Digital Labour – Are you ready?

By Chris Bedi, CIO, ServiceNow

Digital labour can be a game changer. But, so far, we haven’t seen enterprise adoption take off. This will start to change in 2017.

Advances in automated bots, machine learning, cognitive computing, and the availability of data of all kinds will finally start having a real, meaningful impact on the enterprise, enabling organisations and individuals to reach previously unseen levels of productivity and drive growth through new capabilities.

But we should not expect this transformation to happen overnight. The transition from a largely human workforce to one where digital labour plays a major role will be met with many sceptics. The truth is that a lot of IT remains sceptical of the digital labour movement. Over the years, IT has seen too many technologies that promise great things to under-deliver. I believe the technologies are finally there where IT organisations can lead the next wave of productivity across the enterprise. The transition will surely include some elimination of human labour, however, greater value will be achieved by freeing human labour from routine tasks which don’t really create business value. It will cause organisations to redefine jobs and examine future skill set requirements.

Progressive CIOs will move beyond discussions and start experimenting with digital labour solutions in the first half of 2017 with real implementations taking off in the later part of the year. Like other disruptive technologies—cloud, mobile and social—it will take time for enterprise business leaders and workers to get past the FUD (fear, uncertainty and doubt) and understand how digital labour can better their business. We’ll see progress on two fronts this year that will increase workers’ comfort level with digital labour and help to pave the way for enterprise-wide adoption in the future.

The End of Busy Work

Employees spend 2 out of 5 business days each week on routine work that is not core to their jobs. Using manual tools that are ill-suited to the tasks they need to complete—email, spreadsheets, personal visits— they waste almost as much time on busy work as they spend on doing the real work.

A McKinsey multi-year study found about 60 percent of occupations could have 30 percent or more of their constituent activities automated. And IDC believes that by 2020, 60 percent of the G2000 will double their productivity by digitally transforming many processes from human-based to software-based delivery.

Automation can certainly help where there are a lot of manual or semi-manual repetitive processes. CIOs need to assure IT and line of business staff that the robots won’t take over. But repetitive, rule-driven business tasks increasingly will become codified. Not every task is ideal for automation. Processes that are high volume, span multiple systems, collate data from various sources or involve data entry are good candidates for software robots.

It’s the CIO’s role to help business lines identify those capabilities that truly add value and spend less effort on those that don’t. Automating processes can allow workers to focus on business issues, not busy work, and enable companies to reduce costs and improve quality and scalability.

According to ServiceNow’s State of Work research organisations with 5,000 employees collectively across the United States could save $575 billion a year by automating unnecessary tasks and inefficiencies which would equal a 3.3 percent gain in the U.S. GDP, or approximately the combined annual profits of America’s 50 largest public companies.

Machine Learning Lets Us Reach Our Potential

Digital labour is not all about labour costs savings. Because bots access applications through the user interface, just as humans do, they can be a less problematic method for integrating disparate systems. Software robots can compare data gathered from different systems that humans once had to reconcile. But the bots can do it faster, better, and never have to take a break. This will open the door to a new level of intelligence.

As the bot landscape expands and bots improve through machine learning, they will move beyond basic tasks in 2017. Chatbots will provide individual contextual recommendations that will be used to positively alter employee behaviour. Chatbots will serve as digital virtual assistants to help workers reach their highest productivity. Based on ever-increasing data inputs, bots will evaluate how workers’ time is spent, make recommendations to improve productivity and quality, and suggest best practices through the use of algorithms and bot-driven benchmarking. Essentially, all of our data will be synthesised by a machine, and the machine will tell us what to do next—the data will drive our day. According to Gartner, by the end of 2017, at least one commercial organisation will report significant increases in profit margins because of algorithms used to positively alter employee behaviours.

Does this mean we’ll all be taking orders from a bot one day? Possibly. But today, humans are not yet ready to move to a purely robotic world. We are decades away from robots taking over, if ever. At the same time, we are quantifying information like never before—we create 2.5 quintillion bytes of data every day. It is impossible for humans to manage all of this data and analyse all of the relationships between people, information and things. The highest levels of intelligence will be achieved when machines understand activities, context and motivation and can make the appropriate decisions for humans so that we can focus on the issues that only humans can solve.

As technology leaders, CIOs have the opportunity to start the digital labour revolution in their own backyard. By enhancing their own service delivery models through automation, machine learning and artificial intelligence, CIOs and their IT teams can gain the experience needed to deliver, manage and optimise an enterprise-wide rollout of digital labour solutions in the future. Ultimately, the companies that strike the right balance between digital labour and human labour will come out on top.

 

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