“In the future, when Microsoft leaves a security-flaw in their code it won’t mean that somebody hacks your computer. It will mean that somebody takes control of your servant robot and it stands in your bedroom doorway sharpening a knife and watching you sleep.”
~ Daniel H. Wilson
New examples of the hybrid workforce are appearing all the time. Amazon’s warehouse with robotic shelves, the exoskeleton trialled at Daewoo and the financial services startups using algorithms to deliver advice show us what is already possible. We can see that the reality of a hybrid workforce is no longer confined to science fiction. What role will humans play in the future of a 21st Century organisation?
Here are five key management challenges that I believe we will encounter on that journey:
Challenge one: automation versus co-operation
“Whatever you may be thinking when you apply for a job today, you can be sure the employer is asking this: can this person add value every hour, every day – more than a worker in India, a robot or a computer? Can he or she help my company adapt by not only doing the job today but also reinventing the job for tomorrow?”
The current focus within most workplaces is on automation, set in motion by the work of Marvin Minsky, the MIT professor whose seminal Artificial Intelligence (AI) paper “Steps Towards Artificial Intelligence” defined the discipline in its infancy. It is still influencing the way AI is entering the workplace today. He theorised that AI relying solely on computers would increasingly become the norm within organisations. Using robots and algorithms to automate data-oriented tasks, factory jobs and replace call-handling agents has produced massive savings in labour costs over the last decade. Combined with the current predisposition towards Tayloristic management methods, this model holds that the role of managers can be easily codified and easily subsumed by robots or algorithms – a severe disservice to the true role of management within an organisation. But the costs to employees if this model proceeds to its logical conclusion gives us food for thought.
Models of human-machine symbiosis, where machines amplify the intelligence of humans through cooperation, offer a more hopeful view of the future. By taking tasks humans do not excel at and assigning them to machines, we can allow humans to focus on their areas of strength – a more sustainable and rewarding model to pursue. The combination of man and machine outperforms the brute force of relying solely on AI. The co-operative model introduces a separation of management into two distinct disciplines: one focussed on planning, estimating time and resources, crunching big data sets to achieve optimum results; and another role as coach, mentor and leader being championed by humans.
Challenge two: managing performance & amplifying human intelligence
“Anything that could give rise to smarter-than-human intelligence – in the form of artificial intelligence, brain-computer interfaces or neuroscience-based human intelligence enhancement – wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.”
The continuing quest for ever-higher levels of productivity has led us to a place where hyper-efficient management models are required for success. Early approaches are emerging at the extremes: on the one hand companies deploy AI and algorithms to make business decisions, based purely on data; and, on the other, self-managing teams supported by insights and collaborative technologies seek to innovate and outperform.
One area of immediate impact, for teams managed or led by algorithms or robots, is the introduction of an ability to be truly objective, free of mistakes and foibles. At first this might sound attractive from a performance management point of view – after all, who would not like to be judged on what they do, without politics, inter-personal conflicts or misaligned goals? But as social creatures, humans naturally reach out to help others when needed. When we problem-solve, we are not always efficient, but that can also lead to innovation: an algorithm or robot would simply see time spent experimenting with new ways to get things done as not completing our allotted tasks.
We see the consequences of this kind of automation and standardisation everywhere – from the periodic blocking of your bank card to check for fraudulent transactions, to the controversial continuous testing of children through standardised tests, with no way for the teachers who know them best to have any input into the results.
At the other end of the spectrum, we have Toyota, one of the pioneers of replacing factory workers with automation, seeking to re-inject humans, more specifically master craftsmen (often referred to as ‘Gods’) back into the production line. After all, a machine can only perform the tasks it has been taught to do (or predict events based on pre-existing algorithms). Even machines capable of machine-learning require a programme language and predictive models to compute from. Change has become too constant for automation to be the standard for the factory floor, let alone the knowledge worker.
Challenge 3: Leadership versus management
“What about passion, dedication, loyalty? Can a robot provide those? No! On the other hand, it is easier to retire a robot when its day is done.”
It is no surprise that ‘robotic’ managers (those focused purely on the numbers, goals and targets) fail to win the hearts and minds of their workforce. Extending this observation, robot managers may also fail to persuade their human workforce, lacking authority and enthusiasm for a joint mission (a key component of employee engagement).
An AI ‘leader’ would function much like a sociopath – unable to empathise, compromise or have anything other than a cold, calculating nature. With human strengths centred around leveraging emotional intelligence, adaptive thinking and creativity, a clear split develops between leadership from management – the latter being the perfect foil for robots and algorithms, and the former requiring high-value, gap-filling human skills.
The emerging field of big data provides some good emergent examples of how human/machine symbiosis creates a perfect management/leadership combination. At GE aviation, the data gathered from in-flight aircraft is mined in close to real-time (a feat not achievable by humans), and statistically significant data points are fed to human interpreters and leaders for decision-making.
Challenge 4: Social cues, processing tensions and can humans deal with robots being wrong?
“Thank you…motion sensor hand towel. You never work, so I just end up looking like I’m waving hello to a wall robot.”
~ Jimmy Fallon
A clear challenge of humans and machines working side-by-side, day-by-day is the inherent mismatch between the natural human needs to be face-to-face with colleagues, see body language, hear tone, and see focus in order to build a relationship, compared with the lack of these social cues in robots.
Building visual broadcast cues into the interface of a robot would help remove this friction in a hybrid workplace. Robots could express fatigue when their batteries are low, excitement when they have new data to share or curiosity when they need more data to proceed, much in the same way as video game avatars do. Where neither the human nor the machine is entirely autonomous, we need to replace the current tradition of human-centred design, or system-centred design, with a human-machine system design that allows a hybrid team to perform the overall task.
As we see more humanoid robots entering social and customer-orientated situations (such as the Japanese hotel reception staffed by humanoid robots), interface design and social cues through interaction design invent a whole new paradigm.
Pre-conceived notions about the infallibility of robots, coupled with their inability to be truly dynamic ‘in the moment’, could lead to errors in safety and quality, introducing a key human-robot interaction point that will persist.
Challenge 5: Creating a co-operative workforce
“What’s casual for a robot isn’t necessarily what’s casual for a human.”
~ Alan Tudyk
A truly co-operative workforce, where humans and robots work as a seamless team with the lowest possible friction, each playing to their strengths, provides society with a need for an almost total remake of economic models, societal conventions, business models and products, among many other things.
However, it also offers the most exciting vision, whereby analytical, statistical and programming skills are key to maintaining a role in the hybrid workplace. But the hardest people to recruit for and retain will, without a doubt, remain people capable of leading. Not just leading humans with compassion, empathy and interpersonal skills, but also the logic, analytical skills and ability to lead robots too.
I believe that the key challenge to a truly co-operative workforce lies with designers – not just interface designers as indicated earlier, but also designers of organisations, teams and temporary workforce structures. Roles to coach and mentor the workforce will also be needed to ensure change and innovation are underpinned by fast, iterative, agile processes embedded in every agent in the workforce’s day-to-day work. An early model for these workforce coaches can be found in the agile coaches deployed in large, fast moving IT change programmes such as the UK’s Government Digital Services (GDS).
Balancing this re-imagined workforce – and making conscious choices in designing organisations for humans working with machines – is a crucial area for managers and leaders to prove that a hybrid approach is desirable. Leaders brave enough to engage and understand a strengths-based model will need new models for change (making it constant, underpinning, small and iterative) for hybrid management and leadership.
The process of ensuring that leaders provide a major guiding force in building the future hybrid workplace needs to build on the learning from the technological revolution and organisation evolution of the last few decades:
- We need to move from knowledge-centric structures to learning-centric structures: with the amount of growth, innovation and change that will take place during the course of our careers, standing still in terms of capabilities will never again be an option.
- We need to focus on building career portfolios, not career paths: a single planned career path has always been a fallacy, but in a hybrid workforce, the best protection against obsolescence is a portfolio of roles that play to strengths.
- Human-centric models of management will need to develop along human-machine / system-centric models. Managers need to understand that they will no longer be the seat of knowledge, but will take on new roles in defining the ‘why’ and the ‘what’ (i.e. the strategic elements).
- We need to consider how we can fulfil our true human potential: if many of the hands-on roles and knowledge work becomes automated, how can we grow into roles that are of higher value?
Managers and leaders can only establish a competitive future for their organisations by tackling the partnership between humans and machines early and often, pushing boundaries not only inside their organisations, but also in innovation centres such as Silicon Valley, which also have the resources and budgets to set the agenda sooner rather than later.