The terms “AI” and “automation” get thrown around like confetti in the world of startups. Pitch decks are full of them, sales pitches lean on them and founders sprinkle them into product descriptions and conversations whenever they get the chance.
However, do all the people talking about AI automation actually fully understand what they’re talking about? The easy answer is that no, they most likely don’t. And, even worse, a lot of the time people treat these two terms as if they’re synonymous.
But, they’re not. And if you’re building a product, raising money or making decisions about how to scale, mixing them up could cost you real traction, or, at the very least, potential opportunities to do big things.
Automation: Old and Boring But Brilliant
Let’s start with automation. It’s not exciting and it’s definitely not new, but that doesn’t mean it doesn’t have a very important place in the world of business – when used correctly.
Automation is about creating rules. It’s “if this, then that”. You write a script, set up a workflow or trigger an action based on a condition. It’s about removing manual effort in processes that don’t require intelligence, reasoning or deep thinking. It’ll do the tasks for you that normally take forever, and it can, because they’re pretty basic.
Automation is great because it’s predictable – you know exactly what will happen. There’s little ambiguity, and not only that, but it’s reliable, it’s fast and it’s scalable. And because it’s so well understood, it’s easy to sell. You’re not promising anything incredible or super high tech – it’s about improving efficiency and removing monotony.
The irony, however, is that many founders label their automation tools as “AI-powered” because it sounds more innovative – after all, AI is a bit of a buzzword these days.
But in truth, their product is doing exactly what it should be doing – reducing friction through automation. And yes, that’s incredibly valuable, but it’s not artificial intelligence. And that’s okay.
AI: A Different Beast Entirely
Now, artificial intelligence is something else altogether. It’s not just about rules, it’s about patterns, learning and uncertainty. AI doesn’t do exactly what you tell it to do – rather, it guesses, it interprets and it adapts. And sometimes, it gets things wrong in wildly unpredictable ways.
That unpredictability is part of the power, but also the risk. That is, high risk (sometimes), high reward (most of the time).
However, it’s important to bear in mind that founders often overestimate what AI can do in the real world. They imagine chatbots that understand nuance, recommendation engines that know your customers better than they know themselves and content that writes itself.
In practice, AI is only as good as the data, the design and the domain it’s operating in. Garbage in, garbage out, as the experts say! So as much as it has exceptional potential, that potential won’t necessarily be realised by any means unless the whole process is done properly.
Why The Ambiguity and Conflation of AI and Automation?
It’s partly about branding. Using the term “AI” sells better (unsurprisingly). Investors lean forward when they hear it, and customers get curious, because these days, AI is seen as being intrinsically linked with the most modern technology and innovation. But beyond that, the real problem is that founders often fall in love with the promise of intelligence, without doing the hard work to validate whether their product needs it.
A tool that auto-generates blog titles using OpenAI’s API might look smart, but is it really any better than a deterministic script trained on the most-clicked headlines? And more importantly, do users care?
AI is often a distraction. It can add complexity, latency, regulatory overhead and cost and the thing is, ot every problem needs learning. Oftentimes, all that’s really necessary is fewer steps – having the same job being done but quicker, faster and more accurately. And that’s what automation is brilliant at.
What Should Founders Really Be Asking?
Perhaps, instead of asking, “Can we use AI here?” the better question is, “do we need intelligence at all?” If the problem can be solved with basic logic, perhaps it would be wise to do that first. If it can be automated with clear rules, that’s great, but if you truly need something adaptive, uncertain and dynamic, then sure, go ahead and use AI.
But at the end of the day, users don’t care whether it’s AI or automation behind the scenes – rather, they just care that it works. They care that it saves time, reduces errors or improves outcomes. And most of the time, a well-crafted automated process beats a half-baked AI model
Ultimately, what’s starting to become clear as AI continues to progress and become increasingly advanced is that the skill moving forward isn’t necessarily knowing how and when to use AI, it’s about knowing when not to use it.
