Next in Learning: The 2026 Trends Shaping L&D

The 2026 Trends Shaping L&D

The panel’s central message is that AI is moving learning and development beyond isolated courses and basic automation. It is enabling more connected learning ecosystems, faster content production, personalized support, and stronger links between learning and business performance. Realizing those benefits, however, requires high-quality content, cross-functional collaboration, and careful attention to governance.

1. Learning ecosystems are becoming more connected

• AI’s effectiveness depends on bringing together relevant organizational data, which is often spread across L&D, HR, IT, sales, customer support, and other functions.
• Learning technology is increasingly part of a broader ecosystem that may include LMSs, content-management systems, data warehouses, performance tools, and enterprise platforms.
• A single-source content approach can make updates more efficient: maintain information centrally, then distribute consistent, current content across learning and work channels.
• The panel does not expect one all-in-one system to meet every need. Instead, organizations should select appropriate platforms and connect them through APIs or native integrations, ideally giving learners access through a cohesive central portal.

2. AI is accelerating content creation—and freeing teams for strategic work

• AI tools can quickly turn existing documents into summaries, course outlines, learning objectives, and other materials.
• Video creation and localization are also becoming faster and more accessible. A single video can potentially be adapted for different languages and audiences without repeating the full production process.
• The benefit is not just producing the same content faster. Lower production costs can make it possible to create more relevant, personalized learning for audiences who might not previously have received it.
• Generated content still needs review. AI can speed up production, but instructional quality and accuracy cannot be assumed.
• With routine production work reduced, learning teams can spend more time on business needs, program effectiveness, and strategic outcomes instead of responding only to requests for courses.

3. Learner experiences are becoming more personalized

AI can combine information such as a learner’s role, preferences, location, experience, and performance to recommend relevant content. The panel described several possibilities:

• Context-sensitive recommendations for a course, video, or learning program.
• Chatbots or agents that answer questions alongside a course or provide information during daily work.
• Adaptive follow-up lessons based on a learner’s remaining knowledge or skill gaps.
• Support that draws on enterprise information to make it easier to find the right answer when it is needed.

4. Just-in-time support and practice are expanding

• Instead of relying only on formal training before a task, employees can increasingly receive guidance while preparing for or doing the work.
• In one sales example, AI could help a representative research an account, prepare questions, and find relevant product information before a call. During the call, it could provide real-time prompts and identify topics that have not yet been covered.
• AI simulations can let employees repeatedly practice sales conversations, language scenarios, and other interactions without requiring a manager to observe every attempt.
• These tools can provide measurable feedback on specific behaviors, making it easier to assess skill development than through one-off, difficult-to-scale role-play sessions.

5. Human skills can benefit from repeated, contextual practice

• Communication, critical thinking, leadership, and management skills are difficult to develop through information alone; practice is essential.
• AI-based role-play and coaching tools can offer repeated practice and draw on relevant context, such as an employee’s goals or prior feedback.
• For example, a manager-support tool could help prepare for a coaching conversation by bringing together information about an employee’s development goals and project feedback.
• Panelists saw potential for these tools to improve the measurement of learning outcomes and connect learning activity to business performance.

6. Live training is likely to continue

The panel did not expect instructor-led or other live training to disappear. They noted that:

• Some organizations are continuing or increasing live training while using AI to improve preparation and supporting materials.
• Learners may value human interaction and may experience fatigue from digital learning.
• In high-stakes fields, human assessment and connection remain important, particularly where an AI error could have serious consequences.
• AI may change how live training is designed and supported, but demand for it is likely to persist.

7. Adoption requires governance, integration, and experimentation

AI’s potential often runs ahead of an organization’s ability to deploy it. Common obstacles include:

• Data privacy, security, legal exposure, and approval requirements.
• Silos between L&D, IT, and other business functions.
• Content that is not structured or maintained in a way that enables AI tools to return useful answers.
• Slow approval processes that can discourage teams from pursuing promising applications.

Panelists suggested that organizations:

• Treat learning as a product, with a learning business owner and an IT or product counterpart working together.
• Build stronger relationships with technical teams and clarify what the business needs from AI.
• Test ideas in small, safe environments or innovation groups before attempting a broad rollout.
• Demonstrate business value to build support for implementation.
• Keep humans involved in quality assurance and testing, especially in high-stakes applications.

8. Instructional designers’ roles are evolving, not disappearing

Instructional design expertise remains valuable because AI tools still need content designed to help people perform—not merely content that is available to retrieve.

• Instructional designers can help structure, tag, and organize information so AI systems provide useful answers that support behavior.
• They can assess whether content entering an AI tool will actually help employees do their jobs.
• They can also create test cases and observe how AI applications perform across different scenarios.
• One emerging role is an AI content and enablement architect or consultant, combining instructional design with knowledge of AI systems, data, and content operations.
• AI may automate some lower-level tasks and reduce frustrating work, such as repeatedly negotiating basic course objectives, while giving designers more room for strategic work.
• AI-generated learning materials still need to improve in their application of instructional design principles.

9. Advice for 2026: Learn continuously and experiment

The panel acknowledged that the pace of change makes accurate predictions difficult. Its practical advice was to stay curious, gain hands-on familiarity with AI tools, and learn incrementally.

John suggested setting aside a small amount of time each day, estimating that 10 minutes daily could add up to about 50 hours of learning over a year. Angela emphasized treating uncertainty as an opportunity to experiment, learn from what works and what does not, and help shape future roles and practices.

Speakers and sources featured

• Shannon Howard — Director of Customer and Content Marketing at Intellum; moderator.
• John Leh — CEO, lead analyst, and founder of Talented Learning.
• Angela Misella — VP of Workforce Experience Consulting at EPAM.
• Rob Gray — Enterprise Account Manager at Intellum.