AI in Learning & Development Report 2026

Discover how L&D teams are moving from AI experiments to AI-powered learning ecosystems — backed by 20,000+ data points from 421 professionals.

Download the report Watch the webinar

Executive Summary

AI has moved beyond experimentation and into everyday L&D work

87% of respondents are already using AI, and only 2% have no adoption plans. Most are past experimentation, with 36% using AI in defined workflows and 9% beginning to scale it across their organization.

AI’s strongest value today is speed

Today L&D teams use AI mainly for voice generation (63%), content and quiz drafting (60%), video creation (52%) and translation (38%) in the design and development stages. The top benefits are faster production (84%) and better learner experience (66%).

“AI has crossed a threshold in L&D. It has moved from experimental tool to everyday practice — and, for a growing minority, to something closer to operational infrastructure.”
- Dr Philippa Hardman

The next phase is about learner impact

L&D teams expect the biggest future gains in more personalized learning (72%), wider internal reach (65%) and improved learner engagement (56%). Planned adoption is rising for assessments and simulations (36%), adaptive pathways (33%), skills mapping (32%) and AI tutors (29%). And value is shifting from time saved (88%) to clearer business impact (55%) and easier global localization (54%).

The learning ecosystem is becoming more distributed

Only 47% think the Learning Management System (LMS) will remain the backbone of their ecosystem. Expectations for where AI will live split across embedded features (19%), productivity tools (17%), standalone systems (17%) and cross-system agentic layers (19%). 27% remain unsure.

Agentic AI (AI that can autonomously take actions) interest is high, but teams are cautious

Most are excited (27%), cautious (39%) or say they need to learn more (29%). Exploration focuses on AI tutors (49%), coaching and mentoring (43%), personalized guidance (43%) and admin automation (38%).

Budgets remain low and fragmented

39% spend 5% or less of their L&D budget on AI, and 30% don’t know their spend. Dedicated funding is still developing.

Readiness challenges are slowing progress

Security (58%), accuracy (52%), legal constraints (41%) and integration challenges (36%) remain major obstacles. Although 74% say their culture encourages experimentation, only 45% feel IT is enabling AI adoption.

“L&D is entering a new "AI-integrated" era where the real question isn’t which tools to use but how to build a learning ecosystem that drives performance.”
- Kevin Alster

Despite these frictions, optimism is strong

66% believe AI will strengthen L&D’s influence, and 72% think the function will thrive by adapting. L&D teams want practical support, including AI skills and design training (67%), workflow guidance (63%), impact measurement (63%) and integration help (50%).

“ We finally have the tools to create dynamic learning content at a speed and scale we’ve never seen before. The opportunity is massive, but impact still depends on skill. As AI becomes an everyday part of L&D, our focus has to shift from experimenting to upskilling—so we can design learning that’s not just faster, but smarter.”
- Kristen Budd

Our L&D Experts

Dr Philippa Hardman is a leading expert in learning science and AI-powered instructional design, with over 20 years of experience connecting research on how humans learn with the way digital learning is built. She is the creator of the DOMS™ learning design process, used globally to produce evidence-based learning experiences.

Philippa is Co-Founder of Epiphany AI, an Affiliated Scholar at the University of Cambridge, and an advisor to organizations adopting AI to scale learning impact. She regularly consults with global companies and is a keynote speaker at major education and technology conferences.

Kevin Alster is a Strategic Advisor at Synthesia, where he helps global enterprises apply generative AI to improve learning, communication, and organizational performance. His work focuses on translating emerging technology into practical business solutions that scale.

He brings over a decade of experience in education, learning design, and media innovation, having developed enterprise programs for organizations such as General Assembly, The School of The New York Times, and Sotheby’s Institute of Art. Kevin combines creative thinking with structured problem-solving to help companies build the capabilities they need to adapt and grow.

Kristen Budd is a learning experience designer at Synthesia, focused on helping people create impactful instructional content in the age of AI. With a decade of experience across learning design, educational media, and cognitive science, she’s passionate about empowering the L&D community with the confidence, skills, and knowledge to design learning that truly has an impact.

She has designed and scaled learning programs in tech, education, and workforce development, and her research has been published in the Journal of Research on Educational Effectiveness. Kristen has presented at AERA and AEA, with an ongoing focus on evidence-based, multimodal learning.

Survey Methodology

The Survey

The survey was conducted in October–November 2025 with Learning & Development professionals. It was distributed through Synthesia’s audience, Dr. Hardman’s network, and several L&D and instructional design communities to ensure a diverse and representative sample of practitioners.

Responses

421 responses were collected, generating more than 20,000 data points.

Statistical significance

Using a working estimate of ~600,000 L&D and instructional design professionals across the globe, this sample provides a ±5% margin of error at a 95% confidence level, making the results directionally reliable.

Moving Past Experimentation

AI use in L&D is common, even if not yet organization-wide

AI has become a normal part of L&D workflows, even if most teams are still early in their journey. A large majority (87%) of respondents say they feel comfortable using AI. Only 6% express any discomfort. This confidence aligns with how widely AI is already being used inside L&D teams.

Most teams report active or emerging use of AI

The majority say their team is already using AI in learning programs. 57% are actively using it today and another 30% are running early pilots. That means almost nine in ten teams have moved beyond simple experimentation.

13% of respondents aren’t using AI right now, including 10% who are still exploring the possibility and 3% who cite no plans or face barriers. Last year, 20% weren’t using AI.

“ Maturity is rising — but it’s far from uniform. Many teams are still early-stage; a small group is sprinting ahead.”
- Dr Philippa Hardman

AI is now a near-universal part of the L&D toolkit

AI is shifting from individual use to team-level workflows, and is now a near-universal part of the L&D toolkit. Only 2% of respondents say they use no general-purpose AI tools, while the vast majority are relying on tools like ChatGPT (74%), Copilot (54%), and Gemini (39%).

Many respondents describe using AI for specific tasks in design and development, supported by shared prompts, templates, or emerging team norms. This marks a clear shift from isolated experimentation and ad hoc “asset acceleration” toward more consistent, integrated workflows.

How L&D Is Using AI: Today & Tomorrow

AI is now firmly embedded in day-to-day L&D production

84% of respondents said speed is the biggest incentive for using AI as part of their workflows. The heaviest use sits in core production tasks like text-to-speech (63%), quiz generation (60%), video creation (52%) and translation/localization (38%).

These activities cluster in the design and develop stages of ADDIE (Analyze, Design, Develop, Implement, and Evaluate), with more than 65% of respondents now using routinely using AI to create learning materials.

AI helps speed up content production

Teams describe using AI for ideation, scripting, storyboarding, translation and research summarization, supported by human review for quality.

Where AI is going next

The next wave is less about faster production and more about adaptive, intelligence-driven learning. Growth is strongest in adaptive use cases: assessments/simulations (36% piloting), personalized pathways (31% piloting; 33% planning), skills mapping (30%; 32%) and AI tutors/chatbots (30%; 29%).

Current Value vs. Future Value

Early value is expanding beyond speed

Most respondents say AI is already helping them produce learning faster, with 88% reporting value via time saved on content creation. Cost savings are also beginning to materialise, with 45% reporting financial benefits today, though many expect clearer and more measurable gains as AI becomes embedded across more of the workflow.

Moving towards meaningful business outcomes

Budgets are growing, but most organizations are still in the early stages

AI spending in L&D is still modest and often unclear. Most teams invest only small amounts, with 26% allocating 1–5% of their budget and 15% allocating 6–10%. Another 30% do not know their spend at all, which suggests that AI activity is still scattered rather than planned.

Blockers reflect risk, infrastructure and capability gaps

Security is the most common blocker (58%), followed by accuracy concerns (52%), integration challenges (46%) and legal restrictions (41%).

Culture is supportive, but operational alignment is mixed

Only 6% face no blockers at all. Most teams avoid using personal or sensitive learner data with AI (59%), which keeps experimentation focused on low-risk content tasks.

The Future Ecosystem & Agentic AI

The role of the LMS is becoming less certain

The LMS still anchors the learning ecosystem for many teams, but confidence in its long-term position is mixed. Only 47% believe it will remain the backbone of their stack over the next three years, while the rest are neutral or expect the centre of gravity to shift.

Agentic AI is generating interest, even if understanding is still developing

Most respondents see potential in agentic AI. Around 27% say they are already exploring it and another 39% are interested but cautious. Only 4% express concern and none reject it outright. The hesitation appears to come from unfamiliarity rather than resistance.

What’s Next for L&D Teams?

“The next step for L&D teams is learning how to connect everything. The outlook is positive, but the challenge is clear: move beyond just creating assets and start enabling real performance support.