The best AI applications come from organizations that are led as social systems

People often assume – incorrectly – that the book Brian Evergreen is writing on the rise of artificial intelligence (AI) will focus on AI implementation.

The book title, though, offers a clue: Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence. Making the most of AI’s potential and that of other emerging technologies, Evergreen argues, relies on a social systemic approach – one that successfully incorporates social sciences with management and effectively leverages the differing perspectives among technology, industry, and business leaders.

“It’s through a more human future that we can harness the economic potential of AI, as opposed to using AI and happening to steer it at a better future,” he says.

An internationally competitive chess player, music major, entrepreneur who ran a company that taught children chess, and tech visionary, Evergreen takes a long lens, interdisciplinary view on the optimal ways to maximize the benefits of technology. His experience includes global head of Autonomous AI Co-Innovation at Microsoft Research, as well as technology leadership roles at Accenture and Amazon Web Services.

Evergreen recently founded his own consulting company, The Profitable Good Company, to help organizations harness the potential of technology through a new, human-centered approach to management thinking on the foundation of the social sciences.

“Most people don’t talk about that when they talk about AI. They talk about how fast it can move and how complex it is.”

Studies point to a low success rate for AI projects, says Evergreen. Why? AI implementation faces challenges because most organizations still manage and lead with approaches developed within the mechanistic world view of the industrial revolution, he says.

Even though Frederick Taylor penned Principles of Scientific Management in 1911, the main elements of his theories, a core component of the Second Industrial Revolution in the United States, still thrive.

“Today, most of what we do as organizational leaders, most of the disciplines we learn in schools, even the idea of being data-driven, have roots all the way back to that historic moment,” says Evergreen.

The Second Industrial Revolution broke tasks into their most basic elements and automated them, paving the way for the dominance of the mechanistic viewpoint that places more value on machines and processes than on human reasoning and work culture. “We need to move from this mechanistic world view to a social systemic world view,” says Evergreen.

That switch requires a different approach:  Instead of allowing technology to drive change, organizations need to set a vision, work backwards from the vision to see what it would take to reach it, facilitate conversations all along the way to explore paths to the vision, and apply inductive and deductive reasoning at all levels to move forward.

Technologies such as AI, then, become more of a resource than a driver of strategy, says Evergreen. Not realizing the desired outcomes, some organizations are beginning to course correct when it comes to a technology-first philosophy. Examples include organizations that tried fully automated call centers and are pivoting back to human responders with the aim of nurturing personal connections with customers. AI, though, can still play a role by acting as a tool for customer service agents to find answers more quickly for customers. 

While many people are naturally wary of what AI means to their own personal economic security (aka job), AI will create more jobs than it makes redundant, Evergreen says.

“What I’ve seen time and time again, when there is job loss without warning and without transitioning those people to new opportunities within the company, that is a function of a failure to plan, not a function of necessity.”

New technologies take a long time to implement – enough time to develop a roadmap that shows what new jobs will be created, what jobs will be augmented, and what jobs will be eliminated and to begin training and transitioning to avoid the loss of institutional knowledge. “I call that job pragmatism.”

AI and emerging technologies will impact work and jobs in all industries. Organizations with an eye on social systems that emphasize human-centered leadership and collaboration will stand better positioned for success, he says. “Every aspect of the way we work as humans today is going to be touched on and evolve in some way.”

“It’s through a more human future that we can harness the economic potential of AI, as opposed to using AI and happening to steer it at a better future.

Founder and CEO, The Profitable Good Company; Former Global Head of Autonomous AI Co-Innovation at Microsoft Research