During more than 15 years working in telecommunications, MIT Sloan senior lecturer observed a recurring issue: employees approached the same problem from different angles. People in research and development, engineering, and human resources all wanted the same outcomes but they weren’t able to connect, and weren’t taking advantage of lessons learned in other domains.
“They were missing each other,” McDonagh-Smith said. “Things were too often being lost in translation.”
McDonagh-Smith thought of algorithms — a series of step-by-step instructions to get a task completed, normally associated with computer science — as a way to address this problem and unite people within an organization and to unite humans with technology.
“If we truly want to fix problems, we should look for a partnership of humans and machines working together,” he said.
This developed into a concept he termed algorithmic business thinking, a series of interconnected insights, frameworks, and models to help people break complex problems down into their smaller constituent parts, be able to work on them in parallel, and then recombine them so they are opportunities for sustainable growth.
It is useful for reframing problems and encouraging employee exploration, McDonagh-Smith said. Companies are using algorithmic business thinking to explore approaches to complex business problems, such as Walmart optimizing human and machine investments to improve returns, and Boston Consulting Group identifying ways to maintain the accelerated digital transformation the company’s seen over the last 18 months because of the COVID-19 pandemic.
Algorithmic business thinking is a set of ideas — “a toolkit, mindset, and a digital language,” said McDonagh-Smith, who teaches the concept in an MIT Sloan Executive Education course.
The concept isn’t a cure-all, he noted. It’s meant to complement existing methodologies and approaches, not compete with them.
Four cornerstones from computer science
Algorithmic business thinking is based on four cornerstones borrowed from computational thinking:
Decomposition, or the idea of breaking complex problems into smaller parts. For example, a problem could be broken into four smaller problems, and each of those into four smaller problems, until you arrive at a point where you can start solving the smaller problems. This creates momentum and confidence, McDonagh-Smith said.
Pattern recognition, or recognizing patterns of success and failure and being able to apply them in adjacent or different domains. For example, if work has been conducted successfully in one area, that strategy could be transplanted into other areas to drive efficiency.
Abstraction. While many think abstraction has to do with being vague, abstraction in algorithmic business thinking does the opposite — it removes the noise from the signal, McDonagh-Smith said. Amid so much data, being able to abstract and remove things that aren’t necessary for a certain task is especially valuable and allows people to focus on what’s important.
Algorithmic partnership of humans and machines. The first three cornerstones feed into the evolving relationship between humans and machines. “Algorithmic business thinking algorithms are humans and machines working side-by-side, shoulder to shoulder, on problems.”
Human capabilities that make technology work
Research suggests that human characteristics and capabilities make technology effective and useful in organizations. Algorithmic business thinking highlights those human capabilities, and how they are valuable to an organization’s push for digital transformation.
This is illustrated with McDonagh-Smith’s take on the familiar double helix DNA molecule, in which two strands are wound together like a twisted ladder. McDonagh-Smith’s double helix model features the digital world and physical world bound together by human traits, such as critical thinking, collaboration, and compassion. He highlighted three other key traits:
Creativity, which helps people take full advantage of artificial intelligence technologies. “Technology allows us to do great things, but we’ve got to figure out what that means for our business model innovation, what it means for actual work in our organizations, and creativity is key,” McDonagh-Smith said.
Curiosity, which helps companies find new directions and possibilities, and disturb the status quo. This often means being comfortable engaging in new possibilities and new directions. “The more curious you are or the more often you’re able to apply your curiosity, you create a broader landscape to operate in,” McDonagh-Smith said. “It’s probably one of the best tools that we have to counter bias, as well.”
Consilience, which means unification. “I think we can unify physical and digital, human and machine capabilities,” he said. “I think we can unify the past with the future and the present.”
A common digital language to help drive innovation
The disconnect between employees that McDonagh-Smith has observed can be addressed in part with a common digital language, he said. This is especially important during a time of rapid development in technology and human/machine partnership. Humans need to be able to comfortably communicate with each other about machines, and also communicate with the machines themselves. This idea was partially inspired by MIT linguist and professor emeritus Noam Chomsky, who has said that the primary function of language isn’t to communicate, but rather to link interfaces.
A common digital language helps collaboration and communication between technologists and people focused on business, Martha Anderson, the senior director of digital transformation at Walmart, told McDonagh-Smith in a podcast he created for the executive education course.
“This may be more of an opportunity for companies that historically haven’t been thought of as digital natives or technology companies,” she said.
To implement digital language as part of algorithmic business thinking, leaders should look to create shared motivation to engage with technology through incentives, compensation structures, and organizational design, McDonagh-Smith said.
“You’re creating this swimming pool of digital language that people get immersed within,” he said. “But if you don’t create a motivation and you don’t create the environment, it’s never going to happen.”