Wall Street Journal CIO Journal: Human + Machine: The Impact of AI on Business Transformation

Artificial intelligence feels different from previous technologies as it forces us to explore the very boundaries between machines and humans. Will AI lead to whole new jobs and industries and a higher standard of living? Or are we facing a dystopian future, as smart machines increasingly encroach on activities and cognitive capabilities that not long ago were viewed as the exclusive domain of humans?

A new book, “Human + Machine: Reimagining Work in the Age of AI” by Accenture executives Paul Daugherty and Jim Wilson, looks at artificial intelligence’s potential to transform the workplace, citing examples from the leading edge and drawing parallels between today’s human-machine collaborations and how businesses in the past adopted–and were in turn transformed by–earlier technologies. There’s some good news: The “Age of AI” will not come at the expense of human workers as some fear.

Like electricity, IT and the internet, AI is becoming one of, if not the most important technology of the next few decades. But we’re still learning how to best deploy it throughout the economy and society. A recent McKinsey study found few firms deploying artificial intelligence in production at scale. History shows that even after crossing over into mainstream markets, it takes considerable time for transformative technologies like AI to be widely embraced across companies and industries.

“The simple truth is that machines are not taking over the world, nor are they obviating the need for humans in the workplace,” Mssrs. Daugherty and Wilson write. “In this current era of business process transformation, AI systems are not wholesale replacing us; rather, they are amplifying our skills and collaborating with us to achieve productivity gains that have previously not been possible.”

Electricity and standardized processes ushered the first wave of business transformations about 100 years ago. Manufacturing was decomposed into a set of processes and reassembled into the assembly line, leading to the age of mass production. Artificial intelligence, or what the authors call the “third wave,” promises to deliver bigger changes, particularly in business processes.

“Now, the third wave involves adaptive processes,” they write. “This third era, which builds on the previous two, will be more dramatic than the earlier revolutions enabled by assembly lines and digital computers, and will usher in entirely new, innovative ways of doing business… This adaptive capability is being driven by real-time data rather than by an a priori sequence of steps. The paradox is that although these processes are not standardized or routine, they can repeatedly deliver better outcomes.”

As part of their research, the authors surveyed more that 1,500 practitioners, and found that the leading companies in various industries, around 10% of their surveyed sample, already are “reimagining their processes to be more flexible, faster, and adaptable to the behaviors, preferences, and needs of their workers at a given moment… in doing so, they are changing the very nature of work, requiring us to manage our operations and employees in dramatically different ways.”

Key is what the authors call the missing middle, a space where humans and machines collaborate to attain major increases in business performance. In this hybrid space of human-machine activities, humans work closely with their smart machines, each doing what they do best. But, the authors call it missing because only a fraction of companies are seriously pursuing this approach.

“Humans, for example, are needed to develop, train, and manage various AI applications. In doing so, they are enabling those systems to function as true collaborative partners. For their part, machines in the missing middle are helping people to punch above their weight, providing them with superhuman capabilities, such as the ability to process and analyze copious amounts of data from myriad sources in real time. Machines are augmenting human capabilities.”

The book studied what leading firms are doing to benefit from human-machine collaborations and found that they’ve succeeded by adopting five key principles: mindset, experimentation, leadership, data, and skills.

Mindset. Work must be reimagined, with people helping to improve the AI-based applications and tools while the smart machines help augment human capabilities. This is quite different from using technology to automate specific tasks in a job or workflow. The focus instead is on reinventing traditional business processes, making them much more flexible and responsive to real-time conditions.

Experimentation. Looking for ways where AI can improve an existing process and the overall human-machine collaboration. Standard business processes, even best-in-class practices are no longer enough. Rather, companies must continually conduct trial-and-error experiments to learn what works best for their unique set of conditions.

Leadership. AI involves a complex set of new, powerful technologies which must be carefully applied. It’s important to always keep in mind that ultimately it’s the humans working with the AI technologies that are in charge and accountable for the overall decisions and actions. One must always consider the ethical, moral, and legal implications of relying on these advanced technologies. AI systems must be able to explain their behavior and recommendations to the satisfaction of their human partners. This will require people with the proper skills and training.

Data. AI requires extensive volumes and variety of data. Accumulating and preparing the needed information is one of the biggest challenges faced by companies using AI. The needed data may be siloed within different departments in a firm, or only available from a potential data supply chain partner. One must also watch out that the data does not introduce serious, unintended biases.

Skills. To succeed in this third wave of human-machine collaboration, companies need to hire and develop a new set of skills, including “knowing how best to ask an AI agent questions, across levels of abstraction, to get the insights you need”; “teaching AI agents new skills while also undergoing on-the-job training to work well within AI-enhanced processes”; “choosing a course of action amid machine uncertainty”; and “thinking of novel ways to overhaul work, processes, and business models to obtain exponential increases in improvement.”

“For years, the dream of many researchers was to create an artificial intelligence that could rival that of people,” wrote the authors in the books’s concluding chapter. “However, we’re seeing that AI is instead becoming a tool to extend our own human capabilities. In turn, we’re guiding AI systems to evolve into better tools that further extend our capabilities.”

Source: Wall Street Journal