Where even a couple of hundred years ago we worked to survive and put food on the table, today we can pinpoint any number of instances where our sophisticated social structure sees people working for personal fulfilment and other forms of enrichment.
The economist JK Galbraith foretold some of this new wealth in his book The Affluent Society back in 1958. Although Galbraith was lamenting the rise of private sector wealth in the face of income disparity among the lower orders, the point is well made i.e. today we know that some work (in order) to live, whereas others (perhaps more happily) live to work.
As renowned futurist Peter Schwartz has said, today we see people work ‘in pursuit of happiness’. This is because happiness (rather than pure survival) is the main goal for much of the affluent society.
The difference between today and the economic views of the post WWII era is that it is not just the affluent who stand to prosper. Technology democratises opportunity and creates a more equal platform for collaboration, information-sharing and entrepreneurship.
So how is the future of work about to change?
The End Of Peripheral Work
Our own ‘Productivity Drain’ research highlights that we on average spend two out of five business days each week on routine, non-productive work. We do this using manual tools that are ill-suited to the tasks we need to complete, such as email, spreadsheets and some databases.
In many job roles, workers find themselves preoccupied with executing as much routine, peripheral, supporting and administrative work as they do ‘real’ work. When we remind ourselves that real work is typically the tactical implementation and execution of all that background periphery, it is not hard to assess at which end of the spectrum business profits are made.
The key to the end of peripheral busy routine work is automation intelligence and the move from human-based to software-based delivery operations.
This is not the robots taking over; this is human beings using machine automation, software-based event-driven controls and Artificial Intelligence (AI) to drive solutions that control business processes in every possible conceivable business scenario and outcome. For the as-yet-unimagined business scenarios, we will look to higher-level neural network functions and machine learning so that our software can understand new eventualities.
Software-as-a-Service (SaaS) solutions have already sprung up that allow the enterprise to replace basic business tasks with no capital investment or software development. The onward possibilities from this point are potentially limitless.
Consumer (Software) Runs The WorldAmerican entrepreneur and software engineer Marc Andreessen is famed for saying that ‘software runs the world’. What he should have said is: a lot of bad enterprise software often runs the world, but consumer-level usage patterns and individual preferences for which software applications offer the right level of intuitive and compelling usage will ultimately rule the world. Yet that wouldn’t have made quite as punchy a comment.
What our update to Andreessen’s saying is meant to convey is that enterprise software has been on a convoluted route towards consumer-level ease-of-use in terms of its design since before the turn of the millennium. Digital natives and so-called citizen developers have cultivated what has become known and recognised as the ‘maker movement’; people want things the way they want them today, both at home and at work.
Onward from this truth, the rise of personal choice and the impact of this culture means that people refuse to accept off-the-shelf solutions and prefer to make, modify and control the things they interact with every day. Enterprises are realising this and are now architecting and engineering the software from the back end kernel to the front end user interface to reflect this trend.
Micro-compartmentalisation In A Task and Service Economy
Okay let’s fess up; micro-compartmentalisation is not quite a term… not yet. But it does describe our move to a task-based, service-based, micro-centric approach to work in the time of Uber, Taskrabbit, Airbnb and so on. These trends have given rise to the so-called ‘sharing economy’, which in itself has driven the segmentation of tasks, jobs, services, physical goods and other entities into their smaller constituent parts.
Once broken apart, these parts can be provided more efficiently, at better price points and with greater flexibility.
As workers now come together in the new task-based economy, we will see more ‘Agile’ (CAPS A deliberate) work styles brought to bear. Millennials are attracted to this low-friction model of providing services to get work done.
The implication of this is that companies will have to become more Agile in the way they manage, deliver and optimise their business tasks. Just think of how Uber has automated the interactions between drivers and riders. Enterprises will need to build the kinds of systems that enable rapid exchange of talent and services, instead of relying on ad-hoc methods like email.
Bots Are Now A Good Thing
Remember when bots (essentially chunks of computer software engineered to perform specific tasks) were a bad and malicious thing? Often the core code behind botnets and other forms of malware, we have brought the term forward to now describe software ‘agents’ that can be programmed to carry out automated intelligence for positive means and ends.
Today we talk about bots, Chatbots, digital assistant bots and machine-learning bots as essential customer support tools. As the bot landscape expands and bots improve to provide contextual recommendations, we’ll see bots used to positively alter employee behaviour not just improve customer communications.
Chatbots will serve as digital virtual assistants to help workers reach their highest productivity. Based on ever-increasing data input levels, bots will evaluate how workers’ time is spent, make recommendations to improve productivity and quality, plus also suggest best practices through bot-driven benchmarking. Using algorithms, bots will guide positive changes to our behaviour because they will be able to bring ‘individual contextualisation’ forward for each use case.
Learning To Love Machines
The secret to doing more with less is machines, not people. This means that the greatest gains inproductivity will be achieved when the nature and structure of work changes. 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 per cent gain in the U.S. GDP, or approximately the combined annual profits of America’s 50 largest public companies.
Machine automation (including ‘simple’ automation such as moving from email to more automated messaging intelligence) and machine learning will achieve greater productivity. Machines can be taught to understand things like tone or language based on text fragments.
Today, we can take a fragment of text and a machine will tell us which language it is and whether the text is conveying happiness, sadness or anger. In customer service scenarios, this can be an amazing leap forward in terms of call routing and client management. Automation enabled by machine intelligence will personalise and speed all work tasks, boosting customer and employee satisfaction… but most importantly boosting productivity.
My takeaway thoughts…
Our new, task-centric, microcompartmentalised economy makes an automation-enriched pay-as-you-go consumption model for technology more attractive than owning an asset. This mentality explains why a majority of enterprises have become cloud-first. It means that to be successful, IT professionals will need to become experts in brokering services – essentially adopting frameworks such as SIAM (Service Integration and Management).
The future is bright, the future is automated and the future is more intelligent… but more than this, the future has more happiness in it, whatever colour your collar is.