Part 4 in the "Becoming a 10x Operator with AI" Series.

In Part 1, we introduced the concept of the 10x Operator—someone who uses AI to break the rules of efficiency.

Part 2 unveiled the AI Leverage Pyramid, a framework for rethinking your approach to work itself.

In Part 3, we explored how these intelligences interact — and how leverage is created not just from capability, but from combining the four types of intelligence that compound when used together:

• Technical Intelligence (T): working with AI and automation tools (aka "speaking robot")

• Cognitive Intelligence (C): structured thinking, analysis, systems reasoning (the stuff they claim to teach in MBA programs)

• Expertise Intelligence (E): deep domain knowledge (what you probably stayed up too late acquiring)

• Social Intelligence (S): understanding people, power, and how organisations move (the real reason people get promoted)

   

Orchestrated Thinking: Brains, Bots, and Boardrooms

A few months ago, a colleague approached me with a challenge: her board wanted a comprehensive AI strategy, and they wanted it fast.

As an executive with deep domain expertise and organisational savvy, she possessed nearly all the ingredients for success — except one critical component.

She had industry knowledge (Expertise Intelligence), understood the company's inner workings, knew which stakeholders needed to be convinced (Social Intelligence), and was a great strategic thinker (Cognitive Intelligence).

But like many execs, her Technical Intelligence with AI was limited. She hadn't yet developed hands-on capability with the tools.

That's where I entered the picture.

The AI Leverage Pyramid in Action

When we first connected, we didn't immediately dive into tools or technology. Instead, we took stock of what we each brought to the table:

She contributed:

• High Expertise Intelligence: Deep domain expertise

• High Social Intelligence: Understanding internal dynamics and stakeholder needs

• High Cognitive Intelligence: Sharp, analytical, clear thinking

I brought:

• High Technical Intelligence: Fluency with GPT tools, workflows, agents, and prompts

• High Cognitive Intelligence: First principles thinking, frameworks, systems thinking

• Medium general context (±T2 in Social and Expertise) — I have enough experience in this particular industry to orient, but not lead

• AND: A structured process and methodology for combining human and machine intelligence

Rather than one of us trying to compensate for weaknesses, we recognised something more powerful: together, we formed a complete intelligence system that could be exponentially amplified through structured AI use.

Let me share my process:I can show you how: 👉 Schedule a free consultation today and take the first step toward becoming a 10x Operator.

Don’t make excuses: The Outdated Leader's Guide to Avoiding AI (And Ensuring Your Business Fades Into Obscurity😜)

Step 1: The Cognitive Foundation (Before AI Even Entered)

My colleague and I began with what most people skip: dedicated thinking time. 

In the age of AI, human thinking time becomes more valuable, not less. Counterintuitive, but true.

For two working sessions, we simply talked. But this wasn't casual conversation—it was structured cognitive collaboration. She gave me insights. We used first principles thinking to strip away assumptions. We mapped systems on virtual whiteboards. We debated approaches and stress-tested logic chains.

Our combined cognitive power created clarity that neither of us could have achieved alone. We weren't just aligning on what to do—we were building a shared mental model.

Even before AI, the clarity from co-thinking already cut the project time in half. 

Maybe the most valuable thing AI does is force us to articulate what we actually think before we ask for help. There's no "vibe-based strategy" option in ChatGPT (yet).

Step 2: Creating the Context Layer

AI needs context to thrive. 

If you do not give any good context, or are not clear about what you want, AI will give you generalised output. 

For the AI strategy task, I needed context because I didn't have deep industry experience, I took a systematic approach:

• Recorded our sessions and transcribed them into structured notes

• Built context maps (objectives, blockers, politics, strategy goals)

• She shared information about high-level initiatives and past docs

• I researched similar companies' AI strategies using Deep Research

This wasn't just helpful for me — it also gave my colleague clarity on how to frame things for different audiences.

Step 3: Break the Problem into Components

The worst way to use AI?

Throwing everything into a single prompt and hoping for brilliance.

I was systematic. I started by understanding each piece of information that I received, by feeding it into an LLM.

I ended up with 14 parallel AI threads, each focused on a different chunk of the puzzle:

• Some doing research (the nerds)

• Some summarising dense reports (the readers)

• Some testing messaging (the marketers)

• Some stress-testing the logic (the pessimists)

Each stream became a separate AI thread — like building a mini-task force of AI agents, each doing one job well. It's what organizational design would look like if managers weren't afraid of being replaced by the people they hire.

Yes, I really did run 14 AI chats. No, I don't recommend it for your first date with GPT.

And no, I haven't named them yet ;) 

Step 4: Orchestrating the AI Collaboration

I began "orchestrated thinking"—a multi-stage process where AI becomes a true thought partner. I weaved the output from the 14 chats together. I felt like the mickey mouse in Disney’s Fantasia.

For each chat, I followed structured thought process:

• Initial prompting for framework and structure

• Prompt chaining to progressively refine outputs

• Cross-checking between different AI threads for consistency

• Generating multiple approaches to the same question

• Testing for weaknesses and blind spots

I used AI to:

• Summarise dense research materials

• Identify logical gaps and inconsistencies

• Generate targeted counterarguments

• Analyse potential risks and failure modes

• Structure complex ideas for different audiences

• Reframe technical concepts for non-technical stakeholders

• Produce slide-ready outputs

• Develop frameworks and methodologies

This wasn't "generate and go." It was a carefully orchestrated thinking system with AI as both builder and critical evaluator.

I didn't offload my thinking to the AI. Rather, I used AI to enhance how I operated, pushing the boundaries of my Cognitive Intelligence. My brain was tired after all of this, but I was in a state of flow — creating something that without AI would have taken weeks.

Step 5: Human Intelligence as the Critical Filter

Once I had the first-pass outputs, my colleague and I  reviewed them together.

She added insight I didn't have: nuances of stakeholder power dynamics, internal history, risk tolerances. I adjusted, restructured, and regenerated.

Together, we iterated — fast. No waiting on decks. No week-long revisions. Just real-time, high-velocity, high-quality decision-making.

Overview of the Process

The Result: 10x Output with Superior Quality

Within two days, we had a board-ready strategy. Something that would've taken a team weeks — done in a tenth of the time.

The test: Faster, Better, Cheaper, Safer — my favourite 4-word framework for judging AI use cases:

• Faster: Board-ready strategy in a fraction of the time

• Better: Clearer logic, more comprehensive risk assessment

• Cheaper: No need for large teams or consultants

• Safer: Domain expertise filtered out generic recommendations

If your AI isn't delivering on at least one of these dimensions, it's not creating leverage — it's just creating the appearance of intelligence.

Key Lessons about Compounding Intelligence

1. Leverage is a Relationship, Not a Solo Act
   AI didn't replace anyone. It connected strengths. My colleague's insight became exponentially more valuable once it had the tech to scale it.
   
   

2. The Missing Intelligence Bottlenecks the Whole System
   If no one had brought Technical Intelligence, my colleague’s brilliance wouldn't have scaled.
   
   The value of any team is determined by its weakest intelligence, not its strongest.
   

3. Tech Without Domain Insight Creates Elegant Nonsense
    

   If I'd gone solo, I'd probably have built a beautifully structured, totally irrelevant strategy, because I didn’t have insight. My strategy might have sounded impressive (AI is great at sounding confident — even when it's confidently wrong), but the strategy would have been general: good enough, not brilliant.

4. Social Intelligence Scales With Technical Support
   Her understanding of internal politics, incentives, and sensitivities — combined with AI-generated insight and speed — made the final strategy both smart and executable. Like having both a brilliant strategy and knowing which executives need to think it was their idea all along.

5. Structure is a Superpower

   The real unlock? Structure. Breaking the challenge into modular chunks and feeding each into AI like a well-oiled assembly line. Most people try to throw everything into one chat. That's not leverage. That's soup.

6. The Best Operators Curate, Not Delegate
   

This wasn't a copy-paste job. It was a layered, iterative build. AI output isn't the endpoint — it's raw material. We shaped it, challenged it, rewired it

You're not a "prompt engineer." You're a prompt architect, editor, and critic. 

The Research Backing

Harvard Business School's March 2025 working paper "The Cybernetic Teammate" shows that a single professional armed with GPT-4 performed as well as a two-person team without AI when solving real product-innovation problems at Procter & Gamble.

The AI-assisted individuals:

• Worked 12–16% faster

• Delivered higher-quality solutions

• Felt better about their work

• Bridged gaps between R&D and commercial specialists

The headline result: AI can replicate many of the collaborative benefits of a human teammate.

It worked because AI offered:

• 🧠 Cognitive scaffolding – feedback, prompts, critique

• 🧭 Silo-busting – perspectives from outside your speciality

• 💬 Emotional lift – momentum, clarity, reduced overwhelm

In our case, AI helped me bridge my gaps in industry context, gave us both a way to iterate faster, and kept us in flow. It played the role of silent partner — relentless, organised, and just curious enough to ask the questions we might've missed.

Basically, an overachieving intern who's read the entire internet, never needs lunch breaks, but occasionally makes things up with confidence. But still — very helpful.

Final Thought: Compound Your Intelligence

The biggest aha moment: The future doesn't belong to AI specialists or domain experts alone. It belongs to intelligence orchestrators – people who can conduct a symphony of human and machine capabilities. People who leverage, who compound, who multiply.

The age of the 10x Operator isn't about having all the answers. It's about knowing how to combine the right intelligences — human and machine — to ask better questions, build smarter systems, and scale insight at speed.

In the end, alchemy isn't magic. It's method. It's leverage. And when you do it right, it feels a little like magic anyway.

The people who will win in the AI age aren't the ones with the best prompts or the deepest expertise – they're the ones who've mastered the art of intelligent collaboration. And maybe, just maybe, the machines are teaching us how to be better humans after all.

If your org is struggling to turn AI talk into actual leverage, this is what we do.

Where is your organisation on the path to becoming intelligence-native?

The most effective way to accelerate this journey is through systematic development of all four intelligences across your team—creating a shared language, framework, and capability set that compounds over time.

👉 Schedule a free consultation today and take the first step toward an AI-ready business.

If you forget everything else, remember this…