Over the past few years, we’ve had the pleasure of talking to and studying many so-called “early-adopters” of AI. Across our conversations and analysis, one thing is clear: Artificial Intelligence (AI) has been dramatically changing the rules across industries and processes. While critics warn these changes will mean the end of human work, that mindset sorely misses the mark. The true value of AI can only be unlocked when humans and machines work in complementary ways. Indeed, our research shows that human + machine collaborative partnerships are regularly and pervasively outperforming human-only and machine-only teams. How, though, should companies focus their AI activities, attention, and investments to capture this human + machine premium?
Visionary companies that are leading the way have embraced five core principles, which we refer to by the acronym MELDS:
In our survey of 1,075 respondents, firms that had embraced any of the five MELDS principles experienced a substantial improvement in their KPIs (key performance indicators), such as process speed. A company adopting none of the principles may eke out 2x improvements from automation. But even incorporating just one principle (mindset or experimentation, for example) can lead to 3.7x improvement. And truly visionary companies that fully embraced all five principles have, on average, achieved KPI improvements of 6.5x. This is the Human + Machine multiplier effect.
What do these principles look like in practice? Reimagined work can take many forms, but the one common ingredient is a collaboration between human workers and their machine colleagues. In some cases, this may not look so different from the software tools human workers are used to. Such is the case at GNS Healthcare, where AI software churns through quadrillions of hypotheses to flag potentially dangerous drug combinations. By relegating data exploration to machines, and by handing those machine-generated hypotheses to human researchers, GNS discovered in three months what had taken two years through just human observation. In short, by utilizing data as only AI can, GNS has reimagined what’s possible for researchers.
In other instances, reimagined work can look quite futuristic, as is the case in BMW’s Spartanburg, South Carolina, factory, where humans work alongside agile and intelligent robotic arms on the assembly line. AI enables these robots to learn on the job, making them highly adaptable for changing work stations and tasks. They are made of lightweight material, move at low speeds, and are fitted with sensors, making them safe collaborators on the factory floor. A study by MIT’s Julie Shah showed that, compared with the traditional approach of segregating humans and machines, teaming them up slashes the idle time by 85%. Moreover, Shah’s research found that employees actually preferred working with robotic assistants, at times even deferring to them on what to do next.
These are only two examples of what work can look like when it's reimagined in this new era of human + machine collaboration. There's much more that's being done, and vastly more that's yet to be. What's clear now is that AI is here to stay, and it's changing the way that companies need to be thinking about how they do what they do. To learn more about how companies across industries are reimagining processes with AI, and reaping exponential benefits, check out Accenture's Process Reimagined survey and report. Find further discussion on the impact of AI on jobs, and see our MELDS framework applied to real world cases in our book Human + Machine: Reimagining Work in the Age of AI.