How to Create Effective Training Programs with AI Video

You've been asked to create a video explaining the company's remote work policies. You start production in January. Eight weeks later, it's ready. But leadership just revised the policy based on new engagement survey feedback, and now the video is obsolete.

Modern training programs rely heavily on video because it’s visual, engaging, and easy to share. The problem is that traditional production can't keep up with how fast information changes. Every update means re-recording, re-editing, and re-publishing — which slows learning down instead of speeding it up.

So the real challenge isn’t just making training content. It’s keeping it accurate, scalable, and up to date.

The question is: how do you actually integrate AI video into your existing workflow? In this guide, we’ll walk through a practical five-step L&D model—Discover, Design, Develop, Deploy, and Measure—to show how AI-powered video tools streamline training development without sacrificing quality or impact.

Let's start with the first step: understanding what training you actually need to create.

Step 1: Discover 

Discovery is about figuring out what training you actually need to create. Start with a layered analysis. Look at what's happening at the industry level: new regulations, competitive shifts, technology changes. Then, focus on organizational changes, like reorgs or policy updates. Next, reflect on where employees may be struggling, like product launches outpacing sales enablement capacity. Finally, define what content you will create and select the ideal medium. 

With traditional video, production constraints limit how much you can create. AI tools remove that bottleneck. You can now produce 10x more videos in the same timeframe, which means content that was previously too expensive or time-consuming suddenly becomes viable. Our decision guide can help you determine when an AI video is the right choice.

[Insert decision guide]

How to pilot AI video tools:

Look for topics where speed creates immediate value. Which training takes the longest to deliver right now? Where are employees waiting for information they need to do their jobs? What content do you update most often, and how much time does that consume?

Start with a few videos that represent your broader needs. Include a mix of content for testing, including at least one topic that is likely to require updates. This pilot teaches you what works before you scale.

Discovery in practice:

Forecast, a London-based software company, was struggling to scale their training videos. Their production process involved recording voiceovers using Audacity, screen-recording slides in PowerPoint, and syncing audio and visual files in Adobe Premiere Pro. Their output was often hour-length videos that required substantial editing into microlearnings. While they could accelerate this process with third-party providers, it came at the cost of $3,000 per edited minute of footage.

They decided to streamline this production process using AI-powered video. They built Forecast Academy using Synthesia, and prioritized customer onboarding content that needed to be regularly updated, localized, and delivered at scale.

Step 2: Design

Design is where you decide how to structure your training content. For training videos, you write scripts, storyboard each scene, and plan interactive or multimedia elements to keep learners engaged.

With traditional production, you also need to plan for actors and filming locations, not to mention a filming schedule that accommodates multiple takes. When using AI video tools, you're leveraging AI avatars, which means you can design with iteration in mind. There's no need to capture the perfect take.

How to design for AI video:

Keep videos between 2 to 5 minutes in length. (Research from the Association for Talent Development found that 59% of L&D professionals report this as the most effective duration for microlearning videos.) Structure each video the same way: a hook that explains why this matters (5-10 seconds), core content with one concrete example (90-180 seconds), and a quick recap of the key takeaway (10-20 seconds). This formula keeps videos focused while respecting learners' limited attention spans.

Be sure to read your scripts aloud. If you stumble over a sentence, your avatar will too. AI avatars excel at clear, professional delivery. They struggle with delivering complicated syntax or conveying complex emotions (avoid sarcasm and humor). A best practice is to use shorter sentences and conversational language, which also helps reduce the cognitive load for learners. 

Think in templates. If you're creating 10 onboarding videos, build one template with consistent branding, intro, and outro. Clone the template for each new video and just update the script. This maintains quality while dramatically speeding up production.

As you design, consider adding interactive elements: clickable call-to-actions, quizzes to test understanding, branching paths for personalized learning, polls, feedback forms, and hotspots that let viewers explore areas of interest. These features increase engagement and make training more effective.

Design in practice:

You don't have to start from scratch. Forecast adapted their existing training content for AI video rather than building everything new. They took their slide decks, talk tracks, and existing materials and redesigned them for the microlearning format.

This approach saved significant time. Instead of writing entirely new content, they broke long presentations into shorter segments, refined the language for spoken delivery, and updated examples as needed. By leveraging Synthesia's AI avatars and multilingual narration, they could quickly transform existing materials into scalable video content without the constraints of traditional production.

Step 3: Develop 

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Development is where you actually produce the content. With traditional video, this means scheduling talent, booking locations, filming, editing, reviewing, and revising—anywhere from four to six weeks per video. With AI video, production happens in hours, not weeks. You go from script to finished video in the same day.

The workflow is dramatically simpler: upload your script, select your avatar and voice, build your timeline with text and graphics, generate the video, and review. If you need to revise, you edit the script and regenerate in minutes. No reshooting, no re-editing footage.

How to develop with AI video:

Start by creating your first video. Choose your avatar, test different voices, and establish your visual style. This first video may take 2-4 hours as you make design decisions (check out our resources for help through this process). Once you have it right, create a template with your company's branding, a standard intro and outro, and adaptable layout.

Build and test any interactive elements you planned during the design phase—quizzes, branching scenarios, clickable hotspots. Make sure they work smoothly on both desktop and mobile devices before moving to deployment.

Development in practice:

Forecast's designer, Ryan McMeekin, follows this process to develop customer onboarding videos: pastes the script into Synthesia, selects an AI avatar to narrate, and uses the platform’s screen-recording feature that syncs the avatar’s voice with the script automatically. After generating the video, he uploads it to the authoring tool to finalize the course content. 

Step 4: Deploy 

Deployment is about actively engaging your audience and using their feedback to improve. Traditional video training requires a significant upfront investment, meaning that you want to release it as soon as it's finished. AI video changes this by making iteration fast and affordable. Start with a small pilot group to test and refine, then roll out broadly with confidence. The result: higher completion rates and training that actually sticks.

How to deploy AI video:

Choose a pilot group that represents your broader audience. Make videos easy to find: direct links in onboarding checklists, embedded in process documentation, or organized into playlists in your LMS. Test on mobile devices since many learners watch without sound.

Collect feedback deliberately. Send a brief survey after the pilot asking what worked and what didn't. Common issues like "Video 3 was confusing" or "Examples didn't match our work" can be fixed in under an hour. Make those revisions, then launch broadly using pilot participants as champions who can share what improved based on their input.

Deployment in practice:

Forecast launched Forecast Academy as their customer onboarding platform. Rather than releasing one or two videos at a time, they were able to deploy a comprehensive library of over 100 training videos in under six months. They reduced the time spent on video creation by 50%, with an 80% reduction in the time spent syncing audio and video. 

Step 5: Measure

Measurement is the hardest part of L&D work—and the most important. You're not just tracking video views; you're trying to demonstrate how training drives business impact through behavioral change and skill acquisition. With traditional video, analysis happens after production because changes are too expensive to make frequently. With AI video, the low cost of updates enables a continuous improvement model: regularly reviewing available data and modifying the training accordingly. 

How to measure AI video:

Start with engagement metrics—they're easy to track and give you quick signals. Monitor completion rates (while industry averages hover around 20-30%, well-designed interactive training can achieve 90%+ completion), drop-off points that show where people stop watching, and rewatch rates that indicate value. But don't stop there.

The real question is whether training changed behavior or built skills that impact the business. Recent research shows 77% of learning leaders prioritize demonstrating business impact, yet many struggle with data integration. For customer onboarding, did the training reduce support tickets? Did it accelerate time-to-productivity? For compliance training, did error rates decrease? For sales enablement, did deal velocity improve?

Look for correlations between training completion and business outcomes. If employees who complete your onboarding videos reach full productivity two weeks faster than those who don't, that's meaningful data. If support tickets about a specific topic dropped after launching training on it, you're seeing impact.

Review performance monthly. If a video has low completion, check where people drop off and revise that section.  Because regenerating takes minutes, you can A/B test variations and standardize on what works. Retire videos that consistently underperform after several revision attempts.

And remember, not everything that matters can be directly measured. You're creating conditions for growth. You're building the conditions that make engagement, skill development, and productivity more likely.

Measurement in practice:

Forecast tracked clear metrics to validate their investment in AI video. Ryan consolidated his tech stack from three tools (Audacity for voiceovers, PowerPoint for screen recording, Adobe Premiere Pro for editing) down to one platform, reducing both software costs and complexity.

But the real proof came from business outcomes: Forecast Academy now serves more than 50 external customers with up-to-date training that evolves as fast as the product does. Test scores improved, demonstrating that faster production didn't compromise learning effectiveness. And critically, this was achieved with a lean L&D operation—one designer producing what would have previously required a full production team.

Start Building Faster Training Today

The five-step framework—Discover, Design, Develop, Deploy, Measure—works whether you're exploring AI video for the first time or scaling an existing program. The difference is speed: production that used to take weeks now takes days, and updates that cost thousands can be done in minutes.

Ready to see the difference? Create your first training video with a free trial. 

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Need more guidance? Browse our Synthesia Academy for step-by-step walkthroughs on designing microlearning videos, creating templates, and measuring training impact.