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

Re-engineering the Incubation Zone for a Downturn

Leave a reply

Re-engineering the Incubation Zone for a Downturn

GUEST POST from Geoffrey A. Moore

In a prior post, written during the tech boom, I outlined how established enterprises could re-engineer their approach to managing innovation in order to catch the next wave before it caught them. Now we are in a different time, where capital is more expensive, and near-term profitability more necessary. We still need to innovate our way through the challenges ahead, and the management playbook is fundamentally the same, but there are enough nuances to attend to that it is worth revisiting the topic end to end.

The guiding principle is unchanged. Publicly-held enterprises routinely mismanage incubation to such an extent that, when they are successful, the market is actually surprised. Their approach is based on a process model, typically involving crowd-sourcing a large funnel of potential ideas from the workforce, taking those ideas through a well-structured qualification process with clear benchmarks for progressing to the next stage, and funding a handful of the best ideas to get through to a minimum viable product (MVP) and market validation. The problem is that this is a Productivity Zone operating model, not an Incubation Zone model. That is, these enterprises are treating the Incubation Zone as if it were another cost center. Needless to say, no venture capitalist operates in this manner.

Meanwhile, the venture capital industry is routinely successful at managing incubations, be they to successful exits or timely shut-downs. Their operating model has over forty years of established success—and yet it is a rare public enterprise indeed that even tries to implement it. Some of this is due to confusing the venture industry’s business model, which is not appropriate for a publicly held firm, with its operating model, which is perfectly suitable to emulate. It is that model that I want to describe here.

Anchor Tenets

There are at least five key principles that successful Venture Capitalists (VC’s) keep close to their hearts. They are:

  1. Trapped value. VC’s are nothing if not coin-operated, and in that context, the first thing to do is find the coins. In B2B markets, this typically equates to identifying where there is trapped value in the current way of doing business. The value may be trapped in the infrastructure model (think cloud computing over data centers), the operating model (think self-organizing ride dispatching from Uber over the standard call center dispatcher), or the business model (think software subscription over license and maintenance). The point is, if you can release the trapped value, customers will enjoy dramatic returns, enough to warrant taking on the challenge of a Technology Adoption Life Cycle, even in a downturn. This is key because in a downturn, absent a compelling reason to act immediately, pragmatic customers will defer their buying decisions as long as possible. So, innovation for innovation’s sake is not the play for today’s market. You should be looking for disease-preventing vaccines, not life-extending vitamins.
  2. 10X technology. VCs are fully aware that there are very good reasons why trapped value stays trapped. Normally, it is because the current paradigm has substantial inertial momentum, meaning it delivers value reliably, even though far from optimally. To break through this barrier requires what Andy Grove taught us to call a 10X effect. Something has to be an order of magnitude better than the status quo to kick off a new Technology Adoption Life Cycle. Incremental improvements are great for reinforcing the status quo, as well as for defending it against the threat of disruption, but they do not have the horsepower to change the game. So, do not let your Incubation Zone “major in minors.” If there is not something truly disruptive on your plate, wait for it, and keep your powder dry.
  3. Technology genius. 10X innovations do not fall out of trees. Nor are they normally achieved through sheer persistence. Brilliance is what we are looking for here, and here publicly held enterprises face a recruiting challenge. They simply cannot offer the clean slate, venture funding, and equity reward possibilities that private capital can. What they can do, however, is pick up talent on the rebound and integrate it into their own playbook (see more on this below). The point is, top technology talent is a must-have. This puts pressure both on the general manager of any Incubation Zone operating unit and on the Incubation Zone board to do whatever it takes to put an A Team together. That said, there is a loophole here one can exploit in a downturn. If your enterprise needs to catch up to a disruptive innovation, that is, if it needs to neutralize a competitive threat as opposed to instigating a new adoption life cycle, then a “fast follower” leader is just the ticket. This person does not think outside the box. This person catches the box and jumps on it. Microsoft has been the premier example of this playbook from its very inception, so there is definitely money to be made here!
  4. New design rules. The path for breakthrough technology to release trapped value involves capitalizing on next-generation design rules. The key principle here is that something that used to be expensive, complex, and scarce, has by virtue of the ever-shifting technology landscape, now become cheap, simple, and plentiful. Think of DRAM in the 1990s, Wi-Fi in the first decade of this century, and compute cycles in the current decade. Prior to these inflection points, solution designers had to work around these factors as constraints, be that in constricting code to run in 64KB, limiting streaming to run over dial-up modems, or operating their own data center when all they wanted to do was to run a program. Inertia holds these constraints in place because they are embedded in so many interoperating systems, they are hard to change. Technology Adoption Life Cycles blow them apart—but only when led by entrepreneurs who have the insight to reconceive these assets as essentially free.
  5. Entrepreneurial general manager. And that brings us to the fifth and final key ingredient in the VC formula: entrepreneurial GMs. They are the ones with a nose for trapped value, able to sell the next new thing on its potential to create massive returns. They are the ones who can evangelize the new technology, celebrate its game-changing possibilities, and close their first visionary customers. They must recruit and stay close to their top technology genius. They must intuit the new design rules and use them as a competitive wedge to break into a market that is stacked against them. Finally, they must stay focused on their mission, vision, and values while course-correcting repeatedly, and occasionally pivoting, along the way. It is not a job description for the faint of heart. One last thing—in a downturn, instead of starting with visionaries in the Early Market, a far better play is to focus on a beachhead, chasm-crossing market segment from Day One. The TAM is smaller, but the time to close is much shorter, and this gets you traction early, a critical success factor when capital is costly and funders are impatient.

Now, assuming we can embrace these anchor tenets from the VC playbook, the key question becomes, How can a public enterprise, which does not have the freedom or flexibility of a venture capital firm, construct an Incubation Zone operating model that incorporates these principles in a way that plays to its strengths and protects itself against its weaknesses?

An Enterprise Playbook for the Incubation Zone

We should acknowledge at the outset that every enterprise has its own culture, its own crown jewels, its own claim to fame. So, any generic playbook has to adapt to local circumstances. That said, it is always good to start with a framework, and here in outline form is the action plan I propose:

  • Create an Incubation Board first, and charter it appropriately. Its number one responsibility is not to become the next disruptor — the enterprise already has a franchise, it doesn’t need to create one. Instead, it needs to protect the existing franchise against the next technology disruption by getting in position to ride the next wave as opposed to getting swamped by it.
  • In this role, the board’s mission is to identify any intersections between trapped value and disruptive technologies that would impact, positively or negatively, the enterprise’s current book of business. We are in the realm of SWOT threats and opportunities, where the threats take precedence because addressing them is not optional. Another way to phrase this is that we are playing defense first, offense second. This is particularly critical in a downturn because that is a time when visionaries lose power and pragmatists in pain gain power.
  • Given a chasm-crossing mentality, the first piece of business is to identify potential use cases that emerge at the intersection of trapped value and breakthrough technology, to prioritize the list in terms of import and impact, and to recruit a small team to build a BEFORE/AFTER demo that highlights the game-changing possibilities of the highest priority case. This team is built around a technology leader and an entrepreneur. The technology leader ideally would come from the outside, thereby being less prone to fall back on obsolete design rules. The entrepreneur should come from the inside, perhaps an executive from a prior acquisition who has been down this path before, thereby better able to negotiate the dynamics of the culture.
  • The next step is to socialize the demo, first with technology experts to pressure test the assumptions and make improvements to the design, and then with domain experts in the target use case, whether from the customer base or the enterprise’s own go-to-market team, who have a clear view of the trapped value and a good sense of what it would take to release it.
  • The next step is to pitch the Incubation Zone board for funding.

a) This is not an exercise in TAM or SAM or anything else of the sort. Those are tools for determining ROI in established sectors, where category boundaries are more or less in place. Disruptive innovation creates whole new boundaries, or fails altogether in the process, neither of which outcomes are properly modeled in the normal market opportunity analysis frameworks.

b) Instead, focus on beachhead market potential. Could this use case gain sufficient market adoption within a single target segment to become a viable franchise? If so, it will give the enterprise a real option on an array of possible value-creating futures. That is the primary goal of the Incubation Zone.

Whether the effort succeeds or fails, the enterprise will gain something of real value. That is, success will give it a viable path forward, and failure will suggest it need not spend a lot of resources protecting against this flank. The job of the board is to determine if the proposal being pitched is worth prioritizing on this basis.

  • To pursue the opportunity, you want to create an independent operating unit that looks like a seed-stage start-up. Once funded, it should target a specific, value-trapping process in a single industry, ideally managed by a single department, and apply breakthrough technology and laser focus to re-engineering the process to a much better outcome. This will require developing a whole product, defined as the complete solution to the customer’s problem, organized around a core product plus ancillary supporting products and services. The latter can be supplied by third parties, but the effort has to be orchestrated by you.
  • With this problem-specific solution in hand, the final step is to bring it to market via restricted distribution, not general availability. Your goal is to target a beachhead market with a single use case—just the opposite of what general distribution is designed to accomplish. Thus, the entire go-to-market effort, from product launch to pipeline generation, to sales, post-sales implementation, and customer success needs to be under the direct management of the GM of the Incubation Zone operating unit. Success here is measured by classic chasm-crossing metrics, focused on winning a dominant share of the top 30 accounts in the target market segment.

In a downturn, crossing the chasm—not winning inside the tornado—represents the fulfillment of the Incubation Zone’s real option mandate. You want to create a cash-flow-positive entity that protects your franchise from disruption by coopting an emerging technology while at the same time solving a mission-critical problem for a customer who needs immediate help. That is value, in and of itself, over and above the optionality it creates for future category creation.

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

Image Credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Framing Your 2024 Strategy

Leave a reply

Framing Your 2024 Strategy

GUEST POST from Geoffrey A. Moore

Fall is in the air, which brings to mind the season’s favorite sport—no, not football, strategic planning! Let’s face it, 2023 has been a tough year for most of us, with few annual plans surviving first contact with an economy that was not so much sluggish as simply hesitant. With the exception of generative AI’s burst onto the scene, most technology sectors have been more or less trudging along, and that begs the question, what do we think we can do in 2024? Time to bring out the strategy frameworks, polish up those crystal balls that have been a bit murky of late, and chart our course forward.

This post will kick off a series of blogs about framing strategy, all organized around a meta-model we call the Hierarchy of Powers:

Geoffrey Moore Strategy Framework

The inspiration for this model came from looking at how investors prioritize their portfolios. The first thing they do is allocate by sector, based primarily on category power, referring both to the growth rate of the category as well as its potential size. Rising tides float all boats, and one of the toughest challenges in business is how to manage a premier franchise when category growth is negative. In conjunction with assessing our current portfolio’s category power, this is also a time to look at adjacent categories, whether as threats or as opportunities, to see if there are any transformative acquisitions that deserve our immediate attention.

Returning to our current set of assets, within each category the next question to answer is, what is our company power within that category? This is largely a factor of market share. The more share a company has of a given category, the more likely the ecosystem of partners that supports the category will focus first on that company’s installed base, adding more value to its offers, as well as to recommend that company’s products first, again because of the added leverage from partner engagement. Marketplaces, in other words, self-organize around category leaders, accelerating the sales and offloading the support costs of the market share leaders.

But what do you do when you don’t have company power? That’s when you turn your attention to market power. Marketplaces destabilize around problematic use cases that the incumbent vendors do not handle well. This creates openings for new entrants, provided they can authentically address the customer’s problems. The key is to focus product management on the whole product (not just what your enterprise supplies, but rather, everything the customer needs to be successful) and to focus your go-to-market engine on the target market segment. This is the playbook that has kept Crossing the Chasm on entrepreneur’s book lists some thirty years in, but it is a different matter to execute it in a large enterprise where sales and marketing are organized for global coverage, not rifle-shot initiatives. Nonetheless, when properly executed, it is the most reliable play in all of high-tech market development.

If market power is key to taking market share, offer power is key to maintaining it, both in high-growth categories as well as mature ones. Offer power is a function of three disciplines—differentiation to create customer preference, neutralization to catch up to and reduce a competitor’s differentiation, and optimization to eliminate non-value-adding costs. Anything that does not contribute materially to one of these three outcomes is waste.

Finally, execution power is the ability to take advantage of one’s inertial momentum rather than having it take advantage of you. Here the discipline of zone management has proved particularly valuable to enterprises who are seeking to balance investment in their existing lines of business, typically in mature categories, with forays into new categories that promise higher growth.

In upcoming blog posts I am going to dive deeper into each of the five powers outlined above to share specific frameworks that clarify what decisions need to be made during the strategic planning process and what principles can best guide them. In the meantime, there is still one more quarter in 2023 to make, and we all must do our best to make the most of it.

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

Image Credit: Pixabay, Geoffrey A. Moore

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Twelve Digital Disruptions of Your Sales Cycle

Leave a reply

Twelve Digital Disruptions of Your Sales Cycle

GUEST POST from Geoffrey A. Moore

The good news for a salesperson selling into a disrupted industry is that the forces of change are bringing net new budget dollars to the table. The bad news is, the budgets have not yet landed. In effect, then, there are two kinds of sales opportunities to target. You can go after the landed budgets, the incumbent ones, knowing that they are under assault and will be dwindling, but also knowing that at present they can be deployed quickly and readily. Or, one can go after the much larger budgets that have not yet landed, the ones that will power the future of the target industry and your company’s role within it, but with the knowledge that this is a time-consuming effort that requires a completely different approach from the normal sales motion. Basically then, you can make quota in the short term while marginalizing your company’s future, or you can build a platform for the future while putting quota at much higher risk.

Of course, what we need here is an and not an or. And that is possible, provided executive leadership and compensation programs acknowledge this challenge openly and segment the field of play accordingly. The key distinction is simple. Selling into undisrupted industries requires to you to compete to consume budget, whereas in a disrupted one, you must create to consume budget. The first activity is conducted with middle managers charged with deploying operational budgets as efficiently as possible. The second is conducted with executives seeking to reallocate investment assets to meet the new challenge as effectively as possible. As just noted, these are two very different sales motions, and the challenge facing many sales teams today is that, like it or not, they have to do both, and do both well, if their companies are going to succeed.

The Impact of Digital Disruption on the Sales Cycle:

Here are twelve ways in which selling into a digitally disrupted sector calls for a radically different approach from what marketing, sales, and service teams are used to:

  1. Conventional lead generation does not work. It is based on hooking up with mid-level managers who have influence or authority over RFPs and budgets already in place. These people have no influence or authority over sales cycles involving redeployment of assets into new areas. All they will do is steer you to the old regime. Pursuing leads here will ensure you miss the next wave. And cold calling can’t succeed either. Executives employ people called administrative assistants for the express purpose of blocking your call. Instead you need to enable referrals, where a peer or trusted contact of the target executive enables the introduction.
  2. Product narratives don’t work. They are based on having an established view of the problem and of the competitive set. This is very much the case in non-disrupted industries but never so in disrupted ones. So PowerPoint presentations and demos don’t serve. All they do is disappoint and cause executives to redirect the salesperson back to a mid-level manager and an ever-diminishing established budget. Instead you need problem narratives, stories that surface the critical changes under way and that resonate with the business leaders undergoing them. That’s what the early conversations in the sales cycle need to be about.
  3. We need thought leadership here, people! Executives in disrupted industries are hungry for frameworks that can help them diagnose their new situation, envision a novel solution, and engage with peers to discuss their ideas. Slick slogans and asking “What’s keeping you up at night?” won’t cut it. But any vendor, be it a start-up or an established enterprise, who comes with a useful framework will get a good hearing, and the one whose framework gets adopted gets to orchestrate the others in building out a solution architecture. Narratives really, really matter.
  4. Relationship marketing is fundamental. Executives in disrupted industries are open to forming new relationships and are looking for a trusted advisor. To compete for this role salespeople need to monitor industry developments, personal information, and workflow status in real time so they can bring key issues and ideas to the table in a timely manner.
  5. Let’s get vertical, vertical! Digital disruption is unfolding on an industry by industry basis and manifests itself in ways unique to each one. That means that the early framing conversations need to be couched in the language and issues of the target industry, not the technologies and themes of the vendor’s industry. This requires marketing to develop a whole new set of muscles and sales to learn a new foreign language, which calls in turn for some judicious hiring of insider expertise and a sales training capability to get field teams up to speed fast.
  6. Sales and marketing need to map out a new customer journey. All sales cycles are built on an underlying model of the customer journey. These become the backbone of workflows through any CRM system. The problem in a disrupted industry is that the conventional sales cycle maps are all wrong because the journey is taking a very different route. Sales teams need to work with their counterparts in marketing to map out the new journey and align their sales cycles and their CRM systems to it.
  7. Proof-of-Concepts are necessary but not sufficient. To teams used to selling into non-disrupted markets POCs feel like going back in time, but they are key for disrupted industries where neither the problem diagnosis nor the solution prescription is well established. The challenge here is to manage them judiciously. Conservative forces inside the target customer will try to slow roll things here to buy time, whereas visionary sponsors may be too quick to want to leap to the full implementation. The trick is to make sure they are neither an obstacle to sales progress nor become a destination in and of themselves.
  8. Professional services organizations need to lean in. They have to provide insightful pre-sales consulting on a low-latency, cost-efficient basis, while still maintaining billable utilization via their other work. In addition, they have to take the lead in the first few implementations, where their role is often as not to be the chief spear catcher, and then be prepared to package up their expertise and hand it over to partners just when the projects become predictable and profitable. Running professional services inside a technology company is an incredibly important and almost always thankless endeavor. But as the next point makes clear, it is core.
  9. All offers are services-led—period. In a disrupted industry no one buys a product. The early adopters buy projects and the pragmatic majority buys solutions. Both of these offer types are services led. That means all proposals need to be services led as well. That is, they cannot be about products or even ROI; they have to be about changes under way and the responses needed to address them properly.
  10. All sales motions are land-and-expand. No responsible executive underwrites a massive re-engineering undertaking with a single check, even when they already have established a deep relationship of trust with a particular vendor. Most follow a three-phase approach, where the first phase is to prove feasibility, the second, confirm desirability, and the third, achieve scalability. There is no place in disrupted industries for fly-by selling of any kind.
  11. Customers have to step up too. This means that sales teams need to learn diplomatic ways for holding the customer’s feet to the fire, provoking them when they are not rising to the occasion, and holding them accountable when they do. Often this is best done through third parties, so creating communities of interest and sponsoring dialogs among peers become critical sales enablers.
  12. Change management becomes an integral part of every implementation. Getting the new paradigm adopted is key not only to the customer’s success but to the vendor’s continued expansion within the account as well. Service organizations and partners need to be engaged, enlisted, monitored, and compensated accordingly, and this initially at least has to be orchestrated by the sales team who has the winning proposal.

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

Image Credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

The Hard Problem of Consciousness is Not That Hard

1 Reply

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?

Image Credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Aligning Your Culture for Digital Transformation

Leave a reply

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?

Image Credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

A Top-Down Open Innovation Approach

1 Reply

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?

Image Credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

How to Make Your Customers Hate You

Leave a reply

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?

Image Credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

3 Flavors of Product-Service Shift

Leave a reply

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?

Image Credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.3

Bringing Yin and Yang to the Productivity Zone

Leave a reply

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?

Image Credit: Unsplash

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

A Triumph of Artificial Intelligence Rhetoric

3 Replies

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?

Image Credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.