider for a successful implementation.
AI has quickly become one of the most talked-about topics in learning and development. It’s a major topic at industry conferences, featured on nearly every LMS vendor’s website, and a frequent point of discussion across the L&D space.
While the workforce is still divided between those fully embracing AI and those approaching it with caution, one thing is clear: AI is changing how learning and development teams operate. And the momentum is only building.
- 83% of companies say AI is a top priority in their business strategy
- The AI industry is expected to grow nearly fivefold over the next five years
- The market is expanding at a compound annual growth rate of 35.9%
- 75% of professionals acknowledge that AI is impacting their work, necessitating continuous skill development
- AI integration can lead to a 40% reduction in onboarding time, streamlining administrative tasks and enhancing efficiency
In short, more advanced and practical AI applications are on the way. If you’re not already thinking about how AI can support your L&D efforts, now is the time to start.
The Most Common Ways L&D Teams Are Using AI
There is still plenty of buzz surrounding AI, along with bold claims about what it can do. But beyond the hype, many L&D teams are already using AI in meaningful and productive ways. Here are five of the most common applications:
1. Content Creation
AI can automatically generate videos, presentations, quizzes, and other training materials. It can also analyze existing content and create course descriptions and summaries, new ideas, or assessments based on what’s uploaded.
This saves valuable time for instructional designers and trainers. Instead of focusing on tedious execution, they can shift their attention to higher-level strategic and creative tasks.
2. Content Recommendation
AI-powered recommendation engines help personalize the learning experience and learner journeys. By analyzing learner behavior, preferences, performance data, skills gaps, and even information from tools like personality assessments, AI can suggest the most relevant courses and resources.
These recommendations evolve as the learner progresses, making the experience more adaptive and aligned with both individual goals and organizational priorities.
3. Analytics and Insights
AI can turn large volumes of learning data into actionable insights. It helps L&D teams identify trends, measure impact, uncover skill gaps, and optimize learning paths.
AI tools can track outcomes, engagement, and content performance. They can also automate reporting, visualize patterns, and support more inclusive and equitable access to development opportunities. This leads to smarter decisions and better learner experiences.
4. AI Coaching
Coaching is a powerful tool for employee growth, but it often depends on managers who may not have the time or bandwidth to provide consistent support. AI coaching offers a scalable alternative.
AI tools can provide ongoing, personalized guidance by analyzing performance and behavioral data. They allow organizations to deliver consistent coaching across the workforce, making development support more accessible and data-driven.
Platforms like Totara are now incorporating AI tools that assist in setting SMART Goals (Specific, Measurable, Achievable, Relevant, Time-bound) for learners. By analyzing performance and behavioral data, these tools provide ongoing, personalized guidance to learners and managers alike. This ensures that development plans are not only aligned with organizational priorities but also dynamically adapt as employee skills evolve.
5. Skills Alignment
As more organizations move from role-based to skills-based learning models, AI can help close the gap between current employee skills and future needs.
AI can assess individual capabilities and connect employees with the right learning opportunities. This not only helps bridge skill gaps but also strengthens long-term career development and workforce planning.
Key Considerations for Implementation
AI offers powerful opportunities for L&D, but as with any new technology, success depends on thoughtful planning and execution. Here are three key areas to consider:
Data Readiness
Before introducing AI tools, evaluate the quality of your data. Consider where the AI will pull information from, such as your LMS, HRIS, performance management tools, and other systems.
Ask whether your data is accurate, current, and complete. Gaps or inconsistencies can lead to unreliable results. To deliver meaningful insights and personalized learning, AI needs a full picture that includes learning history, performance metrics, skill profiles, and development goals.
Ethics and Privacy
AI often relies on sensitive employee data. In regulated industries like healthcare, data privacy must be a top priority.
Be transparent with stakeholders about what data is collected, how it will be used, and why. Wherever possible, personally identifiable information should be anonymized to maintain compliance with data protection regulations and protect individual privacy.
Change Management
While 79% of leaders believe AI is essential for staying competitive, 60% say they lack a clear implementation plan. Rolling out AI successfully requires more than choosing the right tools. It means managing a cultural shift.
Help your team understand what AI is, how it works, and how to use it. Upskilling in data literacy and AI fundamentals will be important across all roles. Make room for experimentation. Encourage testing, learning from real-world use cases, and sharing insights across teams.
Fostering a collaborative, learning-oriented environment will make it easier for teams to adopt AI with confidence and contribute to meaningful results.
Return On Investment and Use Case Prioritization
Not every AI initiative will deliver equal value, so it’s important to assess which use cases offer the greatest return on investment (ROI). Prioritize projects that balance feasibility with clear financial or operational impact.
Will the AI solution streamline processes, reduce costs, improve learning outcomes, or free up valuable time for your teams? Consider starting with high-impact, lower-complexity use cases to demonstrate quick wins.
Measuring the ROI of AI adoption shouldn’t only be about immediate savings. Each implementation should align with broader business goals and deliver measurable, long-term value.
Efficacy and Continuous Evaluation
AI tools are only as valuable as the outcomes they produce. Early chatbot implementations, for example, offered limited accuracy and impact, but have quickly improved through continuous refinement. The same will be true for AI in learning and development.
It’s essential to establish methods for measuring how effectively AI solutions are performing against their intended goals. Are recommendations actually helpful? Are predictions accurate? Are learning outcomes improving?
Build feedback loops and performance metrics into your AI strategy to monitor efficacy over time. This ensures your AI initiatives stay aligned with evolving business needs and continue to deliver meaningful results as the technology matures.
Leverage AI Capabilities for More Efficient Learning and Development
AI is no longer a distant concept in learning and development. It’s here, and it’s reshaping how teams create, deliver, and measure training. By focusing on practical applications and thoughtful implementation, L&D leaders can harness AI to drive meaningful impact. The key is to start small, stay strategic, and continuously evaluate results as the technology evolves. Those who embrace AI with purpose will be better equipped to support their workforce and meet the demands of an ever-changing business landscape.
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