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

Defining Good and Evil

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Defining Good and Evil

GUEST POST from Geoffrey Moore

This is entry number three in our ongoing reply to Twenty Philosophical Questions That Are Hard to Wrap Your Head Around. Here is the blurb that introduces it:

The intricacies of ethical standards vary across cultures and contexts add complexity to this ever-debated subject. This debate has been published on the Medium website: The Philosophical Debate on the Nature of Good and Evil: Exploring the Boundaries of Morality offering various definitions and viewpoints, on what qualifies as good or evil conduct as well as the reasons and repercussions behind such behavior. The terms “good” and “evil” are often employed to discuss ethical actions. “Good” generally denotes behaviors that are considered upright such, as aiding others in speaking the truth, and avoiding harm. On the other hand, “evil” pertains to actions that are seen as incorrect like lying, cheating, or causing harm.

The blurb suggests that the key question at stake is what we define good and evil to mean, but I don’t think that is where the challenge lies. Sure, there are grey areas that will give us pause, but the more interesting question to me is how we define good and evil. Here’s what I mean.

In religious traditions, the way we define good and evil is relative to a divine being, either personalized as God or impersonally presented as Being or the Absolute. Either way, it is the governing force for all life, and all living things need to align with it. In this context, moral values are grounded in an objective and transcendent reality independent of humanity. Our job is to engage with this reality as best we can, to experience its impact on our consciousness, and to shape our actions in ways that accord with its principles.

There is a lot to like in this tradition, not least of which is that it frees us from the soup of cultural entanglements involved with taking a more humanist relativist approach. Historically, however, religious institutions, authorized to represent divine guidance, have repeatedly succumbed to corrupt appropriations of power and have used both doctrine and sacred texts to justify horrific actions, often at genocidal levels, in the name of religious war. In Western cultural history, such wars were what gave rise to the Enlightenment and its insistence on separating church and state.

One could wish we would have learned much from this movement, but contemporary theocracies, as well as demagogic politicians, show no signs of so doing, positioning themselves instead to be vehicles for patriarchal suppression, ethnic persecution, and terrorist acts. Tolerance, far from being seen as a virtue, is faulted for failing to express appropriate outrage. Spirituality is displaced by zealotry, benevolence by righteousness, and the opportunity to promote acts of evil skyrockets, something we are constantly forced to bear witness to around the world today.

What are we to do? We cannot abandon ethics, for they are foundational to social stability, and we need standards of good and evil to guide ethical engagement. If these standards are not to come from above, then where are they going to come from?

In The Infinite Staircase I argue they come not from above but from below. The staircase as a whole traces the emergence of reality in layers, beginning with the materialist layers of physics, chemistry, and biology, transitioning to the evolutionary layers of desire, consciousness, values, and culture, culminating in the human-specific layers of language, narrative, analytics, and theory. In this hierarchy of systems, good and evil emerge at the layer of values and get institutionalized via the layer of culture. That is, they get defined prior to the emergence of humanity—how is that even possible?!

The answer is that good and evil, as we understand them, are embedded in the mammalian strategy of living. That is, all mammals share two common values. First, they are compelled to nurture their young, regardless of how much sacrifice that might entail. From this comes the definitions of goodness that are centered on love, with evil being defined as violations of same. Second, all mammals self-organize into communities of governance led by socially powered alpha figures, charged with maintaining both internal and external order and security. From this comes the definitions of goodness that are centered on virtue, with evil again being defined as violations of same.

Of course, as humans, we modify our mammalian roots through the higher-level mechanisms of language, narrative, analytics, and theory. In so doing we create separate and conflicting traditions that can indeed make war upon each other. But goodness and evil per se are universal. We are all mammals first, and everything that separates us from one another emerges thereafter.

We may bemoan this separation, but we should honor the fact that community, which is also inherent in our mammalian nature, by definition entails a boundary between them and us. There is no community without that boundary. Culture is an artifact of community, and it evolves separately in each of its instantiations. Language, narrative, and analytics allow culture and community to transcend time and space, but at the end of the day there will always be a boundary that separates an us from a them. How we reach across that boundary to interact with others outside our community is open to us to choose, but dissolving the boundary is not an option.

The key takeaway from this line of thinking is that our values are universal because they are founded in our mammalian roots. Our ethics, on the other hand, are not universal because they are grounded in culture, which is specific to every community. Good and evil belong to the domain of values. Right and wrong belong to the domain of ethics. The former calls for us to be courageous, the latter to be humble. Many of us struggle most of our lives to get this right.

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

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Unlocking Trapped Value with AI

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Unlocking Trapped Value with AI

GUEST POST from Geoffrey Moore

Two topics that have been on my mind a lot lately are generative AI and trapped value, so I am delighted to have them come together in a single blog. Here’s how it plays out.

Releasing trapped value is the engine that fuels ROI from technology deployments. All systems trap value somewhere in their operations, something that is confirmed by the Second Law of Thermodynamics, which means there will never be a time when there is no ROI to be gained. Where it is to be gained from, on the other hand, is up for grabs. The goal of my advisory practice is to help clients target the most impactful sources of trapped value in their target markets and direct their product roadmaps and go-to-market initiatives accordingly.

When I consider the extraordinary potential of generative AI to release trapped value across virtually any set of systems, I think the industry is, at present, overly infatuated with co-piloting and overlooking a far more impactful source of trapped value staring them right in the face. Look, co-piloting is useful, but how much trapped value is there in the delta between unaided and aided performance from white-collar professionals? Some, for sure, but is this really the burning platform that will drive customer spending to a level that might justify the extraordinary valuations generative AI companies are garnering at present? Probably not. So, what could?

My choice would be the market-facing user interface to any and all systems of engagement. This is where enterprises seek to enlist their customers and partners to utilize their systems for purposes of marketing, sales, service, and commerce, and that is precisely what we, as their external constituencies, want to do. But, how well is this working? Well, compared to the twentieth century, when, effectively, we had no systems of engagement, amazingly well. But by twenty-first-century standards, truth be told, they suck.

Where is the trapped value? Two areas jump to mind. The first is passwords, which have gone from being a source of security to a source of breaches, all the while creating massive amounts of aggravation in the process. The second is the user interface itself. No matter how well designed it is, it is inherently unfamiliar to the first-time or even occasional end user. Because of this friction at the point of user interaction, I submit, systems of engagement are delivering far less than their full value-creating potential.

Enter generative AI. The conversational interface that ChatGPT taught us to know and love is familiar to anyone who speaks the same language as the vendor, and the video, sound, and image recognition capabilities, supplemented by information from the idiosyncratic features that every online device entails, are far better equipped to identify and authorize end users than any set of passwords of two-factor authentication. So, the friction-removing technology is already here. What we are lacking is the friction-removing killer app, the one that every enterprise would embrace in a heartbeat were it available to them.

That app is a universal user interface that can be bolted onto APIs from enterprise systems to mediate between the legacy instruction system of the underlying software and the desires and intentions of the occasional end user. This would entail a (Very) Small Language Model to be developed by enterprise application vendors, as well as a companion SLM from cybersecurity system vendors, but really, how hard is that, especially when compared to the ROI rewards?

This is not just a big opportunity. It is an urgent one as well. Governments and NGOs struggle today to finance systems of engagement that allow programs to scale in the social sector, and their adoption is far below what one would wish, in large part because of the layers of friction we continue to impose. Enterprises that are seeking to catch the next wave of disruption roiling their industries are desperate to engage the customer bases, but are falling short because it is just too painful.

It is hard for me to see a more compelling use case for generative AI at this time, and I hope that industry will agree and free us from the Lilliputian bonds that restrict us.

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

— Image credit: Unsplash

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What’s the Truth?

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What's the Truth?

GUEST POST from Geoffrey Moore

This is the second in a series of responses to the MSN post “20 Philosophical Questions That Are Hard to Wrap Your Head Around.” We are on Question #2: Is there a truth? Here’s the MSN blurb:

This inquiry challenges the concept of truths, versus realities prompting introspection on the nature of knowledge and certainty. Many philosophers consider truth to be a concept that cannot be fully explained in terms other than truth itself. The accepted perspective is that truth relates to how well our language or thoughts align, with a world that exists independently of our minds. This theory is known as the correspondence theory of truth.

There is a fundamental premise stated here that needs correction, namely that “truth relates to how well our language or thoughts align with a world that exists independently of our minds.” The correspondence theory is indeed one understanding of what constitutes truth, but there are two others that are equally well-accepted perspectives—the coherence theory and the pragmatic theory. Thus, while correspondence is indeed a measure of how well our language and thoughts align with a world that exists independently of our minds, coherence, by contrast, is a measure of how well any given claim aligns with all the other claims we take to be true, basically our body of understanding of how the world works. Pragmatism provides a third perspective on truth, taking it to be a measure of how well any claim we make aligns with the results we get from acting out that claim in the world.

All three of these perspectives are useful, and the good news is they are not mutually exclusive. The way we normally test for truth is first through coherence theory. That is, when we hear a claim, we check to see if it aligns with our existing understanding of things. If it does, we are likely to just accept it and move on. If it does not, however, then we are moved to investigate whether it corresponds to what is actually the case, doing so by examining evidence for or against the claim. If the evidence does indeed warrant the claim, then we are moved to modify our body of beliefs to absorb this new bit of understanding into a revised coherence model, what we normally call learning or gaining in experience.

Pragmatism overlays another layer of interest, namely, does this claim enable us to have more success in whatever efforts we have underway? Successful outcomes, in other words, are a form of evidence for the validity of the claim. We have to be careful here, however, for the outcome’s success may be due to some factor other than the one claimed. Nonetheless, when things do work, and especially when this applies to things that traditionally have not worked anywhere near as well in the past, it sends us a strong signal to update our coherence and correspondence models accordingly.

Another challenge that pragmatism poses to our understanding of truth is that lying can also deliver successful outcomes. Lying may often defeat itself in the long run, but in the short run, it can be very effective. How we address lying, however, is normally more of a social issue than a philosophical one. For it to be philosophical, one would need to make the case that lying is foundational to wellbeing, and while such an argument is not unimaginable, it carries with it enough problems that we can safely ignore it for the time being.

What we cannot ignore, on the other hand, is the political use of lying. This is foundational to all authoritarian regimes. In order to stay in power, they must continually maintain a heightened sense of fear and anxiety in the population they govern, at a level sufficient to gain its acquiescence to their corrupt and self-serving ways. How citizens can respond to this challenge is something we will address in a moment.

The core question people want answered about truth, though, is whether or not it is a thing. That is, is truth a force in the world, the way love is or the way beauty is? Historically, the answer to this question was yes because truth, like love and beauty, is taken to be an attribute of the divine being that governs all human experience. In a secular world, however, that answer needs to be revisited. When we do so, we see that the desire for love, beauty, and truth still abides. They do indeed exert a force on us. So, if they are forces in the world, where are they to be found?

My answer is that each one is anchored on a different stair in the Infinite Staircase. Love is grounded on the stairstep of values, an inherent part of our mammalian heritage of nurturing the young. Beauty is grounded on the stairstep of culture, a learned appreciation for esthetic experience. Truth, in this context, is located on the stairstep of narrative, subject to verification delivered from the stairstep of analytics.

Narrative, in other words, is the force in the world that mobilizes the power of truth. True narratives are foundational to both personal and social wellbeing. False narratives undermine these same foundations. Let’s look at examples of both.

The power of true narratives manifests when people act in service to their narrative, even at a cost to their personal self-interests. We call this form of truth integrity, and it is genuinely inspiring because it cements the meaningfulness of their commitments. Existentialism represents an entire philosophy based on this core idea. Its life goal is to be authentic, with authenticity defined as behavior aligned with and in service to whatever life narrative you have chosen for yourself. Truth, in this sense, really does make you free, whereas falsehood imprisons you in narrative contradictions that ultimately destroy your wellbeing, a state that is termed bad faith.

False narratives pose a different problem. The power of truth to combat them depends upon the principled deployment of analytics. Unfortunately, the hold of any narrative on the personality, whether true or false, is frequently stronger than analytics can dislodge. As a result, regardless of how strong the evidence is for falsehood, it is simply denied, allowing bad faith and lack of wellbeing to spread pervasively and sustain their position of power far longer than one might imagine. This is the condition of life under an authoritarian regime.

To dislodge such a regime requires going below the stairstep of competing narratives down to the stairstep of core mammalian values and resurrecting those values that the regime is violating. This is dangerous work, for the regime is well aware of its vulnerability to this tactic and will aggressively punish any signs of it. The prospects for success in the short term are not good, and support from entities outside the regime are likely be necessary to dislodge it, so we should not be surprised to see societies continue to persist under painfully oppressive circumstances.

That said, when the global cannot be addressed, we must retreat and draw in the boundaries to find a local that can be. At the extreme, this would be oneself, although as social creatures, we need the help of a community of others to stay the course, family being our most obvious opportunity. Whatever its nature, that community becomes our reason for being, and it is here that we must use truth to establish the relationships of trust that hold us together. Truth in this context looks more like the medieval troth, a term for fidelity and faithfulness, a kind of integrity that is anchored in our pledge to others to be there for them and with them. Keeping faith is a form of integrity that is well suited to coping with adverse circumstances.

So, is there a truth? Well, in this last sense, there certainly is, as people have proved the world over. As we expand outward from our local scenes, and as we raise the level of abstraction, there will be truth as long as, to borrow a phrase from Yeats, “the center holds.” At some point, it will not, and we must reconcile ourselves to the limits of our mortal perspective, embracing humility and foregoing the kind of righteousness that seeks to impose an answer by force.

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

— Image credit: Unsplash

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20 Philosophical Questions to Wrap Your Head Around

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20 Philosophical Questions to Wrap Your Head Around

GUEST POST from Geoffrey Moore

MSN published a piece with this title, and I am complete sucker for such things, mainly as a way to pressure test the views I express in The Infinite Staircase. I plan to answer as many of these questions as I can, one at a time, and invite you to follow along to play the same game in parallel with your own ideas about what’s what.

Today’s Topic? What is the true essence of reality? You gotta love a question that has three words in it, true, essence, and reality, none of which are readily definable! Here’s the paragraph that follows:

The article titled ‘What Is the True Nature of Reality?‘, published on the Curious Matrix website, explains how throughout history countless philosophers and scientists have grappled with the mysteries of reality. Their exploration encompasses both the realm encompassing matter and particles as well as the intangible aspects of existence. This philosophical dualism has sparked an array of probing questions; What truly constitutes the essence of the mind? How does it relate to the body? Do they exist independently? When we ponder the nature of the reality we inevitably question our perceptions. Contemplate the very core of existence.

By the way, it is worth clicking through to read that article because it goes way beyond the mind/matter dualism that is summarized above. Setting that aside, however, how should we reply to the question itself? Note that it has changed the phrasing from “true nature of reality” to “true essence of reality” which actually makes a difference, and I am going to stick with the former because the latter makes the assumption that reality coalesces into an essence, which is not something I can agree to.

My answer is that the true nature of reality is a hierarchy of systems, each one of which emerges from the ones prior, with no top or bottom system clearly in view. In The Infinite Staircase I make the case for a stack of eleven such systems, beginning with three at the bottom that are materialistic in nature (physics, chemistry, and biology), all of which are all governed by the Second Law of Thermodynamics, or what I call the metaphysics of entropy. These are followed by four systems that are organic in nature (desire, consciousness, values, and culture), representing the evolution of life prior to the arrival of mankind, all governed by the dynamics of natural and sexual selection, or what I call the metaphysics of Darwinism. Those underpinnings tee up the topmost set of systems. ones that are distinctively human in nature (language, narrative, analytics, and theory), all governed by symbols and symbolism, or what I call the metaphysics of memes.

The advantage of this framework is that, instead of limiting ourselves to one or two foundations, we can refer to any of eleven possible platforms, alone or in combination with others, to explain any particular aspect of reality. Here’s how one might take advantage of this to answer the questions that are embedded in the latter half of the paragraph quotes above.

What constitutes the essence of mind? Mind is made up of multiple systems in the staircase, but the primary two are narrative and analytics, both leveraging the system of language. That is, most of our conscious experience is taken up with us telling stories to others or to ourselves and processing those stories to extract knowledge or entertainment. In support of this activity, our minds are powered by our brains, hence involving the stairstep of consciousness and all the material systems below it, so no, our minds cannot be independent of our bodies. At the same time, minds are socially developed, bringing in the stairsteps of values, culture, and language.

In sum, the true nature of our minds is that they are a layer cake of systems interoperating with one another in service to us executing our strategies for living. The fact that we have success in these efforts is demonstrated by the real-world outcome of our continued existence. This suggests that we have no pressing need to question our perceptions. The thing to question instead is the notion that there is a “very core” to our existence, a true essence of reality. That concept is way too monistic, given the complexity of the system’s hierarchy and its interdependent operations.

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

— Image credit: Gemini

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How Should CEOs Operate?

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How Should CEOs Operate?

GUEST POST from Geoffrey Moore

A post a little while ago of Dealbook with Andrew Ross Sorkin led off with a great topic: How hands-on should founders be in their companies? As Sorkin notes,

In the framing of Brian Chesky, the Airbnb co-founder and C.E.O., and Paul Graham, the Y Combinator co-founder, it comes down to “founder mode” versus “manager mode.” It’s not a new question — and it draws on longstanding examples like Steve Jobs — but it has taken on fresh significance as start-ups struggle with how to grow while keeping antsy investors happy.

This relates directly to the Zone to Win and the discipline of zone management, so I want to riff off of Sorkin’s column in a Point/Counterpoint way to tease out what I think are the key issues.

My core thesis is that “founder mode” is centered in leadership and is critical to the operating models of the Incubation Zone and Transformation Zone, and that “manager mode” is centered in management and is critical to the operating models of the Performance Zone and Productivity Zone. This is an and, not an or, situation—both are mandatory—but who is in charge where can be up for grabs.

My view, however, is that it should not be. The CEO needs to call the shots when it comes to dealing with disruption and catching the next wave, be that through incubation or transformation. In parallel, the ELT executive suite needs to be in charge of current lines of business in established categories, delivering performance and productivity. The ELT reports to the CEO, so the latter has oversight over the Performance and Productivity Zones and is accountable to the board of directors for their success, but this should largely be accomplished through hiring and promoting the most talented operators, not standing shoulder to shoulder with them on the front lines.

As for the struggle start-ups experience between how to grow and keeping antsy investors happy, the key point is that growth investors are willing to accept short-term shortfalls in financial metrics in order for the enterprise to invest in incubation and transformation initiatives to catch the next wave. Value investors are not. They prefer predictable execution of performance and productivity initiatives that leverage the inertial momentum of established categories. When a high-growth start-up goes public, they get reviewed by financial analysts who are inevitably focused on performance and productivity, and it takes all the narrative skills that the CEO and CFO can muster to build support for any kind of transformation.

Graham, as reported by Sorkin, tells a somewhat different story:

The prime example Graham cited was Chesky. The Airbnb boss shared his story at a recent Y Combinator event, and also discussed it on a podcast: “The less hands-on I was, the more I got sucked into problems. And by the time I got sucked into a problem, it was like 10 times as much work,” Chesky said on the podcast.

Instead, he decided: “I’m going to be involved in every single detail. And Airbnb is not going to do anything more than I can personally focus on.”

That has gotten results, Graham wrote, with Airbnb now having some of the best free cash flow in Silicon Valley. Then again, Airbnb’s stock price is 31 percent below its 52-week high.

Okay, there’s a lot to unpack here. Brian, in my view, is a great CEO leader. The fact that he got sucked into a lot of problems, I suspect, is not because his team does not know how to manage but because the category itself is transitioning from hypergrowth to growth, the competition is catching up, the regulators are weighing in, and so there is still a lot of pathfinding to do. As these paths become more established, he should deliberately step away from those operations to free himself for the next wave of changes. The fact that the company’s stock price is 31 percent below its 52-week high, given that Airbnb has great free cash flow, is far more likely to be due to category dynamics than management execution.

Sorkin goes on to list a number of well-known examples of CEOs, in addition to Jobs, who have taken a very hands-on approach to management, including Jensen Huang, Elon Musk, Mark Zuckerberg, and Sam Altman. All, however, are living in the midst of categorical disruption, where leadership takes precedence over management. That is because categorical stability and inertial momentum, which are the prerequisites for management success, are simply not there yet.

Sorkin also mentions Howard Schultz, who built Starbucks from a local Seattle coffee chain into a global colossus and was famously hands-on, also fits the mold. But here I think we have a CEO who has not orchestrated the transition to management well, witnessed by the volatility in its stock price for the first half of this decade, in a category that is not under disruption.

Finally, there are a handful of examples at the end of the column that can serve as litmus tests for zone management principles:

Dan Rose, who held leadership positions at Amazon and Meta, wrote that Jeff Bezos and Zuckerberg “were both micro-managers, deep in the details of the product and business. They never set expectations of autonomy, and they fired anyone who resisted their oversight.”

Amazon has never stopped disrupting, and the core of its innovation engine, and Bezos’s personal genius, is an incredibly deep and fine-grained inspection of established processes which underpins their radical reengineering to generate astounding competitive advantage. Meta is more of a mixed bag, but again, with a quarter of the Earth’s population on the platform, it is in the midst of every disruption on the planet. The idea that management should have autonomy under such conditions is ludicrous.

Others said there were nuances. Jessica Lessin, the founder of The Information, argued that founders do need capable managers, noting that Jobs relied on Tim Cook to oversee the vast and intricate manufacturing operation that became one of Apple’s most crucial assets. A start-up investor, Henrik Torstensson, agreed that execution mattered, citing Microsoft under Satya Nadella as a “Hall of Fame example” of manager mode done right.

In both instances, Apple and Microsoft are leveraging well-established businesses in personal computing, supplemented by Apple’s success in smartphones and media distribution, and Microsoft’s in business applications and, most recently, cloud computing, while at the same time engaging with the category disruption of AI. This calls for a balance of leadership and management, which is Satya’s gift. Tim has a tougher hand to play because Steve is an impossible act to follow. But since they are running two of the most valued companies on the planet, I think they are probably doing OK.

Stepping back from this whole line of thinking, the key takeaway is that the discipline of zone management is well-equipped to deal with these challenges. The first question to answer is not who should be in charge, but rather, how should we tackle this? What zone, in other words, offers the best operating model for coping with the challenges at hand? Once we decide that, then we know whether leadership or management gets the primary nod, and, thus, whether the CEO or the ELT should take the reins.

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

— Image credit: Geoffrey Moore

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Mixed Reality Headsets Meet the Dead Zone

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Mixed Reality Headsets Meet the Dead Zone

GUEST POST from Geoffrey Moore

My colleague and social media maven, Rich Stimbra, forwards me news items that might spark a blog post, like the following a couple of years ago:

Meta has scuttled development of a high-end mixed-reality headset meant to compete with Apple’s Vision Pro starting in 2027, The Information reports, citing anonymous sources. Production cost was a primary factor, since Meta was using the same micro OLED display tech as Apple, but trying to keep the headset under $1,000 in order to attract buyers. Apple’s $3,500 Vision Pro, meanwhile, is “stuck in neutral,” per Business Insider. Falling U.S. sales will be offset somewhat by its international launch in June, but the Vision Pro has no must-have app yet — and a more affordable model is expected next year.

This was clearly a technology adoption problem, and clients often ask members of The Chasm Group how you can tell where a given category is in the Technology Adoption Life Cycle, and how should that impact your commitment to the opportunity?

One tool we use is called the Pain/Gain matrix, as illustrated in the two charts that follow. The first chart tees up a thought experiment exercise. Looking at the disruptive innovation you are seeking to bring to market, ask yourself how much gain does it deliver, and how much pain must the customer go through to get it? In so doing, don’t just consider the end user, also take into account the other constituencies that must support the purchase. This is particularly germane to B2B enterprises, so you have to squint a bit to apply it to consumer markets. That said, here is the framework:

Pain Gain Matrix

The executive sponsor is the one who has to pay for this, the process owner is the one who has to secure the productivity improvement from it, the systems owner is the one who has to install and support it, and the end user is the one who has to actually extract the benefits from it. Each one has their own pain/gain experience, so they need to be placed separately. (In consumer markets, all four roles could condense into one, although in many families they may be distributed across either one or both parents as well younger and older siblings.)

The goal of the thought experiment is for the team to reach a consensus on the most likely square for each of the four stakeholders based on the current offer in the market today. Once you have all four placed in one (and only one) square, then you can turn to the second chart, which serves as a kind of decoder ring in that it predicts where you are likely to be in the adoption life cycle and what you will have to do to be successful at that stage.

Dead Zone Matrix

Following the Technology Adoption Life Cycle around the edges of the diagram

One might expect that the life cycle would proceed from the upper right to the lower left on a diagonal path, a straight line being the shortest distance between two points. It turns out that is not the case.

For the Early Market, a high-pain, high-gain proposition is OK provided the size of the gain is massive. That is, if you can deliver a truly transformative outcome, early adopters are willing to put up with a considerable amount of pain (and expense) to get it. However, if it is anything less than truly transformative, then they won’t play. That is why the winning Early Market operating model is a bespoke project wherein you and they put serious skin in the game, including assigning your best and brightest to the effort and simply refusing to stop until you have got the thing all the way to bright. The key to this stage is to have a visionary executive sponsor who will commit not only to fund the outcome but to continue to support it through its struggles until it gets all the way to the finish.

For the Bowling Alley (including the critical crossing the chasm step) the critical issue is still very much the size of the gain, what we call the compelling reason to buy, but the ability to absorb pain is much less. Here we have a pragmatist process owner who is under direct pressure to fix a broken mission-critical process that they cannot remediate with conventional solutions. They are desperate for a solution to their problem, what we call the whole product, which will be a huge gain for them. However, they do not have the resources to fund a bespoke project, which means they cannot absorb a great deal of pain. As a result, the vendor has to bring the whole product to them, partners included, and keep everyone committed to stay the course until the fix is up and running and proven successful. The process owner is willing to pitch in here, and so can absorb some pain in order to get to their goal, but nowhere near as much as the Early Market visionary executive.

For a Tornado to form, two things must change. The first is the emergence of a killer app that makes everyone and their mother want the new thing—that’s the big gain that has broad appeal. The second is that there has to be a major reduction in pain—a big drop in the friction that has been holding the category back to date. The goal is to sell a stripped-down, friction-free killer app that has a super-short time to value and then expand over time to address adjacent opportunities that may require more development but which can be readily absorbed because the core product is already installed. During the Tornado proper, particularly at its outset, it is the systems owners who are under the gun to procure and deploy the killer app as fast as possible. Their speed is gaited by whatever friction is still present, so the more this can be removed, the faster the tornado can spin up and the further it can spread. The company that does this best typically takes the field, becomes installed as the category gorilla, and generates outsized returns for many years to come.

Finally, when technology adoption comes to Main Street, we are now in the expand part of the land-and-expand playbook, where the gaiting item is how many different use cases it can support in how many different contexts and whether the offer can be converted from a licensable product to a consumable service. Under such conditions, because the amount of pain to adopt has been reduced to a bare minimum, it takes less and less gain to drive additional market growth. The end users are the ones who most benefit, and success rides increasingly on securing distribution channels that get to them as directly as possible and making it as easy as pie for them to try and buy, or at least try and then get their system owner to buy.

The Dead Zone

As you can see, the Life Cycle follows a path from the upper right to the lower left of the diagram by skirting around the edges. What about all the other squares?

Well, the ones that are blacked out are simply non-starters. The pain exceeds the gain to the point that no one other than a technology enthusiast would want to engage. But the four squares in the very center of the diagram are the ones that are the most dangerous. Collectively they make up the Dead Zone, which is another way of seeing the chasm, or what Intel used to call the Valley of Death.

Products in the Dead Zone deliver good (but not amazing) gains, but they do so at the cost of real (although not excruciating) pain. This combination is the kiss of death. It causes prospective customers to express real interest in the offer (“We’d love to take a meeting and see a demo”), but it does not motivate them sufficiently to get them to actually buy. It is hard to imagine a greater waste of go-to-market resources than that. Start-ups, as well as in-house incubations who venture out into such a desert usually die of thirst, meaning they don’t get that life-supporting next round of funding.

It is kind of like a LaBrea Tar Pit that swallows up whole species, as witnessed by the Sharp’s Personal Digital Assistant, RCA’s 8-track tape, and Mazda’s rotary automobile engine, as well as the . . .

Mixed Reality Headset

This sucker has Dead Zone written all over it. And because it is being positioned as a consumer product, it has a much tougher row to hoe. There is simply no chance of it getting traction unless and until someone can bring to market a killer app at a consumer price point, which will require some very deep pockets to get there, not to mention incredible design skills to get it right.

A much slower but more achievable path would target a B2B customer base with Bowling Alley applications that address urgent problems with mission-critical business processes. An obvious example relates to the “aging out of the workforce” problems that industrial manufacturers are facing, both in the factory and in field services. There simply are not enough trained technicians to meet the need, especially now that many of these industries are moving back onshore. Mixed reality headsets can be used to enable just-in-time training for onboarding inexperienced workers better, faster, cheaper, and even more importantly, safer than conventional alternatives.

Is this a killer app? Heck no, but it is a start; it can support a higher price point, and it can create enough volume to warrant investing in lower-cost approaches. What it cannot do is be rushed. The biggest cause of Dead Zone casualties is a management team that thinks a tornado is upon them when in fact it is just a Hype Cycle hitting its peak.

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

— Image credit: Pexels, Geoffrey Moore

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AI and the Enterprise

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Quo Vadis?

AI and the Enterprise

GUEST POST from Geoffrey Moore

Everybody gets that AI is going to change the world, but nobody is really clear as to how, which would be OK except that CIOs everywhere are under pressure to invest now, at a time when things are still forming, norming, and storming. We need to step back for a moment and survey the landscape so we can prioritize where we should engage first and why.

Let me suggest the following framework as a point of departure:

I submit that most of the value of enterprise IT today is delivered by the two systems highlighted in bold—the systems of record, which include finance, HR, supply chain, purchasing, and the like, and the systems of engagement, which include sales, service, marketing, commerce, and the like. These are the mission-critical stakeholder systems that define every enterprise’s relationship with its customers, partners, investors, regulators, and employees. They are quite simply indispensable, and as such, they are the anchor tenants of the Stack, with all the other elements ultimately justifying their existence by being in service to one or the other of the two.

Both systems rose to prominence in the 1990s as client-server applications running atop the Internet, and in this century, they have been advanced dramatically by two sets of adjacent sets of systems, the systems of infrastructure, which include cloud computing, mobile computing, data management, cybersecurity, and hyperscale outsourcing on demand, and the systems of collaboration, which include video conferencing, file sharing, messaging, threaded discussions, e-signatures, and the like. These have enabled companies to expand both the external reach of their systems of engagement and the internal productivity of their systems of record.

Now, we are seeing the emergence of a third set of systems, still nascent, consisting of systems of intelligence, which include predictive AI and generative AI, and systems of autonomy, which include agents. Both are based on advanced statistical software that can learn, and both are driven by machine learning feasting on all the data it can hoover up, including massive troves of log files that were never before examined except forensically.

The question before us is how will systems of intelligence and systems of autonomy impact the operation of the Stack as we have known it to date.

The first claim of this framework is that the AI-enabled systems will operate in the middle of the Stack, not at the top, and not at the bottom. That is, they will not displace any of the other systems but rather, will enhance them dramatically by operating behind the scenes. To get to more specific claims, let’s look at each layer of the stack one at a time.

Systems of Infrastructure

With respect to our systems of infrastructure, the most astounding impact of AI to date is the realization of just how much compute and storage it can consume. To generate a competitive Large Language Model (LLM) requires a hyperscale compute footprint that only a handful of companies have the resources to deploy. When we hear that Microsoft has invested $13 billion in OpenAI, we can rest assured that the bulk of that will come in the form of compute service. Ditto for Amazon, Meta, and Google.

Everyone else will need to license one or more of these LLMs and then adapt it for use in their own Stack. These adaptations, in turn, will rely heavily on first-party data from the enterprise’s own systems of record and engagement, supplemented by data from their systems of collaboration, as well as licensed second-party data, and publicly available third-party data. Extracting all that data, normalizing the metadata, filtering out the information that is protected by data sovereignty regulations, and staging the rest in a data lake for real-time use cases represents the most immediate challenge for CIOs today.

For many real-time applications in the physical world, latency issues will dictate that some processing needs to be done at the edge rather than in the core. This calls for a new kind of PC server armed with a GPU as well as a CPU, running a real-time operating system, connected under next-generation cyber-security protection. By contrast, digital-only applications for the virtual world, including most use cases for systems of record and systems of engagement, require modest infrastructure changes, if any. The current generation of co-pilots all run on the current end-user platforms, with the heavy lifting all being done by either in the core.

Systems of Record

Systems of Record, as we have already noted, are the foundation of enterprise operations. They are intentionally conservative by design. This ensures their integrity, but at the expense of ease of use and adaptability to circumstance. AI changes this game dramatically.

Generative AI augments the click-based UI of the core system, which users need to be trained on, with a natural language interface that is much more forgiving and can be learned through trial and error. Advanced use cases can be created through prompt engineering, allowing a general-purpose LLM to be used in tandem with proprietary enterprise data to ensure privacy, integrity, and relevance. These prompts can be reused and are likely to become an important reservoir of trade secrets.

Predictive AI is an even bigger game-changer. We have been doing this long before the current AI wave but via software that is preprogrammed and does not learn. Machine learning allows for continuous discovery of next-best actions, be they for predictive maintenance, fraud detection, energy optimization, demand forecasting, or product sourcing. It is like having a six-sigma black belt on duty 24/7.

Agents are a bit more dicey, particularly for regulated applications or ones that pose liability issues. Here a human-in-the-loop co-pilot model is likely to prevail for a long time to come, even after it has been shown conclusively that agents can do the job as well as, or even better than, humans. Think X-ray diagnosis or self-driving cars.

Systems of Engagement

We are skipping up to the top because systems of record and systems of engagement have much in common. That said, they are less conservative because they must continuously adapt to unpredictable workflows based on prospect, customer, transaction type, market, and use-case.

Generative AI has a much bigger role to play in these market-facing applications because, in addition to all the UI benefits mentioned earlier, it can actually substitute for human beings in Level One interactions, including creating and running email marketing campaigns, fielding customer service requests through both email and chat, supplying field engineering professionals with on-demand technical support information, alerting sales professionals to next-best actions, and the like.

Predictive AI, on the other hand, is more of a stretch because human factors are less predictable than system behaviors. Nonetheless, data-driven decision-making trumps intuition in the long run, and advanced statistical software that learns outperforms even the best humans eventually, as our friends at DeepMind taught us with respect to the game of Go. Sales forecasting and market campaign attribution are two areas in particular where there is low-hanging fruit to pick. It just takes more patience and, given the reputational risks involved in market-facing interactions, a more prudent approach to rolling anything out at scale.

Agents fall somewhere in between in that, for highly routine interactions, they can shine and actually provide better customer service than humans, as we all learned many years ago from ATM machines who trumped in-bank tellers from day one. One thing to guard against here is to measure the success of such programs by cost savings instead of friction reduction, the latter creating a much bigger payoff in customer loyalty, active use, and churn reduction.

Finally, co-pilots for all customer-facing functions are really a no-brainer. They do not lose context, they do not lose track of time, and they do not get bored with routine. In addition, they are great at prompting and taking a first cut at any natural language task. The critical issue here is focus. The more tuned the co-pilot is to your specific business, the more impactful its contributions will be.

Systems of Collaboration

First of all, we need to appreciate the fact that systems of collaboration are a differentiating source of information for any enterprise. That is, more than any other source, they represent the quality and texture of all your relationships in flight, not just their progress toward achieving your targeted outcomes. They embody who you are and what you care about. By including such data in data lakes that feed your AI models, not only will you improve your systems of collaboration themselves but also make better recommendations to your systems of record and engagement.

Specific to improving collaboration itself, AI excels at summarizations that cut through the clutter of long communication threads, timely reminders that keep workflows from bogging down, and sentiment analyses that can detect concerns and improve tone. The more your enterprise depends on nuanced knowledge work, the higher such improvements need to be on your priority list.

Systems of Intelligence

As you can see from the foregoing, in my view, we should not think of systems of intelligence as a separate layer in the Stack but rather as a technology infusion that changes its very nature. That is, our core business systems are poised to become more intelligent. Indeed, a fruitful metric might be something like a “system IQ test,” which CIOs could use both to assess their current state and to target their future state, all to be done by overlaying and integrating a layer of advanced statistical software that learns. As the BASF slogan used to say, “We don’t make the product, we make it better.”

What we should not endorse is the notion that Systems of Intelligence, or indeed any form of AI, is going to “take over.” Yes, they could have unintended consequences, but no they could not have Game of Thrones ambitions.

With one possible exception.

Systems of Autonomy

Systems of autonomy are the natural extension of systems of intelligence wherever the task in question can be done better by a machine than a person. We use them to place digital advertising, to get astronauts to space stations, to orchestrate the Internet, to run the GPS applications that help us navigate the world, to detect military threats, to prevent spam. They are an indispensable part of the digital transformation we are still in the midst of, and their role will only increase going forward.

The question is when you take the human out of the loop, who or what is in charge? This is not an intractable problem, but it is also not one that will get solved theoretically. This one will require lived experience, a history of successes and blunders, some pleasant surprises, and some very unpleasant unintended consequences. We should not be shocked. Life never stops evolving, and natural selection never stops operating. We just need to step up.

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

— Image credit: Gemini, Geoffrey Moore

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The Language of Thought

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The Language of Thought

GUEST POST from Geoffrey Moore

Where do thoughts come from? Is there any structure to them before they manifest themselves in language? Is there a universal grammar that underlies every actual grammar?

These questions have been asked many times before. My answers are as follows:

  • They come from below, not above.
  • They do have a readily describable structure.
  • That structure does indeed underly every actual grammar.

The first answer is the most important one. If the language of thought precedes symbolic language, then symbolic language is emergent from it, and the way to study is not through self-examination but rather by observing the behavior of non-language speaking agents. Of these my favorite two are babies and dogs. Both exhibit a myriad of strategic behaviors that imply thought but clearly do not entail language. So, based on observing them, what can we say about the structure of such thinking?

Babies and dogs, I propose, process the following five concepts routinely and effectively:

  1. Agents. They recognize and respond to people and animals that can interact with them, as indicated by their eye contact, their coming and going, and response to commands and gestures.
  2. Objects. They recognize and can discriminate among objects that interest them, both inanimate and animate, including foods, toys, playmates, and parents. They do not distinguish between animate and inanimate objects in any consistent way.
  3. Actions. They initiate and respond to actions in ways that further their interests, be that in feeding, playing, or getting attention. They are inherently attracted to action in any form.
  4. Valence. They discriminate between things they like and things they don’t like, seeking the former and avoiding the latter, as witnessed by their eating and their expressions of mood.
  5. Uncertainty. When uncertain, they hesitate and respond tentatively until they can resolve their uncertainty.

My claim is that these five concepts map directly to the fundamental syntax of every one of the six thousand or so symbolic languages currently in use. Agents and objects both convert to nouns and noun-like entities that serve as subjects and predicate objects in declarative statements. Similarly, actions convert to verbs and verb-like phrases. When we combine nouns with actions, we get predications, or what I like to term claims, which are the fundamental units of symbolic discourse. Valences foreshadow the use of adjectives, adverbs, and other modifiers that add nuances to our claims, and uncertainty is represented by modal verbs expressing possibility, probability, or necessity.

As much ground as all this covers, it is important to understand what the language of thought does not entail. Take the sentence “John is tall.” That thought would never occur to either a baby or a dog. It is inherently symbolic in nature, and until you have a symbolic language, it cannot exist. In The Infinite Staircase, it belongs to the stairstep of analytics, whereas the language of thought can reach no higher than the stairstep of narrative.

It is on the stairstep of narrative that the language of thought passes the baton to symbolic language. We cannot tell stories without symbolic language, but it is clear from the behavior of both toddlers and long-term pets that we want to. The connection that binds the two at this point is a realization of cause and effect. Narrative is how we process cause and effect. The breakthrough of symbolic language is that we can not only be a lot more precise in our communication, increasing its efficacy, but also that we can abstract from one narrative concepts and patterns that can be applied elsewhere. This is the miracle of analytics, and it only comes with symbolic language.

To close, let’s revisit our first question—where exactly do our (symbolic-language-expressed) thoughts originate from? This is not a brain-processing question—that landscape is still under investigation—but rather a question of lived experience. Where does it feel like they come from?

My answer is that they are chemically generated responses that represent fluctuations in our homeostatic balance. Like all organisms, we are compelled to seek this balance all the time. The more we get out of balance, the stronger the chemical signal to respond, the more intense the resultant stream of thoughts will be. This applies to dreams as well as to when we are awake. We are continually processing a stream of thoughts that emerge into consciousness and finding their linguistic expression as they do. We register these thoughts through listening to ourselves and shape them further through talking to ourselves. It is only when we get to the talking that we are fully immersed in symbolic language.

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

— Image credit: Pixabay

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AI ROI and Market Valuations

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AI ROI and Market Valuations

GUEST POST from Geoffrey Moore

A recent CNN Nightcap lamented the lack of ROI given the massive investments to date in AI, fueled by equally massive market valuations. This is true but misleading. ROI is a metric that tracks success in the Performance Zone. The big AI players are all playing a game in the Transformation Zone. Investors need to understand the difference and manage their expectations accordingly.

Back in the late ‘90s, Paul Johnson, Tom Kippola, and I published The Gorilla Game, a guide to investing in technology booms. The core concept was Paul’s: investors value companies based on their expectations of future earnings, and they base those in turn on two factors: the Competitive Advantage Gap (GAP) that separates the company’s offerings from their competition, and the Competitive Advantage Period (CAP) that represents the length of time they can sustain that GAP.

In established markets, GAP and CAP oscillate with product release cycles, and over time the industry consolidates around company power as measured by market share. Ecosystems organize around market-share leaders, and customers follow ecosystem leaders, all of which make for a remarkably stable pecking order. Risk-adjusted returns are modest but reliable, and ROI is indeed the right measure for success and is reflected in market valuations by the P/E ratio.

In technologically disrupted markets, a different dynamic is at work. Category power is obsoleting company power, displacing the current ecosystem, calling into question the traditional valuations of current market leaders, and giving rise to very untraditional valuations for next-generation challengers. Whereas the GAP and CAP in traditional markets are modest but stable, say single-digit advantages in GAP with three-to-five-year lengths for CAP, the GAP of a disruptive technology is extraordinary, triple-digits and beyond, and the length of GAP is based on the life of the category itself, typically multiple decades. Risk-adjusted returns are enormous despite the deep J-curve that must be passed through to get to them, something partially captured by a “Rule of 40” metric that combines current revenue growth with current gross margins and ignores profits and cash flows for the foreseeable future.

The CNN nightcap quite reasonably categorized these investments as suitable for venture capital, not for public markets, but a funny thing is happening to capital accumulation as the global economy becomes more and more digital. Whereas the industrial economy is capital-constrained, needing constant investment in factories, inventory, logistics, and distribution to keep it running smoothly, the digital economy is much less so. Yes, it needs factories — aka data centers — but it has no inventory, and it has multiple “asset light” plays when it comes to logistics and distribution. As a result, because management is still incented to maximum returns, capital has been accumulating in massive pools, especially in the coffers of the digital market leaders. This is what allows Microsoft, Google, Meta, Tesla, Amazon, and their ilk to make eye-popping investments in technologies that have yet to deliver meaningful ROI.

Should their shareholders be concerned? Are they managing for shareholder value? Not for short-term value investors, that’s for sure, but for long-term growth investors, the answer is perhaps. It depends on whether the category really does take off, what we call going inside the tornado, and whether their company can win enough market share and generate a sufficiently competitive ecosystem to ride the wave through to the end. It is not an easy bet to make, but the fates of iconic companies like Kodak, Nokia, and AT&T, as well as the current challenges facing the equally iconic Intel, show that not making the bet is not a safe path either.

There is one last wrinkle to mention, and that is the impact of what the Gartner Group has called the Hype Cycle.

Gartner Hype Cycle

This model looks very similar to the Technology Adoption Life Cycle, but ironically it is time-shifted such that at the Peak of Inflated Expectations, which is an Early Market phenomenon, many become convinced that the category is instead inside the tornado and commit to massive investments just as they are about to hit the chasm. So, note to all you visionaries: When you get a vision of the future, which you are very good at doing, please look for a calendar to find out what year it is.

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

— Image credit: Gemini

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Compelling Narratives

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Compelling Narratives

GUEST POST from Geoffrey Moore

As human beings we cling to two misconceptions of life. The first is that life is fragile. That is not quite right. Living things are certainly fragile — we are all subject to injury and will eventually die. Even whole species can go extinct. But life itself is anything but fragile. It originated some 3.5 billion years ago and has subsequently taken over the entire planet — land, water, sky, even deep within the earth — and shows no signs of loosening its grip. Life per se, in other words, may be the most powerful force we can experience.

Our second misconception is that, when it comes to our own lives, we are the ones in charge. There is some truth in this, but there is also truth in the notion that life is in charge of us. That is, life per se has an agenda that is independent of us, an agenda in which we are inexorably embedded, one that is grounded in the two universal functions that have enabled it to withstand the test of time — metabolism and reproduction.

We know a lot about these two functions from our study of the microbiology of the cell. Both entail a mindboggling number of chemical reactions chained together by a myriad of cascading pathways that are in turn governed by a byzantine array of positive and negative feedback loops. What is truly astounding is that, working in tandem, they have, from their very inception, been both dynamic — ever-changing—and persistent — continuity-maintaining. Life per se, in other words, is characterized by sustained dynamic equilibrium.

To be sure, life has evolved enormously since its cellular inception. After about 2 billion years of single-celled organisms only, cells began to collaborate with one another, leading to the emergence of additional layers of complexity. Over the 1.5 billion years since, they have generated all the variety of living things we see today, as well as a huge variety of species that have passed out of existence. But amidst all that change, over that entire immensity of time, at the level of the cell itself, this absurdly complex concatenation of cascading processes has never ever stopped. Based on this history, I think we must conclude that living things are compelled to live, and that includes us.

Yes, we can end our lives through suicidal interventions, but we cannot will our hearts to stop beating or our lungs to stop breathing. We can’t stop sleeping, and we can’t stop waking up. In short, we are embedded in something that is much larger and more powerful than our conscious selves, and we need to appreciate how this shapes our lived experience.

It turns out there is a whole tradition in philosophy called phenomenology that takes this premise as its starting point. It is organized around the work of Edmund Husserl and Martin Heidegger, but not in a way I am happy with. Like most philosophers, Husserl and Heidegger anchor their work in analytics and then search for narratives to illustrate their concepts. This runs counter to the fundamental premise of The Infinite Staircase, namely that the stairstep of Narrative precedes the stairstep of Analytics, both in evolutionary time and in order of priority when establishing meaning. That is, you need to get your narratives in focus first and then apply analytics to them, not the other way around.

The reason this is so important is that lived experience expresses itself in narratives, not analytics. Analytics abstract from narratives underlying principles which can be used to refine our strategy for living. This is an incredibly important function, one that underlies virtually all the amelioration of the human condition that we have accomplished over the past several centuries. But analytics cannot substitute for narratives. They cannot capture the immediacy of experience nor engage our sensibility with anything like the power of narratives. We are story-telling creatures, first and foremost. That’s how and where we develop our understanding of cause and effect.

Now, if we take the notion that we are compelled to live and we add it to the notion that narrative is our primary mode for processing lived experience, we can reasonably surmise that we are compelled to tell stories. What might that mean?

Well, for those of us in business, this is not a big surprise. We spend the bulk of our time engaged in storytelling and listening. What are you working on? How is it going? What’s the plan? How did the call go? Did you find that bug? What did Harry want? You get the idea. Stories are our lived experience, even more so if we are working remotely.

More formal occasions call for more formal storytelling, be that a proposal, a report, an analysis, an earnings call, or an annual plan. The point is, in all those situations, we are compelled to tell a story. Now, to be sure, our story may well be challenged–that is the function of analytics—but it is not OK not to tell one. The reason is that all our future actions will ultimately be grounded in a story that we agree to accept. Even the most routine bureaucratic approval cycles involve storytelling at some point in the process. We cannot act without a storyline to guide us. Thus, we are compelled to tell stories.

This is even more the case in the realm of politics. Today we are awash in political storytelling, much of which is being labeled misinformation. But the term is misleading. It implies that the teller has got their facts wrong. But that is not what is happening. Instead, they are telling a story that they believe will generate the political response they are seeking, be that to get themselves elected, or to prevent someone else from being elected, or to get legislation passed, or to block legislation from being passed. Our job as responsible citizens is to apply our analytics to their narratives to detect the ones we endorse and those we repudiate.

In both business and politics, pressure-testing the narratives we are presented with requires both judgment and experience. We normally do not have access to an irrefutable body of facts. Instead, we have to assess the character of the storyteller based on how they come across to us, how self-serving their story might be, how plausible it is, and the like, and we often draw on the opinions of professional commentators whom we have found to be trustworthy. There is no guarantee we are going to get things right, but the more due diligence we do, the more likely we will get close.

But what about all the lies? What about all the blatant liars that are just getting away with it? Why aren’t they being held to account? Why do their supporters believe them? The answer, in my view, is because they want to. People are not stupid. They know when other people are lying. The reason they accede to such lies and bully each other into accepting them is because the story being told validates their lived experience. You or I might repudiate the narrative, but we cannot repudiate their lived experience. That is theirs, not ours.

What we can do is share lived experiences with each other with the goal of finding common values. This requires a level of civility that we see openly mocked and that mockery is perhaps the most dangerous current in our present circumstances. It is not the narratives that are the problem. It is the sneering. We can work our way out of the toughest situations if we respect each other’s perspectives. We cannot do so when we demean or demonize our opponents. Stories and storytelling are the currency of our being. We cannot let it be debased.

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

— Image credit: 1 of 1,500+ FREE quotes available at http://misterinnovation.com

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