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

Contemporary Science versus Natural Language

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Contemporary Science versus Natural Language

GUEST POST from Geoffrey A. Moore

Item 1. The fastest human-created spacecraft goes 165,000 mph. Pretty amazing. But for it to travel one light year would take roughly 3000 years—basically, the length of recorded human history. The closest star system that hosts an earth-like planet (Alpha Centauri) is 4.4 light years away. Thus, it would take today’s fastest vehicle 14,000 years to make a one-way trip. On our earth, 14,000 years ago humanity’s most sophisticated technology was a stone axe. Thus, while we love to talk about space travel outside the solar system, as well as aliens in UFOs coming to Earth, neither is remotely possible, not now, not ever.

Item 2. There are 30 trillion cells in the average human body. There are 100 trillion atoms in a typical human cell. That means there are three thousand trillion trillion atoms, give or take, in you or me. Atoms are so small that it is not clear any words we have would apply to how they actually operate. Particle and wave are two of the ones we end up using the most. Neither of them, however, can coherently explain something as simple as the double-slit experiment.

Item 3. The metabolic reactions that support all life are mind-bogglingly fast. Take mitochondria for example. They are the organelles that produce the bulk of our ATP, the energy molecule that drives virtually all life’s chemical reactions. Of the 30 trillion cells in your body, on average each one uses around 10 million molecules of ATP per second and can recycle all its ATP in less than a minute. There is simply no way to imagine something happening a million times per second simultaneously in thirty million different places inside your own body.

Item 4. Craig Venter has been quoted as saying, “If you don’t like bacteria, you’re on the wrong planet. This is the planet of the bacteria.” In one-fifth of a teaspoon of seawater, there are a million bacteria (and perhaps 10 million viruses). The human microbiome, which has staked out territory all over our body, in our gut, mouth, skin, and elsewhere, harbors upwards of three thousand kinds of bacteria, comprising some 3 million distinct genes, which they swap with each other wherever they congregate. How in the world are we supposed to keep track of that?

Okay, okay. So what’s your point?

The point is that contemporary science engages with reality across a myriad of orders of magnitude, from the extremely small to the extremely large, somewhere between sixty and one hundred all told. Math can manage this brilliantly. Natural languages cannot. All of which means: philosophers beware!

Philosophers love analogies, and well they should. They make the abstract concrete. They enable us to transport a strategy from a domain where it has been proven effective and test its applicability in a completely different one. Such acts of imagination are the foundation of discovery, the springboard to disruptive innovation. But to work properly they have to be credible. That means they must stand up to the kind of pressure testing that determines the limits to which they can be applied, the boundaries beyond which they must not stretch. This is where the orders of magnitude principle comes in.

It is not credible that there could be a cause that is a million million times smaller than its effect. Yes, it is theoretically conceivable that via a cascading set of emergent relationships, one could build a chain from such an A to such a B, but the amount of coordination that would be required to lever something up a million million times is just ridiculously improbable. So, when philosophers refer to the uncertainty principles embedded in quantum mechanics, and then infer or imply that such uncertainty permeates human affairs, or when they trace consciousness down to quantum fluctuations in messenger RNA, when, in short, they are correlating things that are more than a trillion, trillion times different in size and scope, then they are misusing both the mathematics of science and the resources of natural language. We simply have to stay closer to home.

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

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The Role Platforms Play in Business Networks

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The Role Platforms Play in Business Networks

GUEST POST from Geoffrey A. Moore

A decade and a half ago, my colleague at TCG Advisors, Philip Lay, led a body of work with SAP around the topic of business network transformation. It was spurred by the unfolding transition from client-server architecture to a cloud-first, mobile-first world, and it explored the implications for managing both high-volume transactions as well as high-complexity relationships. Our hypothesis was that high-volume networks would be dominated by a small number of very powerful concentrators whereas the high-complexity networks would be orchestrated by a small number of very influential orchestrators.

The concentrator model has played out pretty much as expected, although the astounding success of Amazon in dominating retail is in itself a story for the ages. The key has been how IT platforms anchored in cloud and mobile, now supplemented with AI, have enabled transactional enterprises in multiple sectors of the economy to scale to levels previously unimaginable. And these same platforms, when opened to third parties, have proved equally valuable to the long tail of small entrepreneurial businesses, garnering them access to a mass-market distribution channel for their offerings, something well beyond their reach in the prior era.

The impact on the orchestrator model, by contrast, is harder to see, in part because so much of it plays out behind closed doors “in the room where it happens.” Enterprises like JP Morgan Chase, Accenture, Salesforce, Cisco, and SAP clearly extend their influence well beyond their borders. Their ability to orchestrate their value chains, however, has historically been grounded primarily in a network of personal relationships maintained through trustworthiness, experience, and intelligence, not technology. So, where does an IT platform fit into that kind of ecosystem?

Here it helps to bring in a distinction between core and context. Core is what differentiates your business; context is everything else you do. Unless you are yourself a major platform provider, the platform per se is always context, never core. So, all the talk about what is your platform strategy is frankly a bit overblown. Nonetheless, in both the business models under discussion, platforms can impinge upon the core, and that is where your attention does need to be focused.

In the case of the high-volume transaction model, where commoditization is an everyday fact of life, many vendors have sought to differentiate the customer experience, both during the buying process and over the useful life of the offer. This calls for deep engagement with the digital resources available, including accessing and managing multiple sources of data, applying sophisticated analytics, and programming real-time interactions. That said, such data-driven personalization is a tactic that has been pursued for well over a decade now, and the opportunities to differentiate have diminished considerably. The best of those remaining are in industries dominated by an oligopoly of Old Guard enterprises that are so encumbered with legacy systems that they cannot field a credible digital game. If you are playing elsewhere, you will likely fare better if you get back to innovating on the offering itself.

In the case of managing context in a high-complexity relationship model, it is friction that is the everyday fact of life worth worrying about. Most of it lies in the domain of transaction processing, the “paperwork” that tags along with every complex sale. Anything vendors can do to simplify transactional processes will pay off not only in higher customer satisfaction but also in faster order processing, better retention, and improved cross-sell and up-sell. It is not core, it does not differentiate, but it does make everyone breathe easier, including your own workforce. Here, given the remarkable recent advances in data management, machine learning, and generative AI, there is enormous opportunity to change the game, and very little downside risk for so doing. The challenge is to prioritize this effort, especially in established enterprises where the inertia of budget entitlement keeps resources trapped in the coffers of the prior era’s winning teams.

The key takeaway from all this is that for most of us platforms are not strategic so much as they are operational. That is, the risk is less that you might choose an unsuitable platform and more that you may insufficiently invest in exploiting whatever one you do choose. So, the sooner you get this issue off the board’s agenda and into your OKRs, the better.

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

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Unlocking Trapped Value with AI

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Unlocking Trapped Value with AI

GUEST POST from Geoffrey A. Moore

Anyone who has used Chat GPT or any of its cousins will testify to its astonishing ability to provide valuable responses to virtually any query. This is hardly a threat—indeed, it is a boon. So, what are we worrying about?

Well, there is the issue of veracity, of course, and it is true, GPT-enabled assistants can indeed make mistakes. But, come on—humans don’t? We are not looking for gospel truth here. We want highly probable, highly informed answers to questions where we need guidance, and it is clear that GPT-enabled applications are outstanding at meeting this need, for at least three reasons. They are remarkably well-informed. They are available 24/7 on demand with no hold time. And they have infinite patience. So, let’s not kid ourselves. We are massively better off for their emergence on the scene.

What we should be worrying about, on the other hand, is their impact on jobs to be done, employment, and career development. A simple way to think about this is that for any of us to earn money, we have to release some form of trapped value. A bank clerk helps a customer get access to the trapped value in their savings account. A bus driver helps a passenger cope with their trapped value by transporting them to the location where they need to be. A lawyer helps a client get access to trapped value by constructing a contract that meets their needs while protecting against risk. A teacher helps a student access trapped value by helping her solve problems she couldn’t handle before. The principle applies to every job. All systems have points of trapped value, and all jobs are organized around releasing and capturing that value.

Now, let’s introduce generative AI. All of a sudden, a whole lot of trapped value that funded a whole lot of jobs can now be released for free (or virtually for free). Those jobs can be protected in the short term but not forever. In other words, the environment really has changed, and we must assess our new circumstances or fall behind. This is Darwinism at work. Evolution never stops. It can’t. As long as there is change, there will be dislocation, which in turn will stimulate innovation. That’s life.

But here’s the good news. The universe can never eliminate trapped value, it can only move it from place to place. That is, there are always emergent problems to solve, always new opportunities to capitalize on, because every system always traps value somewhere. What Darwinism requires is that we detect the new value traps and redirect our activity to engage with them.

Publicly funded agencies sometimes interpret this as a mandate for training programs, but we have to be careful here. Training works well for disseminating established skills that address known problems. It does not work well, however, where the problems are still being determined and the skills are as yet undeveloped. Novelty, in other words, demands creativity. It is simply not negotiable.

Getting back to the impact of generative AI, we should understand that it is an advisory technology. It is not automation. That is, it is not eliminating the need for human beings to make judgment calls. Rather, it is accelerating the preparation for so doing and framing the options in ways that make decision-making more straightforward. By solving for the old value traps, it is giving us the opportunity to up our game. It’s our job to step up to add net new value to the equation.

The best way to do this is to ferret out the emerging new value traps. Who is the customer now? What is the bottleneck that is holding them back? How could that bottleneck be broken open? What is the reward for so doing? These are the fundamental questions that drive any business model. We know how to do this. It’s just that we have been riding on the inertia of the past set of solutions for so long we may have atrophied in some of the muscles we need now. One thing we need not worry about is the universe running out of trapped value. If you are ever in doubt, just read the day’s headlines and be reassured. The world needs our help. Any tool that helps us do our part better is a blessing.

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

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Which Go to Market Playbook Should You Choose?

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Which Go To Market Playbook Should You Choose?

GUEST POST from Geoffrey A. Moore

Life-cycle go-to-market has been the focus of much of my life’s work, and I had the opportunity to recap that experience at a recent chalk talk at the HackerDoJo in Mountain View. It turned out that most of what I had to say was captured on a single slide. For readers over the age of X, this may be familiar territory; for those under the age of Y, it may prove new.

This framework highlights four different go-to-market playbooks, each optimized for a different stage of the Technology Adoption Life Cycle. The two key takeaways are:

  1. The playbook that creates success in any given stage will under-perform at any of the other three, and
  2. The playbooks do not blend; instead, they actually undercut each other when combined.

Thus, the number one job of the go-to-market strategy-setting leader is to get the entire team aligned around one, and only one, playbook.

Now, full disclosure, because different segments of the market can be in different phases of the life cycle, a go-to-market organization can be running more than one play at the same time. What they must not do is run more than one at the same time in the same place!

The Early Market Playbook

The focus of this play is to engage with a visionary customer executive who wants to leverage disruptive technology to change the world. Because your technology has yet to be adopted, the category does not yet exist, and thus there is no budget for your product. As a result, it must be funded as a project, and the customer executive has to be senior enough to have the clout to extract the necessary funds from the enterprise’s existing resource pool. Your job is to inspire that executive, hence the emphasis on thought leadership marketing to connect your breakthrough technology to their compelling business vision. It makes for a wild ride, to be sure, but when successful, it puts your company on the map as the company that did what!?!? There still is no market, there still is no budget, but there is buzz, and that buzz is associated with you, provided, that is, that your target customer is a marquee brand that people look up to. For Salesforce in its early days, this was Merrill Lynch. For Amazon Web Services in its early days, this was the CIA. For OpenAI recently, this was Microsoft.

The Bowling Alley Playbook

This is the playbook described in Crossing the Chasm. Its focus is to engage with a pragmatic business manager who is responsible for a deteriorating business process that is causing increasing problems for their enterprise, and thus, urgently needs a fix. All the conventional approaches have been found wanting, and so this prospect is open to a disruptive approach, but only if it commits to solving its specific problem. There is budget to spend here although at present it is allocated to traditional approaches. As a result, the sales cycle begins with winning the right to redirect that spend. Sales success depends on your company demonstrating a deep understanding of the problem state followed by a clear explanation of why your technology can succeed where traditional approaches fail. Implementation success depends on bringing together a team that can solve the problem end to end, leveraging domain expertise with technological leverage, to deliver what Ted Levitt taught us to call the whole product (the minimum set of products and services needed to eliminate the problem). From a market development strategy point of view, the key is to focus on a single use case in a single industry in a single geography, the goal being to develop a congregation of successful companies that will serve as a reference base as well as a loyal customer base. That is how desktop publishing helped give birth to the Mac Faithful.

The Tornado Playbook

This is the playbook that drives The Gorilla Game, a market share land grab that catapults a single company to stratospheric valuation, dragging a cohort of close contenders in its wake, resulting in the gigantic market caps that motivate early-stage venture capital investing. It is triggered by a tipping point in the adoption life cycle when pragmatic customers’ resistance to early adoption is overcome by their fear of missing out. In a flash, the new paradigm becomes the new mandate—we must have mobile apps, we must transition to cloud computing, we must procure software as a service. Budgets sprout up everywhere like mushrooms, and they are there for the picking. All this rewards a “Just win, baby” approach to go-to-market, characterized by as broad a coverage model as possible combined with highly disciplined sales tactics. RFPs (Requests for Proposals) are prevalent, driving both pilot projects and bake-offs, with marketing focusing primarily on competitive differentiation and pricing discounts. Importantly, whichever vendor wins the first pick becomes that customer’s incumbent, giving it privileged access to future purchases. Just as importantly, if one company becomes the clear market share leader, then the ecosystem of supporting companies rallies around it, elevating its competitive advantage to gorilla status.

The Main Street Playbook

This is the playbook that drives sustained earnings growth in markets that have adopted the new technology and now seek to maintain it over as long a useful lifetime as possible. At this stage, customers prefer to work with their incumbent vendors and over time to consolidate around a smaller set of integrated suites. These suites serve as platforms for ongoing innovations that are sustaining rather than disruptive, something that bores visionaries but appeals greatly to pragmatists and even more so to conservatives. In the land-and-expand as-a-service business model, we are in the expansion phase, and the growth goal is to cross-sell and up-sell new service transactions, and the earnings goal is to maximize renewals and minimize attrition. Telemetry about user adoption and feature usage is mission-critical to this effort, enabling both account managers as well as the software itself to guide the customer’s buying decisions. Product-led growth supported by self-service transactions is mission-critical for consumer applications and other user-driven offers. For enterprise sales, packaging up sets of requirements and aligning with the customer’s procurement cycle calls for the kind of account management we used to call farming and now call customer success.

Final Takeaway

Each of these playbooks makes distinctly different demands of the marketing, sales, and services teams running the go-to-market effort. People talented at one type of play may struggle with another. Our tendency as human beings is to want to stick with what we are good at, so it is usually wise to empower a new leader whenever you change playbooks.

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

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Is it Time to ReLearn to Work?

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Is it Time to ReLearn to Work?

GUEST POST from Geoffrey A. Moore

In white-collar industries where remote work is not only viable but often highly productive, we are still struggling to find a post-pandemic formula for integrating office attendance into our weekly routine. Continuing to waffle, however, does no one any good, so we need to get on with things. Part of what has been holding us back is that we have been talking about getting back to the office as an end. It is not. It is a means. The question it begs is, what is the end we have in mind? Why should we get back to the office?

Let’s start by eliminating one reason which gets frequent mention—we can manage better. This is not a good why. Supervision is an artifact of a prior era. Digitally enabled work logs itself, and we can hold each other accountable for all our KPIs, OKRs, and MBOs without having to be collocated. Managers may feel more in control with people in sight, but that is a poor return on the overall commute investment entailed.

A far better reason to return to the office is to reactivate learning. The biggest problem with remote work is that we do not learn. Specifically, we do not:

  • Learn anything new about ourselves, because we need the input of others to do so.
  • Learn new soft skills, because online courses don’t cut it.
  • Learn about our teammates, because video calls lack the needed intimacy.
  • Learn about our customers, because we need to go to their offices to do so (going to our offices would at least let us share the ride)
  • Learn about the current state of our company, because that kind of thing never gets published.

In short, just as our children experienced a learning gap at school, so we inherit the same dynamics with remote work. We consume the skills we have, but we do not develop the ones we need next. We are harvesting, but we are not seeding, and there will be a reckoning if we do not alter our course.

So, there is a good why for returning to the office, but that in turn begs the question of how? Here we need to be clear. We do not know how. We do not know what is the right formula. Unfortunately, waiting won’t help either, so now what?

Let me suggest that the best course of action is to implement a clear policy effective immediately with the following provisos.

  1. We publicly acknowledge that we suspect this policy is wrong.
  2. We are putting it in place for 90 days.
  3. We want everyone to abide by it religiously so that we get the right signals.
  4. We will review the policy publicly and transparently after 90 days and implement a new policy at that time.
  5. We will put that policy in place for 90 days, following the same protocols as before.
  6. We will rinse and repeat until no longer necessary.

The point is, we have to get on with getting on, and running the experiment is the fastest way to get there.

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

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Don’t Slow Roll Your Transformation

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Don't Slow Roll Your Transformation

GUEST POST from Geoffrey A. Moore

Business pundits love to talk about transformation, and consultants drool at the opportunity to tap into a limitless budget, but the truth is, transformations suck.

At minimum, transformation consists of re-engineering your operating model while continuing to operate, with even greater disruption involved if you are revamping your business model at the same time. Now, if you are a privately held enterprise, you might be able to sell this to your board as a “pivot,” and indeed, in the venture world, there is some accommodation built in for such moves. Not so, however, for companies whose shares are publicly held. If this describes you, fasten your seatbelt and read on.

Transformations come with “J curves”—financial projections that have you swimming underwater for some considerable period before you emerge reborn on the other side. Public investors hate J curves. They also worry prospective customers, as well as ecosystem partners, not to mention your own employees. Only a VC loves a J curve, but their attention is on a younger generation.

Nonetheless, everyone understands there are situations where transformation is warranted. For public companies, the most common cause is when the entire franchise is under existential threat. A new technology paradigm is going to categorically obsolete the core franchise, as digital photography did to Kodak, as digital media did to BusinessWeek, as wireless telephony is doing to wireline. It was an existential threat that caused Microsoft to displace its back office software business with Azure’s cloud services, even though the gross margins of the latter were negative while the net margins of the former were stupendous. It was an existential threat that drove Lou Gerstner to reengineer IBM’s hardware-centric business model to focus on services and software. Failure to transform means dissolution of the enterprise. If you are to survive, there are times when you simply have to bite the bullet.

That said, you still have to confront the issue of time. Everyone understands that a transformation will take more than one year, but no one is willing to tolerate it taking three. That is, by the end of the second year you have to be verifiably emerging from the J curve, head out of water, able to breathe positive cash flow, or else you are likely to be written off. That means transformational initiatives should be planned to complete in seven quarters, plus or minus one. That’s the amount of time you can be in the ICU before you risk getting transferred to hospice care.

So, if a transformation is in your future, and you really cannot work around it, then start your planning with the end in mind and calendar that end for seven quarters out. Now, work backward to determine where you will have to be by each of the intervening quarters in order to meet your completion date. When you get back to the current quarter, expect to see you are already two or three quarters behind schedule (not fair, I know, but I already told you that transformations suck). Suppress panic, conduct triage, and start both your engines and the clock.

Final point: given the lack of time and the amount of risk involved, there is only one sensible way to approach a transformation. Prioritize it above everything else, and keep everyone focused on making the intermediate milestones until you are well and truly out of danger. Transformations are no joking matter. Most companies lose their way. Don’t let that be true of you and yours.

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

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Back to the Basics of the Performance Zone

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Back to the Basics of the Performance Zone

GUEST POST from Geoffrey A. Moore

As the global economy gropes its way to a new normal, with buyers still looking to regain their confidence to invest, most companies are dealing with sluggish performance—not terrible, but not great. In such circumstances, management attention gravitates to the Productivity Zone, where the focus is internal on ourselves, and the goal is to optimize our processes to prop up our operating margins. All good, but only half the solution.

The other half is to reengage with the Performance Zone. The goal of this zone is to not to improve–it is to win the game. There is no process for doing this (if there were, then Germany would win the World Cup every year), so internal focusing will not help. Instead, we need to reexamine our relationship with others, specifically with our customers and our competitors. Strategy begins, in other words, when we divert our attention from us and put it on them.

Investigating our Customers

In a doldrums economy, we know that existing budgets are tight, so if we are to find growth opportunities, we need to detect where new budgets are emerging. In other words, we are looking for forces at work in our target markets that are changing the investment priorities of our target customers. The key unit of examination here is the use case.

Use cases live at the intersection of our portfolio of offerings and customer value realization. We already have libraries of established use cases, but those are the ones that are under budget constraint. We are looking for emerging use cases, typically gnarly problems that are possible to solve with our stuff, but only with net new innovation and additional attention from us. Such use cases are at odds with our Productivity Zone focus on efficiency, but they are key to finding growth opportunities in trying times.

Each use case is a shorthand representation for a mini-TAM (Total Addressable Market). We are not looking for big here, we are looking for urgent. We want use cases that will activate customers to invest now, even when budgets are tight, keeping in mind that even the most highly focused use case with the smallest immediate TAM is normally a harbinger of bigger things to come. First-mover advantage in an emerging use case is like winning an early primary election—it is modestly valuable in itself, but even more so in terms of its impact on later competitions in bigger venues.

To detect these opportunities we need to interrogate our customer-facing teams in sales, solution engineering, and customer success to extract from them anecdotal evidence of novel use cases, regardless of who the vendor is. We also want to hear stories about customers struggling with problems that no one is solving. The question we are trying to answer is, what does the world really want from our company now? What would cause prospective customers to line up to spend money with us today?

To be sure, pursuing net new use cases requires investment at our end, and we too are under budget pressure, so there can be no “spray and pray” here. We need to stack rank whatever opportunities we detect on a risk/reward gradient and focus on the top one or two only, the limiting factor being that whatever we do fund must get “all the way to bright.” Adding even just one more opportunity than there is budget to fund results in all opportunities getting underfunded and nothing getting over the finish line. It is the most common cause of companies losing their way and drifting into irrelevance.

Learning from our Competitors

Here again we should divide up the landscape into legacy versus future competitors, as we will treat each differently. The legacy group are competing for the same constrained budgets as we are, using tactics we are now quite likely to be familiar with. This is the realm of execution, not strategy. It rewards campaigns led by the Productivity Zone focused on extracting the best returns we can from what is a low-yield, but also a low-risk, situation. Our customers are not going away, but they are going to sweat their assets and consolidate vendors wherever they can. Inertia here is our friend, and we need to leverage it as best we can by eliminating any sources of friction that would diminish our returns.

On the other hand, our future competitors do warrant strategic attention, for any number of reasons. For example, any recent wins they may have had could signal an emerging new use case, one that we too should be checking out. Alternatively, we may learn they are attacking our own target use case, in which case we need to differentiate quickly and dramatically in order to block them out early (a mini-TAM is too small for more than one winner). A third possibility is that we may be getting blindsided altogether, our installed base under some whole new form of attack, potentially jeopardizing the future of our entire franchise. It’s a wake-up call nobody likes to get, essentially forecasting an existential threat, but that is often what it takes to prod an established enterprise to adjust to a changing market landscape.

The standard unit of work for investigating future-oriented competition is the win/loss analysis. Again, we need to bring in the customer-facing teams to get their anecdotal evidence. Analyst reports don’t help much—they tend either to track us and our legacy competitors in established markets, or to glom onto the next potential disruptive technology and make extravagant extrapolations of its future returns. Instead, we want to look closely at the new use cases, regardless of whether we have won or lost, to see what the customer ended up prioritizing and why that drove their buying decision. As always, we prefer to win, but it is imperative regardless that we learn.

Changing the Narrative

Once we have focused on others, once we have revised our understanding of what the world wants from us, and who we are going to be competing with, we can now legitimately focus our attention on ourselves and our stakeholders. These include our installed base, our ecosystem partners, our investors, and our employee workforce. Our new strategy calls for a change in our course and speed, and we need everyone in our boat to row in the same direction. This can only happen if we change the narrative.

It is hard to overemphasize this point, so let me put it another way. If we do not change the narrative, nothing new will happen. No one will change course and speed. Even if we make clear the course corrections we are making, things still won’t change. That’s because everyone always assumes that things will be more or less the same, and that goes especially for established franchises. Getting stakeholders to turn a big boat requires a big signal.

The structure of the successful new narrative is always the same. It is never about you. Nobody cares about you (well, except your mom, of course, God bless her). Stakeholders have plenty on their own plates to worry about without taking on stuff on yours. What they do care about, on the other hand, is what is happening in their world, how it impinges on their hopes and plans, where it is creating risk for them, and what, if anything, you might be able to do to help them mitigate that risk. That’s what your new narrative must be all about. It’s a new you because it is a new world, and you are rising to meet the occasion. Not only does this change people’s focus, it energizes those whom it attracts, giving a real boost to the team at a time when everyone can use one.

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

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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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