Supercharging productivity with AI and automation isn’t worth it if you also supercharge risk exposure
Technology is constantly reshaping the way we work. And the speed of change is only getting faster as we move into the era of artificial intelligence (AI) and automation.
According to McKinsey, 65% of organizations are regularly using generative AI - nearly double the percentage from their survey ten months prior. This increasing usage was matched by rising expectations. Three-quarters of the businesses surveyed predict generative AI will disrupt their industries in the years ahead.
But we should keep our feet on the ground when it comes to AI. Because supercharging productivity isn’t worth it if you also supercharge exposure to risk.
It takes two to tango with AI.
AI is only as effective as the instructions and context it receives. Just as people rely on clear direction to produce meaningful work, AI depends on precise tasking and relevant data to deliver reliable results.
For example, if we ask AI to rewrite the lyrics to Meatloaf’s iconic 1993 hit “I’d Do Anything for Love (But I Won’t Do That)”, and make it about the business benefits of AI and productivity, the results are instant…
(Chorus)
Oh, I would do anything for productivity,
Handle the mundane so you can do what’s new.
Yes, I would do anything for productivity,
But I won’t steal the spark that belongs to you.
No, I won’t do that.
A quick rewrite of a song is no problem at all. Effectively leveraging AI in a business context is a bigger challenge.
There has been a lot of attention on the various AI models available to businesses. However, there is a lot less focus on understanding and providing the context these AI models need. In many cases, effective uses of AI are the result of specific individuals within a company tasking models effectively. But this is a step in the wrong direction in a landscape where businesses need to de-risk, reduce costs and control revenue.
For instance, imagine a retailer using an AI-powered recommendation system to suggest products to its customers.
If the AI is given limited context or a dataset that only reflects the shopping patterns of a limited demographic, its recommendations may not resonate with a broader customer base. This leads to poorly-targeted suggestions that frustrate customers, resulting in lower engagement and lost sales.
AI is only as good as the context we give it. So what is holding AI back? In many cases it’s us, the users.
You can’t rely on humans to provide the context AI needs.
Most people approach prompting an AI in the same way they would use a search engine. They will list some keywords, and feel disappointed when they see jumbled results.
The best way to approach AI is to treat it like you would a colleague. Imagine giving just keywords to a real colleague? They wouldn’t know where to begin. Providing clear objectives, detailed context, and relevant information helps the AI better understand the task and align its output with your goals.
But why does this matter? If businesses want to experience the productivity benefits of automation, they need to first reduce the risks created by humans.
There are over 1.2 billion knowledge workers globally across the likes of banks, law firms and consultancies.
These businesses are built on ultra-specific skill sets. These experts have a detailed understanding of their roles, niches and market context.
However, they also have to deal with administrative processes and repetitive, document-heavy tasks. This is where human error can cost businesses. In the UK alone, banks and fintechs spend £21.4k per hour fighting financial crime and fraud, pushing the UK’s annual compliance bill to £38.3bn.
The bigger the business, and the more knowledge workers, the greater the risk.
AI is reshaping the knowledge work landscape. Not because they’re capable of fulfilling their roles, but because tasks like document generation can now be automated, reducing the risk of human error and cutting compliance costs.
Businesses should do anything for productivity. Unless it means increasing risk.
The less time people spend doing manual document or data work, the more time they can spend doing the things that drive value. And the less humans are involved in automation, the less risk businesses face.
You May Also Like
