AI for Training and Development: How to Use AI to Improve Learning Outcomes

AI is reshaping how training and development teams design and scale learning, but using it well requires more than choosing tools. As AI adoption accelerates, the real challenge has shifted from whether to use AI to how to apply it in ways that actually improve learning and performance.

This post breaks down how L&D teams apply AI in practice and where human judgment remains essential.

AI in everyday L&D workflows

87% of L&D teams are using AI, according to Synthesia’s 2026 AI in Learning & Development Report, which draws on insights from more than 400 L&D professionals across roles, industries, and regions.

The report shows that most AI use today is concentrated in content design and development, including voice generation, content and quiz drafting, video creation, and translation and localization.

Even with this increased ability to design and develop content, learning outcomes haven’t reliably improved. Production has accelerated faster than the learning systems that help behaviors stick.

Designing for behavior change isn’t new in L&D. What’s changed is the feasibility of sustaining that design consistently and at scale — which is where AI starts to matter differently.

Design for the behavior you want to change

Learning succeeds or fails in specific moments—how people open a sales call, handle an objection, or decide what to ask next.

L&D teams already know these moments matter. What AI changes is how consistently those moments can be supported as work happens.

Consider a sales team that has rolled out training on running better discovery calls. Reps complete the training, pass knowledge checks, and can explain the recommended framework.

But when managers listen to call recordings, they still hear wide variation in how calls are opened and how problems are explored. The training is complete, yet the behavior hasn’t settled.

At this point, outcome metrics don’t offer much clarity. The earliest signs of progress tend to show up in everyday behavior.

• Practice frequency: Are reps practicing how to open a call, ask diagnostic questions, and respond to common scenarios more than once? 
• Feedback timing: Are reps getting input on how they handled a discovery question shortly after a call, or only later in pipeline reviews?
• Application in daily work: Are managers starting to hear more consistent call openings, deeper problem exploration, and fewer feature-led pitches without prompting?
• Consistency at scale: Are reps practicing the same core discovery behaviors and receiving comparable guidance across teams or regions?

These signals tend to change before traditional KPIs do. When they move, it’s a strong sign that learning is translating into behavior change.

AI tools like Synthesia help L&D teams sustain these conditions over time by making it easier to deliver repeated practice, timely guidance, and consistent reinforcement at scale.

Getting started: embedding AI responsibly into L&D workflows

What matters next is how AI becomes part of everyday L&D work.

Teams that make this shift successfully tend to follow a few consistent patterns:

• Start hands-on with low-risk tasks
  Begin by applying AI to everyday work with limited downside, such as drafting scripts, updating existing training, or localizing content. These tasks make AI’s strengths visible quickly and help teams build shared confidence through direct use.
• Make value visible early
  ‍Early use cases focus on areas where impact is easy to see, including video creation and localization. Clear improvements in speed, consistency, and reach help teams recognize value and build momentum for broader adoption.
• Anchor AI to real L&D problems
  Apply AI to specific challenges such as slow content updates, inconsistent delivery across regions, or limited reinforcement after training. Tying AI to concrete problems keeps usage focused on outcomes and performance needs.
• Be explicit about boundaries and ownership
  ‍Define which parts of the workflow AI supports (e.g., drafting, scaling, or maintaining consistency), and which decisions remain human-led (e.g., learning design, performance standards, and ethical judgment). Clear boundaries support trust and sustainable use.

Once teams are clear on the behaviors they want to change and the signals they’re looking for, tools come into play as a way to execute consistently.

Tools that support this in practice

The most effective way to get started is to choose formats that naturally support practice, reinforcement, and consistency.

Short, scenario-based videos work particularly well because they make expectations visible, reduce cognitive load, and give learners a clear sense of what “good” looks like in action.

Teams tend to see the most progress when they start with a small number of repeatable formats tied to real behavioral moments.

Here are a few examples of templates that align well with this approach:

Customer service training‍

These templates are useful for modeling conversations, practicing responses, and reinforcing consistent behaviors over time, especially in roles where tone, sequencing, and judgment matter.

Onboarding‍

These templates help reinforce expectations and decision-making beyond a first-day experience, supporting consistency as new hires encounter real situations on the job.

Standard Operating Procedure (SOP)

Compliance training

These templates are well suited for reinforcing high-stakes behaviors clearly and consistently, with short, focused videos that learners can revisit when needed.‍

For a deeper look at how L&D teams are moving from AI experimentation to everyday impact, read our AI in Learning & Development Report.