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I’LL DO ANYTHING FOR PRODUCTIVITY (BUT I WON’T DO THAT)I’LL DO ANYTHING FOR
PRODUCTIVITY (BUT I WON’T DO THAT)

Supercharging productivity with AI and automation isn’t worth it if you also
supercharge risk exposure

Christian Lund, Co-founder at Templafy

December 13, 2024

4 Min Read
Getty images


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.




CONTEXT IS KING 

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.

Related:Generative AI, Digitalization Combined Drive True Data Democratization

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. 


PROMPTING PROGRESS 

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. 

Related:Understanding the Data Governance Divide

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. 


PRODUCTIVITY IN THE AGE OF AI 

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. 




ABOUT THE AUTHOR

Christian Lund

Co-founder at Templafy, Templafy

Christian Lund is the co-founder of Templafy, the AI-powered document generation
platform for business professionals who need to create high-performing, accurate
and compliant business documents with little effort. 

During the early days of the cloud age, Christian saw millions of knowledge
workers wasting enormous amounts and time creating documents rather than
generating business value and he and his co-founders decided to create a company
missioned to eliminate manual document work across enterprise workforces. Over
the past 10 years, Templafy has developed into a mission-critical technology for
thousands of global enterprises with a need to enable their people to create
better-performing documents faster. The company now leads the way to safely
bringing AI to enterprise document generation.

Outside of Templafy, Christian is also an active investor and advisor helping
new tech entrepreneurs succeed.

See more from Christian Lund
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