Is Your AI Program Failing? Here's Why...

Why most AI training creates curiosity, not capability

👋🏽 Hey, it's Anita. Welcome to AI@Scale! My weekly(ish) newsletter for leaders navigating the messy reality of getting hundreds (or thousands) of people actually capable with AI.

Read Time: 7 minutes

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I keep seeing this thing happen over and over: companies spend serious money on AI upskilling programs, everyone completes them, leadership feels good about the metrics, and then six months later... nothing has actually changed about how people work.

Maybe it's just me, but I think most of us are solving for the wrong thing.

Hear me out… I think what’s probably happening is that your program is creating AI curiosity, not AI capability.

The Curiosity Trap That's Costing You

There's this popular idea that because people love exploring ChatGPT, we should design all our workplace AI training around curiosity and discovery. Everyone's reading about "curiosity-driven learning" and thinking that's the answer.

But, there's a massive gap between being curious about AI and actually using it to do your job better.

I worked with someone recently who could spend hours asking ChatGPT fascinating questions about industry trends (I mean who hasn’t fallen into a GPT rabbit hole?). She was super curious and totally engaged with the technology, but when Monday morning rolled around and she needed to write her quarterly report? I saw her pull up that tried and true excel spreadsheet.

Substack doesn’t let me double underline things so im just going to make it look nice…
curiosity isn't the same thing as capability.

What's Actually Happening in Your Failed Program

From what I'm seeing with my clients, most AI training programs are accidentally optimizing for the wrong outcome.

✅You design training that feels engaging - lots of exploration, "what's possible" sessions, tool demos, brainstorming about use cases.

✅People get excited - they complete the modules, ask good questions, maybe even experiment with some tools.

✅Everyone feels good about it - high completion rates, positive feedback, leadership thinks it's working.

❌Then nothing changes - six months later, people are still doing their actual work the exact same way.

🚩The problem is that you solved for curiosity when you needed to solve for capability.

Why Curiosity Doesn't Create Capability

When it comes to new technology, in this case AI, we're trying to solve two completely different problems with the same solution.

Problem 1: People don't understand what AI can do This is where curiosity-driven learning actually works. People explore, get excited, start to see possibilities.

Problem 2: People can't integrate AI into their actual workflow This is where curiosity falls apart completely. Building capability requires practice, habit formation, and workflow integration - none of which happen through exploration alone.

Most programs try to tackle both problems at once, which is why you end up with people who can have smart conversations about AI in meetings but still do their actual work exactly the same way.

What AI Capability Actually Looks Like

I was working with someone from our content services team a few months ago, and one person had become genuinely AI-capable. Not AI-curious. Actually capable.

Here's what that looked like:

She didn't think "Oh, I should try AI for this task" - she just automatically opens Claude when organizing learning modules. It had become muscle memory. She had created a project where she had added all her seminal data, fed it her logic, and made sure it had examples of her previous work.

She had developed her own shortcuts and templates that made AI faster than her old process, not slower….but most importantly gave her a draft output in the style she was looking to edit.

When AI gave her something that didn't quite work, she knew exactly how to adjust instead of giving up.

Most importantly: her work had noticeably improved AND gotten easier. That's when you know you've built capability, not just curiosity.

Try This: The Two-Track Fix

I think the solution is treating these as two separate tracks instead of one big program.

Track 1: AI Awareness (3-4 weeks)

• Focus: What's possible, what's changing in our industry

• Format: Curiosity-driven exploration actually works here

• Goal: People understand AI's relevance to their role

• Success: Can have informed conversations about AI applications

Track 2: Workflow Capability (3-6 months)

• Focus: Integrating AI into specific job tasks

• Format: Hands-on practice with their real work, lots of coaching

• Goal: AI becomes automatic in their normal work habits

• Success: Measurable improvement in output or efficiency

The thing is, you can't skip Track 1 because people need the context and motivation. But Track 1 alone won't create any business impact. And you definitely can't rush Track 2 - capability takes time and repetition.

Track 1 creates the curiosity. Track 2 creates the capability. You need both, but trying to do them simultaneously is where everything falls apart.

What This Actually Looks Like in Practice

Track 1 Example: I'll show a team 10 different ways AI is being used in their industry. Let them explore, ask questions, get excited. Keep it broad and inspiring.

Track 2 Example: Pick ONE specific thing multiple people create - like project proposals. Spend 8 weeks helping them integrate AI into just that one workflow until it becomes automatic. Then move to the next thing.

Track 1 builds motivation. Track 2 builds habits. You need both, but in sequence, not simultaneously. This is where most organizations get it wrong…

Why Your Current Approach Isn't Working

From what I'm seeing:

❌You're teaching concepts instead of building skills. "Here's how prompt engineering works" versus "Let's practice writing better project updates together."

❌You're optimizing for completion instead of adoption. 90% completion rates don't mean anything if 90% of people stop using what they learned after two weeks.

❌You're treating AI like software training. "Click here, type this, get that result." But AI capability feels more like learning to drive - it requires judgment and intuition that only comes from practice.

❌You're ignoring the workflow problem. Even if someone masters ChatGPT, that doesn't mean they know how to fit it into their existing tools, processes, and team dynamics.

Yes, It’s Uncomfortable

Here's what's uncomfortable: most AI training initiatives can't prove ROI because they're not designed to create measurable behavior change.

If your "AI fluency program" is courses people complete and never think about again, you're not building capability. You're just creating lots and lots of curiosity (which is definitely not a bad thing... but it's definitely not going to get you far when trying to justify that learning budget to leadership).

Real AI capability shows up as:

• People finishing tasks faster

• Higher quality output

• Voluntary use of AI for things you didn't train them on

• Less time spent on routine/admin stuff

• Better job satisfaction (because work gets less tedious)

But you only get this if you commit to the messy, time-intensive work of actually changing work habits.

What I'd Try Next

If you're dealing with this gap between AI curiosity vs. actual capability training:

Audit what you're currently doing. Are you teaching concepts or building capabilities? Optimizing for course completion or behavior change?

Separate the tracks. Design Track 1 to create understanding and motivation. Design Track 2 to create habit change.

Pick ONE workflow for Track 2. Don't try to make everyone AI-capable at everything. Pick the highest-impact thing and focus there.

Measure behavior, not completion. Track whether people are actually working differently, not whether they finished your modules.

The curiosity revolution is real. The problem is that curiosity without capability is just a bunch of people who can talk about AI but can't actually use it when it matters.

Your AI training isn't failing because people don't understand AI. It's failing because understanding AI isn't the same thing as being able to use it to do better work.

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Anyone else seeing this gap between AI training completion and actual workplace change? I'd love to hear what's happening on the ground at your organization - especially if you're finding approaches that actually create lasting capability.