Anita began by asking Phanish about the main message of “Re-Humanize.”  How can we make organizations, and especially corporate life, more human in an age of AI?   What does re-humanizing mean when companies are under such incredible pressure to deliver financial performance?  

Phanish wrote the book because he sees organizations as having two primary functions, which are sometimes in tension with each other:  (1) a social technology for accomplishing goals, and (2) the natural habitat for our species.  By the latter, Phanish means that we thrive in groups.   We desire membership in organizations.   The tension arises because sometimes amplifying the adhesions that tie us together can blunt performance.   But it is the second of these two functions that gives us meaning.

Now we can use AI to advance either of these functions.  “Re-Humanize” is about helping us be more human in groups.   There are good economic reasons for doing this as investing in the organization as a great place to work creates firm-specific human capital, which means creating a great culture internally.  This is what makes people want to work for your organization. 

Human centricity is about making the firm more attractive to workers.  Some of this could involve doing *some* drudgework that might otherwise be automated, by the way, because a bit of drudgework makes you better at discovering new ways of doing things.   We’ll need a kind of hybrid of AI and human engagement with paperwork to assure that innovation occurs.

What are some examples of rehumanization that you find inspiring?   How can our listeners rehumanize even as they struggle to understand how to use AI to make their organizations more efficient and more profitable?

It’s still so early in this process that it’s hard to find examples of full-scale Re-Humanization, but there are some examples of organizations that have excelled in various ways.  Devious Bank in Singapore upskilled call-center workers after implementing AI.  Salesforce has long had a cultural emphasis on collaboration enabled by technology.  Microsoft uses GenAI copilots within the organization.

As a leader/CEO, the question is:  How will you adopt AI to make your organization attractive as a place to belong.   To answer this question effectively, its important to tap into basic human values.  You use GenAI to support collaboration and communication as a design choice.

As an individual, it’s a question of making sure that you preserve the skills that continue to make you relevant to your organization’s competitive advantage.  You want to belong, and to grow in your skills in ways that truly matter.

This book reflects a lifetime of scholarship on what makes organizations more than the sum of the parts.  In other words, you have long studied how organizations generally can accomplish things that cannot be accomplished without some their structured constraints.   What’s at the essence of the challenge of organizational design to juice up team performance?    Are there any dimensions or rules of organization that you recommend that we focus on?

The way that Phanish has studied organizations is as any group with a goal.  There are a number of critical design choices for all organizations, including AI-enabled organizations:  Task division, task allocation, motivation systems, coordination systems, and exception handling.

All organizations must devise design principals on each of these dimensions.  Phanish’s research recently has focused on hybrid AI-human organizations as places where these choices arise.   “Ensembling,” for example, is orchestration of labor allocation through a committee-like structure rather than a team.  The goal is to activate algorithms in tandem with humans so as to replace authority with a collaborative mechanism for allocation.               

How does your earlier work on governance – i.e., on private-public partnerships, NGOs, and government – square with these ideas?

Some of the advisory work that Phanish has done with the UNHCR and the scholarly work that we’ve done on public-private partnerships raises these precise issues, although the contexts are often quite complex.   Non-profits and governments often don’t have the same AI technologies and mandate for organizational change as in the private sector.   PPPs and other collaborations are areas where organization design may be the most difficult and yet the most necessary.   We do know something about platforms and open-source models, for example, as designed organizations.  We know that there are ways in which technology can advance human-centricity in public organizations.  But we need to know much more.

What makes you optimistic about the future?

First, we know that often the best predictor of the future is the past.   And, in the past, we’ve done a lot – and all through organizations – to elevate prosperity and improve lives.   Organizing to take advantage of technological opportunities is something that we do well.

Second, optimism is rational.   This reminds me of an old story that our mutual friend, Gautam Ahuja, often tells about a guy who complains in his prayers to God that he has not yet won the lottery.  The punchline?  God asks the fellow to meet him halfway by buying a lottery ticket.   With AI, we’ve got to buy the lottery ticket.  The subtext in what Phanish is saying is that human-centric design principles lead us to ensure that AI amplifies our humanness.  It might be risky.  It might be hard.  It might even be unlikely that we succeed.  But we’ve got to try to create the possibility.