close up of robot toy
PHOTO: Rock n Roll Monkey

I’ve been spending a lot of time recently talking about intelligent platforms that use artificial intelligence (AI) or machine learning (ML) to help out in the workplace. In fact, just a few hours before sitting down to write this I was asked to participate in yet another industry conference panel on the subject.

An Early Introduction to the Two Sides of AI

AI has always fascinated me. I think the first encounter I had with the concept of an autonomous system helping out in the workplace was Robert the Robot the robotic co-pilot of the World Space Patrol craft Fireball XL5 on its eponymous puppet show back in the early '60s (I was very young — honest). I always laughed at his funny voice as he took the controls and uttered his catchphrase of “on our way home” in his metallic voice.

Several years later I saw 2001 at the movie theater where the HAL computer taught me another lesson: AI may not always be the beneficial workplace aid we want it to be.

Robert and HAL were two sides of the potential impact of intelligent platforms. One that sits aside us and helps us achieve our goal; and one that takes over to the point that we can no longer complete the simplest of tasks without its input.

Related Article: We Need Ethical Artificial Intelligence

AI Has Moved Mainstream

As much as AI in fiction can either inspire or teach us cautionary tales, we now live in an age where AI has become a reality. But what does that mean for today’s workplace?

I think before we answer that question we need to define what we mean by artificial intelligence. The dictionary defines it as “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” But I find it to be a very fluid term, in that it is often used to describe the technology in the experimental and early stages of adoption, but once something gets accepted as mainstream it is no longer considered AI. 

Consider voice assistants. We now talk to our computers (let’s face it, it's not really a phone we keep in our pockets anymore is it?) several times a day, I talk to my TV and my car. I use facial recognition to open apps, to help tag photos on my social media platforms of choice, and more.

With friends and family all over the world, I use translation platforms on a regular basis. But these are not the platforms I’m talking about at industry conferences these days — yes they are intelligent platforms, yes they meet the definition of AI, yes they help out both in the workplace and the home (and that’s a distinction that has become less clear in recent months). I believe they have moved past the point of adoption where these various recognition technologies are considered as anything special. The AI has moved mainstream.

Related Article: Getting Your Workers and Culture Ready for AI in the Workplace

Why Confusion Around AI Lingers

When I talk about AI in the workplace these days, it is less about the idea of man-like machines like Robert, and more about how the intelligent platforms that handle our data can learn what to do with that data, and extrapolate the stories the data is telling us. ML is regarded as a subset of AI that is based on the study of algorithms and statistical models that enable a computer system to perform specific tasks without needing explicit instructions. Instead, the system operates based on patterns and inference. Each time an ML process is run, the results can be used to validate the degree of accuracy and thereby learn. This approach is ideally suited for solving complex content optimization problems, such as asset recognition and metadata application. Research has shown that for high volume, low complexity tasks, one ML bot can do the work of three to four full-time employees. While ML can drive efficiency, the real power comes from when bots and humans work together and there is a growing trust in the value and consistency of the results from the tasks that the ML undertakes.

As mentioned earlier we all encounter AI in some form or another almost every day, be it voice assistants, mapping services, or receiving personalized marketing messages or customized digital experiences based on our online behavior. Additionally almost every enterprise is now using some form of AI in their day-to-day business operations as it is becoming increasingly embedded within the tools and technologies they have deployed.

According to a recent Gartner survey of CIOs, the adoption of AI has increased by 270% over the last four years. AI is here to stay.

Yet there is still confusion around what AI is and can do. While we may use it (often unknowingly), most of us don’t know how it really works, and as a consequence often have unclear and unrealistic expectations. These perceptions are often further clouded by vendors that over-promise on the capabilities of their AI-enabled products, or use AI and ML as buzzwords that can’t deliver on expectations.

When you consider what AI, and ML, in particular, is fundamentally designed to do, i.e. automate and assist with repetitive tasks that need some degree of human intervention and specialist knowledge, then the benefits of their application become clear: optimizing operations, and creating and acting upon advanced insights to help make our lives easier and freeing us up to do what we do best — provide the human touch. Something that neither Robert nor HAL could ever provide.