AI adoption; have we just got digitally lazy?

The prompt

I'd like to write an educational and somewhat humorous blog on the paradigm that AI might just be a lazy way of solving problems.  I want this to be in Australian spelling.  Can you please organise ideas clearly and for the main part in the same order as below.  Can you aim the content at CIO/CFO type roles, people that need to make decision about AI adoption and care about the bottom line - the basis of the entry is below.  Please include any references or weblinks in the copy you create.  Thank you AI! 

This blog might be a hard pill to swallow.  My analogy here is like when we started to use a 4GL instead of using C code to do everything.  The 4GL felt like a cheat and an easy way out, like using visual basic over C++. (Oh yes, I am that old).  Is that like what we are seeing now, where you can perform a sales order entry by creating an AI agent that has access to your postman collection which exposes the Sales order entry process from your ERP of choice.  So, you could create a form with mandatory fields and have 2 way repetitive validation of all of the forms...  Or now, we use AI to have a conversation with the parameters of the API, so that it can continue to ask you for missing parameters and have a conversation with you about the data.  I'd love to know the difference in CPU cycles for each implementation there (let's not include building the LLM in the first instance).  So yeah, the choice as using AI to solve the entire problem is interesting.  But, imagine that one LLM could interrogate the API for the Purchase order system....  Then using that data translate and create the sales order...  Wait - now we are getting somewhere!  This is starting to get impressive.

But on the contrary, if you use AI to write a nice tight piece of code...  And you use AI to regression test it and make it as efficient as possible, iterating through 1000s of options in seconds... Wait and then get AI to do security vulnerability testing and patching... and you get to use that code to make wonderful rules based decisions for years to come - then this is going to SAVE you time and money and probably save CPU cycles...  We have a conundrum! 

I agree that integrations and sales orders and finance and $ need to be super accurate and that LLM based AI is not amazing at maths, but it can call functions that do pricing and calculate numbers - so the solution is looking better and better.  But, it's still expensive - from a CPU cycle POV.  Oh, and you need AI for ever and ever... 

We'll still have humans in the loop, or we might just have more AI that does anomaly detection and will flag orders that need a human to have the ultimate say.

Are we just using compute and being lazy about solutioning?



Here is what chatGPT 40 had to say on the matter: Absolutely — here’s a blog post crafted in Australian English, aimed at CIOs and CFOs, with the requested structure, tone, and humour. The core theme is: Is AI just a lazy way to solve problems… or is it a new kind of efficiency?


Is AI Just a Lazy Way to Solve Problems? Or the Smartest Shortcut We’ve Ever Invented?

This might be a hard pill to swallow—especially for those of us old enough to remember writing C code and feeling personally affronted when 4GLs showed up. Remember that moment when Visual Basic made something that took hours in C++? Felt a bit like cheating, didn’t it?

Fast forward to now, and AI is the new 4GL. Only this time, it’s not just making coding easier—it’s changing how we solve problems altogether. But the question remains: Are we solving better, or just...lazier?

The AI Shortcut: Helpful Hack or Hidden Cost?

Let’s say you want to enable sales order entry from your ERP. Old-school approach:

  • Build a front-end form.

  • Add validation logic.

  • Ensure mandatory fields are ticked.

  • Run it through user testing.

  • Deploy, then fix it all again after launch.

Now? You expose the ERP’s API via a Postman collection, and let an AI agent chat to it. Missing parameters? The agent just asks. Invalid data? It checks and clarifies. Need to know what a field means? It explains, with a side of dry humour if you're lucky.

It’s a conversation, not a form. Elegant? Definitely. Lazy? Maybe. But undeniably fast.

From Sales Orders to Self-Writing Code: The Real Magic

But let’s flip the script.

What if you use AI to write a tight, efficient piece of code?

Then use AI to:

  • Regression test it,

  • Optimise it over thousands of iterations,

  • Perform vulnerability scans,

  • Auto-patch the results...

Now we’re talking about something deeply valuable. That code might make decisions for years to come, using rules-based logic that’s explainable, repeatable, and CPU-efficient.

In this model, AI isn’t lazy—it’s disciplined. It's not skipping steps; it's compressing time.

But AI Still Isn’t Great at Maths (Yet)

We all know LLMs can hallucinate numbers. You probably don’t want an AI guessing your month-end financials or calculating your margin.

But here’s the kicker: they don’t need to. AI doesn’t need to do the maths. It just needs to know where to ask.

Have a pricing API? A finance calculation engine? AI can call those functions and pull the results back into its workflow. The maths stays trusted, and the experience becomes conversational. It's not replacing logic—it's orchestrating it.

It’s Expensive… But So Is Everything That Works

Of course, this all costs compute. And no, we’re not talking about kilobytes anymore. LLMs are CPU hogs. They burn through memory, cycles, and cash like a kid with a gift card at JB Hi-Fi.

But you’re not just buying raw compute—you’re buying:

  • Speed to solution,

  • Flexibility,

  • Agility in your processes.

Yes, you need AI forever—it’s not a one-off project. But neither is good customer service, or sound architecture, or paying for your ERP licence.

Humans Still Have a Role (It’s Just Changed)

Humans aren’t going away—they’re just moving up the value chain.

AI will still need:

  • Anomaly detection agents to flag the dodgy stuff,

  • Approval flows for anything risky,

  • Business users to guide, correct, and override when things go weird.

And let’s face it—AI is still not great at navigating ambiguity when there’s no data to lean on. Humans remain the ultimate context engine.

So... Are We Being Lazy? Or Just Efficient in a New Way?

If your AI is just rephrasing search results and flooding your CPU to do what a form could handle—then yes, maybe that’s lazy.

But if your AI is orchestrating complex tasks, integrating trusted logic, learning from patterns, flagging exceptions, and freeing up your people to work smarter?

Then no, it’s not lazy. It’s strategic efficiency—with a lot of compute behind the scenes.

Bottom Line for the Bottom Line

CIOs and CFOs, here’s your TL;DR:

  • AI can look lazy—but often, it’s just replacing manual drudgery with higher-order thinking.

  • Shortcuts aren’t always bad—especially when they’re secure, repeatable, and governed.

  • Compute is a cost, yes. But how much would you pay to deliver in days instead of quarters?

AI isn’t cheating—it’s levelling the playing field. The only real question is whether you’re still trying to hand-code your ERP integrations, or you’re ready to have a conversation with your business systems.

(And hey—at least you don’t have to debug Visual Basic anymore.)


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