Plug it in and make it magic

There’s a new phase in the corporate lifecycle - somewhere between “we need to modernise” and “someone please find the person who owned this spreadsheet” - where an executive suddenly sits up, eyes glowing with the confidence of someone who has not read a single technical document since 2009, and declares:

“We need AI.”

It’s said with the certainty of a man spotting a new cordless tool in Bunnings. They don’t know what it does, but they want, no, they need it in the garage.

No one asks what problem AI is supposed to solve.
No one asks whether the data is ready.
No one asks why the company still can’t reliably onboard a new starter without summoning three separate departments and a ritual sacrifice.

Because asking questions complicates things. And executives don’t want complexity.

They want magic.

The comforting illusion of the ‘AI Strategy’ slide

There’s something soothing about the idea that AI will glide into the organisation like a benevolent wizard, tap its wand on the servers, and suddenly all the messy, human, organisational nonsense will sort itself out.

Staff will stop entering dates in 14 different formats.

Old processes will melt away like snow in spring.

Those systems nobody has touched since all the monitors were big ivory boxes will become “integrated”.

In the executive imagination, AI arrives the same way the hero does in a Marvel movie: just in time, perfectly choreographed, hair blowing in the breeze.

Never mind that the data is a series of disconnected puddles in a swamp of different machines.

Never mind that your workflows require a map, a compass, and emotional resilience.

Never mind that half your systems communicate via Excel export as attachments and hope.

Magic is appealing. Reality is not.

But reality keeps poking its head round the door

The uncomfortable truth is that AI is perfectly capable of solving problems - just probably not your problems.

At least not until you fix:

the processes everyone follows differently
the workflows nobody owns
the data pipelines held together with sticky tape
the reporting layer built entirely by Bob, who retired without handover
the documentation last updated just after the dinosaurs died.

Companies want AI the same way people want a robot vacuum: press the button, walk away, and imagine the house is gently tidying itself while you contemplate your leadership principles.

But AI is not the robot vacuum itself. AI is more the moment you finally buy a robot vacuum, set it loose, and discover that your home is 60% cable spaghetti, 20% abandoned socks, and 20% “mysterious debris.”

The robot doesn’t clean - it loudly and enthusiastically alerts you to every corner of your home you have neglected for a decade.

And AI behaves the same way:

You didn’t want a vacuum.

You wanted the comforting illusion that your mess would remain theoretical.

The “Magic Button” problem

There is always - always - someone who asks if AI can just “look at all our data and tell us what’s wrong.”

This is organisational astrology. They want the AI equivalent of reading star charts: “Your KPIs are low because Mercury is in retrograde… and also Karen keeps overriding the CRM data manually.”

They want a glowing dashboard that says:

AI SUMMARY: EVERYTHING IS FINE EXCEPT THAT ONE TEAM YOU SECRETLY SUSPECTED ANYWAY.

They want the machine to be omniscient, decisive, and conveniently aligned with their pre-existing biases.

And maybe, one day in the far future, when our brains are uploaded to servers and nobody uses spreadsheets anymore because we’ve evolved past pain - maybe then AI will tell you your organisational destiny.

But today?

AI is basically the world’s most sophisticated amplifier. Whatever mess you give it, it will give you back a louder, more expensive version of that mess.

AI as a metaphorical house renovation

Think of AI like hiring a tradie to renovate your kitchen.

You call them in, expecting new worktops and sleek appliances, the whole Pinterest perfect fantasy.

But the tradie walks in, looks at the walls, taps a few beams, and says: “Yes, lovely idea, but your house is actually… well… a bit shit.”

Then they take out their iPad full of photos of all the flaws, faults and expensive f#$k ups in your house you didn’t want to see.

This is what most AI projects are: a very polite professional pointing at everything structurally wrong with your organisation.

The data silos? Rotten floorboards.
The 87-step approval process? A load-bearing mistake.
The operational workflow written by someone who left in 2014? An electrical hazard wrapped in nostalgia.

AI reveals the nonsense. It does not cover it up with quartz countertops.

The deeply unsexy prerequisites of “being ready for AI”

If companies truly want AI to work - really work - they need to do the things that never make it into keynote speeches:

define a process
stick to the process
pick a single source of truth
clean the data
centralise the data
stop emailing Excel sheets around like digital carrier pigeons
retire systems old enough to vote
document everything - literally everything.

AI is not the prize you get for announcing ambition. AI is the prize you get for doing the boring, tedious, organisational homework that everyone has avoided for years.

And yes, this is exactly why so few companies actually get value from it.

Doing software wrong… but now with a robot

This is just the next chapter of the same saga we always live through:

the belief that software becomes “done” just because it ships
the belief that support is someone else’s problem.

AI doesn’t replace these delusions. It simply adds a shiny chrome finish. You’re not supposed to get it perfect. You’re supposed to get it less wrong than yesterday.

Fix your data.
Fix your processes.
Fix the ancient systems quietly decaying in the corner like a neglected house plant withered and drying to dust.

Then - and only then - AI will stop being a corporate fantasy and start being genuinely useful.

And if that feels disappointing, congratulations.
You’re doing software wrong. Exactly as intended.