The Missing Link in AI Implementation Success

Why Your AI Training Programs Are Not Yet Working

Here's what we see happening in every organization trying to do something very general: Someone creates a "ChatGPT for Everyone" workshop.

Everyone attends. Nothing changes.

Want to know why?

After conducting hundreds of AI readiness workshops and AI+ leadership development sessions (oh God, one of my very first was for the International Coaching Federation in Toronto in November 2023 – you would be rolling on the floor laughing if you saw my slides back then… :))) ),

one pattern became crystal clear: your core personality drivers predict your AI behavior with scary accuracy.

What's missing from your AI adoption isn't better tools or training.

It's understanding why Sarah from Finance approaches AI completely differently than Mike from Innovation.

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The Kahler's Drivers Behind AI Behavior

Let's talk about those deep-seated patterns that determine how you and your team approach AI. They're hardwired responses that kick in especially when things get tough.

So before diving into AI adoption patterns, let's understand the core concept: personality drivers. These aren't just preferences; they're deep-seated motivational patterns that define how we approach challenges, process information, and react under stress.

These drivers, as defined by Taibi Kahler's research, represent our core motivational patterns:

Let's try to understand the concept. Yes, psychologists and coaches are studying these for hundreds of hours but believe me, even scratching the surface is much more useful than throwing anything on your organization without caring about your people's inner drive first.

"Be Perfect" and AI: A Love-Hate Story

• Will create the most sophisticated prompt library you've ever seen

• Tests every AI output against three different sources

• Creates complex validation systems that actually work

• But will abandon any AI tool after one significant error

• Struggles with AI's probabilistic nature

The good news? They'll ensure quality.

The challenge? Getting them to accept that 95% accuracy might be enough.

"Be Strong" and AI: The Resistance Is Real

• Needs to see clear evidence of AI's value

• Won't implement without proper control mechanisms

• Creates robust governance frameworks

• Secretly tests tools at home to avoid showing weakness

• Actually reads those Terms of Service

The good news? They'll ensure responsible implementation.

The challenge? Moving from planning to action.

"Please Others" and the Human Side of AI

• Primarily focused on AI's impact on team dynamics

• Creates the most human-centric implementation plans

• Excellent at getting buy-in from resistant team members

• Worries about AI replacing human connection

• Will make sure nobody feels threatened

The good news? They'll maintain the human element.

The challenge? Sometimes machines just need to be machines.

"Try Hard" in the AI Playground

• Will find creative uses for AI that nobody thought about

• Creates multiple parallel implementation streams

• Excellent at pushing boundaries and innovation

• Struggles with finishing what they started

• Always chasing the next AI breakthrough

The good news? They'll discover unprecedented possibilities.

The challenge? Actually implementing them.

"Be Quick" and the AI Rush

• Implements AI solutions at lightning speed

• Gets quick wins that motivate teams

• Creates momentum for change

• Might skip crucial validation steps

• "We'll fix it in production"

The good news? They'll get things moving.

The challenge? Dealing with the aftermath.

Why This Actually Matters

Understanding these patterns isn't just psychological entertainment. It's crucial for:

1 Designing AI Training Programs

• Different drivers need different approaches

• One-size-fits-all training will fail

• Some need technical depth, others need emotional safety

2 Planning Implementation

• Map your team's driver patterns

• Anticipate resistance points

• Plan support structures accordingly

3 Managing AI Crisis

• Under stress, these patterns intensify

• Knowing your team's distress sequences helps predict and manage AI incidents

• Different drivers need different support during AI failures

The Path Forward

The key isn't changing these patterns - it's working with them.

1. Assess your team's drivers

2. Design implementations that respect these patterns, or/and train your AI trainers to deal with these behaviors!!! (or choose professional coaches like we are..:) But jokes aside, you HAVE TO train your AI trainers before letting them touch your organizations)

3. Create support systems for each type

4. Choose your AI advocates based on their personalities.

5. Plan for predictable resistance points

Because here's the truth: AI implementation isn't failing because of technology. It's failing because we're ignoring human psychology. The technology implements itself without even doing anything. But you have to deal with the change meanwhile. And change management changed nothing.

Your personality won't change for AI.

Your drivers won't adapt to digital transformation.

But understanding these patterns? That's what makes the difference between another failed AI project and successful transformation.

The Bottom Line

Stop treating AI resistance as a technical problem. Start seeing it as a predictable psychological response. Start using this knowledge to create implementations that actually work.

Because in the end, AI adoption isn't about artificial intelligence. It's about very human intelligence.

And that's not changing anytime soon.