Managing Digital Labor–Are You Ready?
by Betsy Burton, Jim Lundy
During Aragon Research’s research community meeting this week, Jim and I were discussing what it will take to manage your workforce in the future; a hybrid mixture of humans and digital labor. The discussion got us thinking about what it means to manage technology versus what it takes to manage people, and how this will change as organizations introduce AI-enabled technologies.
In this blog, we explore the differences and similarities between managing humans and technology to understand how management will change as we introduce technologies that can learn, recognize patterns, and change/respond.
Your Future Hybrid Workforce
Digital labor is a term that applies to the automation of tasks that are performed by computer applications. Our future workforce will be a hybrid combination of humans and AI-enabled technologies (i.e., bots, assistants, robotics, etc.), supported by traditional non-AI technology. This workforce may include employees, contractors, AI-enabled cloud services, AI-enabled “things”, and AI-enabled assistants.
Organizations have long supported well-defined business processes, governance, and resources for managing both people and technology, but it has always been independently. The complexity is that AI-enabled technologies introduces technology (bots, assistants, robots) that need to be “taught” and that will evolve as they collect more information. This evolution begins to blur the disciplines of human capital management and technology management.
Managing and Leading Are Not the Same
There is a social, cultural, and moral imperative to leading and developing humans that requires empathy, compassion, and mentorship. It is critical for our survival that we continue to develop, educate, and inspire other humans. Otherwise, all this technology is for naught. And furthermore, we risk exacerbating the income, education, and social justice equality gaps.
In addition, there are a collection of management/administration tasks that are similar when managing people and technology (see figure 1), including: scheduling, measuring, and regulatory management. In addition, there are management tasks related to humans and technology that are parallel but significantly different, including hiring/acquiring and evaluating.
If we could streamline these management/administration tasks between humans and technology, could it offer managers and executives more time to focus on leading, inspiring, mentoring, and developing our human resources?
AI-enabled Systems Require Some “Human-like” Management Tasks
Managing AI-systems, and particularly advanced learning systems, have some parallels to managing humans that non-AI technologies do not have in common, including teaching/learning, supervising, and collaborating.
- AI-enabled technologies (bots, assistants, robotics, etc.) are based on complex models and algorithms that enable them to use massive amounts of data, pattern recognition, and analytics to learn. This means they need to be initially trained and given more information over time, so that they increase their ability to recognize patterns. Like on-boarding and training humans, AI technologies need to learn how to do the job at a particular organization to be effective.
- We all know organizations continue to face enormous challenges with data quality—garbage in and garbage out. Since AI technologies are “learning” all the time based on information that is specifically input, and information it gathers during usage, AI technologies must be supervised to ensure that they are not learning to analyze and respond in ways that are counter to their mission and value. On one hand, AI systems could learn a few harmless dirty words as Watson did—or it could be much worse. They could learn detrimental or even illegal parameters that would harm humans, the environment, or assets.
- While we are clearly a long way from doing a team-building event like a workshop, sports, or team dinner with our AI systems, there is some investment needed to enable AI technologies to work with the humans they are supporting, and visa versa. An AI-based robotic system in surgery needs to be able to learn and take into account different surgeons’ techniques, preferences, and approaches to fully augment and enhance the work of a surgeon.
Managing AI-based technologies is not equal to or a replacement for managing humans. But we can learn from managing humans and apply this knowledge to managing AI systems, and vice versa. Humans, by nature, are more forgiving and adaptable as compared to the literal application of instructions that AI-based systems will apply.
Organizations must define specific plans, and budget resources and time, to managing AI systems in a very different way from non-AI technologies as a part of their future digital labor and workforce.
AI systems evolve and change over time based on the information input and gathered through use. This means they must be supervised, trained, and developed to meet business objectives, and more importantly, to keep them from causing harm or damage by using bad data and analysis.