When the microwave oven first entered our kitchens in the 1970s, it was hailed as a miracle machine. Suddenly, meals could be reheated in minutes, popcorn could be popped without a pan, and defrosting was no longer a test of patience.
The microwave was disruptive, changing not only how we cooked, but how we thought about time in the kitchen. For a while, it was used for everything. People experimented – some successfully, some less so, learning (sometimes the hard way) that putting a metal bowl inside one was a recipe for sparks, noise, and a potential fire hazard.
Fast forward a few decades, and the air fryer joined the party. With promises of crispy chips without the oil and roast chicken without the mess, the air fryer quickly gained a countertop position in many homes. Again, people began using it for anything and everything: baking cakes, reheating pizza, cooking steaks. Over time, its best uses emerged: crispy textures without deep-frying, faster cook times, and less mess. People worked out that while it could do a lot, it was exceptional in certain areas and perhaps less so in others.
The pattern here is familiar. A new technology arrives, full of promise and potential. We rush to apply it broadly, test its limits, learn what works and what very much doesn’t. Ruined dinners, smoky kitchens, and inedible meals are the consequences of misuse, but they’re also part of the learning curve.
Now, we find ourselves facing another disruptive technology: AI. Much like the microwave and the air fryer, AI has sparked excitement and experimentation. Can it automate tasks? Write emails? Code? Make decisions?
The answer is YES… to some extent. But, just like previous innovations, finding the right uses takes time and thoughtful application. AI isn’t magic. It’s a powerful tool with enormous potential when used wisely.
The real challenge isn’t just in discovering what AI can do, but in understanding the consequences of how we use it. Not all consequences are negative: many are transformative in the best way. But, from an information governance perspective, they must be well understood, measured, and documented. Misusing AI, just like misusing a kitchen appliance, can lead to poor outcomes, only this time, the stakes are higher than a burnt dinner.
And if you’re unsure where to start? That’s ok. It’s perfectly acceptable to observe and learn while others test the boundaries. You can still be leading edge without being bleeding edge. Let others explore, fail, and refine. Then, when the time is right, work with experts like the team at Fivium to implement AI safely, securely, and with purpose. You won’t miss out. In fact, you’ll be ready to make it count when it really matters.
Want to understand more about AI and Information Governance?
Watch “Rage agAInst the machine” when experts Rowenna Fielding, Raz Edwards, Glenn Phillips and Lynn Wyeth navigate through the hype.
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