Author Archives: Geoffrey Moore

About Geoffrey Moore

Geoffrey A. Moore is an author, speaker and business advisor to many of the leading companies in the high-tech sector, including Cisco, Cognizant, Compuware, HP, Microsoft, SAP, and Yahoo! Best known for Crossing the Chasm and Zone to Win with the latest book being The Infinite Staircase. Partner at Wildcat Venture Partners. Chairman Emeritus Chasm Group & Chasm Institute

Performance Management and Accountability

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Performance Management and Accountability

GUEST POST from Geoffrey A. Moore

Accountability begins with a voluntary commitment to put yourself in service to bringing about an outcome. To frame this effort for you and your team, I have found Salesforce’s V2MOM management system to be an invaluable tool. In that context:

  1. Vision describes the outcome you are all in service to.
  2. Values shape the approach you will all take to bringing it about.
  3. Methods present what each one of you will do to achieve the outcome and are assigned to single accountable leaders.
  4. Obstacles call out the challenges the leaders anticipate having to deal with, and
  5. Measures are the objective signals that everyone will use to assess your degree of success.

Performance management begins with securing each individual’s voluntary commitment to the outcomes associated with their jobs to be done as well as to the values to be honored while doing it. It then moves on to review their methods, obstacles, and measures to test them for coherence, feasibility, and credibility, and to ensure each person is confident they are set up to succeed and that they want to be held accountable for that success. The day-to-day work of performance management consists of inspecting, detecting, dissecting, course-correcting, and resurrecting the stream of work to keep it on track. Most of this effort consists of self-management, supported by regular check-ins with the team leader and quarterly reviews with the higher-ups. The majority of the work is focused on the near term, but this must be balanced with investments in the mid and long-term for sustained success.

That all said, that is not what most people think of when you bring up the topic of performance management. Instead, they associate it with a mandate to manage out under-performers. The word under-performer has unfortunate connotations, and this has cast a cloud over the entire effort.

To set things straight, begin by realizing that everyone is an under-performer at something. If you are unsure about what you personally under-perform at, just ask your spouse or your children, and they will let you know. The point is, there is no shame in under-performing per se. We just don’t want to persist in it.

When it comes to the workplace, under-performance shows up as a series of repeated shortfalls in our measures despite our best efforts to overcome our obstacles by course-correcting our methods. To ignore these signals without taking remedial action is to fall prey to Einstein’s definition of insanity, namely, doing the same thing over and over again and expecting a different result. Instead, one needs to intervene by invoking the “horse, rider, trail” principle. The horse is the offering, the rider is the person accountable for its success, and the trail is the target market. Changing any one of these factors will materially alter the dynamics of the situation such that you can expect a different result. Just understand that you probably won’t get to do this more than once, so choose wisely.

Finally, understand that while everyone is an under-performer at something, they are also likely to be an overachiever at something else. As a manager, you should act as a steward of your team members’ careers. If they are not the right fit for the job they are in, then both they and you need them to move on. Under-performing in this context is just nature’s way of telling us we are playing the wrong position, perhaps even playing the wrong game. Nobody likes to under-perform, and nobody is served by it. Meanwhile, our world is a needy place, so the sooner we can get people into their right roles, the better we all shall be.

That’s what I think. What do you think?

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Everyone Clear Now on What ChatGPT is Doing?

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Everyone Clear Now on What ChatGPT is Doing?

GUEST POST from Geoffrey A. Moore

Almost a year and a half ago I read Stephen Wolfram’s very approachable introduction to ChatGPT, What is ChatGPT Doing . . . And Why Does It Work?, and I encourage you to do the same. It has sparked a number of thoughts that I want to share in this post.

First, if I have understood Wolfram correctly, what ChatGPT does can be summarized as follows:

  1. Ingest an enormous corpus of text from every available digitized source.
  2. While so doing, assign to each unique word a unique identifier, a number that will serve as a token to represent that word.
  3. Within the confines of each text, record the location of every token relative to every other token.
  4. Using just these two elements—token and location—determine for every word in the entire corpus the probability of it being adjacent to, or in the vicinity of, every other word.
  5. Feed these probabilities into a neural network to cluster words and build a map of relationships.
  6. Leveraging this map, given any string of words as a prompt, use the neural network to predict the next word (just like AutoCorrect).
  7. Based on feedback from so doing, adjust the internal parameters of the neural network to improve its performance.
  8. As performance improves, extend the reach of prediction from the next word to the next phrase, then to the next clause, the next sentence, the next paragraph, and so on, improving performance at each stage by using feedback to further adjust its internal parameters.
  9. Based on all of the above, generate text responses to user questions and prompts that reviewers agree are appropriate and useful.

OK, I concede this is a radical oversimplification, but for the purposes of this post, I do not think I am misrepresenting what is going on, specifically when it comes to making what I think is the most important point to register when it comes to understanding ChatGPT. That point is a simple one. ChatGPT has no idea what it is talking about.

Indeed, ChatGPT has no ideas of any kind — no knowledge or expertise — because it has no semantic information. It is all math. Math has been used to strip words of their meaning, and that meaning is not restored until a reader or user engages with the output to do so, using their own brain, not ChatGPT’s. ChatGPT is operating entirely on form and not a whit on content. By processing the entirety of its corpus, it can generate the most probable sequence of words that correlates with the input prompt it had been fed. Additionally, it can modify that sequence based on subsequent interactions with an end user. As human beings participating in that interaction, we process these interactions as a natural language conversation with an intelligent agent, but that is not what is happening at all. ChatGPT is using our prompts to initiate a mathematical exercise using tokens and locations as its sole variables.

OK, so what? I mean, if it works, isn’t that all that matters? Not really. Here are some key concerns.

First, and most importantly, ChatGPT cannot be expected to be self-governing when it comes to content. It has no knowledge of content. So, whatever guardrails one has in mind would have to be put in place either before the data gets into ChatGPT or afterward to intercept its answers prior to passing them along to users. The latter approach, however, would defeat the whole purpose of using it in the first place by undermining one of ChatGPT’s most attractive attributes—namely, its extraordinary scalability. So, if guardrails are required, they need to be put in place at the input end of the funnel, not the output end. That is, by restricting the datasets to trustworthy sources, one can ensure that the output will be trustworthy, or at least not malicious. Fortunately, this is a practical solution for a reasonably large set of use cases. To be fair, reducing the size of the input dataset diminishes the number of examples ChatGPT can draw upon, so its output is likely to be a little less polished from a rhetorical point of view. Still, for many use cases, this is a small price to pay.

Second, we need to stop thinking of ChatGPT as artificial intelligence. It creates the illusion of intelligence, but it has no semantic component. It is all form and no content. It is a like a spider that can spin an amazing web, but it has no knowledge of what it is doing. As a consequence, while its artifacts have authority, based on their roots in authoritative texts in the data corpus validated by an extraordinary amount of cross-checking computing, the engine itself has none. ChatGPT is a vehicle for transmitting the wisdom of crowds, but it has no wisdom itself.

Third, we need to fully appreciate why interacting with ChatGPT is so seductive. To do so, understand that because it constructs its replies based solely on formal properties, it is selecting for rhetoric, not logic. It is delivering the optimal rhetorical answer to your prompt, not the most expert one. It is the one that is the most popular, not the one that is the most profound. In short, it has a great bedside manner, and that is why we feel so comfortable engaging with it.

Now, given all of the above, it is clear that for any form of user support services, ChatGPT is nothing less than a godsend, especially where people need help learning how to do something. It is the most patient of teachers, and it is incredibly well-informed. As such, it can revolutionize technical support, patient care, claims processing, social services, language learning, and a host of other disciplines where users are engaging with a technical corpus of information or a system of regulated procedures. In all such domains, enterprises should pursue its deployment as fast as possible.

Conversely, wherever ambiguity is paramount, wherever judgment is required, or wherever moral values are at stake, one must not expect ChatGPT to be the final arbiter. That is simply not what it is designed to do. It can be an input, but it cannot be trusted to be the final output.

That’s what I think. What do you think?

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Push versus Pull in the Productivity Zone

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Push versus Pull in the Productivity Zone

GUEST POST from Geoffrey A. Moore

Digital transformation is hardly new. Advances in computing create more powerful infrastructure which in turn enables more productive operating models which in turn can enable wholly new business models. From mainframes to minicomputers to PCs to the Internet to the Worldwide Web to cloud computing to mobile apps to social media to generative AI, the hits just keep on coming, and every IT organization is asked to both keep the current systems running and to enable the enterprise to catch the next wave. And that’s a problem.

The dynamics of productivity involve a yin and yang exchange between systems that improve efficiency and programs that improve effectiveness. Systems, in this model, are intended to maintain state, with as little friction as possible. Programs, in this model, are intended to change state, with maximum impact within minimal time. Each has its own governance model, and the two must not be blended.

It is a rare IT organization that does not know how to maintain its own systems. That’s Job One, and the decision rights belong to the org itself. But many IT organizations lose their way when it comes to programs — specifically, the digital transformation initiatives that are re-engineering business processes across every sector of the global economy. They do not lose their way with respect to the technology of the systems. They are missing the boat on the management of the programs.

Specifically, when the CEO champions the next big thing, and IT gets a big chunk of funding, the IT leader commits to making it all happen. This is a mistake. Digital transformation entails re-engineering one or more operating models. These models are executed by organizations outside of IT. For the transformation to occur, the people in these organizations need to change their behavior, often drastically. IT cannot — indeed, must not — commit to this outcome. Change management is the responsibility of the consuming organization, not the delivery organization. In other words, programs must be pulled. They cannot be pushed. IT in its enthusiasm may believe it can evangelize the new operating model because people will just love it. Let me assure you — they won’t. Everybody endorses change as long as other people have to be the ones to do it. No one likes to move their own cheese.

Given all that, here’s the playbook to follow:

  1. If it is a program, the head of the operating unit that must change its behavior has to sponsor the change and pull the program in. Absent this commitment, the program simply must not be initiated.
  2. To govern the program, the Program Management Office needs a team of four, consisting of the consuming executive, the IT executive, the IT project manager, and the consuming organization’s program manager. The program manager, not the IT manager, is responsible for change management.
  3. The program is defined by a performance contract that uses a current state/future state contrast to establish the criteria for program completion. Until the future state is achieved, the program is not completed.
  4. Once the future state is achieved, then the IT manager is responsible for securing the system that will maintain state going forward.

Delivering programs that do not change state is the biggest source of waste in the Productivity Zone. There is an easy fix for this. Just say No.

That’s what I think. What do you think?

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Coping with the Chasm

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Coping with the Chasm

GUEST POST from Geoffrey A. Moore

I’ve been talking about crossing the chasm incessantly for over thirty years, and I’m not likely to stop, but it does beg the question, how should you operate when you are in the chasm? What is the chasm itself about, and what actions is it likely to reward or punish?

The chasm is a lull in the Technology Adoption Life Cycle, one that comes after the enthusiasts and visionaries have made their splash and before the pragmatists are willing to commit. At this time the new category is on the map, people are talking about it, often quite enthusiastically, but no one has budgeted for it as yet. That means that conventional go-to-market efforts, based on generating and pursuing qualified leads with prospects who have both budget and intent to purchase, cannot get traction. It does not mean, however, that they won’t entertain sales meetings and demos. They actually want to learn more about this amazing new thing, and so they can keep your go-to-market engine humming with activity. They just won’t buy anything.

Crossing the Chasm says it is time for you to select a beachhead market segment with a compelling reason to buy and approach them with a whole product that addresses an urgent unsolved problem. All well and good, but what if you don’t know enough about the market (or your own product for that matter) to make a sound choice? What if you are stuck in the chasm and have to stay there for a while? What can you do?

First of all, take good care of the early adopter customers you do have. Give them more service than you normally would, in part because you want them to succeed and be good references, but also because in delivering that service, you can get a closer look at their use cases and learn more about the ones that might pull you out of the chasm.

Second, keep your go-to-market organization lean and mean. You cannot sell your way out of the chasm. You cannot market your way out either. The only way out is to find that targetable beachhead segment with the compelling use case that they cannot address through any conventional means. This is an exercise in discovery, so your go-to-market efforts need to be provocative enough to get the meeting (this is where thought leadership marketing is so valuable) and your sales calls need to be intellectually curious about the prospect’s current business challenges (and not presentations about how amazing your company is or flashy demos to show off your product). In short, in the chasm, you are a solution looking for a problem.

Third, get your R&D team directly in contact with the customer, blending engineering, professional services, and customer success all into one flexible organization, all in search of the beachhead use case and the means for mastering its challenges. You made it to the chasm based on breakthrough technology that won the hearts of enthusiasts and visionaries, but that won’t get you across. You have to get pulled out of the chasm by prospective customers who will make a bet on you because they are desperate for a new approach to an increasingly vexing problem, and you have made a convincing case that your technology, product, talent, and commitment can fill the bill.

Finally, let’s talk about what you should not do. You cannot perform your way out of the chasm. You have no power. So, this is not a time to focus on execution. Instead, you have to find a way to increase your power. In the short term, you can do this through consulting projects—you have unique technology power that people want to consume; they just don’t want to consume through a product model at this time. They are happy to pay for bespoke projects, however, and that is really what the Early Market playbook is all about. Of course, projects don’t scale, so they are not a long-term answer, but they do generate income, and they do keep you in contact with the market. What you are looking for is solution power, tying your technology power to a specific use case in a specific segment, one that you could deliver on a repeatable basis and get you out of the chasm. Often these use cases are embedded in bespoke projects, just a part of the visionary’s big picture, but with more than enough meat on the bone to warrant a pragmatist’s attention.

Sooner or later you have to make a bet. You can recognize a good opportunity by the following traits:

  • There is budget to address the problem, and it is being spent now.
  • The results the prospect is getting are not promising and, if anything, the situation is deteriorating.
  • You know from at least one of your projects that you can do a lot better.

That’s about all the data you are going to get. That’s why we call crossing the chasm a high-risk, low-data decision. But it beats staying in the chasm by a long shot.

That’s what I think. What do you think?

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Are You Leading in the Wrong Zone?

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Are You Leading in the Wrong Zone?

GUEST POST from Geoffrey A. Moore

I get tired of listening to “experts” explain how leaders need to be bolder. Usually what they are advocating for is more disruptive innovation, less business as usual. But this completely ignores the impact of context and ends up patronizing behavior that may actually be well-grounded. It depends on which zone you are operating out of.

In the Performance Zone, the goal is to deliver on the quarterly plan. It is not the time or place for disruptive innovation. Leadership means getting your team to the finish line despite whatever roadblocks may crop up. Grit and resourcefulness, combined with attention to tactics, is what is wanted here.

In the Productivity Zone, the goal is to be there for the long haul. Again, disruptive innovation is not on the docket. Analysis and optimization are the keys here, and leaders must be willing to step back, take a systems view of things, and invest in efforts that will enable the Performance Zone to perform better in the future.

By contrast, the Incubation Zone is all about disruptive innovation, and most pundits champion a leadership style that is a perfect fit for this zone. So, if you are in this zone, by all means embrace hypothesis testing, agility, fast failure and the like. Just remember that what works here does not work well in any of the other three zones.

Finally, the Transformation Zone is where the pundits ought to be focusing because transformation is a bear, and no one can ever really tame it. Business lore celebrates the amazing disrupters here — Jobs, Musk, Bezos, etc. — as well we should. But in so doing we should not ignore the amazing disruptees, the leaders who redirected their enterprises to bring them kicking and screaming into a new age — Gerstner, Nadella, Iger, and company. For my money, their leadership style is the single most important one for any aspiring CEO to master.

That’s what I think. What do you think?

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What We Have Learned About Digital Transformation Thus Far

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What We Have Learned About Digital Transformation Thus Far

GUEST POST from Geoffrey A. Moore

We are well into our first decade of digital transformation, with both the successes and the scars to show for it, and we can see there is a long way to go. Realistically, there is probably never a finish line, so I think it is time for us to pause and take stock of what we have learned, and how best we can proceed from here. Here are three lessons to take to heart.

Lesson 1: There are three distinct levels of transformation, and operating model transformation is the one that deserves the most attention.

The least disruptive transformation is to the infrastructure model. This should be managed within the Productivity Zone, where to be fair, the disruption will be considerable, but it should not require much in the way of behavior change from the rest of the enterprise. Moving from data centers to cloud computing is a good example, as are enabling mobile applications and remote work centers. The goal here is to make employees more efficient while lowering total cost of IT ownership. These transformations are well underway, and there is little confusion about what next steps to take.

By contrast, the most disruptive transformation is to the business model. Here a company may be monetizing information derived from its operating model, as the SABRE system did for American Airlines, or overlaying a digital service on top of its core offering, as the automotive makers are seeking to do with in-car entertainment. The challenge here is that the economics of the new model have little in common with the core model, which creates repercussions both with internal systems and external ecosystem relationships. Few of these transformations to date can be said to be truly successful, and my view is they are more the exception than the rule.

The place where digital transformation is having its biggest impact is on the operating model. Virtually every sector of the economy is re-engineering its customer-facing processes to take advantage of ubiquitous mobile devices interacting with applications hosted in the cloud. These are making material changes to everyday interactions with customers and partners in the Performance Zone, where the priority is to improve effectiveness first, efficiency second. The challenge is to secure rapid, consistent, widespread adoption of the new systems from every employee who touches them. More than any other factor, this is the one that separates the winners from the losers in the digital transformation game.

Lesson 2: Re-engineer operating models from the outside in, not the inside out.

A major challenge that digital transformation at the operating model level must overcome is the inertial resistance of the existing operating model, especially where it is embedded in human behaviors. Simply put, people don’t like change. (Well, actually, they all want other people to change, just not themselves.) When we take the approach of internal improvement, things go way too slowly and eventually lose momentum altogether.

The winning approach is to focus on an external forcing function. For competition cultures, the battle cry should be, this new operating model poses an existential threat to our future. Our competitors are eating our lunch. We need to change, and we need to do it now! For collaboration cultures, the call to action should be, we are letting our customers down because we are too hard to do business with. They love our offers, but if we don’t modernize our operating model, they are going to take their business elsewhere. Besides, with this new digital model, we can make our offers even more effective. Let’s get going!

This is where design thinking comes in. Forget the sticky notes and lose the digital whiteboards. This is not about process. It is about walking a mile in the other person’s shoes, be that an end user, a technical buyer, a project sponsor, or an implementation partner, spending time seeing what hoops they have to go through to implement or use your products or simply to do business with you. No matter how good you were in the pre-digital era, there will be a ton of room for improvement, but it has to be focused on their friction issues, not yours. Work backward from their needs and problems, in other words, not forward from your intentions or desires.

Lesson 3: Digital transformations cannot be pushed. They must be pulled.

This is the hardest lesson to learn. Most executive teams have assumed that if they got the right digital transformation leader, gave them the title of Chief Transformation Officer, funded them properly, and insured that the project was on time, on spec, and on budget, that would do the trick. It makes total sense. It just doesn’t work.

The problem is one endemic to all business process re-engineering. The people whose behavior needs to change—and change radically—are the ones least comfortable with the program. When some outsider shows up with a new system, they can find any number of things wrong with it and use these objections to slow down deployment, redirect it into more familiar ways, and in general, diminish its impact. Mandating adoption can lead to reluctant engagement or even malicious compliance, and the larger the population of people involved, the more likely this is to occur.

So what does work? Transformations that are driven by the organization that has to transform. These start with the executive in charge who must galvanize the team to take up the challenge, to demand the digital transformation, and to insert it into every phase of its deployment. In other words, the transformation has to be pulled, not pushed.

Now, don’t get me wrong. There is still plenty of work on the push side involved, and that will require a strong leader. But at the end of the day, success will depend more on the leader of the consuming organization than that of the delivery team.

That’s what I think. What do you think?

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Are We Doing Social Innovation Wrong?

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Are We Doing Social Innovation Wrong?

GUEST POST from Geoffrey A. Moore

The Volume Operations business model kicks in when you have hundreds of thousands of users and goes up from there. 100,000, for those of us who are not math majors, is 10 to the power of 5. Uber-successful volume ops businesses operate at 10 to the power of 9 and up—millions of users or customers. But if you are a start-up, you are looking at 10 or maybe 100. How do you get from here to there?

The key thought to keep in mind is the old chestnut “what got you here won’t get you there.” That is, whatever operating model you have, keep in mind it can scale to two exponents but never to three. That means for every two exponents you have to change operating models, which likely means you have to change executive leadership in order to go forward.

To illustrate this idea, I’d like to focus on the non-profit sector and ask the question, what would it take to really solve for any widespread social problem? Homelessness was the first one that came to mind, but hunger is another obvious one, drug addiction a second, street crime a third. They are all seemingly intractable issues that, despite the best intentions of a whole raft of people, and regardless of how much funding is supplied, stubbornly resist any sustainable improvement.

The question I want to address is not what programs would work—because I actually think a whole lot of programs would work—but rather, how could we organize to deploy these programs successfully at scale.

Following our principle of what got you here won’t get you there, we need a ladder of operating models that can take us, exponent by exponent, from 10 to the power of 1 to, say, 10 to the power of 7. What might that look like?

Scaling Social Innovation

Consider this a straw man, a place to start, something to edit. It conveys a key lesson from the high-tech sector, namely that the fastest way to kill a disruptive innovation is to race to scale by skipping over one or more of these “exponential steps.” It just doesn’t work. There are too many emergent factors at each new level you must learn to cope with in order to succeed. The only reliable way to scale is to ratchet your way up this staircase, adapting your systems and operations as you go.

Unfortunately, that’s not what politicians do. They want to make a big impact right away. That means they start everything on one of the upper stairs. Driven by impatience, they ignore the dynamics of adoption and demand mass deployment from the get-go. They think the problem is simply one of getting enough funding. It’s not. It’s one of operational innovation. Scaling prematurely simply wastes the funding. And then when programs do flounder, as they inevitably will, they blame it on execution when in reality they simply did not do the hard, time-consuming work of building up their foundation step by step from below.

One of the implications of this framework is that social services should be incubated in the private sector where freedom from regulatory constraints supports agile innovation. But as they scale, the importance of regulatory oversight increases and more communal engagement is required. The goal should be to keep this oversight as local as possible as long as possible, doing as much as we can to empower the people delivering the service itself. Once that operating model solidifies, then, and only then, is there a proper foundation for scaling to state and federal programs.

Today, we do not lack the empathy to support social services. Nor do we lack the funding. But we are failing nonetheless. We can do better. We need to do better.

That’s what I think. What do you think?

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Career Development at Its Best

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Career Development at Its Best

GUEST POST from Geoffrey A. Moore

A colleague recently forwarded me an article about upskilling one’s team. I am on board with the first part—innovating from within—but I am not comfortable with the concept of “upskilling.” Here’s why.

Upskilling implies that the challenges you face lend themselves to known skilled responses. Combined with the idea of training, it also implies that you have that skills knowledge already in-house and simply need to transfer it to a new cohort. I can see both of these ideas being applicable to technical work, but I don’t think they reflect the realities of knowledge work. There, instead, I think we need to substitute learning for training and experience for upskilling.

Whereas training puts the responsibility for a successful outcome on the trainer, learning puts it on the learner. In the world of knowledge work, that’s where it has to sit. We need to develop ourselves—no one else can do it for us. But we need experience to do so, and this is where managers can have a big impact. It is not the training we give our teams but rather the assignments we entrust to them that let them build new muscles.

Assigning an inexperienced person to a new challenge is always a risk—for them, for their manager, and for the customer of the work to be done. Unfortunately, without risk, there is no learning. There is no risk-free way to learn. So the manager’s goal is to create opportunity while mitigating risk.

One of the best means for so doing is assigning people the role of the Single Accountable Leader (SAL). The need for a SAL arises in any cross-functional initiative where hierarchy of authority is replaced by team collaboration. Just as a football team needs a quarterback to run a play that involves 11 people collaborating toward one outcome, so does a cross-functional initiative need a single accountable leader to be the focus of decision-making as the team adapts to unfolding events. This person need not be an expert. They need to be vigilant. They need to stay on top of things. They need to inquire and inspect, to detect when the effort is going off course, to focus the team on redirecting it, to get counsel from their mentors, and to escalate issues that are beyond their ability to address. Most of all, they need to care.

When people care, they learn. They go the extra mile. They bring out the best in others. And in so doing, they build the new muscle that will qualify them for a larger role in the enterprise. This to me is career development at its best. The SAL assignment is the most precious gift that managers and mentors can bestow upon their charges. I hope you can take advantage of it.

That’s what I think. What do you think?

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Exploring Performance Management

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Exploring Performance Management

GUEST POST from Geoffrey A. Moore

Performance management is a tool for managing Performance Zone commitments. These are the outcomes and deliverables that your organization, or for that matter you yourself, are being funded to deliver. In the Vision, Value, Methods, Obstacles, and Measures (V2MOM) framework, they will be represented by one or more of your Methods and will be tied directly to a corresponding set of Measures.

The primary goal of performance management is to ensure success in meeting such commitments. This includes early detection of things going off track—hence the need for frequent check-ins. It includes coaching and mentoring to help team members succeed—something we all need at one time or another. It asks us all to balance empathy for times when people don’t succeed with accountability for the need to succeed. People need help when they are down, but it is not fair to continue to accept funding for outcomes that do not get delivered.

When people under-deliver against their commitments, nobody wins. Most importantly, and this is something a lot of people miss, the person who is under-delivering is not winning. When coaching and mentoring are not getting them to success, the likelihood is that they are in the wrong role. Keeping them in that role, which we sometimes think of as protecting them, just prolongs the agony. A far better response is to step back and assess what would be the right role for this person, whether it be another one on the same team, or one on a different team, or one in another organization, or one in another company. The point is, rather than obsessing about what they are doing badly, we need to focus instead on what they could be really good at and get them into that role as swiftly as possible.

Let me be even more clear. It is obvious that when under-performing folks are kept in a role, everyone else on the team has to work harder. What is not so obvious is that when under-performing folks move on, even if their roles are not back-filled, the team discovers it has less work to do! The reason is that under-performing people absorb everyone else’s time. After all, they are trying to help, they just aren’t succeeding. And since helping teammates is baked into collaborative cultures, we give them extra time even though it is not productive to do so.

Again, nobody is winning here. We need an intervention. In this context, policies that call for managing out the bottom five percent are simply a heuristic that says, in any organization at any time, there are bound to be some number of round pegs in square holes, and leaving them in place doesn’t help anyone.

So, why then do we still bristle at the notion of performance management? There’s a ton of psychology behind this question, more than I am competent to address, but the net effect is that performance management puts enormous pressure on a set of social skills many of us lack. That’s not going to change anytime soon, so we should not be surprised at our reluctance to engage. But it is not OK to dodge our responsibilities either. Our best bet, in my view, is to bake into our protocol a discussion of positive next steps that includes concrete recommendations, and cope with our emotional challenges as best we can.

That’s what I think. What do you think?

Image Credit: Unsplash

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Time is Not Fundamental

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Time is Not Fundamental

GUEST POST from Geoffrey A. Moore

For all my life I have been taught that time is the fourth dimension in a space-time continuum. I mean, for goodness sake, Einstein said this was so, and all of physics has followed his lead. Nonetheless, I want to argue that, while the universe may indeed have four dimensions, time is not one of them, nor is it a fundamental element of reality.

Before you think I have really jumped off the deep end, let me just say that my claim is that motion is a fundamental element of reality, and it is the one that time is substituting for. This is based simply on observation. That is, we can observe and measure mass. We can observe and measure space. We can observe and measure energy. We can observe and measure motion. Time, on the other hand, is simply a tool we have developed to measure motion. That is, motion is fundamental, and time is derived.

Consider where our concept of time came from. It started with three distinct units—the day, the month, and the year. Each is based on a cyclical motion—the earth turning around its axis, the moon encircling the earth, the earth and moon encircling the sun. All three of these cyclical motions have the property of returning to their starting point. They repeat, over and over and over. That’s how they came to our attention in the first place.

If we call this phenomenon cyclical time, we can contrast it with linear time. The latter is time we experience as passing, the one to which we apply the terms past, present, and future. But in fact, what is passing is not time but motion, motion we are calibrating by time. That is, we use the cyclical units of time to measure the linear distance between any given motion and a reference location.

As I discuss in The Infinite Staircase, by virtue of the Big Bang, the Second Law of Thermodynamics, and the ongoing rush to greater and greater entropy, the universe is inherently in motion. Some of that motion gets redirected to do work, and some of that work has resulted life emerging on our planet. Motion is intrinsic to our experience of life, much more so than time. As babies we have no sense of time, but we immediately experience mass, space, energy, and motion.

Because mass, space, energy, and motion are core to our experience, we have developed tools to help us engage with them strategically. We can weigh mass and reshape it in myriad ways to serve our ends. We can measure space using anything as a standard length and create structures of whatever size and shape we need. We can measure energy in terms of temperature and pressure and manipulate it to move all kinds of masses through all kinds of spaces. And we can measure motion through space by using standard units of time.

The equation for so doing is typically written as v = d/t. This equation makes us believe that velocity is a concept derived from the primitives of distance and time. But a more accurate way of looking at reality is to say t = d/v. That is, we can observe distance and motion, from which we derive time. If you have a wristwatch with a second hand, this is easily confirmed. A minute consists of a wand traveling through a fixed angular distance, 360°, at a constant velocity set by convention, in this case the International System of Units, these days atomically calibrated by specified number of oscillations of cesium. Time is derived by dividing a given distance by a given velocity.

OK, so what? Here the paths of philosophy and physics diverge, with me being able to pursue the former but not the latter. Before parting, however, I would like to ask the physicists in the room, should there be any, a question: If one accepted the premise that motion was the fourth dimension, not time, such that we described the universe as a continuum of spacemotion instead of spacetime, would that make any difference? Specifically, with respect to Einstein’s theories of special and general relativity, are we just substituting terms here, or are there material consequences? I would love to learn what you think.

At my end, I am interested in the philosophical implications of this question, specifically in relation to phenomenology, the way we experience time. To begin, I want to take issue with the following definition of time served up by Google:

a nonspatial continuum that is measured in terms of events which succeed one another from past through present to future.

From my perspective, this is just wrong. It calls for using events to measure time. The correct approach would focus on using time to measure motion, describing the situation as follows:

an intra-spatial continuum that can be measured in terms of time as one event succeeds another from a position of higher energy to one of lower energy.

The motive for this redefinition is to underscore that the universe is inherently in motion, following the Second Law of thermodynamics, perpetually seeking to cool itself down by spreading itself out. We here on Earth are born into the midst of that action, boats set afloat upon a river, moving with the current on the way to a sea of ultimate cool. We can go with the flow, we can paddle upstream, we can even divert the river of entropy to siphon off energy to do work. The key point to register is that motion abides, inexorably following the arrow of entropy, moving from hot to cold until heat death is achieved.

If motion is a primary dimension of the universe, there can be no standing still. Phenomenologically, this is quite different from the traditional time-based perspective. In a universe of space and time, events have to be initiated, and one can readily imagine a time with no events, a time when nothing happens, maybe something along the lines of Beckett’s Waiting for Godot. In a universe of space and motion, however, that is impossible. There are always events, and we are always in the midst of doing. A couch potato is as immersed in events as a race car driver. Or, to paraphrase Milton, they also move who only stand and wait.

A second consequence of the spacemotion continuum is that there is no such thing as eternity and no such thing as infinity. Nothing can exist outside the realm of change, and the universe is limited to whatever amount of energy was released at the Big Bang. Now, to be fair, from a phenomenological perspective, the dimensions of the universe are so gigantic that, experientially, they might as well be infinite and eternal. But from a philosophical perspective, the categories of eternity and infinity are not ontologically valid. They are asymptotes not entities.

Needless to say, all this flies in the face of virtually every religion that has ever taken root in human history. As someone deeply committed to traditional ethics, I am grateful to all religions for supporting ethical action and an ethical mindset. If there were no other way to secure ethics, then I would opt for religion for sure. But we know a lot more about the universe today than we did several thousand years ago, and so there is at least an opportunity to forge a modern narrative, one that can find in secular metaphysics a foundation for traditional values. That’s what The Infinite Staircase is seeking to do.

That’s what I think. What do you think?

Image Credit: Pixabay

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