Let’s dive into the world of AI and unpack some serious truths!

This episode is all about the misconception that AI can magically solve chaos and improve workflows without any groundwork. We kick things off with a hilarious analogy to assembling flat-pack furniture—just like tossing aside the instructions can lead to a wobbly bookshelf, applying the same casual approach to enterprise AI can result in catastrophic outcomes.

We explore Heather's insightful piece, "AI Does Not Make Chaos Disappear," and discuss how AI actually amplifies existing messiness instead of cleaning it up.

By the end, we’ll share how to build a solid structure before unleashing the power of AI, ensuring it enhances our capabilities rather than just creating faster chaos. So grab your favourite cuppa, and let’s get cracking on this enlightening journey together!

This Deep Dive podcast is AI generated from the Start With AI Newsletter on LinkedIn - linkedin.com/newsletter/start-with-ai

Chapters:

• 00:03 - The Challenge of Flat Pack Furniture
• 03:19 - Understanding AI's Impact on Business
• 05:57 - The Dangers of Unconscious AI Implementation
• 09:08 - Understanding the Ghost Note Concept in AI
• 15:29 - The Structural Integrity of AI Integration

Takeaways:

• Flat pack furniture assembly is a light-hearted metaphor for the chaos of AI deployment, highlighting the dangers of skipping foundational understanding.
• AI doesn't simplify processes; it amplifies existing workflows, for better or worse, depending on the structure underneath.
• The 'ghost note' concept illustrates unseen patterns in AI usage, which can dictate output quality without users being aware.
• Organisations often mistake rapid AI output for efficiency, but without clarity and structure, this results in accelerated chaos.
• Heather's journey shows the importance of understanding AI's mechanics rather than just using it, avoiding the trap of false competence.
• To truly harness AI, businesses must focus on structural integrity and human capability, rather than just tool acquisition.

Companies mentioned in this episode:

• Claude
• Anthropic
• Klarna
• OpenAI
• ChatGPT
• Gemini

AI Bootcamp - 2 Weeks to master using your AI with integrity and Creating YOUR VOICE template through Modelling the true you. Email 'Waitlist' to [email protected] to advance notification.

So we have all been there with flat pack furniture.

Right.

You bring the heavy box home, slice it open, and just stare at, I don't know, a hundred wooden dowels and scattered screws.

Oh, yeah.

And you just toss the manual aside.

Exactly.

You skip the instructions, assuming you'll just, you know, figure it out as you go.

And for a bookshelf, it's a completely normal, harmless beginner's error.

At worst, you end up with a wobbly shelf.

Right.

Or maybe a few leftover screws that make you slightly nervous.

Yeah, exactly.

But applying that exact same I'll figure it out on the fly mentality to enterprise artificial intelligence.

That is catastrophic.

Oh, completely.

It shifts from this quirky weekend habit to a profound systemic risk.

Especially when you consider that organizations are currently building the core operational foundation of their entire business on top of these tools without actually understanding the architecture, which is wild.

So for today's deep dive, we are pulling from a piece of source material that tackles this head on.

It's such a great title.

It really is.

So our mission today for you listening is to basically dismantle the illusion of competence that so many of us get when we use AI.

We'll explore why these models actually amplify messy workflows rather than magically fixing them.

Yeah.

Which is a huge misconception.

Totally.

And most importantly, we'll talk about how you can step back and build real structure before deploying the technology.

Right.

And I think before we analyze how massive multinational corporations get this wrong, we have to look at the micro level.

Right.

We have to look at how we fail on a personal level by mistaking, you know, basic tool operation for actual skill.

Heather shares this wake up call in her piece that I think resonates with anyone who uses these tools daily.

She describes feeling this widening, uncomfortable gap in her work.

On one side, she was using AI tools constantly, but on the other, she realized she lacked a genuine structural understanding of what she was actually doing with them.

Yeah.

She calls it a false sense of competence, which I love.

It wasn't necessarily a gap that stopped her from working.

Right.

I mean, she was still producing output.

Right.

But she was carrying on with this illusion that she had mastered the technology simply because, well, she knew how to interact with the chat interface and get a coherent response back.

And that false competence is such a subtle trap to break out of it.

She actually enrolled in the Claude Code bootcamp.

Yeah.

And went Deeper into anthropic training programs.

Wow.

So she really committed.

She did.

She explicitly states her motivation wasn't to, you know, buy into the industry hype, but rather to strip away the illusion.

She wanted to see the actual mechanics underneath.

Right.

And what she found mirrors the biggest myth in the corporate AI space right now.

The pervasive idea that AI is a simplification engine.

It isn't.

AI is an amplification engine.

Let's break down what that amplification actually looks like in practice.

Because if your underlying business system is highly structured, logical and strong, AI helps you scale that excellence.

Right.

With unprecedented consistency.

Exactly.

But if your underlying system is messy, poorly defined, or just chaotic, the AI will just amplify that mess and it.

Will do it exponentially faster than a human team ever could.

Okay, but hold on.

Let me play devil's advocate here.

Let me challenge the premise that a faster mess is inherently a bad thing.

Okay, go for it.

If I look at this purely from a productivity standpoint, you know, if an AI model is just doing what my team already does, but doing it 10 times faster, isn't that still a net positive?

Like, even if my process is a bit disorganized, plowing through the work quicker sounds like a win for the bottom line.

Right, and that is the exact rationale driving so much of the current corporate AI strategy today.

Right, but it is a complete trap.

Really?

Why?

Because a faster mess is disastrous.

You are producing a higher volume of output while simultaneously degrading clarity.

You might be generating, say, 10 times the amount of reports or marketing copy or code, but if the foundational logic is chaotic, you are literally just scaling technical and operational debt.

Oh, wow.

So you end up with a volume problem masquerading as an efficiency gain.

Precisely.

You nailed it.

The author points out that this leads to profound organizational fragmentation.

You get a proliferation of tools, deeply fragmented workflows, highly inconsistent results across departments, and just a massive amount of rework.

Which people then dress up as innovation, Right?

Yes.

They think because they are deploying a new AI agent to fix the problems created by their previous AI agent that they are being cutting edge.

Oh, man, that is so true.

The issue isn't the underlying model.

The issue is the complete lack of critical thinking and baseline standards surrounding how the model is used.

You know, Heather makes a brilliant connection to NLP here to explain this dynamic.

And just to be clear to everyone listening who works in tech, she doesn't mean natural language processing, right?

Right.

She is pulling from Neuro linguistic programming,.

Which is a vital distinction to make neuro.

Linguistic programming, at its core is a modeling discipline.

It's about identifying reproducible patterns in human language and behavior.

Specifically, it focuses on the underlying structure of how people achieve results.

The goal of an NLP practitioner is to take hidden unconscious behavioral patterns and make them conscious so they can be.

Deliberately replicated or improved.

Exactly.

Which maps flawlessly onto how we interact with large language models.

AI is entirely structure dependent and it's highly pattern sensitive.

It constantly reflects the quality of the behavioral and structural model that sits underneath it.

Yes, it's a mirror.

Right.

And the danger is that while an NLP practitioner actively tries to make those patterns conscious, most organizations today are using AI entirely unconsciously.

They are just feeding chaotic patterns into the machine without even realizing it.

And we can see the ultimate consequence of this unconscious frantic efficiency by looking at a macro level case study.

The source material points at Klarna, which honestly serves as a massive warning sign for the entire industry.

Oh, the Klarna story.

Yeah.

On paper, the initial headlines about Klarna look like an unmitigated triumph for automation.

They really did.

They deployed an AI assistant that reportedly handled around two thirds of all their customer chats.

Metrics showed drastically improved response times and a significant reduction in repeat contacts from customers.

Alright, so if you are a board member looking at that spreadsheet, you think you have just solved customer service forever.

Exactly.

It was paraded as the ultimate success story of AI replacing costly human effort at scale.

But, and here's where it gets really interesting for you listening the second half of the Klarna story, the part that doesn't make the aggressive tech press, is that they later had to walk the strategy back.

Yep, they actively shifted course to increase their human customer support.

Again because they realized customers still fundamentally required human help.

Right.

But let's look at the mechanism of that failure.

Why were customers frustrated if the AI was supposedly resolving things faster?

It really comes down to the difference between answering a query and actually resolving a complex problem.

The AI didn't fail at Klarna because the technology itself was useless.

The AI models are incredibly capable.

It failed because it was deployed as a total replacement strategy for an experience that was never properly designed for end to end end automation in the first place.

That makes a lot of sense.

Right?

Used well, AI handles high volume routine tasks of basic pattern recognition.

Used badly, it becomes a way of scaling decisions that lack nuance.

Like if a customer asks for their balance, the AI is perfect.

Exactly.

But if a customer says, hey, my package was stolen off my porch, the Merchant won't refund me, and I need to pause my payment schedule while I file a police report.

I mean, that requires structural empathy, negotiation, and exception handling.

And if your system hasn't consciously designed a pathway for that complexity.

The AI just loops the customer through policy documents.

It is efficiently disappointing them.

Wow.

Efficiently disappointing them.

That's a great way to put it.

It's essentially like installing a hyperspeed conveyor belt in a factory.

But the factory is producing half assembled products, right?

Upgrading the conveyor belt to move at lightning speed doesn't magically assemble the widget.

You aren't fixing the product.

You are just delivering broken things to the customer.

Much f then wondering why your return rate is skyrocketing.

Efficiency without structural completeness is just accelerated failure.

Klarna isn't a story about a chatbot malfunctioning.

It is a story about a system architecture failing to account for what humans actually need and just assuming the AI would figure it out.

Okay, so if that is how a massive corporation trains its system to fail at a macro scale, we need to understand how this same trap happens right at your own desk.

Like, how do thousands of micro interactions build up to that kind of structural failure?

And this brings us to one of the most compelling concepts Heather introduces.

The ghost note.

Yes, I love this part.

It describes the hidden, almost invisible layer of AI usage that the vast majority of people miss entirely when they interact with a model.

Right.

So in drumming and music theory, a ghost note is a hit on the snare drum, played so softly that you feel the groove rather than actually hear the strike.

It doesn't stand out in the mix, but it fundament dictates the underlying rhythm and feel of the entire song.

And that musical analogy is perfect for understanding AI context sensitivity.

You have to realize that AI does not function like traditional software.

Well, if you open a spreadsheet and type in a sum formula, it behaves the exact same way every single time, regardless of your mood or your previous inputs.

It is fixed in uniform.

Right.

AI systems, however, are fluid.

They vary their outputs based on your prior interactions, the specific syntactic framing of a prompt, and the feedback patterns you provide.

So in practice, the model begins to act as a mirror.

It performs in context learning based on the groove you establish.

If you are a thoughtful, precise, discerning user who demands rigorous analysis, the AI's outputs get brilliant.

It rises to the standard you set.

But the flip side is where the degradation happens.

If you are vague, lazy, or reactive, if you are willing to accept weak, fluffy output without Challenging the system, the model learns that this is the acceptable standard.

Wait, so you're saying if I let the AI get away with a mediocre answer today, I'm actively training it to give me weaker answers tomorrow?

It's adopting my bad habits.

Exactly.

It replicates those bad patterns.

The unstated assumptions and the lazy framing are your ghost notes.

You don't see them written explicitly in the final paragraph the AI generates, but they dictate the underlying logic of the response.

Okay, but let me push back on this a little bit.

These foundational models, whether it's Claude, ChatGPT or Gemini, they are pre trained on billions of parameters by some of the smartest engineers on the planet.

Sure, their baseline intelligence is locked in.

How much damage can my one lazy prompt on a Tuesday afternoon actually do?

Does the model really adopt my bad habits, or is it just giving me a brief, low effort answer to a low effort question?

Question.

It is a great question, and it really comes down to how the context window operates.

You are right that the foundational weights of the model don't change based on your single prompt.

Okay, but the model is essentially a highly sophisticated probabilistic prediction engine.

It is predicting the next most likely token based on the immediate context window you provide.

So when you provide a lazy generic prompt, you are narrowing the probability distribution toward average generic, uninspired text.

Oh, I see.

So it's not that the model forgets how to be smart, it's that it assumes, based on my input, that I want the mediocre version of its capabilities.

Exactly.

That the feedback loops rely on your confirmation and correction.

Yeah, the system isn't just producing isolated answers.

It is building a localized structure around what you tolerate within that session.

Right.

If an organization doesn't understand this dynamic, the results across their team won't just vary randomly.

They will steadily and aggressively degrade over time as poor lazy usage becomes normalized.

Wow.

That completely shifts the responsibility away from the developers and onto the user.

It means the degradation of AI output isn't an IT failure.

It is a reflection of our own degrading standards 100%.

The root problem organizations and individuals face isn't a lack of technical capability.

The technology is more than capable.

The real problem is our own fragmented thinking, our bad habits, and a severe lack of governance over how we apply our own intelligence.

Which forces us to ask how we actually fix this.

If the root cause is our own lack of structure, we have to stop tinkering with the symptoms.

Buying a new AI subscription or downloading a cheat sheet of, you know, a hundred perfect pumps isn't going to solve a structural deficit.

No, not at all.

And to solve this, Heather turns to a framework from organizational psychology known as Diltz Logical levels.

This is a model traditionally used to understand change and learning within complex systems.

And it perfectly articulates why most AI strategies fail.

Okay, let's unpack this Gilt's model for the listeners.

Sure.

So Gilt's pyramid has several levels, and the fundamental rule of the model is that you cannot fix a problem at a lower level if the higher levels are broken or misaligned.

Most people and most massive companies try to solve their AI problems exclusively at the bottom two levels, environment and behavior.

Let's ground this in a concrete scenario.

Imagine an employee tasked with generating a weekly marketing analysis report using AI at the environment level.

The only question being asked is about the tool.

Tools like should we use anthropic's Claude or OpenAI's ChatGPT for this?

Exactly.

And moving one step up to the behavior level, the focus is entirely on the action.

What specific prompt should I type into the box to make it summarize these metrics?

Right.

Which is entirely surface level.

If your strategy stops there, you are just optimizing the speed at which you generate potential garbage.

Dilt's model demands that true change has to be driven from the higher levels of the pyramid.

So as we move up from environment and behavior, we hit capability.

Okay, the question shifts from what prompt do I use to do I actually possess the skills to validate this output?

Does the employee know how to verify the statistical significance of the marketing metrics the AI just summarized?

Because if the capability to discern truth from hallucination isn't there, the behavioral prompt is completely irrelevant.

Yes.

And then above capability, we reach beliefs and values.

This is where the structural integrity of a project is really tested.

What does that employee fundamentally believe this marketing report is for?

Right.

Do they believe it is just a bureaucratic checkbox that needs to be filled as quickly as possible?

Or do they value it as a critical tool to drive actual business strategy?

Because if they believe it's just a checkbox, they will accept the first piece of fluff the AI spits out.

Their belief dictates their standard of quality, and that feeds directly into the next level up.

Identity.

Identity.

Who is this employee becoming in their relationship with the technology?

That is a profound shift in how we view white collar work.

Are you stepping into the identity of a passive consumer just acting as a rubber stamp for generated text?

Or are you maintaining the identity of an active, discerning editor and strategic thinker.

And finally, at the very peak of Dilt's pyramid, you have purpose.

What is the ultimate goal of the marketing department?

What is the organization actually trying to achieve in the market?

When you look at AI integration through that lens, you realize none of those higher level questions are technical questions.

No, they are purely leadership questions.

If your fundamental beliefs are muddled, if your professional identity as a critical thinker is eroding, and if your ultimate organizational purpose is weak, no amount of clever prompt engineering is going to save your workflow.

Wow.

Without asking these higher level questions, deploying AI does not create organizational transformation.

It just creates drift.

You drift faster into chaos, generating more outputs that mean absolutely nothing.

Which brings us to the core takeaways from Heather's refined perspective.

After navigating the boot camps, unpacking the NLP connections, and recognizing the subtle trap of the ghost note, her metric for success completely shifted.

She is no longer interested in AI as a shiny object.

She doesn't care about the hype cycle at all.

The only real metric of success for deploying these tools is does the AI strengthen human capability or does it quietly erode it?

Does it actually improve your judgment?

Does it create more coherence and alignment in your projects?

Does it reduce unnecessary, repetitive effort without weakening your sense of ultimate responsibility for the final product?

And the businesses and individuals who are going to succeed and thrive over the next decade won't be the ones who procure the most tools.

They won't be the ones obsessing over the behavior level of prompt engineering.

Right.

They will be the ones who meticulously design how their human systems operate around those tools.

Right.

We have to stop asking the basic question of how do we use AI more?

The necessary question is what kind of structural system are we putting the AI into?

Because AI does not make chaos disappear.

It simply reveals, often quite ruthlessly, whether there was any real structure there in the first place.

It forces us to build the architecture before we turn on the machine.

It really does.

Well, thank you for joining us on this deep dive.

We've covered a massive amount of ground today, from the dangers of the illusion of competence to the hidden ghost notes dictating your outputs.

It's been a great discussion.

Absolutely.

But we want to leave you with one final lingering thought to mull over.

We've established that AI operates as a highly sensitive mirror, perfectly reflecting the structure, clarity, and discipline of the person sitting at the keyboard.

So when you open your laptop tomorrow morning and you look at your most recent AI outputs.

What exactly is it reflecting back about you?

Before you go building that cabinet, maybe it's time to stop, step back and finally write the instructions.