By Predrag Jakovljevic, Principal Analyst, Technology Evaluation Centres
Artificial Intelligence is a divisive technology. Its potential to improve the way we work and live is enormous, but people are also uneasy about the so-called “rise of the machines” and having everything they do tracked by software. Even those companies that are pioneering AI applications in the consumer space are at odds about its implications, most notably Facebook’s Mark Zuckerberg and Tesla’s Elon Musk who recently revealed their opposing views on the technology.
In reality we all use AI in some form already, but as with any new technology we have let our guard down in cases where it has been commoditised. Take personal assistants like Siri or Google’s image search. In the workplace, some people have started using HR chatbots for simple requests instead of taking up HR’s time when we need something.
These are all examples of AI in action. What distinguishes AI from classic “if-then” computing is that today’s powerful software allows companies to dynamically refine their decision-making based on experimentation and a wide range of data. In other words, AI is advanced number crunching done at a scale beyond human capability.
Now on to machine-learning, which refers to algorithms that allow a system to recognise patterns in large datasets and apply those to new data.
To illustrate how this works, consider an organisation’s shared services function, where robotic process automation (RPA) is already being used to speed up administrative processes and cut costs. With machine learning added to the mix, the shared services function will be elevated into a self-improving mechanism that constantly searches for ways to become more efficient and reduce the risk of errors.
The dynamic discounting capability in Oracle ERP Cloud is an early example of this concept in practice. The application monitors supplier invoices and flags those that offer discounts for early payment, allowing finance teams to better prioritise their disbursement. This approach has a place across the business, from shared service to supply chain management.
If all of this paints a positive picture of AI, that’s because the future is largely bright. Data becomes more valuable to a business when it is no longer just retrospective and actually helps it to better prepare for the future. Whether that materialises in the form of more efficient processes, lower costs or delivering smarter customer service, the possibilities of AI are virtually endless and we are only now scratching the surface.
People’s fear that they will be part of a real-life “big brother” experiment is understandable, but mostly reveals our discomfort with concepts we don’t fully understand. For instance, Disney recently revealed prototype facial recognition technology that tracks how people are enjoying its movies. The idea that a screen is watching us back may be unnerving for some, but when one considers alternate applications for an ability to accurately detect emotional markers in a person’s body language, such as health and wellbeing monitoring, its value becomes more apparent.
AI is still in its early days and discussion around its relative merits and pitfalls will only become more complex as the technology becomes more powerful. However, if the progress we’ve seen so far is any indication of what’s to come, we can expect significant gains and an increasingly measured public opinion of AI as its benefits come to light.