AI in Industrial Skills Training: What Works and What Doesn’t

Key Takeaway

AI is helping training teams move faster, but not smarter on its own. At HSI, our instructional designers and animators use AI to speed up tasks like drafting content, building visuals, and generating ideas. But without human expertise, quality checks, and strong instructional design skills, AI can introduce risk, inaccuracies, and gaps in real-world relevance. The takeaway: AI works best as a support tool but NOT a replacement.

How the Experts Are Using AI Right

AI is showing up in training development faster than pizza disappears in a staff meeting.

In Industrial Skills training, where accuracy and safety matter, the focus isn’t on whether AI is useful, but on how to use it the right way.

To answer that, we went straight to the source: HSI’s Industrial Skills subject matter experts (SMEs). Instructional designers, animators, and content leaders share how AI is actually used in day-to-day training development and where it helps or hurts.

What follows is a look behind the curtain.

AI Is Everywhere - But It’s Not One-Size-Fits-All

Across the team, one theme came up consistently: AI is a tool, not a solution.

When used thoughtfully, it can save time and streamline workflows. But it doesn’t replace the need for strong instructional design, subject matter expertise, or quality assurance.

Trellis Manning, Manager of Training Development, recently demonstrated AI features she uses with training. The results were clear: AI can quickly organize and generate content, but it still requires human review, structure, and alignment to learning objectives.

In other words, AI accelerates production, but not accuracy.

Where AI Is Helping Training Development

Speed and Efficiency

AI is most valuable when it handles repetitive or time-consuming tasks.

Instructional designers can generate:

• Microlearning modules
• Assessment questions
• Scenario-based exercises

This allows teams to spend more time on strategy, quality, and learner experience.

Content Support and Idea Generation

Eric Monterrosa, Animator, uses tools to help:

• Refine writing and communication
• Pre-visualize graphics and layouts
• Break down complex topics for better understanding

He’s also experimented with generating background 3D assets to support production.

While not perfect, Eric notes that the technology is improving and becoming more useful as a starting point.

Faster Production for Media and Visuals

Animator Nathan Bates shared a practical example from a recent marketing project.

He created an on-screen presenter with voiceover without needing a studio, actor, or traditional production setup.

The result:

• Reduced production time
• Lower costs
• Faster turnaround

Nathan also uses tools to source visuals quickly. However, he notes results often require manual adjustments and sometimes produce unexpected errors (like incorrect physical features).

Instructional Design Support

Instructional designers are using AI to enhance course development in targeted ways.

Vonita Williams uses AI to:

• Generate and refine assessment questions
• Add context and improve distractor quality
• Create draft visuals based on SME input

She emphasizes that prompting is critical. Clear, detailed prompts produce better results, but AI still requires human validation.

Marcy Hall applies AI across multiple stages of development, including:

• Rewriting lesson summaries into more effective formats
• Running quick word counts and content checks
• Translating content for review
• Generating mockups for graphics and interactions
• Evaluating alignment with learning objectives and Bloom’s Taxonomy

These uses help streamline workflows but always require review before final production.

Content and Process Optimization

Chad Johnson, Sr. Manager of Content and Curriculum Development, uses AI for:

• Drafting and refining content
• Generating prototype images
• Translating text
• Analyzing data and supporting course descriptions

Again, the pattern holds: AI supports the process but doesn’t replace expertise.

Where AI Falls Short

Despite the benefits, the team was clear. AI introduces real risks if used incorrectly.

Inaccuracy and Lack of Context

AI does not understand your systems, your environment, or your standards. It generates outputs based on patterns, not real-world applications.

Quality Risks Without Human Review

Unchecked AI content can:

• Include errors
• Miss critical nuances
• Reduce overall training quality

Sensitivity to Poor Inputs

AI produces exactly what you ask for. Vague or incomplete prompts lead to weak or incorrect outputs.

Gaps in Real-World Application

Industrial skills training requires deep contextual understanding, something AI cannot replicate.

The Bottom Line: AI With Human Review Is the Right Approach

At HSI, AI is used to support, not replace, the training development process.

It helps:

• Save time on routine tasks
• Generate ideas and drafts
• Accelerate production workflows

But human expertise ensures:

• Accuracy
• Safety alignment
• Instructional quality
• Real-world relevance

Every AI-generated output still goes through quality assurance and quality control by instructional designers and SMEs.

What’s Next

Our team will continue to explore AI where it adds value, but with clear guardrails in place.

Because in industrial skills training, speed alone isn’t the goal. Accuracy, safety, and reliability come first, and those still depend on people. That’s what keeps training aligned with real-world conditions and expectations.