Category: Knowledge work

During my first job out of college I was assigned to work as staff on several financial audits. Consulting work was slow and the firm did not believe in idle hands. I was assigned to help with the audit of a major brokerage firm.

As part of the audit process, we had sent out letters to the firm’s several hundred thousand account holders asking them to return a form acknowledging that their account statements were correct or noting discrepancies if there were any. My task that first day was to sit at a conference table and count those forms. Each time I reached 100 forms I raised my hand and waited for an audit senior to put two rubber bands around my stack and take it away. I then counted another stack and the day continued.

I was granted a promotion the next day for my diligence at counting to 100. The slips of paper I had counted the day before had only a customer signature. Other slips had comments on them from customers. Some of those comments noted a discrepancy to be investigated; they claimed they held 115 shares of IBM, not 100, for example. Other slips contained what were deemed “gratuitous” comments; observations about a broker’s parentage or legitimacy were potentially entertaining but not pertinent to the audit.

I was not asked to make such a rarified judgment call; that was a task for trained and experienced auditors. I was considered qualified, however, to deal these slips out onto the glass of a photocopier, copy the slips front and back, assemble the results, and bind them into files to be saved as part of the audit working papers.

As mind numbing as you might think this experience was, it did drive home an idea about knowledge work that still echoes four decades later.

For all the exhortations on math tests to “show your work” I believed in answers. Showing your work was what you had to do when you didn’t get it.

Outside the world of classes and tests, showing your work was also something you needed to do when someone else didn’t get it either. Clients and managers weren’t necessarily doing to accept an answer just because you offered it up. You needed to be able to walk them through how you got there.

Lawyers call it a “chain of evidence”, scientists keep lab notebooks, artists make sketches on the way to a finished work, programmers version control everything.

The path between germ of an idea and final product can be long and convoluted. We so want to reach the end of the path—the answer—that we often fail to manage the trip effectively. That management task can be eased if we are mindful about the ways we design and introduce intermediate work products that support and fold into a final deliverable.

When I was in the fourth grade, we figured out that I needed glasses. I was complaining about having trouble reading what was on the blackboard and after moving to the front row didn’t solve the problem, I was dispatched to an eye doctor. Sure enough, I was nearsighted. A few weeks later I got my first pair of glasses.

I particularly remember the sense of wonder at discovering that street signs were something you were supposed to be able to read from inside the car as you drove by. My eyes weren’t shaped to see the world in 20/20 on their own but I inhabited a world where a simple prosthetic compensated for that limitation.

I also lived in a world and a time when nuns were quite happy to provide the structure and discipline a daydreaming young boy might need to complete his lessons. External supports, innate curiosity, and a few extra IQ points took me a pretty long way.

I survived—actually thrived—for a long time because I operated in environments that offered supports matched to my deficits. I was well into my 40s before I suspected that I had ADD. As my responsibilities and environment changed, I kept trying to get closer to the blackboard until I ran out of rows. What I didn’t have was a way to figure out what constituted glasses or how to get the right prescription.

What I had instead was a story of the Peter principle in action. I had blown past my abilities. My credentials were accidental; past performance is not an indicator of future performance. Now, it also turns out that ADD and depression are often correlated, so add that to the equation.

Having a way to name what was going on was a necessary but not sufficient step. It made it possible to have productive conversations with the experts. We experimented with the various meds that worked for many, but not for me. The compensating strategies I had developed over time were matched to environments I no longer operated in. One choice would have been to return to a matching environment. Unfortunately, those environments have continued to shrink in our modern world.

So that leaves the choice of formulating a prescription that lets me see the board in front of me. The benefit of this particular metaphor is that I don’t have to be discouraged when a prescription that works for some doesn’t work for me.

The tricky part is that we talk more about what the prescription is than we do about why and how it works. When we talk about eyesight, we know how to assess and correct for myopia and astigmatism. And we know why the corrections work. When we’re trying to compensate for deficits in managing focus and attention doing complex knowledge work, we have to dig deeper and build provisional theories as we experiment.

This is clearly a work in progress. I am curious about how others might be tackling presumably similar challenges of matching their work practices to the unique demands of their environments? What metaphorical myopias and astigmatisms are you dealing with? How have you gone about designing and implementing corrections that work?

Tomorrow’s healthy organizations must make innovation routine. That’s a natural consequence of organizations becoming increasingly knowledge intensive while the half-life of knowledge also continues to shrink.

Routine innovation requires a shift from scripted responses to widely distributed improv capacity.

At first glance this is a straight reversal of organizational best practice. Organizations succeeded and scaled by transforming craft into standard operating procedure. The logic of the industrial revolution was anchored in scripting and standardization. We programmed technology and people to produce products and services at scale. We programmed people when the technology was too expensive or insufficiently flexible. As the technology evolved, we dispensed with the people whose work was now programmable.

Over time, this increases the proportion of people in organizations whose work is to do the design and programming; the script writers flourish until there are no more scripts to write. Concerns about AI and robotics boil down to script writers—programmers, analysts, data scientists, advertising creatives, etc—anxiety over whether or when they will write their final script.

What happens when the audience—or the market—demands performances faster than the scripts can be written and produced? There are three answers. The first is to speed up the writing and producing of scripts. The second is to settle for lower quality scripts. The third is to learn how to work without a script—to improvise.

The first approach seems to be business as usual for most organizations, the second, sadly, is appearing more and more frequently. It is the third which most interests me.

There’s a classic piece of career advice offered to those starting out, “fake it until you make it.” This is advice rooted in a scripted world where experts are thought to be those with the most robust collection of scripts. I think a more relevant view of expertise comes from a definition offered by Nobel Laureate Niels Bohr, “an expert is a man who has made all the mistakes which can be made in a very narrow field.”

From this perspective, an expert is someone who knows how to improvise safely in new situations.

If you grant this as a working definition of an expert, then experts are not those with the largest collection of memorized scripts. Beyond the scripts they have accumulated, they have learned a meta-skill. Beyond a deep understanding of multiple scripts, experts understand the structure and components of scripts so well that they can invent a new script in the moment in response to particular situations and moments. In short, they can improvise.

One of the mistakes that brought me around to this line of thought happened during a visit with a highly desired prospective client. We had gotten an introduction to this Fortune 500 organization through an academic colleague. Cracking open this account could keep multiple teams busy for years; we brought three partners and our CEO to the meeting.

After introductions and some talk about our general qualifications, one of the prospective client executives laid out a problem they were wrestling with. We pulled out our solving a strategic problem script and started working the problem in the meeting. Actually, a pretty solid way to showcase our expertise.

But, we were in script mode. The script runs all the way to an answer to the problem, which our CEO divined and promptly shared. We launched into a soliloquy when being fully in the improvisational moment would have led us back to a dialogue about the relationship we both desired not the transaction in the moment.

We never did any work with that client. We learned an immediate lesson about script selection. I’m still teasing out the implications of taking an improv point of view. I believe that the improv perspective is more suited to the mix of problems we now face. I believe that perspective is learnable. I suspect that it is not teachable but may be coachable.

If we’re lucky we connect with good mentors during our careers. I first worked with Mel in the late 1970s. Our paths intertwined over the years; I moved from Boston to Chicago to become one of his partners in the consulting firm we founded in 1994. He was CEO and holder of the central vision of what we became. I was Chief Knowledge Officer and Chief Learning Officer, which was a very grand title when we were 25 people; it became more representative as we grew to over a thousand in the next several years.

One of our points of disagreement crystallized on a drive home after another long day. His critique was that I cared too much about insight. As he put it, “95% of the people in most organizations are just pulling on the oars, they don’t need insight.” My response was to ask where did sails come from if everyone was pulling on the oars?

Mel was anchored in a classic individual contributor/hierarchical manager model of organization. Success depended on faithful execution of the scripts; managers monitored and controlled, contributors rowed. New ideas appeared infrequently and by magic; after careful vetting, new scripts were written, rehearsed, and deployed.

Organizations that survive and thrive do so because they are well-adapted to their environments. When environments evolve slowly, so can organizations.

Those environments don’t exist anymore. The current term of art is that today’s environments are VUCA—Volatile, Uncertain, Chaotic, and Ambiguous. Yet the show must go on; the curtain rises each day and the audience awaits.

The usual response is to row faster. A new script is written—or found, quick rehearsals take place, and we hope the audience is tolerant of the cast and crew’s mistakes.

The better answer is to reorient to an improv mindset and learn how to act more responsively and in the moment. This demands more of everyone in the organization. No one can be content to simply learn their lines or wait to execute their cue.

In the old scripted world, you could set up the joke and plan the special effect. It is a place of grand vision broken down into tactical execution.

An improv, VUCA, world exists between those two poles. The grand vision must relate to the audience/market’s interests and responses in the moment. Tactical bits must be selected and sequenced to move in the general direction of the evolving vision.

This calls for performers who can straddle the divide and support crew who can assemble platforms, bridges, and illumination in real time. Developing skill and competence in this dynamic performance demands more capable performers and practices designed to be adaptable.

“Can we fly six of the chorus girls during the 2^nd act opening number?”

Directors always have crazy ideas. That particular idea took a week of design work, rigging, and rehearsal to pull off. The one technical detail you need to know is that the key safety issue was properly balancing the rigs while the dancers got on and off the playground swings they were sitting on.

The rig was balanced for two dancers, one on either side of the stage. When they were both on the swings, a stage hand could raise them 15 feet in the air with one hand. Before getting off the swings, we had to replace their weight with sandbags clipped to the rigs offstage. That kept the system safely in balance. We had rehearsed the switch multiple times; it was a complex piece of offstage choreography in its own right involving nine stage hands and six dancers.

Opening night, the scene runs smoothly, the curtain comes down, and one of the dancers hops off her swing early before the sandbags have been clipped on. As her counterpart starts what is about to be a very rapid ascent into the rafters, I see Mark, the stagehand at the rail controlling the rig, reach up about two feet, grab the ropes, and use his bodyweight as a temporary counterweight. The sandbags were clipped on, dancer number 2 came safely back to the ground, and the show went on without interruption. My heart restarted several minutes later.

The audience saw none of this. The director delivered his moment of magic. The crew got a story to talk about at the cast party.

Most people seem content to simply enjoy the magic. I find the magic more compelling when I understand how it is made. Making magic takes work; the more you understand of the work, the better the magic you can make.

We live in a world that appears magical. But it has been built by designers and engineers and carpenters and stagehands. If you leave the magic to the experts you are bound by their imaginations. If you are prepared to come backstage and invest in learning something about how the magic is done, then you can become another collaborator in imagining and creating new magic.

“Point of view is worth 80 IQ points” – Alan Kay

If you’ve interacted with me for more than a few minutes, there’s a good chance you’ve heard me quote Alan Kay. If not, it’s a pretty safe bet that you have no idea who Alan is, even though you are probably reading this on a device that can trace its roots back to Alan’s work.

Alan is a computer scientist who was one of the original members of Xerox’s Palo Alto Research Center. Both the Macintosh and Microsoft Windows are built on work that Alan did first. If you Google Alan be prepared for a long list of articles to read and videos to watch. It would also be time well spent.

I first learned of Alan’s work by way of a consulting project I did for Andersen Consulting while I was in graduate school. They were interested in whether they should take deeper interest in the ideas of object-oriented programming. Since Alan is generally regarded as the father of object-oriented programming I learned of his existence. That work led to a working relationship between Accenture and Alan and I went on with my studies.

Jump forward about ten years. I’m moving to Chicago to join Diamond, a new consulting firm that I am founding along with former colleagues from Andersen. Diamond’s CEO, Mel Bergstein, was my client at Andersen and cut the deal with Alan based, in part, on my earlier work. Mel asked Alan to serve on Diamond’s Board.

I got to transform my arms length knowledge of Alan into a working relationship. Alan worked with us in client settings and internally. I invited Alan to talk to our consultants in various workshops and I got to watch Alan interact in multiple client settings. I went from professional admirer to full-on fanboy.

Alan is a polymath and has a collection of awards that constitute a resume in their own right. On paper, he is the definition of “scary smart.” In person, he is not immediately intimidating. Watching him think on his feet, however, is a master class in focused inquiry.  He’s also, first and foremost, an engineer more interested in how to make something work than anything else.

That pragmatic focus drives Alan to the middle space that bridges the gap between blue sky concept and picayune detail. Moreover, his engineering point of view values solving problems so that they stay solved. This is not always the perspective you encounter with managers and executives; they are often under pressures that favor things that look like rather than are solutions. Watching Alan think provides lessons in managing and manipulating points of view to gain extra IQ points and discover answers that are both practical and enduring.

There’s a letter in my files dated May 31, 1974. It’s on the letterhead of the Trustees of the Princeton University Triangle Club and signed by Peter Putnam. Class of 1942, then the Graduate Secretary of the Trustees. In it Peter says.

An axle or a stage manager is not a showy part of a vehicle, but without one, a car won’t go, and without the other, a company does not perform.

I was that stage manager.

That bit of praise from one professional to another sits at the heart of my career. I know I need to revisit it to discern what comes next.

There are two basic questions that interest me.

The first is about searching for the essential parts that make things go. I’ve sometimes talked about this as the messy middle. What are the critical connections between grand goals and effective execution?

The second question has to do with what it takes to make a living operating in those non-showy, essential, places. Particularly in a world that extols and rewards stars.

There’s an old adage that shows up in motivational posters and gets attributed to various sages. One version is “there is no limit to what a man can do who does not care who gains the credit for it.” A laudable sentiment but the reality is that getting things done requires gathering and distributing resources and credit.

What can you do with a stage manager temperament in a world that sees stars?

I don’t quite know what I mean by that yet but I’ve learned to trust the turns of phrase that pop into my head.

Back in the earliest days of my third cycle of higher education, my advisor commented that this was the last opportunity I would have to invest in building my store of intellectual capital. I think this was his way of justifying the two books a week he was assigning in his doctoral seminar, the 50-page reading list in our organizational theory seminar, and the comparable workloads in industrial economics and research methods.

I did ultimately earn the piece of paper certifying me as an expert. But my advisor was wrong about that store of intellectual capital. Neither of us correctly factored in the half life of knowledge in the technology, data, and information realms we inhabited. You can’t build an academic career mining the work you did in graduate school. In today’s world, you can’t build any kind of career mining what you used to know.

This creates a new kind of learning problem. How do you approach learning if you’re already an expert? How do you manage the process of acquiring new knowledge when you are creating it at the same time?

I want to figure out what it means to do expert learning. I suspect the answers will look more like exploratory research than like taking a class from the designated expert. Your task isn’t so much to follow a curriculum as it is to design one in real time.

Perhaps what I’m working out here is an expanded definition of being an expert. We talk about SMEs—subject matter experts. Being a SME implies that you possess a particular knowledge base—a body of knowledge—plus knowledge of deeper principles and themes that aren’t readily apparent to the less expert.

Experts also know who the other experts are and hang out with them. We call these communities of interest and practice when we’re trying to impress people.

Finally, there has to be another layer of skill/expertise. You need a collection of methods and practices for updating, extending, and refactoring your existing knowledge base. There also needs to be a level of mindfulness about this layer. Maintaining and extending expertise has to be an explicit and deliberate practice.

I used to think it would be cool to go to school permanently. Turns out that the modern world requires it. Everybody has become a lifelong student. The part that got left out was that we are also expected to be lifelong teachers as well.

Things are the way they are because they got that way

– Ken Boulding

This is a quote attributed to Boulding by Jerry Weinberg in his excellent Secrets of Consulting. My students will tell you that I am fond of bringing it into many a class discussion. It’s somehow more useful to me than trotting out Santayana’s observation about learning history.

We’re all hot for the new, new thing and innovation is always better when it’s disruptive. For all that, when thinking about new technology we would do well to spend time and thought understanding how we got to the current situation. Predicaments today grew out of decisions yesterday that made sense when they were made. If we want to leave fewer predicaments for our successors, we need to understand how those earlier logic trains derailed.

For example, consider the Y2K issue of the late 1990s; the transition from 1999 to 2000 raised the threat that computer programs might fail because of the common practice of storing only the last two digits of the year in computer files and databases. That common practice grew out of technology limits with storage and data access. There was also an unexamined assumption about the likely longevity of the systems being built in the 1960s, 70s, and 80s. Why worry about a problem that was decades in the future; surely the programs would be replaced long before then.

There seems to be a deeper issue at work that is troubling me. Design is always driven by constraints and requirements; what limits exist on what you hope to do. We fail to appreciate how technology constraints evolve very differently from other constraints. We treat certain limits on our designs as if they were integral to solving the problem at hand when they are actually temporary speed bumps in the technology. We solve the immediate problem—the dates in our files fit the space available—at the expense of creating a bigger problem later.

We talk of “technical debt” but I don’t think the notion works with less technical decision makers. Debt suggests you trade future interest payments for a solution now. What we do instead is lock the organization’s future within boundaries that disappear and make us look stupid to those who designed smarter.

Organizational decision making is often hampered by shortchanging the future. We don’t seem to have good methods for avoiding these mistakes in technology. Temporary technology limits become design constraints which become design assumptions that get baked into design patterns and then outlive their usefulness.