The idea of AI (artificial intelligence) more often than not tends to conjure up images of a hyper futuristic world with flying cars, robots, altered realities and the list goes on. Often these views are distorted with ideas of machines taking over the world like so many popular Hollywood movies have depicted. And while this may or may not be a realistic prediction of the future, we often ask the question of whether or not we should be hurtling down the path of discovery and development of these intelligent tools.

I think the word “tool” is perhaps a better way to look at things. If we think of AI as merely a new tool with which to achieve our goals and initiatives, we can start to think more accurately and more realistically of how this can impact the way we, as people, work and live. We can start to realize the necessity of AI.

In today’s world we are relying more and more on data, insights and evidence to augment the decision making process, from simply using your phone to lookup the weather forecast for the weekend to leveraging complex dashboards and data visualisations to determine whether we should adjust farming strategies to accommodate for adverse weather conditions and climate change. Things start to make a little more sense and so we see the ever growing requirement to obtain and process more and more of this data.

It’s no longer viable to only use one dimensional data. Analysts require a deeper angle of insight requiring multiple dimensions and often multiple data sources in order to paint a holistic picture. We have already seen how trends like IoT have unleashed new capabilities by unlocking data from the physical environment, as well as Edge computing, which helps to process massive data sets locally before sending the nuggets of value back to the cloud.

What would take analysts weeks and months to achieve with more traditional methods, is taking only minutes and seconds in this new augmented age with the help of AI and machine learning.

The pursuit of this new commodity “data”, has lead us to the “big data” problem… having too much information to process, or more accurately, too much information to process in time.

I believe there are two sides to this coin. The first revolves around our ability to use AI and machine learning effectively to automate a lot of our existing processing. This is where AI has already proven extremely useful, helping to mine and crunch large data pools of information. Figuring out what information is useful, analyzing past trends and making evidence based predictions for the future.

The manual effort required to analyze, process, plan and act on so much information is simply inefficient, as is the reliance on restrictive rule based systems. What would take analysts weeks and months to achieve with more traditional methods, is taking only minutes and seconds in this new augmented age with the help of AI and machine learning.

We are shifting away from this traditional approach of trying to actively drive our wills into our tools by explicitly instructing them what to do and how to do it. And moving toward a more implicit approach, by simply providing a set of goals, and constraints, and allowing the AI to explore the entire solution space to figure out, test and validate the best solution.

This brings me to the other side of the coin. Now that we are able to leverage AI to automate the management of data, cutting down time and cost exponentially, it now enables us to do so much more. The sheer volume of data that can be processed and the scale of problems we can potentially solve are much larger than previously possible.

Unlocking massive new opportunities – new ways to understand, new ways to act, engage or respond to feedback in near real-time. For example, our ability to sense the world around us, sense how our products are being used by people, sense whether or not someone enjoys shopping in our store, sense if the rules and regulations we impose are actually benefiting people or harming them in the long term.

Companies spend a tremendous amount of money on marketing techniques trying to convince consumers to want to buy their products, compared with a future where better decision making enables companies to manufacture meaningful products that people actually want and need. This feedback is invaluable.

With the advancements in technology unlocking so many exciting opportunities for the future, we can start to reinvent the way we work and live right now, and this feedback will impact future decisions in a significant way. Allowing machines to do what they are really good at doing, positions them well as tools for processing repetitive and resource intensive tasks.

This is not to say that machines and AI’s will replace us as humans but rather free us up to pursue things that we are inherently good at doing, things like leveraging common sense, intuition, creativity and emotions – this combinationation and duality of being able to augment one another and work together will help us to solve much bigger problems – solutions that, without AI and augmentation, would otherwise be impossible.

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