Data analytics, artificial intelligence, machine learning - these are some of the favourite terms of many business executives and corporates who dub it ‘the future of business’. But truthfully, not many are able to keep up with the trends or translate any of these areas into actual business productivity.
As a result, there exists lots of ideas and projects, but without any fruitful results. That is why the area of augmented intelligence is crucial in the next frontier of artificial intelligence. In short, it is the process of AI and human intelligence working hand-in-hand towards a goal. Augmented intelligence is a subset of AI, which follows the process of automated learning through the influence of humans. It can be broken down in the following way:
The above process should quell any fears of AI ‘replacing’ humans. Humans are continuously involved in the process in guiding machines, and as a result, the optimum decision or conclusion can be reached.
A study by the Harvard Business Review found that companies in America waste about $3 trillion every year on inefficient processes. For organisations, the use of augmented intelligence is a no-brainer to simplify and improve process and business intelligence.
Augmented intelligence and decision making broken down more simply : Let’s take the example of a factory plant that manufactures high-end shoes. The shoe leather, heel, laces etc. are all part of the shoe. In analytics terms, this would be data. Much like you would need the best designer to guide the process of the high-end shoe to be designed, so you would need someone to translate complicated data and guide the process. The designers in this case would be data engineers, stewards and subject matter experts.
Any organisation that has AI at its core or part of its processes knows that it can plug data into the models to reach a conclusion. However, they do not account for context and real-life scenarios. That’s where the marriage of human and artificial intelligence comes to play. The output from AI needs to be prioritized and also interpreted within a governance framework.
Most data scientists that make themselves stand out from the rest have the ability to translate theoretical data models into a business for real life results. That’s why any organisation should prioritise to put data scientists in a decision making space. For example, the Head of Financial Investments of an investment firm needs to regularly have consultations with the Head of Data Science for that same company. Both areas and depths of expertise is where AI will thrive. That’s where success lies. You need extremely skilled experts and data scientists. However, you also need data engineers or people that can translate the data into actionable decisions. In business terms, this would mean linking and translating the data models into real business outputs such as ROI, Debt to equity ratio, Customer satisfaction. Through augmented intelligence, people are able to detect patterns, develop strategies and make insight-evidence based decision making.
Broken down even further, Augmented Intelligence provides the ‘who, what, where and when’ to a problem. Humans then work on the ‘why (i.e. why it matters’ and then the ‘how’- the decision that needs to be taken to solve a problem. In a company setting, augmented intelligence is your set of tools that allows you to do things. This includes, cleaning a data set, providing predictions, conducting risk analysis, acting as an aide to customer service and finally enhancing decision making.
For example, Amazon: Let’s say that you purchased a laptop. At the bottom, it will say “customers who bought this also…” and then suggest products that might interest you. And when you return to your homepage, these products related to a laptop will also appear. That’s based on a basic algorithmic model that helps customers and provides convenience and is an obvious business enhancer for sales. In decision making, AI acts in the same way, by providing suggestions and nudges to key-decision makers in the company to move in the direction that will increase business results.
In an ideal world, your business decision making should be based on 70% AI and 30% manual intervention from humans. To cultivate this expertly, consider the following guidelines:
Have proper governance and compliance in place to ensure that the roles played by AI and humans are ethical and within the company values.
Have data scientists / engineers at the executive decision making table
Have a solid change management plan in place to ensure that AI is introduced into the workplace without fear and concerns of ‘replacing humans’.
Ensure that domain experts are at the forefront of inputting procedures and data into technology
Have a fast-fail approach in which input is provided as to why things are not working or failing
Work on an iterative strategy where each project is a stepping tone for the next.