Artificial Intelligence: Distinguishing science fiction from delivering actual business value

The first Terminator movie introduced the world to Skynet, a revolutionary artificial intelligence (AI) system that develops self-awareness and fights back when its creators try to deactivate it.

The movie came out in 1984, which was also the year that Apple launched the Macintosh, the first affordable personal computer to offer a graphical user interface. If you never had the pleasure of booting up that 9 inch black and white screen with a floppy disk, then it may be hard to imagine just how obscene a fiction an all powerful AI would have seemed in 1984.

Fast forward 35 years to the present day and AI has become the quintessential business buzzword, touted as the answer to nearly every business challenge. According to the hype, companies should be using AI in every department to revolutionize their business or risk falling behind.

While I am an avid believer in AI for business value, I caution those who think of AI as a singular silver bullet.

Even Google did not become the technology giant it is today through the development of an AI. They started with a single value-add problem: to make internet searches better. And they accomplished this through a single and comparatively small advancement, PageRank.

Google did one thing slightly smarter than the competition and by continuing to leverage a foundation of smaller advancements (inclusive of AI), in aggregate, it has become the massive organization we know today – and to many Google has become associated with AI advancement.

Rather than one giant revolutionary change to the way businesses do things, I see AI as a set of tools that can be used to help businesses evolve, to continually improve and eventually make large impacts.

Offering real solutions, not science fiction

Incremental improvements is the approach that we’re taking at Mintel to offer better solutions for our clients. Not science fiction, but real improvements that will enable insight.

As an intelligence based organisation, we know how valuable data is and we’ve had a team analysing data for several years. We are now taking this forward, using the latest data analytic tools to help our research teams join the dots much quicker, so they can focus on providing actionable analysis – rather than spending their time wading through data.

We know that Mintel has undisputed expertise in consumers, categories and markets. We are using data science and AI techniques to help our research experts enhance their expertise in order to:

• Leverage anomaly and change point detection, in order to identify market movements earlier;
• Build smarter data acquisition platforms that automate mundane tasks, so that we can expand our global reach and acquire exciting new data points to our existing platforms;
• Use smarter language processing and sentiment analysis to help us provide more meaningful consumer insights.

While Mintel has always been predictive (see our new You Heard It Here First blog series for more details), AI gives us the opportunity to incrementally do this even more, with greater degrees of confidence.

To 2054 and beyond

Skip ahead another 35 years to 2054, the year in which the plot of The Minority Report takes place.

The likelihood of us having an all knowing AI running every business decision is about as likely as a PreCrime division in every police force, with the ability to operate as a group mind.

We are not living in science fiction. I very much doubt that we will have a truly intelligent machine within a single generation. However, AI will continue to give us a growing tool set for identifying insight from data.

And while other companies may be selling you their fictional version of the world in 2054, Mintel will be there, with recommendations that you can actually trust to help you to make better decisions faster.

Benjamin Korallus leads Mintel’s data science practice, responsible for expanding its analytical capabilities via advanced data analysis, predictive modelling and machine learning. He holds a Master of Science in Analytics from the University of Chicago and is an adjunct professor of Information Systems at Loyola University Chicago.