AI myths and misconceptions

In this blog we welcome guest blogger Mariano Kristensen, Software Sales Executive at SAP Centre of Excellence, EMEA North. Mariano will be one of our experts at the upcoming OpenText™ Innovation Tour Stockholm on 20 April at The Grand Hotel.  

The advent of Artificial Intelligence (AI) and Machine Learning (ML) over the past couple of years has been incrementally accelerating, moving from theoretical to tangible solutions that are indeed providing “a way to do it better”. Edison would no doubt embrace all the technology disruption around us if he were alive to see it.

“There’s a way to do it better – find it,”Thomas Edison

Let me give you an example. I recently met with a large construction company who was looking to find a better way to decide which big RFIs it should participate in, and which it should decline to bid. The contract for a major new bridge, for instance, might be worth billions of euros, but the company’s tender process would need to involve hundreds of engineers just to create a proposal.

Bidding for such projects is expensive and there’s a risk the company might not win the contract. That’s where AI and ML come into their own by finding common patterns of tenders that have been won and lost and identifying which RFIs have a higher percentage of success.

Fact: AI can do things humans cannot

Unlike analytics, which looks at historical data to predict something that’s possible in a linear or vertical environment, AI and ML can do things we humans can’t: analyse data at its lowest level – such as images, text, and information that resides in unstructured repositories at the application level – and link it with structured information.

In our own organisations, we all want to find ways to be inspired or constructively provoked to think about our current business models differently. But when I speak with customers, I often find they have a few myths and misconceptions, particularly around Artificial Intelligence.

These typically range from assuming Artificial Intelligence solutions think like humans, will take away their jobs and do everything better than humans can, or that they can somehow replicate human behaviour just because they can process data faster. Of course, most of these myths are rooted in fear and lack of understanding. AI and ML can analyse data from multiple sources quickly and easily (and yes, faster than you) to advise a preferred course of action.  But speed is not a replacement for judgement.

The 3 categories of AI

Broadly speaking, there are three categories of AI – data (a range of raw facts), information (facts put into context that have some meaning), and knowledge (the ability to make decisions based on meaningful information with dynamic input/output). Most people assume AI is just Big Data but this is a mistake. There are many hidden pearls of valuable information residing in content repositories and information systems, as well as in the heads of experienced employees. I’d urge you to look at all three categories of AI to harness the full spectrum of knowledge, regardless of where data resides or what format it’s in.

Change is inevitable, particularly in the current disruptive machine-assisted environment. Progress, however, is optional. I hope you’ll grab it with both hands.

I’ll be discussing some of these issues in greater depth at the upcoming OpenText™ Innovation Tour Stockholm on 20 April at The Grand Hotel.  I hope you’ll join me in finding out how you can make the most of AI and your unstructured enterprise data.

You can reserve your seat at the OpenText Innovation Stockholm event here.

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