AI can look like magic. You type in a prompt, and out comes a fluent response. But of course, it isn’t magic at all. Behind the curtain sits mathematics and design: probability models, semantic maps, billions of training examples.
The magic only works because of careful design choices that remain invisible to the user.
Performance Support Has the Same “Magic” Problem
Effective performance support can look deceptively simple. A checklist, a quick-reference guide, a chatbot response — nothing flashy. To the business, it may even seem obvious. “Why did we need L&D for this?”
But that simplicity masks the design effort that went into making it effective. Just as GPTs depend on hidden maths, performance support depends on hidden design discipline.
The Hidden Design Principles
What makes performance support sticky isn’t the format on the surface, it’s the thinking underneath:
- Stripping away irrelevant detail.
- Sequencing information in just the right way.
- Designing for usability in the flow of work.
Those design choices are invisible to the learner but they make the difference between a resource that gets used — and one that gets ignored.
Where AI and L&D Meet
AI has the ability to simplify huge amounts of complexity into something usable. L&D brings the ability to apply design principles that make that usability stick.
That pairing matters. Because while AI can generate text, what it can’t do is understand context without guidance. That’s where L&D steps in:
- Framing outputs around the learner’s actual flow of work.
- Shaping scaffolding that aligns with UID principles — fewer clicks, clearer paths, better performance support.
It’s the combination of AI’s power and L&D’s perspective that turns “magic” into something meaningful.
Why This Matters Now
AI will only increase the sense that performance support is easy — automatic, even. But if the business sees the output and thinks “that looks obvious,” the risk is that the design expertise behind it gets side-lined or forgotten in the rush to make more use of the technology.
That’s why PerformaGo is being built around performance support design principles. It’s not about pushing technology for its own sake, but about embedding the design discipline that ensures AI outputs are genuinely useful in the workplace.
Strip away the curtain and you see: it isn’t magic, it’s design. And that’s exactly why L&D’s role is so critical — to make sure the apparent simplicity of performance support is backed by invisible, intentional design.
If you’d like to see the personal reflection that sparked this, take a look at this week’s diary post: In Awe of the Math I’ll Never Do
