Artificial intelligence is no longer a side experiment for Learning and Development teams. It is becoming part of their everyday operations.

Synthesia’s AI in Learning & Development Report 2026, based on responses from 421 professionals and more than 20,000 data points, found that most L&D teams are already using or testing AI.

The challenge is no longer simply adopting AI. It is using the technology to improve learning, employee performance and measurable business outcomes.

AI Adoption Has Entered the Mainstream

The report found that 57% of surveyed teams are actively using AI, while another 30% are running early pilots. Only a small proportion have no adoption plans or continue to face barriers.

Although adoption is widespread, maturity remains uneven. Some teams use AI only for individual tasks, while others are building shared workflows, approved toolsets and organization-wide learning systems.

Speed Is Only the Beginning

For many L&D teams, the first benefit of AI has been faster content production.

AI is currently used for tasks such as:

• Voice generation
• Learning-content development
• Quiz creation
• Video production
• Translation and localization
• Research and summarization

According to the report, 84% of respondents identify speed as a primary reason for using AI, while 88% already see value through time saved during content creation.

These efficiencies allow teams to create, revise and localize training materials more quickly. However, producing content faster is only the first stage of meaningful AI adoption.

The Focus Is Shifting to Learner Impact

The next phase of AI-powered learning will be less about generating more content and more about providing relevant support when employees need it.

Over the next two years, L&D professionals expect AI to contribute to:

• More personalized learning
• Greater learner engagement
• Wider access to internal training
• Faster global localization
• Clearer business outcomes
• Better performance support

Personalization represents one of the largest expected changes. While 24% of respondents report seeing this benefit today, 72% expect AI to deliver more personalized learning in the future.

Chart legend:

• First bar: Value reported today
• Second bar: Value expected in the future

Rather than requiring every employee to follow the same standardized course, AI could help create learning pathways based on an individual’s role, existing skills, performance needs and professional goals.

Learning Is Moving Into the Flow of Work

Traditional workplace learning often takes place through scheduled courses hosted in a Learning Management System. AI is beginning to expand that model.

Future learning experiences may appear directly within the tools employees already use. An AI assistant could answer questions, recommend resources, provide coaching or guide someone through a task without requiring them to leave their workflow.

This does not necessarily mean the LMS will disappear. It may instead become one part of a broader ecosystem connecting learning platforms, productivity tools, knowledge systems and intelligent assistants.

Only 47% of respondents believe the LMS will remain the backbone of their learning ecosystem over the next three years. Others expect workplace learning to become increasingly distributed.

Agentic AI Could Create Responsive Learning

Interest is also growing in agentic AI, which can take actions, respond to changing needs and coordinate activities across multiple systems.

L&D teams are exploring agentic AI for:

• AI tutors
• Personalized coaching
• Mentoring support
• Course development
• Automated assessments
• Administrative workflows

AI tutors are the most frequently cited area of exploration, selected by 49% of respondents. Personalized guidance and coaching were each identified by 43%.

These capabilities could move workplace learning away from static course libraries and toward continuous support that adapts to each employee.

However, autonomous learning systems require reliable information, human oversight and clear boundaries around how employee data is accessed and used.

Adoption Is Moving Faster Than Readiness

Although enthusiasm is high, many organizations are not prepared to scale AI responsibly.

Security, accuracy, integration and internal capability remain significant concerns.

Many organizations encourage AI experimentation, but their governance policies, technical infrastructure and approval processes have not developed at the same pace.

This gap will become more important as AI moves from low-risk content creation into personalization, assessment and performance support. These applications may involve sensitive employee information and require coordination between L&D, HR, IT, security and legal teams.

Organizations therefore need:

• Approved AI tools and usage policies
• Clear data-protection standards
• Defined ownership and accountability
• Human review processes
• Reliable system integrations
• Training for L&D professionals

Human Expertise Remains Essential

AI can draft content, create videos and analyze feedback, but it cannot independently guarantee that learning is accurate, ethical or effective.

L&D professionals remain responsible for:

• Applying learning science
• Understanding organizational context
• Verifying AI-generated information
• Protecting employee data
• Maintaining quality and brand standards
• Connecting learning to business priorities

Human oversight should be built into every AI-enabled workflow.

The future of L&D is not about removing professionals from the learning process. It is about allowing them to spend less time on repetitive production and more time designing meaningful experiences.

Measuring More Than Time Saved

Saving time is valuable, but it does not automatically mean employees are learning more or performing better.

As AI adoption matures, organizations should measure:

• Knowledge retention
• Skill development
• Learner engagement
• Behavior change
• Time to competency
• Job performance
• Business outcomes

The strongest AI strategies will connect efficiency with effectiveness. They will ask not only whether training was created faster, but whether it helped employees perform their work more successfully.

From Content Creation to Performance Enablement

The evolution of AI in L&D can be viewed as a progression from isolated experimentation to a connected learning ecosystem.

The 2026 picture of AI in workplace learning is optimistic but unfinished.

Most L&D teams have moved beyond isolated experiments, yet many are still developing the skills, governance and infrastructure required for deeper adoption.

The next stage will require organizations to connect their systems, protect employee data, train their teams and measure meaningful results. Those that succeed will use AI not simply to produce more content, but to create responsive learning environments that support employees when and where they need help.

AI can provide speed, scale and personalization. Human expertise must ensure that workplace learning remains relevant, responsible and connected to real performance.

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Based on findings from the Synthesia AI in Learning & Development Report 2026. The survey included 421 L&D professionals and may overrepresent early AI adopters because it was distributed primarily through AI-oriented networks.