In an era characteristically defined as "uncertain", there is one fact that every industry can agree on: artificial intelligence (AI) will be one of the most disruptive technologies of the next decade. The global business value derived from AI in 2018 will be $1.2 trillion, an increase of 70% on last year’s figure, according to Gartner. It will continue to grow over the next four years, reaching $3.9 trillion in 2022.
Global GDP will be 14% higher in 2030 as a result of AI, estimates PWC. That’s the equivalent of an additional $15.7 trillion - more than the current output of China and India combined. This forecast growth is exponential. These predictions make AI the biggest commercial opportunity for businesses in today’s fast-changing economy.
Though some AI predictions succumb to the hype, occasionally verging on the hysterical, in reality we are witnessing the evolution of smarter humans accompanied by smarter machines. A better way to cut through and make sense of the noise is to reframe AI as augmented intelligence, rather than artificialintelligence. Instead of falling for the common narrative of a new technology that is here to take jobs from people, why not see AI's ultimate goal as assisting people in doing their jobs better and more effectively than before? That’s where the real innovation will happen.
Professionals will be more productive, more efficient and better revenue creators by using machine learning, data, analytics and automation tools. But human roles will need to play critical parts, from the start, in order to realize these benefits. It’s important for businesses to realize that automation and augmentation do not work for every process and every industry. Before institutions can determine where to use AI effectively, they must assess which aspects of a particular process are best suited to it. Businesses often fall into the trap of having technology look for a problem – a classic case of the tail wagging the dog. This is exacerbated because technologists, either within the business itself or as consultants, don’t always understand the real use cases and professional workflows they’re trying to change. This is a challenge that extends well beyond single industries.
A (seemingly) simple solution lies in more dialogue between technology experts and the business managers who understand their clients and the challenges they face. Better mutual understanding and collaboration means that AI can be used as a tool to solve real problems, and be used as a human-centric tool from the start. But communicating with multiple parties, from varying backgrounds, is easier said than done.
However, there are cases of industries starting to get augmented intelligence right. Healthcare is one of the most active areas leveraging AI, as the abundance of training data makes numerous use cases more feasible in comparison to other industries. One example is in the area of ophthalmology, where doctors have collaborated closely with technologists and healthcare agencies to detect early onset diabetic eye disease that if left undetected could progress into irreversible blindness for patients. This research, published in the Journal of the American Medical Association, highlights how collaborating on augmented intelligence can help doctors perform better and more efficiently while solving real-world issues using a human-centric approach.
Another industry that’s starting to hone in on specific business use cases, instead of taking a technology-first approach, is financial services. Though the last few years have mainly seen companies looking at AI in the forms of automating content operations, enhancing trading tools and improving customer service, they’re now demonstrating how AI can tackle much larger societal issues such as financial crime. Currently, only 1% of financial crime that happens through the banking system is stopped. AI has a real opportunity to bring together industry, government and regulators to consider a new approach. Various businesses – both start-ups and larger corporations alike – are making strides in fraud identification, sanctions screening, money-laundering, anti-bribery and corruption. Augmenting these processes enhances their transparency, accuracy and flexibility, without eliminating the people who ultimately make the final decisions.
Successful AI use cases that are really making an impact within and across different industries are those that approach the emerging technology as something that can help, rather than hinder, humans. It takes a combination of human expertise, domain knowledge and technical proficiency to truly understand this. Building technological solutions that undergo constant human interaction to validate, update and regulate them makes them far from artificial. So let’s move past the man vs. machine argument and acknowledge that together, we can be smarter humans with smarter machines.