AI vs Automation
“We need AI.”
I hear it a lot. And I get it; AI can be a valuable resource and offers a wealth of opportunities to those willing to embrace it (much to Sarah Connor’s horror).
Fast forward half an hour into that conversation, and often it turns out that what’s really needed is a workflow that moves data from A to B without manual copy-pasting between a dozen different systems.
This is a job for automation, not necessarily AI. So, let’s explore that distinction a bit…
AI vs Automation: What’s the Difference?
In short, automation is rules-based and deterministic. It follows a set structure and doesn’t deviate. When X happens, do Y. It’s fast, reliable and can save oodles of time.
AI, on the other hand, is about judgment and reasoning. You can give it messy input, and it will make what it can out of it. It’s useful when rules are fuzzy or data is unstructured, but it needs guardrails.
For me, the most worrying distinction is the reliability of output.
Think of the classic puzzle with two guards. One tells the truth, and one lies. I’m not saying AI will always lie, but it’s the only one of the two capable of lying to you at all…
So why do they get muddled up?
I think this mainly comes down to the hype around AI, and it becoming a bit of a buzzword at the moment. It’s become a shorthand for “make it smarter”.
Generally, I think most people believe “we implemented AI” sounds better than “we automated it”, even if it’s not the most appropriate or straightforward solution.
When will Automation do the job?
Automation is extremely powerful when you’re dealing with repeatable steps, structured data, and repeatable outcomes. Think things like lead handling, internal process reporting, support tickets and workflows, and back-office admin tasks.
Some signals that automation can help include:
- The inputs are predictable (forms, fields, checkboxes).
- The rules are clear (“if”, “then”, “else”).
- You need consistency more than creativity.
- Failure costs are high (so you prefer 100% repeatable outcomes).
An added bonus is that automation can be quick to pilot, test and iterate, making it the perfect “first port of call”.
When do you actually need AI?
AI is powerful when rules fall apart because inputs are messy or ambiguous, which means interpretation is an important step in the process.
AI can be integrated to understand unstructured data, make judgment calls and prioritise based on context. It can personalise, draft summaries and translate tone or intent.
Some red flags if you’re considering integrating AI into your processes include:
- You have too little data.
- Absolute accuracy is needed, and you can’t tolerate “nearly right”.
- There is no plan to monitor or update the models on an ongoing basis.
- You’re implementing AI just to say you have!
A Sweet Spot: AI + Automation
Sometimes, both can work together, responsible for their own remit, to supercharge your processes, depending on what you’re trying to achieve.
Automation collects, cleans and routes the input data. AI interprets the input or drafts the output. Automation applies rules, permissions, alerts and escalations. Human oversees and approves.
An example of how this might look:
You get an inbound email (think support ticket or enquiry) > AI classifies & extracts the required details > Automation rules create or update the CRM entry, record the input, and push it to the right queue/pipeline > AI drafts a response > Human approves > Automation sends and logs.
You get speed, control and reasoning all baked into one flow.
Final Thought
Before you spend, here is a general rule of thumb: Do you need a brain (AI), or just a reliable conveyor belt (Automation)?
Let’s chat!
If you’re still not sure which bucket your use-case lives in, reach out for a chat! Send me a message on LinkedIn or email me at richard@hydracreative.com.
