Working with Big Data and global analytics, I hear the term “Artificial Intelligence,” or AI batted around a lot. And truth be told, it’s inevitable that AI will — to some effect — impact every job or career at some point or another.
Nearly every Fortune 500 company now uses some form of automation, whether it’s for hiring employees or weeding out potential risks. C-suite executives — many well-informed, and others that are not — are actively investigating how the company’s next disruptive innovation could leverage Artificial Intelligence to streamline their business operations. And, according to a recent survey by the Northeastern University, three out of four Americans believe that it will negatively impact the job market, with one out of four worried about losing their own job to Automation.
But while there’s an awful lot of talk about what impact AI will have in the future, the reality is that today’s AI storylines are essentially a work of fiction. For critical tasks and insight-based decision making, AI technology simply can’t make the kinds of informed decisions that human analysts remain so good at.
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While AI systems are perfectly capable of analyzing specific streams of data, drawing some basic insights, and even recommending similar data for exploration, they struggle to notice subtlety or draw bigger-picture conclusions based on more than just the data immediately in front of them. And when it comes to forecasting, their abilities aren’t even remotely close to those of human analysts. This ability to predict big picture outcomes based on fuzzy data is one of the bedrocks of corporate decision making.
Given the clear gap between the idea and actual reality of AI, I suggest that, at least for the foreseeable future, we move away from the notion that the “A” stands for “artificial.” Instead, let’s call it what it is: Augmented Intelligence.
Augmented Intelligence refers to the combination of the human brain and the most reliable facets of AI. It emphasizes an enhancement of human intelligence — not a replacement for it. For lay people, Augmented Intelligence could be explained through the current experience of using Google search. You type in a few words, and Google predicts the rest of the question. But it isn’t yet capable of knowing what you’re going to ask before you open the screen!
To use another example, we’ve recently learned that self-driving cars are not quite ready for prime time. But cruise control, that oft-forgotten partner of the endless highway, has gotten way smarter. Today, many new cars have cruise controls that can see the car ahead of and behind you and adjust speed accordingly to maintain a safe distance. You are still responsible for steering, but the car is doing a lot of heavy lifting.
This same basic principle applies when discussing how Augmented Intelligence applies to businesses, or business analytics and decision-making. It assumes that while machines tend to be faster analysts, humans still tend to be better decision-makers.
So, when it comes to business analytics, the concept of Augmented Intelligence takes into account that the automated processes still require an active and engaged team of human analysts.
A good example of Augmented Intelligence in action is the case of a major North American automobile manufacturer. They’re leveraging Augmented Intelligence to analyze billions of rows of data, structured and unstructured, from internal and external streams including social media, Safety Alerting Data, customer complaints, warranties, contact center records, and legal claims.
Fusing these data sets together requires an element of Machine Learning or Artificial Intelligence, as algorithms learn to find commonalities within the data that analysts can later explore. But once those data streams are fused together, it is the human analysts reading the output of that data who are responsible for making decisions based on it.
The coupling of proven analytic methodologies and Augmented Intelligence technology enables human analysts to filter the information they are aggregating to only the most meaningful and actionable. This means that analysts are discovering, consuming and analyzing more specific data that is also more relevant and can be leveraged more efficiently.
To be clear, I am by no means anti-Artificial Intelligence. AI does work in certain situations, and will work better and better in years to come across a range of functions. Machines can effectively listen, learn and adapt — as long as there is an actual human managing the data source. Signafire, the company I work for, was created to help public and private organizations benefit from it. But what I have found is that people are talking about Artificial Intelligence so much that it’s creating improbable expectations about how it can impact business decision making in today’s world.
I believe that at this point in the game, it is more important to be realistic about the promises of Artificial Intelligence. As the technology evolves, we must continually separate fact from fiction, and recognize the essential function analysts’ skills, knowledge and methodologies still contribute to pattern and trend analysis. For now, an “augmented” approach is simply more neutral and will enable businesses to grow and take advantage of the advances to improve products and services — not replace the human interface.
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