November, 28, 2017
Machine learning will be a defining technology of 2018, doing more to change how we live and work than any technology since the internet.
To understand why, we first need to get past sensational headlines about robots ‘stealing our jobs’. Innovation and the use of tools to make life easier have been a marker of progress throughout history, through agricultural and industrial revolutions. We are now in a data revolution and as we progress, so some of the roles people do will change, but progress has consistently brought the creation of new jobs, new business models and whole new industries. Far from making us obsolete, machine learning will augment humanity and make us more effective.
Machine learning is already all around us; written into the software on our phones, in our cars and homes and in the business software we use as work, helping us access information and make better, more informed decisions, more quickly.
According to Gartner, AI technologies will be present in “almost every new software product” by 2020, making this an exciting and potentially decisive moment in time for software providers and a crucial crossroads for the businesses who buy from them.
Media heads may be turned by robots and driverless cars and these will certainly be key areas of development in the years to come, but machine learning is changing the world around us in significant ways right now. Its ability to dramatically reduce the time and improve the effectiveness of our decision-making may sound less sensational than driverless cars, but it is what will make machine learning an era defining technology.
Organisations that harness the power of machine learning will quickly leap ahead, due to the speed and efficiency of the improved decision-making it powers. No business can afford to sit back and wait. If they do, they will be left behind.
The adoption of machine learning is growing hand-in-hand with the growth of cloud computing, for good reason. The seamless integration of cloud applications, platforms and infrastructure are crucial to the growth and effectiveness of machine learning; they open up machine learning to ever greater pools of data, breaking down silos and drawing in data from across organisations and their networks.
Trying to unleash machine learning in pockets within an organisation is like trying to grow plants in the dark. The algorithms that drive machine learning need data, as much, and from as many sources, as possible. The more it feeds on that data the smarter it becomes and the greater its decision-making potential.
The growing maturity and adoption of cloud technologies add to the reasons why 2018 represents such a sweet spot for machine learning. Cloud is an integral part of almost every business’s IT strategy, driving their digital transformation and ability to exploit the value of their data.
If big data promised us there were riches to be found in digital transformation and cloud provided the fundamental building blocks for digital transformation then machine learning is the first truly industrialised tool for unlocking those riches at scale.
Strategy is crucial in all of this. The key to getting the most from machine learning is to look for applications that deliver long term strategic value, which fundamentally transform functions or critical processes within the business, rather than delivering a short term ‘wow factor’.
Reducing the time taken to create accurate, reliable forecasts can have a significant impact not only on a business’s ability to plan, budget and resource effectively, but all those things combined will have a considerable financial upside for any company.
The beauty of machine learning is that its uses are almost limitless. Where there is value in rapidly analyzing and deriving understanding from data, it has a role to play. Where there is value in identifying trends or anomalies in vast data sets it can have a transformative effect, from clinical research to compliance and security.
Right now, machine learning is driving a revolution in customer service.
In almost any customer facing industry a vast number of enquiries fall into a limited number of categories and many are simple to predict and respond to using chatbots that are powered by machine learning that refines and hones their ability to accurately respond to customers. This reduces waiting times and frustration among customers and makes businesses more efficient. It also frees up customer service agents to handle the limited number of complaints that are more unique and require human intervention.
This last example is perhaps the most tangible instance where machine learning is augmenting the ways in which people work. It isn’t going to replace people but it can certainly make people better at whatever they do.
If there is a risk with machine learning, it is in ignoring it. 2018 should be the year businesses commit to explore and unlock the value of machine learning if they are not doing so already.