Automation vs AI: What’s the Difference and Why Does it Matter

Automation and AI are terms that are thrown about a lot these days, particularly for streamlining or simplifying office-based tasks. These two things - often used interchangeably - will transform your workflows and make you endlessly more efficient.

There's a lot of hype.

And there’s some truth to it.

But let’s dig into the basics first, shall we?

What is Automation?

Automation is when a process is carried out without the explicit input of a human, due to prior instruction. Basically, following a chain of command - if X, then Y.

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Humans set the rules and parameters, machines carry out the actions. The point is to relieve us of tedious and repetitive tasks. And this is incredibly useful, because humans are much more prone to error when carrying out these mundane, repetitive tasks than machines are.

It removes the risk of human error, improves consistency across work, and frees your team up for more high-value tasks. It’s a win for everyone.

What is AI?

Artificial Intelligence, on the other hand, is more of an ability of the machine to mimic human intelligence or to ‘think’ for us. AI is able to analyze data, recognize patterns, solve problems, and learn from inputs or feedback. Crucially, AI will evolve and develop over time as it learns how to more efficiently and effectively complete the task.

AI is often used for similar mundane, repetitive tasks, such as pattern matching, analyzing data, and reviewing documents, because it can take on the heavy lifting of the task, while leaving the interpretation and ‘skilled work’ for the human team members. At the same time, they can once again avoid that human error that is common with boring, repetitive work.

However, AI can also mimic human ‘intelligence’ and often think for us. A key way many businesses are using AI is to understand, interpret, and get insights from information without having to spend time reading and analysing it themselves. Reading through and summarizing reams of information to give lawyers the most relevant information upfront is a huge benefit.

However, the risks of relying too heavily on AI are well publicised. Aside from ‘inventing’ fake information and missing information in its summaries, another worry is that without that mental work, the human members of the team lower their overall understanding of what they are working on.

What is the difference between AI and Automation?

One of the main issues we see in technology is the use of these terms interchangeably. Automation and AI are not the same thing. And while you can use AI within your automated processes, the point of AI is to mimic human intelligence and perform tasks that it can then learn from and improve on.

Here are the main differences -

• Works best for regular, repetitive tasks
• Have clear rules and workflows set by humans
• Need to be regularly checked and updated to ensure correct performance

• Works best for analyzing and summarizing
• Requires detailed, well-written prompts
• Can learn and improve independently

However, the confusion is not altogether unsurprising given that there are core similarities between automation and AI, too.

• Productivity and efficiency - both take on the workload of repetitive or time-consuming tasks to free people up to perform more highly skilled or relationship-based work.
• Assigned tasks - both are assigned the goal of completing a specific task, set by humans, within clear parameters.
• Overlap of needs - both can be used together to create a more flexible and detailed workflow than is possible with just automation.

Why Does It Matter?

So, why do these differences matter? When both can help businesses with productivity and efficiency, why do we care if it is through automation or AI?

The key answer to that is risk and investment.

Automation is a fairly simple process - and although the automations themselves can have several steps and branches - they are usually operated by a simple trigger. So the worst outcome is that the automation didn’t work when it was supposed to, or vice versa. Embarrassing, possibly (who hasn’t had an ‘oops’ email from one brand or another), but unlikely to cause serious harm.

Similarly, automations are relatively simple to set up. While it may be complicated to map out and implement, the skills and experience needed can be developed on the job. A bit of trial and error is expected which is why testing is built into automated workflows.

Conversely, AI can come with a much higher investment of time, money, and expectations. The prompts need to be carefully crafted if they are to deliver the output you want, and it is easy to get them wrong. Training is essential.

In addition, the risks of something going wrong with AI are higher. As it is involved in understanding, analyzing, and summarizing, people rely on that information being accurate, but there is a real risk that it may not be. It’s not that AI deliberately misunderstands, but it has the potential to invent information that it feels is lacking. On top of this, is the well-known risk that AI draws information from sources that it does not have copyright agreement to use. Both of these risks, when combined with the increased cost and time of implementation, should give organizations pause before they introduce it into their processes.

Before introducing automations or AI to your workflows, consider the goals of what you are hoping to achieve, the investment you’re willing to make and the outcome you expect. This will help you determine which approach works best for you.

How to use them together?

While most businesses are still getting their heads around AI (and the legal or compliance ramifications of introducing it to their workflows) there is a growing push for using AI in automated processes to further reduce the need for manual input.

Automation can start the process, then AI can be pulled in to tell the workflow what direction to go in or to add summarized information for the people using or monitoring the workflow.

Imagine you’re creating an alert - your automation will pull in the sources based on your keywords, and AI could create summaries of those articles by scanning them. This would provide more information than just the available metadata and would allow the end-user to more easily identify where they want to dig down.

As mentioned above, the risks of hallucinations and compliance remain, so these processes would need to be monitored and sanity-checked by a person. But with the right checks and balances, it could completely transform how you deliver your alerts.

Are you desperate to use AI to overhaul your automated workflows? Or do you think it’s an unnecessary risk?