'The test isn’t whether AI can do something. It’s whether it can make the process measurably better': We hear why businesses need to be more selective about where they’re using AI

Does every business process really need AI?

‘The test isn’t whether AI can do something. It’s whether it can make the process measurably better’: We hear why businesses need to be more selective about where they’re using AI

As AI becomes an ever-present in many businesses, most will now be looking to determine not just when AI will genuinely make a difference - as opposed to when it might just add cost and complexity.

So does every business process really need AI, or should companies be focusing on specific needs which will help them the most?

We spoke to Gregg Aldana, Senior Vice President, Head of Global Solutions Consulting at Appian, to find out more.

There’s a growing assumption that if AI can be applied to a business process, it should be. Is that the wrong starting point? How should businesses decide where AI genuinely adds value - and where it’s simply adding complexity?

The wrong starting point is asking, ‘Where can we use AI?’ because that puts the technology before the business problem.

Organisations should start by asking what they are actually trying to solve and where the biggest bottlenecks or inefficiencies sit. That means looking closely at which decisions they are trying to improve, what data those decisions rely on and where human oversight needs to sit.

That discovery should involve the people who understand the process and its challenges, from IT and business teams through to executive leadership. Only then can businesses judge whether AI is genuinely going to make a difference.

The test isn’t whether AI can do something. It’s whether it can make the process measurably better.

How do you distinguish between a process that actually needs AI and one that can be handled more effectively with traditional automation, rules or workflow?

The key distinction is whether the task follows predictable rules and has a clearly defined outcome. If it does, traditional automation or business rules can often get you there faster and more cheaply.

In those cases, adding AI may not improve the outcome and can introduce complexity that simply isn’t needed.

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