I’m seeing, with increasing frequency compared to previous years, posts championing the need for procurement professionals to expand and deepen their talents and skills across a list of critical areas to remain relevant in the AI era:
- Prompt engineering
- Data literacy
- AI tool selection
- Use case identification
- Change management
- Digital fluency
- Strategic thinking
- Cross-functional collaboration
The lists are comprehensive. The advice is sound. The frameworks are detailed.
What surprises me is that I have yet to see meta-awareness on any of the lists.
From my standpoint, such an absence is the equivalent of wanting to drive 250 miles with 50 miles of gas in your tank.
You have the vehicle (AI tools). You have the destination (business outcomes). You even have the map (skills frameworks).
But without meta-awareness—the fuel that powers effective human-AI collaboration—you’ll get 50 miles down the road and stall.
This is one of the main reasons why AI initiatives are constantly stuck in what Jim Collins calls the Doom Loop: the cycle of unmet expectations, half-finished pilots, and perpetual “we’re still evaluating” status.
Let me show you what I mean with a case study from the last 24 hours.
Section 1: The Doom Loop Pattern
What I’m observing in the market:
Post after post championing:
- “10 skills procurement professionals need for AI”
- “Critical competencies for the AI era”
- “How to stay relevant in procurement 2025”
The skills listed are legitimate:
- Technical literacy ✅
- Analytical thinking ✅
- Prompt engineering ✅
- Strategic vision ✅
- Change leadership ✅
But here’s what’s happening:
- Procurement professional reads list
- Takes courses on listed skills
- Learns prompt engineering
- Understands AI capabilities
- Attempts to use AI in work
- Gets mediocre results
- Wonders: “I have the skills—why isn’t this working?”
- Tries harder (more prompts, different tools)
- Still mediocre results
- Conclusion: “AI isn’t ready for our use case” OR “I’m not good at this”
This is the Doom Loop.
And it’s not because the skills lists are wrong.
It’s because they’re missing the fuel: meta-awareness.
The Doom Loop continues because practitioners acquire Layer 1 capabilities (technical skills, tools, knowledge) without developing Layer 2 readiness (meta-awareness of collaboration patterns).
Sound familiar? It should.
For 27 years, I’ve documented this exact pattern in procurement technology implementations: Organizations deploy Layer 1 (technology capability) without Layer 2 (behavioral readiness) and wonder why 70-95% of implementations fail.
The same principle applies to AI collaboration.
People acquire AI skills (Layer 1) without developing meta-awareness (Layer 2) and wonder why their results are mediocre despite “having all the skills.”
You’re trying to drive 250 miles on 50 miles of gas.
Section 2: What Is Meta-Awareness (And Why It’s Not on Any List)
Meta-awareness is the ability to observe and refine your own collaboration patterns with AI in real-time.
It includes:
- Recognizing when an AI output is “good enough” vs. “needs refinement”
- Knowing when to challenge AI defaults vs. accept them
- Developing “feel” for what makes outputs stronger
- Iterating strategically based on conscious quality criteria
- Being aware of your own assumptions, constraints, and blind spots during collaboration
Why it’s not on skills lists:
- It’s not a tool (can’t take a course on it)
- It’s not technical (doesn’t sound as impressive as “prompt engineering”)
- It’s behavioral (Layer 2, not Layer 1)
- It’s hard to measure (no certification)
- It requires self-awareness (uncomfortable territory)
But here’s the truth:
Without meta-awareness, every other AI skill is like having a car with no gas.
You can:
- ✅ Know how to drive (technical literacy)
- ✅ Have a map (use case identification)
- ✅ Understand the vehicle (AI tool knowledge)
- ✅ Have a destination (business outcomes)
But you won’t get 250 miles on 50 miles of gas.
And that’s why AI initiatives stall at mile 50 (pilot phase) instead of reaching mile 250 (scaled deployment).
The Difference
The difference is recognizing the following:
- AI tools (same)
- More technical knowledge (same)
- More time available (same)
The difference is meta-awareness:
- Observing when outputs don’t meet quality thresholds
- Questioning defaults and iterating strategically
- Testing against real constraints, not assumptions
- Recognizing context
- Managing collaborative ego (challenging both your assumptions and AI’s)
No matter how good your vehicle (AI tools), your map (skills frameworks), or your destination (business outcomes).
Meta-awareness is the fuel.
Without it, you’re driving 250 miles on 50 miles of gas.
The Five Components of Meta-Awareness
Based on 27 years of observing what separates successful technology adoption from failure, here are the five components that power meta-awareness:
1. Pattern Recognition
The ability to notice when AI outputs follow predictable patterns that signal generic vs. specific, surface vs. deep, buzzword vs. insight.
2. Strategic Questioning
Knowing when to challenge AI defaults vs. accept them, and how to question productively without blindly accepting or rejecting everything.
3. Constraint Awareness
Holding real-world constraints in mind during collaboration and iterating until they’re perfectly met—testing against actual requirements, not assumptions.
4. Quality Threshold Recognition
Knowing when “good enough” isn’t good enough, and having an internal barometer for excellence that guides iteration decisions.
5. Collaborative Ego Management
Being willing to challenge your own assumptions AND AI outputs without defensiveness on either side, treating AI as a thought partner rather than an oracle or servant.
Each component is a learnable skill. None requires special talent. All require deliberate practice.
One Practice Exercise to Get Started
Here’s one practice exercise to begin developing meta-awareness. This develops Component 1: Pattern Recognition.
The Three-Version Comparison
For your next important output (email, analysis, LinkedIn post, supplier assessment):
Step 1: Generate Version A
- Use AI to create first draft
- Don’t edit yet—just save it
Step 2: Generate Version B
- Same task, different angle
- Prompt: “Give me an alternative version emphasizing [different aspect]”
- Example: If A emphasized cost savings, ask for B emphasizing risk mitigation
Step 3: Generate Version C
- Third approach
- Prompt: “Give me a version that’s more [concrete/strategic/data-driven]”
- Example: “Give me a version with specific examples instead of general principles”
Step 4: Compare side by side
- Read all three carefully
- Notice differences in:
- Specificity: Concrete examples or general statements?
- Depth: Surface level or genuine insight?
- Impact: Forgettable or memorable?
- Evidence: Assertions or proof?
Step 5: Articulate why one is stronger
- Don’t just feel it—write down WHY
- What patterns make it resonate?
- What creates impact vs. falling flat?
- Document these observations
Step 6: Synthesize the best elements
- Identify strongest elements from each
- Create Version D combining best patterns
- This becomes your final output
Commitment: Do this once per week for four weeks.
By month’s end, you’ll recognize generic patterns instantly—and know how to refine toward high-impact outputs without needing three versions every time.
The pattern recognition muscle builds through deliberate, documented practice.
This Is One of Five
This practice exercise develops Pattern Recognition—Component 1 of meta-awareness.
In the coming weeks, I’ll share practice exercises for:
- Component 2: Strategic Questioning – How to know when to challenge AI defaults
- Component 3: Constraint Awareness – How to test outputs against real requirements
- Component 4: Quality Threshold Recognition – How to develop your “excellent” barometer
- Component 5: Collaborative Ego Management – How to challenge assumptions without defensiveness
Each component has specific, actionable practices that systematically build the meta-awareness muscle.
Because here’s the truth that every procurement skills list is missing:
You can have every AI skill on every competency list—prompt engineering, data literacy, tool selection, use case identification, change management, digital fluency, strategic thinking, cross-functional collaboration—but without meta-awareness, you’re still trying to drive 250 miles on 50 miles of gas.
The technical skills are the vehicle. The frameworks are the map. The use cases are the destination. Meta-awareness is the fuel.
And you can’t complete the journey without it.
This is why AI initiatives stall in the Doom Loop:
- Unmet expectations (assumed 250 miles of range)
- Half-finished pilots (stalled at mile 50)
- Perpetual “still evaluating” (wondering why it won’t go further)
The vehicle is fine. The map is accurate. The destination is reachable.
You just need to fill the tank.
Fill the Tank Today
Next time you see a procurement AI skills list, ask: “Where’s meta-awareness?”
And when it’s not there—and it won’t be—recognize what you’re looking at:
- A vehicle manual ✅
- A destination guide ✅
- A road map ✅
But no fuel gauge. No gas station. No acknowledgment that you need fuel for the journey.
All useful. None sufficient.
The Doom Loop continues until you fill the tank.
This week, run the Three-Version Comparison exercise once.
Pick one important output. Generate three versions. Compare them. Document what makes one stronger. Synthesize the best.
It takes 45 minutes.
And it begins building your pattern recognition muscle—the first component of meta-awareness.
Because the future doesn’t belong to those with the most AI skills on their resume.
It belongs to those with the highest meta-awareness of how to collaborate with AI effectively.
The technical skills are commoditizing. Everyone will have them.
Meta-awareness is the differentiator.
Build yours. Start today.
Follow along over the next five weeks as we unpack each component with practical exercises you can implement immediately.
Because after 27 years of documenting why technology implementations fail:
Layer 1 (capability) without Layer 2 (behavioral readiness) = failure.
For procurement transformation: Layer 2 = organizational readiness For AI collaboration: Layer 2 = personal meta-awareness
Both required. Neither is sufficient alone.
Fill the tank. Start today.
30
Procurement Insights
250 Miles on 50 Miles of Gas: Why Meta-Awareness Is the Missing Skill in Every AI Competency Framework
Posted on October 28, 2025
0
I’m seeing, with increasing frequency compared to previous years, posts championing the need for procurement professionals to expand and deepen their talents and skills across a list of critical areas to remain relevant in the AI era:
The lists are comprehensive. The advice is sound. The frameworks are detailed.
What surprises me is that I have yet to see meta-awareness on any of the lists.
From my standpoint, such an absence is the equivalent of wanting to drive 250 miles with 50 miles of gas in your tank.
You have the vehicle (AI tools). You have the destination (business outcomes). You even have the map (skills frameworks).
But without meta-awareness—the fuel that powers effective human-AI collaboration—you’ll get 50 miles down the road and stall.
This is one of the main reasons why AI initiatives are constantly stuck in what Jim Collins calls the Doom Loop: the cycle of unmet expectations, half-finished pilots, and perpetual “we’re still evaluating” status.
Let me show you what I mean with a case study from the last 24 hours.
Section 1: The Doom Loop Pattern
What I’m observing in the market:
Post after post championing:
The skills listed are legitimate:
But here’s what’s happening:
This is the Doom Loop.
And it’s not because the skills lists are wrong.
It’s because they’re missing the fuel: meta-awareness.
The Doom Loop continues because practitioners acquire Layer 1 capabilities (technical skills, tools, knowledge) without developing Layer 2 readiness (meta-awareness of collaboration patterns).
Sound familiar? It should.
For 27 years, I’ve documented this exact pattern in procurement technology implementations: Organizations deploy Layer 1 (technology capability) without Layer 2 (behavioral readiness) and wonder why 70-95% of implementations fail.
The same principle applies to AI collaboration.
People acquire AI skills (Layer 1) without developing meta-awareness (Layer 2) and wonder why their results are mediocre despite “having all the skills.”
You’re trying to drive 250 miles on 50 miles of gas.
Section 2: What Is Meta-Awareness (And Why It’s Not on Any List)
Meta-awareness is the ability to observe and refine your own collaboration patterns with AI in real-time.
It includes:
Why it’s not on skills lists:
But here’s the truth:
Without meta-awareness, every other AI skill is like having a car with no gas.
You can:
But you won’t get 250 miles on 50 miles of gas.
And that’s why AI initiatives stall at mile 50 (pilot phase) instead of reaching mile 250 (scaled deployment).
The Difference
The difference is recognizing the following:
The difference is meta-awareness:
No matter how good your vehicle (AI tools), your map (skills frameworks), or your destination (business outcomes).
Meta-awareness is the fuel.
Without it, you’re driving 250 miles on 50 miles of gas.
The Five Components of Meta-Awareness
Based on 27 years of observing what separates successful technology adoption from failure, here are the five components that power meta-awareness:
1. Pattern Recognition
The ability to notice when AI outputs follow predictable patterns that signal generic vs. specific, surface vs. deep, buzzword vs. insight.
2. Strategic Questioning
Knowing when to challenge AI defaults vs. accept them, and how to question productively without blindly accepting or rejecting everything.
3. Constraint Awareness
Holding real-world constraints in mind during collaboration and iterating until they’re perfectly met—testing against actual requirements, not assumptions.
4. Quality Threshold Recognition
Knowing when “good enough” isn’t good enough, and having an internal barometer for excellence that guides iteration decisions.
5. Collaborative Ego Management
Being willing to challenge your own assumptions AND AI outputs without defensiveness on either side, treating AI as a thought partner rather than an oracle or servant.
Each component is a learnable skill. None requires special talent. All require deliberate practice.
One Practice Exercise to Get Started
Here’s one practice exercise to begin developing meta-awareness. This develops Component 1: Pattern Recognition.
The Three-Version Comparison
For your next important output (email, analysis, LinkedIn post, supplier assessment):
Step 1: Generate Version A
Step 2: Generate Version B
Step 3: Generate Version C
Step 4: Compare side by side
Step 5: Articulate why one is stronger
Step 6: Synthesize the best elements
Commitment: Do this once per week for four weeks.
By month’s end, you’ll recognize generic patterns instantly—and know how to refine toward high-impact outputs without needing three versions every time.
The pattern recognition muscle builds through deliberate, documented practice.
This Is One of Five
This practice exercise develops Pattern Recognition—Component 1 of meta-awareness.
In the coming weeks, I’ll share practice exercises for:
Each component has specific, actionable practices that systematically build the meta-awareness muscle.
Because here’s the truth that every procurement skills list is missing:
You can have every AI skill on every competency list—prompt engineering, data literacy, tool selection, use case identification, change management, digital fluency, strategic thinking, cross-functional collaboration—but without meta-awareness, you’re still trying to drive 250 miles on 50 miles of gas.
The technical skills are the vehicle. The frameworks are the map. The use cases are the destination. Meta-awareness is the fuel.
And you can’t complete the journey without it.
This is why AI initiatives stall in the Doom Loop:
The vehicle is fine. The map is accurate. The destination is reachable.
You just need to fill the tank.
Fill the Tank Today
Next time you see a procurement AI skills list, ask: “Where’s meta-awareness?”
And when it’s not there—and it won’t be—recognize what you’re looking at:
But no fuel gauge. No gas station. No acknowledgment that you need fuel for the journey.
All useful. None sufficient.
The Doom Loop continues until you fill the tank.
This week, run the Three-Version Comparison exercise once.
Pick one important output. Generate three versions. Compare them. Document what makes one stronger. Synthesize the best.
It takes 45 minutes.
And it begins building your pattern recognition muscle—the first component of meta-awareness.
Because the future doesn’t belong to those with the most AI skills on their resume.
It belongs to those with the highest meta-awareness of how to collaborate with AI effectively.
The technical skills are commoditizing. Everyone will have them.
Meta-awareness is the differentiator.
Build yours. Start today.
Follow along over the next five weeks as we unpack each component with practical exercises you can implement immediately.
Because after 27 years of documenting why technology implementations fail:
Layer 1 (capability) without Layer 2 (behavioral readiness) = failure.
For procurement transformation: Layer 2 = organizational readiness For AI collaboration: Layer 2 = personal meta-awareness
Both required. Neither is sufficient alone.
Fill the tank. Start today.
30
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