Chapter One

1. Unearthing an Adaptive Workplace

I became interested in the topic of new workplace organizational designs enabled by technology in 2004 when I was working as a business and strategy consultant at Perot Systems. Perot Systems was a world-class IT outsourcing company. Typically, when a business outsources their IT to another company, they hire away “key” managers and replace everyone else with employees from the outsourcing company to achieve economies of scale. Perot Systems did this too. For reasons it took me awhile to identify, I had the nagging feeling that there was something amiss with how we were servicing our clients. We talked to them about their businesses as though they were machines formed of processes, technologies, titles, and org charts.

What we did not discuss was the value built up over time in the relationships among the employees, like axons among neurons. I have no data to back this up, but I suspect that if you were to find a way to measure the amount of energy we all put into our working relationships, we might discover that this is where we are spending the majority of our energy every day. To torture the brain metaphor a bit more, there are roughly eighty-six times the number of axons in the brain than there are neurons.

Yet we never studied, leveraged, or even acknowledged the relationships and strong kinship that staff members had already established with one another. We completely overlooked the most essential element of any business—the relationship element and how the work was getting done. Each employee has value that is unlocked by their collaboration with others. The potential for each individual is embodied in their relationships. Productive relationships yield maximum potential. Unproductive relationships mute potential. Relationships are not peripheral to business results; they are essential to it. The hive is defined more by the way bees work with one another than by the individual bee.

Instead of thinking about any of this, Perot Systems did what most companies do—we treated employees like fungible cogs in a machine. The reasoning goes that if you have a manager over a department that produces widgets, and that person manages people who know how to produce widgets, then the most important asset is the manager who knows how to run a department of widget makers. After all, you can always find another widget maker. Replacing a widget maker should be seamless, right? Of course, it is anything but.

I realized that the org chart is almost incidental to how work actually gets done. Titles, promotions, and reporting lines all are components of an idealized machine for producing market value. Relationships are not in an org chart as a first-class entity the way a title is. Instead, they are implicit, assumed to be among members of a team. Yet these relationships govern how work gets done.

It was no surprise, then, that making decisions based exclusively on an org chart yielded suboptimal results. The more I saw our process result in bad staffing decisions, the more I realized something needed to change. I also suspected I knew exactly what we were missing. I thought back to a job I held in 1998, at a startup called the Technical Resource Connection (TRC), where I worked in a variety of roles, including information architect and technology consultant to C-suites. The organizational structure of TRC was flat, consisting of a general manager and a bunch of team members, each of whom held their own badge of honor within the company, which they’d genuinely earned. It was an extraordinarily egalitarian organization. Everyone was happy to pitch in however they could because we loved our tribe, and it felt good to contribute in both big and small ways to a cause we believed in, a cause bigger than ourselves.

Perot Systems ultimately bought TRC. As a consultant for Perot, I had the opportunity to study dozens of companies from a dozen different industries. The more I studied, the more I saw a recurring theme: Companies start out small, agile, and tribal. Collaboration is fast and effective. As the number of employees grows, the number of relationships increases exponentially and the collaboration model becomes unwieldy, and so they begin to use the only model they know how to scale—the org chart. With the org chart comes a transition from a tribal human organism to a machine formed from fungible components. The former can’t scale while the latter can. At least it used to be that way before collaboration bots, but I’m getting ahead of myself.

The Power of Social Networks

In 2004, I had the opportunity to work with a fantastic CIO at a large, prestigious hospital system in Northern California. This hospital system intended to outsource its IT to Perot Systems. The CIO and I agreed that we were going to handle the staffing decisions differently because a seminal book—The Hidden Power of Social Networks by Robert L. Cross and Andrew Parker—had just been published. The premise of the book is that work gets done through complex social networks that are fluid and have only a loose correlation to the org chart. It describes a methodology for mapping out how work actually gets done through what the authors called “social network analyses,” commonly referred to today as organizational network analyses or ONAs. The book argues that a company benefits more by optimizing the fluidity of a network than by following the org chart.

To better understand how work got done, the CIO and I agreed we would try creating a social network analysis as spelled out in the book, ignoring titles and org charts. The results were shocking. We found that most of the work was getting done by people who had quietly developed a massive amount of influence, or soft power. The startup was still alive and kicking in plain sight if you knew where to look within this well-established hospital. It’s just that, now, it was hidden inside the machine.

Below is an actual example of an organizational network analysis done using the first version of the Adaptive Engagement Platform.

This diagram has no correlation to the org chart, but it does provide an incredibly accurate depiction of how work got done in these teams, of which there are several as indicated by the different shapes. Whereas I used to create these kinds of graphs manually, now chatbots collect and analyze all this data for me. There are lots of terrific books on network diagrams like the one above that I will touch on throughout this book. The focus of this book is how collaboration platforms and bots that operate inside of them are changing the way companies can organize themselves.

So, for now, the critical thing to consider is that the relationships depicted in the graph above outnumber the people; it is simple math—three people have potentially six relationships, and the number of potential relationships grows exponentially with each new person.

When you reorganize a team, you are potentially destroying a world of invisible value greater than what you can see, which is much more than the employees and their titles. Sometimes that might be a disaster (as would be the case with the circle team shown in the left half of the diagram), and sometimes that might be the right thing to do (in a case such as the triangle team). Incredibly, this kind of destruction happens every day without a second thought for the hidden value of the social networks.

This analysis led me to realize that reorganizing based only on org charts tears apart tribes that are getting work done, effectively lobotomizing the resulting organization and turning them into a zombie. Before acquisition or restructuring, tribes of people hang together, sometimes in terrible cultural environments, doing the best they can to deliver. By coming in and imposing new structures, processes, and organizational models, companies like Perot Systems inadvertently destroyed the very tribes that we needed to deliver value. In effect, we were laying waste the intelligence built up in the existing tightly interwoven fabric of working relationships. It’s sad, really.

Mergers and acquisitions (M&A) often create this destruction of corporate value on an industrial scale. Outsourcing can inadvertently lobotomize one functional department of a company. M&A, on the other hand, have the potential to destroy everything in two companies. Raise your hand if you have survived a merger. Now keep your hand raised if the combined company was better than either one of the original companies. Really? Nobody? Well, of course not.

The worst mergers I’ve ever seen were the purest form of weaponizing the org chart. A team of outside consultants would come in and create a massive spreadsheet with the names of every employee, and columns for things like their titles, salaries, functional areas, and geographic location. To find redundancies, they sorted the spreadsheet by column and then deleted rows.

It was, in effect, a blender into which outside consultants unknowingly threw the company’s most valuable and underrated asset—network knowledge. A prominent leader in the organization later said to me of this merger, “If you are running a dairy farm, stop killing the cows for their meat.” This is a truly gruesome, but also apt, visual for what many companies do: they treat...