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

The Hard Problem of Consciousness is Not That Hard

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The Hard Problem of Consciousness is Not That Hard

GUEST POST from Geoffrey A. Moore

We human beings like to believe we are special—and we are, but not as special as we might like to think. One manifestation of our need to be exceptional is the way we privilege our experience of consciousness. This has led to a raft of philosophizing which can be organized around David Chalmers’ formulation of “the hard problem.”

In case this is a new phrase for you, here is some context from our friends at Wikipedia:

“… even when we have explained the performance of all the cognitive and behavioral functions in the vicinity of experience—perceptual discrimination, categorization, internal access, verbal report—there may still remain a further unanswered question: Why is the performance of these functions accompanied by experience?”

— David Chalmers, Facing up to the problem of consciousness

The problem of consciousness, Chalmers argues, is two problems: the easy problems and the hard problem. The easy problems may include how sensory systems work, how such data is processed in the brain, how that data influences behavior or verbal reports, the neural basis of thought and emotion, and so on. The hard problem is the problem of why and how those processes are accompanied by experience.3 It may further include the question of why these processes are accompanied by that particular experience rather than another experience.

The key word here is experience. It emerges out of cognitive processes, but it is not completely reducible to them. For anyone who has read much in the field of complexity, this should not come as a surprise. All complex systems share the phenomenon of higher orders of organization emerging out of lower orders, as seen in the frequently used example of how cells, tissues, organs, and organisms all interrelate. Experience is just the next level.

The notion that explaining experience is a hard problem comes from locating it at the wrong level of emergence. Materialists place it too low—they argue it is reducible to physical phenomena, which is simply another way of denying that emergence is a meaningful construct. Shakespeare is reducible to quantum effects? Good luck with that.

Most people’s problems with explaining experience, on the other hand, is that they place it too high. They want to use their own personal experience as a grounding point. The problem is that our personal experience of consciousness is deeply inflected by our immersion in language, but it is clear that experience precedes language acquisition, as we see in our infants as well as our pets. Philosophers call such experiences qualia, and they attribute all sorts of ineluctable and mysterious qualities to them. But there is a much better way to understand what qualia really are—namely, the pre-linguistic mind’s predecessor to ideas. That is, they are representations of reality that confer strategic advantage to the organism that can host and act upon them.

Experience in this context is the ability to detect, attend to, learn from, and respond to signals from our environment, whether they be externally or internally generated. Experiences are what we remember. That is why they are so important to us.

Now, as language-enabled humans, we verbalize these experiences constantly, which is what leads us to locate them higher up in the order of emergence, after language itself has emerged. Of course, we do have experiences with language directly—lots of them. But we need to acknowledge that our identity as experiencers is not dependent upon, indeed precedes our acquisition of, language capability.

With this framework in mind, let’s revisit some of the formulations of the hard problem to see if we can’t nip them in the bud.

  • The hard problem of consciousness is the problem of explaining why and how we have qualia or phenomenal experiences. Our explanation is that qualia are mental abstractions of phenomenal experiences that, when remembered and acted upon, confer strategic advantage to organisms under conditions of natural and sexual selection. Prior to the emergence of brains, “remembering and acting upon” is a function of chemical signals activating organisms to alter their behavior and, over time, to privilege tendencies that reinforce survival. Once brain emerges, chemical signaling is supplemented by electrical signaling to the same ends. There is no magic here, only a change of medium.
  • Annaka Harris poses the hard problem as the question of “how experience arise[s] out of non-sentient matter.” The answer to this question is, “level by level.” First sentience has to emerge from non-sentience. That happens with the emergence of life at the cellular level. Then sentience has to spread beyond the cell. That happens when chemical signaling enables cellular communication. Then sentience has to speed up to enable mobile life. That happens when electrical signaling enabled by nerves supplements chemical signaling enabled by circulatory systems. Then signaling has to complexify into meta-signaling, the aggregation of signals into qualia, remembered as experiences. Again, no miracles required.
  • Others, such as Daniel Dennett and Patricia Churchland believe that the hard problem is really more of a collection of easy problems, and will be solved through further analysis of the brain and behavior. If so, it will be through the lens of emergence, not through the mechanics of reductive materialism.
  • Consciousness is an ambiguous term. It can be used to mean self-consciousness, awareness, the state of being awake, and so on. Chalmers uses Thomas Nagel’s definition of consciousness: the feeling of what it is like to be something. Consciousness, in this sense, is synonymous with experience. Now we are in the language-inflected zone where we are going to get consciousness wrong because we are entangling it in levels of emergence that come later. Specifically, to experience anything as like anything else is not possible without the intervention of language. That is, likeness is not a qualia, it is a language-enabled idea. Thus, when Thomas Nagel famously asked, “What is it like to be a bat?” he is posing a question that has meaning only for humans, never for bats.

Going back to the first sentence above, self-consciousness is another concept that has been language-inflected in that only human beings have selves. Selves, in other words, are creations of language. More specifically, our selves are characters embedded in narratives, and use both the narratives and the character profiles to organize our lives. This is a completely language-dependent undertaking and thus not available to pets or infants. Our infants are self-sentient, but it is not until the little darlings learn language, hear stories, then hear stories about themselves, that they become conscious of their own selves as separate and distinct from other selves.

On the other hand, if we use the definitions of consciousness as synonymous with awareness or being awake, then we are exactly at the right level because both those capabilities are the symptoms of, and thus synonymous with, the emergence of consciousness.

  • Chalmers argues that experience is more than the sum of its parts. In other words, experience is irreducible. Yes, but let’s not be mysterious here. Experience emerges from the sum of its parts, just like any other layer of reality emergences from its component elements. To say something is irreducible does not mean that it is unexplainable.
  • Wolfgang Fasching argues that the hard problem is not about qualia, but about pure what-it-is-like-ness of experience in Nagel’s sense, about the very givenness of any phenomenal contents itself:

Today there is a strong tendency to simply equate consciousness with qualia. Yet there is clearly something not quite right about this. The “itchiness of itches” and the “hurtfulness of pain” are qualities we are conscious of. So, philosophy of mind tends to treat consciousness as if it consisted simply of the contents of consciousness (the phenomenal qualities), while it really is precisely consciousness of contents, the very givenness of whatever is subjectively given. And therefore, the problem of consciousness does not pertain so much to some alleged “mysterious, nonpublic objects”, i.e. objects that seem to be only “visible” to the respective subject, but rather to the nature of “seeing” itself (and in today’s philosophy of mind astonishingly little is said about the latter).

Once again, we are melding consciousness and language together when, to be accurate, we must continue to keep them separate. In this case, the dangerous phrase is “the nature of seeing.” There is nothing mysterious about seeing in the non-metaphorical sense, but that is not how the word is being used here. Instead, “seeing” is standing for “understanding” or “getting” or “grokking” (if you are nerdy enough to know Robert Heinlein’s Stranger in a Strange Land). Now, I think it is reasonable to assert that animals “grok” if by that we mean that they can reliably respond to environmental signals with strategic behaviors. But anything more than that requires the intervention of language, and that ends up locating consciousness per se at the wrong level of emergence.

OK, that’s enough from me. I don’t think I’ve exhausted the topic, so let me close by saying…

That’s what I think, what do you think?

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Aligning Your Culture for Digital Transformation

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Aligning Your Culture for Digital Transformation

GUEST POST from Geoffrey A. Moore

A quote you often hear is, “Culture eats strategy for lunch,” typically attributed to Peter Drucker (whether correctly or not). Regardless, it puts a spotlight on the power of culture to resist even the most compelling strategic narratives. These days it’s hard to come up with a more compelling narrative than digital transformation. But it can definitely find itself at odds with culture, so what chance could it possibly have?

In my work with successful companies, two cultures show up over and over again. One is a competition culture, where teams get up every morning driven to be the best. The other is a collaboration culture, where teams strive to be the best for others. Both cultures can create great companies, and, if you play your cards right, each can be enlisted as an ally of change. You just have to get it aligned properly.

To do so, you need to use your culture to focus people on a driving force of change that is outside of your company:

  • In the case of a competition culture, this would be a competitor using disruptive technology to steal your market share. Think Google for Microsoft, Lyft for Uber, Nvidia for Intel, or Arista for Cisco. Transform or they win! That’s the sort of thing that galvanizes change in a competition culture.
  • In the case of a collaboration culture, the driving force is fear of letting your customer down as the world shifts to a new platform. Think of Salesforce championing machine learning, Docusign championing systems of agreement, or Proofpoint championing people-centric security. These are changes that could put your customers’ franchises at risk. No customer left behind! That’s the battle cry that brings a collaboration culture to attention.

The key point here is that, regardless of whether you have a competition or a collaboration culture, the force for change must be external, not internal. Either culture, internally focused, simply will not transform. Instead, everyone will spend all their time listening to radio station WIIFM—What’s in it for me? And what they will learn is that there are not a lot of good songs playing. Transformation requires sacrifice. We are going to have to step back before we step forward.

People are willing to sacrifice for the right cause outside the company, but not inside. So, when you are leading a transformation, be sure to keep people’s attention focused on a North Star that transcends their individual issues, not on the career compass they are holding in their hand.

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

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A Top-Down Open Innovation Approach

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A Top-Down Open Innovation Approach

GUEST POST from Geoffrey A. Moore

For high-tech in much of the 20 century, when start-up capital was scarce and the need for it was great, innovation began at the core and migrated to the edge. Today we have the reverse. Start-up capital is plentiful, the need for it is modest, and innovation is thriving at the edge and moving reluctantly to the core, fearful of the inertia it will encounter once it gets there.

Yet if innovations are going to scale, they must leverage the core-edge dynamic in both directions. That means, in addition to enabling innovation from the bottom up—something today’s start-up enterprises are having great success in doing—we must also be able to manage it from the top down, from the core out, from the acquiring-sponsoring enterprise to acquired-innovating start-up. Here success is not so widespread, but there is a fix for that.

Geoffrey Moore Return on Innovation

In the core-edge dynamic, the job of the core acquiring institution is not to innovate—it is to get a return on innovation from wherever it is sourced. This could be an internal skunk works project, a major R&D project, a tuck-in acquisition, or a merger with another mature enterprise. The challenge is not, in other words, to bring innovation into existence but rather to capitalize on it in a meaningful way. That is what the pie chart above is all about.

The key claims of this model are 1) that there are three ways to get a positive return from an innovation investment and 2) that they are mutually exclusive. (There are also at least five ways to get a negative return which we will get to in a moment.)

The winning returns can come from:

  1. Differentiation. To win here you must create an offer that dramatically outperforms its competitive set on at least one vector of innovation. You are playing for competitive separation, looking for a 10X result on at least one chosen vector, either in product performance, customer delight, or operational savings. This sort of thing creates the highest return on innovation possible. Think Apple iPad over any prior tablet (or arguably any tablet since).
  2. Neutralization. To win here you must catch up to a competitor’s innovation sufficiently to get your offer back in the hunt. This means getting to “good enough” as quickly as possible. Here you are playing for speed—how fast can you get back in the game. Think Google Android catching up to (and then overtaking) the Apple iPhone.
  3. Optimization. To win here you produce essentially the same offer on a better, faster, cheaper basis. Basically, you are extracting resources from an established effort in order to hit a new price-point, repurpose them for innovation elsewhere or simply taking to the bottom line. Here you are playing neither for separation nor for speed but rather for money. Think Nokia’s long history of success with feature phones.

The critical thing to note about these three sources of return is that they are at odds with one another. If you are going to get maximum separation, you cannot tell exactly when that will occur, so you cannot play for speed. Conversely, if you are playing for speed, you must suppress any impulse to go beyond a “good enough” standard. But in both cases you are willing to spend extra money to achieve your primary goal, be that separation or speed. That puts both approaches at odds with optimization, where the goal is to extract cost from the system.

The net of this is that top-down management of innovation requires leaders to charter their innovation teams with one—and only one—of these objectives. Where you have multiple needs, you need multiple teams. To understand why, let’s turn to look at how innovation investments fail to pay off.

There are at least five ways this can happen, as follows:

  1. The innovation doesn’t work. Ouch. But that is the price of playing innovation poker. In fact, if you have no failed experiments, you probably are not taking enough risk.
  2. The differentiation doesn’t go far enough. Yes, you create something different, but it is a far cry from a 10X separation, and so the market accepts it as good but does not grant you any competitive advantage for it. Basically, you just spent your R&D budget and have nothing to pay you back for it. HP and Dell have both suffered here greatly in recent years.
  3. The neutralization doesn’t go fast enough. The team got caught up in out-doing the competition rather than simply getting to good enough. The problem is, the market will not pay you any return on improvements beyond good enough, so all you have done here is waste time, which is the one thing you cannot afford to waste when your product is out of the game. Nokia was a prime offender here with respect to its tardy response to the iPhone challenge.
  4. The optimization doesn’t go deep enough. Basically, you optimize around the edges and do not attack any of the sacred cows (typically meaning you do not touch either engineering or sales). The gains are minimal, and the bottlenecks that are holding you back are still deeply in place. Ginny Rometti made a version of this point in one of IBM’s earnings calls, but so could every other Tech 50 CEO in any given quarter. This is a really big problem because tech has never been good at optimization.
  5. The innovation project blended two or more goals. The problem here is that either the differentiation goal slowed you down or the neutralization goal dumbed you down or the optimization goal tied you down. One way or another, you went down.

So the net here is simple. Managing innovation is a different discipline from innovating per se. It is all about controlling the charter, targeting one and only one kind of return, and then focusing the team solely on that set of outcomes. It isn’t all that cool. It is just very, very important.

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

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How to Make Your Customers Hate You

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One Zone at a Time

How to Make Your Customers Hate You

GUEST POST from Geoffrey A. Moore

My most recent book, Zone to Win, lays out a game plan for digital transformation based on organizing your enterprise around four zones. They are the:

  1. Performance Zone, where you make, sell, and deliver the products and services that constitute your core business.
  2. Productivity Zone, where you host all the cost centers that support the Performance Zone, functions like finance, HR, IT, marketing, legal, customer support, and the like.
  3. Incubation Zone, where you experiment with next-generation technologies to see if and how they might play a role in your future.
  4. Transformation Zone, which you bring into existence on a temporary basis for the sole purpose of driving a digital transformation to completion.

The book uses these four zones to help you understand your own company’s dynamics. In this blog, however, we are going to use them to help you understand your customer’s company dynamics.

Here is the key insight. Customers buy your product to create value in one, and normally only one, zone. Depending on which zone they are seeking to improve, their expectations of you will vary dramatically. So, if you really want to get your customers to hate you, you have to know what zone they are targeting with your product or service.

To start with, if your customer is buying your product for their Productivity Zone, they want it to make them more efficient. Typically, that means taking cost out of their existing operations by automating one or more manual tasks, thereby reducing labor, improving quality, and speeding up cycle time. So, if you want to make this customer hate you, load up your overall offer with lots of extras that require additional training, have features that can confuse or distract end users, and generally just gum up the works. Your product will still do what you said it would do, but with any luck, they won’t save a nickel.

Now, if instead they are buying your product to experiment with in their Incubation Zone, they are looking to do some kind of proof of concept project. Of course, real salespeople never sell proofs of concepts, so continue to insist that they go all in for the full Monty. That way, when they find out they can’t actually do what they were hoping to, you will have still scored a good commission, and they will really hate you.

Moving up in the world, perhaps your customer has bought from you to upgrade their Performance Zone by making their operations more customer-focused. This is serious stuff because you are messing with their core business. What an opportunity! All you have to do is over-promise just a little bit, then put in a few bits that are not quite fully baked, turn the whole implementation over to a partner, and then, if the stars align, you can bring down their whole operation and blame it entirely on someone else. That really does get their dander up.

But if you really want to extract the maximum amount of customer vitriol, the best place to engage is in their Transformation Zone. Here the CEO has gone on record that the company will transform its core business to better compete in the digital era. This is the mother lode. Budget is no object, so soak it to the max. Every bell, whistle, doo-dad, service, product—you name it, load it into the cart. Guarantee a transformational trip to the moon and back. Just make sure that the timeline for the project is two years. That way you will be able to collect and cash your commission check before you have to find other employment.

Of course, if for some reason you actually wanted your customer to like you, I suppose you could reverse these recommendations. But where’s the fun in that?

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

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3 Flavors of Product-Service Shift

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Which One is Yours?

3 Flavors of Product-Service Shift

GUEST POST from Geoffrey A. Moore

The most profound change in enterprise computing in this century to date has been the shift in value delivery modality from product to service and the corresponding rise is XaaS or Everything-as-a-Service. The current bull market leaders in the tech sector take this for granted, and the prior generation of incumbents are still scrambling to get themselves onto the new model. For consumers this is an all-upside proposition; for enterprises, it is a balancing act of open fluidity versus secure compliance. But everyone seems to know their place in the new order—or do they?

As the product-service shift unfolds, it can manifest itself at three very different levels of value delivery, each of which has its own priorities. When you are looking to help your organization navigate the transition, it would be good to get clear as to which path you are on:

1. Infrastructure Model Transformation

This is the easiest to absorb, the impact for the most part contained on the vendor side within Finance and Legal and on the customer side within the IT organization itself. Basically, all you are doing is changing the contract from a license to a service level agreement, and staging a series of leasing payments out of op ex instead a one-time purchase out of cap ex. For clarity sake, think of this as a move to subscription, not yet to For most people in the organization, it is a non-event.

2. Operating Model Transformation

This move has the most impact on incumbent vendors and their installed base. As Todd Hewlin and J B Wood described in Consumption Economics, the shift is based on a change from the customer to the vendor as the one who must absorb goal attainment risk. In a product model, once the customer has bought and paid for it, the customer owns virtually all the risk. That can readily lead to a lot of drive-by selling, the sort of thing that built out empires of shelfware in the late 1990s. In a service model, by contrast, the vendor can never stop owning the success of the offering, not if they want to protect against their installed base churning out from underneath them. This is the true product-service shift, and even now it is sufficiently novel that both customers and vendors are still sorting out the implications for what staffing and expertise is needed on both sides of this relationship.

3. Business Model Transformation

This is the most impactful for venture-backed start-ups and the incumbent franchises they are looking to disrupt. Typically the former are re-architecting an established but aging value chain by substituting digital services for physical-world interactions. The biggest disruptions we have seen thus far are in retail, print media, financial services, transportation, hospitality, and communications, with lots more to come. They all represent daggers pointed at the heart of established enterprises because even when the latter can find ways to re-engineer their own offers to match the new paradigm, it is still painfully hard to bring the rest of their ecosystems up to speed to deliver the whole product. And to a lesser extent, the same goes for their customer bases. That is why disruption usually starts with targeting customers who have been disenfranchised by the old solution. It is only over time that the Innovator’s Dilemma bill comes to for the established vendors, but when it does, it hits with a wallop.

For most companies, the path you want to double-click on is the Operating Model Transformation, and in the next post, I want to dig in a lot deeper there.

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

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Bringing Yin and Yang to the Productivity Zone

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Bringing Yin and Yang to 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 1, 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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A Triumph of Artificial Intelligence Rhetoric

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Understanding ChatGPT

A Triumph of Artificial Intelligence Rhetoric - Understanding ChatGPT

GUEST POST from Geoffrey A. Moore

I recently finished reading 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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Mission Critical Doesn’t Mean What You Think it Does

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Mission Critical Doesn't Mean What You Think it Does

GUEST POST from Geoffrey A. Moore

God bless NASA for giving us the phrase “mission critical,” and God bless The Princess Bride for teaching us that not all words mean what we think they do.

In the case of mission-critical, specifically, the term has two distinct connotations, each of which leads to a distinctively different management priority.

1. Must achieve this outcome to succeed. This is what most people first think of when they hear the phrase. We will put a man on the moon and bring him back by the end of the decade. Anything that is on the critical path to that objective is mission critical.

2. Must not fall below this standard or we will be disqualified. This refers to a host of other things that, if not done properly, could have catastrophic consequences for the mission. Securing adequate funding, managing finances carefully, acquiring and maintaining proper facilities, and complying with pertinent regulations all come under this heading. You get no prize for doing any of these things right, but there can be a whopping penalty for getting them wrong.

When mission-critical equates to achieving success, the goal is to allocate the maximum amount of resources to the activity in question because it is the source of highest return. Indeed, it is your whole reason to be. Often in this situation there is no fixed upper boundary as to how much success can be achieved, so more is always going to be better here. That is why managers seeking budget for their efforts like to position them as mission-critical.

When mission-critical equates to disqualification risk, however, this approach backfires. That’s because there is a natural human tendency in risk-bearing situations to over-allocate resources as a hedge against what potentially could be a catastrophic failure. No one wants to get blamed for anything like this. Thus there is almost always an unproductive use of resources associated with these workloads and processes.

The proper goal for managing disqualification risk is to deploy the least amount of resources needed to achieve an acceptable level of risk, understanding that risk itself can never be eliminated entirely. To do this requires investing both in governance systems and in cultural discipline—the better the systems, the more disciplined the culture, the fewer the resources will be required.

Entrepreneurial cultures who grew up with the mantra “We don’t need no stinkin’ systems” will find it hard to execute this playbook, but until they do, they will be unable to scale. Conversely, risk-averse cultures who are unwilling to even approach the efficient frontier of risk will also fail here as well. You cannot compete effectively if a host of your best players are tied up on the sidelines. In short, there is no substitute for getting disqualification risk right, and successful organizations will testify this is always a work in progress.

So the next time you hear the word mission-critical, perk your ears up and apply this filter. Whatever is under discussion, for sure you are going to want to do this thing right. But before that, make sure you are doing the right thing.

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

Image Credit: Pixabay

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Understanding Permission

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Understanding Permission

GUEST POST from Geoffrey A. Moore

Permission is one of the most powerful concepts in strategic marketing, but it is an easy one to misinterpret if you are not careful. Here are five things you need to know:

1. Permission is anchored in the idea of what the world wants your company to be.

It may surprise you that the world has an agenda for your company, but it does—even if it has never heard of you. The world has unmet needs, and it is always looking for someone credible to enlist in meeting those needs. If you are a start-up, then you are credible at being highly disruptive. If you are an established and vibrant enterprise, then you are credible as a responsible steward. If you are an aging enterprise looking to rejuvenate, then you are credible as an agent of change within a conservative community.

2. Permission means you get more than one bite at the apple.

It means the customer base, and even the industry ecosystem, are willing to cut you some slack because, in their view, you represent the best chance for them to accomplish their agenda. In effect, you are granted a competitive advantage out of the box, given a status that none of your peers are getting. It is an amazing gift and is key both to start-up successes and enterprise turnarounds.

3. Permission is absolutely necessary at the point of business transformation.

There are plenty of times when you can overcome lack of permission through tenacious persistence, but not at the point of transformation. When you are turning the boat, when you are changing the direction of your enterprise, when you are entering a new space, your first encounters need to be with natural allies. They may not have bought in just yet, but they must be naturally aligned with your agenda, or you need to go elsewhere. You are simply too vulnerable otherwise.

4. Permission is an invitation to display generosity.

The prize you are after is a relationship of trust, one which will cause your target customers to privilege your company above other vendors. That’s what will secure your future in the new world. To accelerate getting to that point, begin with acts of strategic generosity, ones that demonstrate respect for the challenges your customers are hoping you can solve, and evidence that you have the wherewithal to solve them. Get these offers in play ASAP, and let the market’s viral properties drag you forward.

5. Permission has a sell-by date.

When issues and concerns come to the fore, there is a period of several years during which the pragmatist herd will wait to see who is going to step up to meet its new set of needs. If you have the wherewithal to lean in at this time but hold back instead, perhaps because you fear you will cannibalize some other aging product line, you will not be forgiven downstream. Gather the rosebuds while ye may.

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

Image Credit: Unsplash

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A Shortcut to Making Strategic Trade-Offs

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A Shortcut to Making Strategic Trade-Offs

GUEST POST from Geoffrey A. Moore

I read with interest the following article posted on hbr.org. It highlights the challenge facing every Executive Leadership Team in securing alignment around what they should prioritize, short versus long-term gains, high versus low-risk initiatives, and disruptive versus sustaining innovation. The article notes that conflicts requiring sacrifices are common across industries, and that to handle them better, CEOs should introduce a “calculus of sacrifice” to ensure greater alignment in decision-making:

“By making the degree of sacrifice explicit among such conflicting objectives and quantifying it, CEOs can reframe decision-making and give executives the tools to make decisions aligned with their vision. Instead of advocacy-based deliberations, in which proponents of different courses of action make affirmative cases, discussion focuses on sacrifice: How much of one thing are we willing to give up in order to get more of something else?”

I take this to be a very reasonable point of departure, but from here the article goes on to propose a lengthy set of dialogs between the CEO and every member of the ELT digging into their personal approach to these issues and working toward a collaborative consensus about the best course of action. I don’t think this is either realistic or efficient. Instead, let me advocate for a zone-based approach.

As readers of this blog will be aware, the zone management model identifies four “zones of interest” within any enterprise, each with its own mission, metrics, and governance model, as follows:

  1. Performance Zone: Focus on executing this year’s annual plan with particular emphasis on meeting or beating the financial guidance given to investors.
  2. Productivity Zone: Focus on supporting the Performance Zone by attending to all the processes required to operate the enterprise efficiently, effectively, and in compliance with regulations.
  3. Incubation Zone: Focus on disruptive innovations that could have substantial impact on the enterprise’s future success, and develop real options for incorporating them into a future portfolio.
  4. Transformation Zone: Focus on taking a single disruptive innovation to scale, thereby changing the overall valuation of the enterprise’s portfolio.

Each of these four zones entails a different “calculus of sacrifice,” one that is built into the mission and metrics of that zone. Rather than ask the Executive Leadership Team to chart a path forward by keeping all four in mind, a simpler way forward is to use the annual budgeting process to allocate a percentage of the total available resources of the enterprise to each one of the four zones. The question is not, in other words, what should we do with this specific situation, but rather, how much of our operating budget do we want to spend in each of the four areas? It is still a tough question to answer, but it is bounded, and you can reach closure on it at any given point in time simply by having the CEO say, this is what it is going to be.

Once the allocations are settled, then decision-making can go much faster, because each member of the ELT is making calls in one, and only one, zone, using the calculus of that zone and ignoring those of the other three. In other words, stop trying to make your colleagues more or less innovative or risk-averse, and instead, let them play to their strengths in whatever zone represents their best fit.

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

Image Credit: Geoffrey Moore

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