Human + Machine: Reimagining Work in the Age of AI, by Paul R. Daugherty and H. James Wilson
MANY companies are already using or intending to use artificial intelligence (AI). AI is defined by authors Paul R. Daugherty and H. James Wilson of Accenture as “systems that extend human capability by sensing, comprehending, acting, and learning”.
There is a widespread misconception that AI systems will gradually replace humans in one industry after another. However, the authors’ research indicates that although AI can be used to automate certain functions, the technology’s greater power is in complementing and augmenting human capabilities.
Rather than replace the need for humans, AI will perform the tedious grunt work, collecting data and doing the preliminary analysis, and so freeing human being to perform work only they can.
A brief overview of the evolution of businesses is necessary to position the authors’ insights.
The first wave of business transformation involved standardised processes. Henry Ford’s extraordinary contribution was to deconstruct the manufacture of automobiles so that they could be made on an assembly lines powered by men.
With each step made measurable, the assembly line could be optimised and standardised, with enormous efficiency gains.
The second wave was the automation of processes that began in the 1970s and reached its peak in the 1990s.
This ‘business process re-engineering’ was propelled by the ubiquity of computers, large databases and the automation of numerous back-office tasks. Many people were replaced by machines, and at the time Walmart was the gold standard.
The third wave rests on the previous two waves, but is a completely new way of doing business. This combination will adapt to the behaviours, preferences and needs of workers at a given moment. It will be powered by real-time data rather than by a pre-organised sequence of steps.
When this third wave is optimised, it will allow organisations who take full advantage of AI. They will be able to produce individualised products and services which are satisfying beyond the capabilities of the mass-production of the past – and deliver more profit.
The difference can be compared to travelling to a destination you are unfamiliar with. In the past, you used a map-book, and then you used the early version of the GPS, which was very similar to a map under glass.
The third wave is like ‘Waze’. It is a combination of AI algorithms and real-time data to create a living, dynamic, optimised map to get you to your destination on the quickest route possible at that exact time.
Waze doesn’t replace the driver; rather, it amplifies our navigation skills and collaborates with us to achieve productivity gains in navigation that have previously not been possible.
Humans and machines are partners, not adversaries
“Humans and machines aren’t adversaries, fighting for each other’s jobs. Instead, they are symbiotic partners, each pushing the other to higher levels of performance,” the authors demonstrate.
Infused with AI, factories, for example, are acquiring more humanity, with jobs changing in nature and increasing in number. But AI is also creating brand new roles and new opportunities for people up and down the industrial and even the back-office value chain.
In the past, engineers would have to program a robot and then re-program it when the job changed. The new robotic arms, developed in Japan, adapt on their own using an AI technique called ‘deep reinforcement learning’, which involves giving the robot a picture of the desired outcome.
The robot then uses trial and error to figure out how to do this. It takes eight hours to become 90% proficient, and can then ‘teach’ hundreds of other robots in the network! The arm is now an autodidact, freeing humans for other tasks.
Mechanical arms are perfect for highly repetitive and heavy tasks. However, there is always a subset of tasks that are just too complex to program into a robot.
This could be a factory-floor task such as positioning numerous small wires or handling awkward or dynamic objects. Or it could be a back-office task that requires judgement, after the AI-programmed computer performs an analysis from a data set too large for a person to manage.
A human is still needed in the loop.
In a pilot project using AI to analyse big data and workers’ routines, Hitachi was able to instruct employees to meet real-time fluctuating demand and on-site kaizen objectives. This produced an 8% productivity improvement in logistics tasks.
With all the benefits of the third wave, we need to have a much deeper understanding of how humans and machines must collaborate so that people are augmented and not replaced.
The ‘missing middle’
The authors call this the “missing middle”. There is a great deal of information and excitement about AI and “almost no one talks about… how to fill this crucial gap.”
The authors’ method requires five deliberate changes which can be summarised by the acronym MELDS.
The first is a change of ‘Mindset’. This requires reimagining the work, then discovering how people can improve AI, and how smart machines can give humans superpowers.
You can get to your destination as rapidly as possible even if you don’t know the way, or the number of cars on the route, or that an accident has occurred that will block the short cut.
The second change is “Experimentation”. Businesses need to be actively looking for parts of processes where AI can be introduced, and then learn and scale that process, with the enhancing power of people.
Only people could conceive of the entirely new, such as the agricultural model – the “vertical farm” – in which plants are grown in thirty-foot-high stacks of trays in urban warehouses.
The third change is the responsible use of AI by the “Leadership” of the business. It is too easy to look for the next quarter’s improvement, and overlook the long-term catastrophe of unemployed people.
The fourth change is to see “Data” in its rightful place. Data is the fuel of any intelligent system and, not unlike fossil fuel, requires the building of a data “supply chain” to ensure efficient delivery.
Data is not a static body, but an ever-growing one that requires the same attention to delivery that is required of any other resource.
The fifth change involves a new set of eight “Skills” that need developing, that the authors call ‘fusion skills’. Each skill draws on the fusion of human talent and machine ability within a business process, to create better outcomes than could be achieved working independently.
These skills are more than learning what the machine can do; rather it is the machine learning from the person and the person learning from the machine.
“The AI revolution is not coming; it is already here, and it is about reimagining your processes, across all functions of the company, to get the most benefit from this technology’s power to augment human capability.”
This book not only makes fascinating reading about the AI revolution, it offers a coherent and practical tool to get the most out of the human and machine interaction.
That said, we face the frightening reality of huge unemployment, growing by hundreds of thousands annually. The ‘Leadership’ element of the model has a Herculean challenge.
Readability: Light —+- Serious
Insights: High -+— Low
Practical: High -+— Low