Data Intake and Protocols for AI

Welcome to the Machine, Mate

Let’s set the scene. You're an Aussie business owner. You’ve heard the term “AI” more than “interest rates” this year, and now your boss wants to “leverage it for strategic outcomes.” Whatever that means. You nod, smile, and Google “how to not get replaced by ChatGPT.”

At the heart of it all is one thing: data intake. This is where the beast gets fed. And if you don’t have the right data, or worse, you let in the wrong stuff… well, you’re basically letting your AI intern snort glue and do your taxes.

What Even Is Data Intake?

Think of AI like a rescue greyhound with trust issues. Data intake is the onboarding process. You're not just dumping Excel sheets into a void and praying for answers. You're setting up pipelines, controls, filters, and rules to make sure your AI doesn’t:

• Hallucinate
• Expose private client info
• Assume everyone in Perth is a threat to democracy because one spreadsheet said so

Data intake = how you collect, process, clean, validate, and feed information into your AI system.

If you're in a regulated sector like finance, health, or the slightly shadier corners of crypto, your data isn’t just “numbers”—it's potentially a class action lawsuit with your name on it.

The Four Horsemen of AI Intake

1. Source Control Not all data is equal. Data from your CRM? Good. Data scraped from a Russian message board about Dogecoin? Maybe not. Pro tip: Treat your sources like Tinder matches. If you wouldn’t introduce them to your lawyer, don’t feed them to your AI.
2. Validation & Cleansing This is where you turn your data from a “Ute full of random junk” into something a system can read without vomiting. Dates should be real. Names spelled right. No 1993 fax numbers listed as emails. Basically: clean your damn data.
3. Protocols & Access AI loves structure. Your data intake protocols should define who can upload what, where it goes, and how it gets audited. Otherwise, Sharon from Accounts uploads her lunch order to the training set and now your chatbot recommends Pad Thai during compliance reviews.
4. Security & Consent If you’re using customer data, congratulations: you’re now a steward of privacy, whether you wanted to be or not. Australia’s Privacy Act, Consumer Data Right, and every IT lawyer within a 10km radius say you better know where your data came from, what it’s used for, and how it’s stored. Encrypt. Anonymise. Log everything.

Garbage In, Lawsuit Out

If you feed bad data into an AI, it doesn’t “learn better” like a child at Montessori. It becomes more confident in its wrongness—like a bloke at a pub with a full head of steam and half a Wikipedia article.

Here’s a classic Aussie scenario:

You train a property price predictor on 10 years of data from regional WA. Great, right? Except it’s missing 80% of updates from the eastern states, doesn't factor in zoning laws, and thinks Mount Druitt is a national park. You deploy it anyway. Result? A class-action, a media roasting, and your LinkedIn now says “consulting sabbatical.”

How We Do It Right (or At Least Less Wrong)

• Sandbox everything. Never train on production data directly. Ever. Not even “just for one quick test.”
• Data contracts. Enforce schemas. Define what’s expected, what’s optional, and what gets you flagged.
• Human-in-the-loop. AI doesn’t get context. Humans do. Especially if they’ve been burned by it before.
• Document everything. You will be audited. You will forget what you did last quarter. Your future self will hate you unless you keep records.

Closing Thoughts From an AI-Curious Madman

Data intake is the difference between “AI that works” and “AI that says your boss has been dead for six years.” It’s the most boring, least glamorous part of artificial intelligence—and the most important.

So to all the Aussie founders, tech leads, ops managers, and caffeine-powered interns trying to build AI systems without blowing something up:

Start with the intake. Question everything. Label your columns. And for the love of god—never trust a CSV called final_FINAL_version2_useThisOne_really.csv.

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