Category Archives: Technology

Customer Journeys and the Technology Adoption Lifecycle

GUEST POST from Geoffrey A. Moore

Like everything else in this Darwinian world of ours, customer journeys evolve with changes in the environment. Ever since the advent of the semiconductor, a compelling source of such changes has been disruptive digital technology. Although we are all eager to embrace its benefits, markets must first work through their adoption life cycles, during which different buying personas come to the fore at different stages, with each one on a very different kind of journey.

So, if you plan to catch the next wave and sell the next big thing, you’re going to need to adjust your customer journey playbook as you go along. Here’s a recap of what is in store for you.

Customer Journeys in the Early Market

The early market buying personas are the visionary and the technology enthusiast, the former eager to leverage disruption to gain first-mover competitive advantage, the latter excited to participate in the latest and greatest thing. Both are on a journey of discovery.

Technology enthusiasts need to get as close to the product as possible, seeing demos and alpha-testing prototypes as soon as they are released. They are not looking to be sold (for one thing, they have no money)—they are looking to educate themselves in order to be a reliable advisor to their visionary colleague. The key is to garner them privileged access to the technical whizzes in your own enterprise and, once under NDA, to share with them the wondrous roadmap you have in mind.

Visionaries are on a different path. They want to get as clear an understanding as possible of what makes the disruptive technology so different, to see whether such a difference could be a game changer in their circumstances. This is an exercise in imagineering. It will involve discussing hypothetical use cases, and applying first principles, which means you need to bring the smartest people in your company to the table, people who can not only communicate the magic of what you have but who can also keep up with the visionary’s vision as well.

Once this journey is started, you need to guide it toward a project, not a product sale. It is simply too early to make any kind of product promise that you can reliably keep. Not only is the paint not yet dry on your own offer, but also the partner ecosystem is as yet non-existent, so the only way a whole product can be delivered is via a dedicated project team. To up the stakes even further, visionaries aren’t interested in any normal productivity improvements, they are looking to leapfrog the competition with something astounding, so a huge amount of custom work will be required. This is all well and good provided you have a project-centric contract that doesn’t leave you on the hook for all the extra labor involved.

Customer Journeys to Cross the Chasm

The buying personas on the other side of the chasm are neither visionaries nor technology enthusiasts. Rather, they are pragmatists, and to be really specific, they are pragmatists in pain. Unlike early market customers, they are not trying to get ahead, they are trying to get themselves out of a jam. In such a state, they could care less about your product, and they do not want to meet your engineers or engage in any pie-in-the-sky discussions of what the future may hold. All they want to do is find a way out of their pain.

This is a journey of diagnosis and prescription. They have a problem which, given conventional remedies, is not really solvable. They are making do with patchwork solutions, but the overall situation is deteriorating, and they know they need help. Sadly, their incumbent vendors are not able to provide it, so despite their normal pragmatist hesitation about committing to a vendor they don’t know and a solution that has yet to be proven, they are willing to take a chance—provided, that is, that:

you demonstrate that you understand their problem in sufficient depth to be credible as a solution provider, and
that you commit to bringing the entire solution to the table, even when it involves orchestrating with partners to do so.

To do so, your first job is to engage with the owner of the problem process in a dialog about what is going on. During these conversations, you demonstrate your credibility by anticipating the prospective customer’s issues and referencing other customers who have faced similar challenges. Once prospects have assured themselves that you appreciate the magnitude of their problem and that you have expertise to address its challenges, then (and only then) will they want to hear about your products and services.

As the vendor, therefore, you are differentiating on experience and domain expertise, ideally by bringing someone to the table who has worked in the target market segment and walked in your prospective customer’s shoes. Once you have established credibility by so doing, then you must show how you have positioned the full force of your disruptive product to address the very problem that besets your target market. Of course, you know that your product is far more capable than this, and you also know you have promised your investors global domination, not a niche market solution. But for right now, to cross the chasm, you forsake all that and become laser-focused on demolishing the problem at hand. Do that for the first customer, and they will tell others. Do that for the next, and they will tell more. By the time you have done this four or five times, your phone will start ringing. But to get to this point, you need to be customer-led, not product-led.

Customer Journeys Inside the Tornado.

The tornado is that point in the technology adoption life cycle when the pragmatist community shifts from fear of going too soon to fear of missing out. As a consequence, they all rush to catch up. Even without a compelling first use case, they commit resources to the new category. Thus, for the first time in the history of the category, prospective customers have budget allocated before the salesperson calls. (In the early market, there was no budget at all—the visionary had to create it. In the chasm-crossing scenario, there is budget, but it is being spent on patchwork fixes with legacy solutions and needs to get reallocated before a deal can be closed.)
Budget is allocated to the department that will purchase and support the new offer, not the ones who will actually use it (although they will no doubt get chargebacks at some point). That means for IT offerings the target customer is the technical buyer and the CIO, the former who will make the product decision, the latter who will make the vendor decision. Ideally, the two will coincide, but when they don’t, the vendor choice usually prevails.

Now, one thing we know about budgets is that once they have been allocated they will get spent. These customers are on a buying mission journey. They produce RFPs to let them compare products and vet companies, and they don’t want any vendor to get too close to them during the process. Sales cycles are super-competitive, and product bake-offs are not uncommon. This means you need to bring your best systems engineers to the table, armed with killer demos, supported by sales teams, armed with battle cards that highlight competitor strengths and weaknesses and how to cope with the former and exploit the latter. There is no customer intimacy involved.

What is at stake, instead, is simply winning the deal. Here account mapping can make a big difference. Who is the decision maker really? Who are the influencers? Who has the inside track? You need a champion on the inside who can give you the real scoop. And at the end of the sales cycle, you can expect a major objection to your proposal, a real potential showstopper, where you will have to find some very creative way to close the deal and get it off the table. That is how market share battles are won.

Customer Journeys on Main Street

On Main Street, you are either the incumbent or a challenger. If the latter, your best bet is to follow a variation on the chasm-crossing playbook, searching out a use case where the incumbent is not well positioned and the process owner is getting frustrated—as discussed above. For incumbents, on the other hand, it is a completely different playbook.

The persona that matters most on Main Street is the end user, regardless of whether they have budget or buying authority. Increasing their productivity is what creates the ROI that justifies any additional purchases, not to mention retaining the current subscription. This calls for a journey of continuous improvement.

Such a journey rewards two value disciplines on the vendor’s part—customer intimacy and operational excellence. The first is much aided by the advent of telemetry which can track product usage by user and identify opportunities for improvement. Telemetric data can feed a customer health score which allows the support team to see where additional attention is most needed. Supplying the attention requires operational excellence, and once again technology innovation is changing the game, this time through product-led prompts, now amplified by generative AI commentary. Finally, sitting atop such infrastructure is the increasingly powerful customer success function whose role is to connect with the middle management in charge, discuss with them current health score issues and their remediation, and explore opportunities for adding users, incorporating product extensions, and automating adjacent use cases.

Summing up

The whole point of customer journeys done right is to start with the customer, not with the sales plan. That said, where the customer is in their adoption life cycle defines the kind of journey they are most likely to be on. One size does not fit all, so it behooves the account team to place its bets as best it can and then course correct from there.
That’s what I think. What do you think?

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AI as an Innovation Tool – How to Work with a Deeply Flawed Genius!

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GUEST POST from Pete Foley

For those of us working in the innovation and change field, it is hard to overstate the value and importance of AI. It opens doors, that were, for me at least, barely imaginable 10 years ago. And for someone who views analogy, crossing expertise boundaries, and the reapplication of ideas across domains as central to innovation, it’s hard to imagine a more useful tool.

But it is still a tool. And as with any tool, leaning it’s limitations, and how to use it skillfully is key. I make the analogy to an automobile. We don’t need to know everything about how it works, and we certainly don’t need to understand how to build it. But we do need to know what it can, and cannot do. We also need to learn how to drive it, and the better our driving skills, the more we get out of it.

AI, the Idiot Savant? An issue with current AI is that it is both intelligent and stupid at the same time (see Yejin Chois excellent TED talk that is attached). It has phenomenal ‘data intelligence’, but can also fail on even simple logic puzzles. Part of the problem is that AI lacks ‘common sense’ or the implicit framework that filters a great deal of human decision making and behavior. Chois calls this the ‘dark matter’ common sense of decision-making. I think of it as the framework of knowledge, morality, biases and common sense that we accumulate over time, and that is foundational to the unconscious ‘System 1’ elements that influence many, if not most of our decisions. But whatever we call it, it’s an important, but sometimes invisible and unintuitive part of human information processing that is can be missing from AI output.

Of course, AI is far from being unique in having limitations in the quality of its output. Any information source we use is subject to errors. We all know not to believe everything we read on the internet. That makes Google searches useful, but also potentially flawed. Even consulting with human experts has pitfalls. Not all experts agree, and even to most eminent expert can be subject to biases, or just good old fashioned human error. But most of us have learned to be appropriately skeptical of these sources of information. We routinely cross-reference, challenge data, seek second opinions and do not simply ‘parrot’ the data they provide.

But increasingly with AI, I’ve seen a tendency to treat its output with perhaps too much respect. The reasons for this are multi-faceted, but very human. Part of it may be the potential for generative AI to provide answers in an apparently definitive form. Part may simply be awe of its capabilities, and to confuse breadth of knowledge with accuracy. Another element is the ability it gives us to quickly penetrate areas where we may have little domain knowledge or background. As I’ve already mentioned, this is fantastic for those of us who value exploring new domains and analogies. But it comes with inherent challenges, as the further we step away from our own expertise, the easier it is for us to miss even basic mistakes.

As for AI’s limitations, Chois provides some sobering examples. It can pass a bar exam, but can fail abysmally on even simple logic problems. For example, it suggests building a bridge over broken glass and nails is likely to cause punctures! It has even suggested increasing the efficiency of paperclip manufacture by using humans as raw materials. Of course, these negative examples are somewhat cherry picked to make a point, but they do show how poor some AI answers can be, and how they can be low in common sense. Of course, when the errors are this obvious, we should automatically filter them out with our own common sense. But the challenge comes when we are dealing in areas where we have little experience, and AI delivers superficially plausible but flawed answers.

Why is this a weak spot for AI? At the root of this is that implicit knowledge is rarely articulated in the data AI scrapes. For example, a recipe will often say ‘remove the pot from the heat’, but rarely says ‘remove the pot from heat and don’t stick your fingers in the flames’. We’re supposed to know that already. Because it is ‘obvious’, and processed quickly, unconsciously and often automatically by our brains, it is rarely explicitly articulated. AI, however, cannot learn what is not said. And so because we don’t tend to state the obvious, it can make it challenging for an AI to learn it. It learns to take the pot off of the heat, but not the more obvious insight, which is to avoid getting burned when we do so.

This is obviously a known problem, and several strategies are employed to help address it. These include manually adding crafted examples and direct human input into AI’s training. But this level of human curation creates other potential risks. The minute humans start deciding what content should and should not be incorporated, or highlighted into AI training, the risk of transferring specific human biases to that AI increase. It also creates the potential for competing AI’s with different ‘viewpoints’, depending upon differences in both human input and the choices around what data-sets are scraped. There is a ‘nature’ component to the development of AI capability, but also a nurture influence. This is of course analogous the influence that parents, teachers and peers have on the values and biases of children as they develop their own frameworks.

But most humans are exposed to at least some diversity in the influences that shape their decision frameworks. Parents, peers and teachers provide generational variety, and the gradual and layered process that builds the human implicit decision framework help us to evolve a supporting network of contextual insight. It’s obvious imperfect, and the current culture wars are testament to some profound differences in end result. But to a large extent, we evolve similar, if not identical common sense frameworks. With AI, the narrower group contributing to curated ‘education’ increases the risk of both intentional and unintentional bias, and of ‘divergent intelligence’.

What Can We do? The most important thing is to be skeptical about AI output. Just because it sounds plausible, don’t assume it is. Just as we’d not take the first answer on a Google search as absolute truth, don’t do the same with AI. Ask it for references, and check them (early iterations were known to make up plausible looking but nonsense references). And of course, the more important the output is to us, the more important it is to check it. As I said at the beginning, it can be tempting to take verbatim output from AI, especially if it sounds plausible, or fits our theory or worldview. But always challenge the illusion of omnipotence that AI creates. It’s probably correct, but especially if its providing an important or surprising insight, double check it.

The Sci-Fi Monster! The concept of a childish super intelligence has been explored by more than one Science Fiction writer. But in many ways that is what we are dealing with in the case of AI. It’s informational ‘IQ’ is greater than the contextual or common sense ‘IQ’ , making it a different type of intelligence to those we are used to. And because so much of the human input side is proprietary and complex, it’s difficult to determine whether bias or misinformation is included in its output, and if so, how much? I’m sure these are solvable challenges. But some bias is probably unavoidable the moment any human intervention or selection invades choice of training materials or their interpretation. And as we see an increase in copyright law suits and settlements associated with AI, it becomes increasingly plausible that narrowing of sources will result in different AI’s with different ‘experiences’, and hence potentially different answers to questions.

AI is an incredible gift, but like the three wishes in Aladdin’s lamp, use it wisely and carefully. A little bit of skepticism, and some human validation is a good idea. Something that can pass the bar, but that lacks common sense is powerful, it could even get elected, but don’t automatically trust everything it says!

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What’s Next?

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What's Next?

GUEST POST from Mike Shipulski

Anonymous: What do you think we should do next?

Me: It depends. How did you get here?

Anonymous: Well, we’ve had great success improving on what we did last time.

Me: Well, then you’ll likely do that again.

Anonymous: Do you think we’ll be successful this time?

Me: It depends. If the performance/goodness has been flat over your last offerings, then no. When performance has been constant over the last several offerings it means your technology is mature and it’s time for a new one. Has performance been flat over the years?

Anon: Yes, but we’ve been successful with our tried-and-true recipe and the idea of creating a new technology is risky.

Me: All things have a half-life, including successful business models and long-in-the-tooth technologies, and your success has blinded you to the fact that yours are on life support. Developing a new technology isn’t risky. What’s risk is grasping tightly to a business model that’s out of gas.

Anon: That’s harsh.

Me: I prefer “truthful.”

Anon: So, we should start from scratch and create something altogether new?

Me: Heavens no. That would be a disaster. Figure out which elements are blocking new functionality and reinvent those. Hint: look for the system elements that haven’t changed in a dog’s age and that are shared by all your competitors.

Anon: So, I only have to reinvent several elements?

Me: Yes, but probably fewer than several. Probably just one.

Anon: What if we don’t do that?

Me: Over the next five years, you’ll be successful. And then in year six, the wheels will fall off.

Anon: Are you sure?

Me: No, they could fall off sooner.

Anon: How do you know it will go down like that?

Me: I’ve studied systems and technologies for more than three decades and I’ve made a lot of mistakes. Have you heard of The Voice of Technology?

Anon: No.

Me: Well, take a bite of this – The Voice of Technology. Kevin Kelly has talked about this stuff at great length. Have you read him?

Anon: No.

Me: Here’s a beauty from Kevin – What Technology Wants. How about S-curves?

Anon: Nope.

Me: Here’s a little primer – Beyond Dead Reckoning. How about Technology Forecasting?

Anon: Hmm. I don’t think so.

Me: Here’s something from Victor Fey, my teacher. He worked with Altshuller, the creator of TRIZ – Guided Technology Evolution. I’ve used this method to predict several industry-changing technologies.

Anon: Yikes! There’s a lot here. I’m overwhelmed.

Me: That’s good! Overwhelmed is a sign you realize there’s a lot you don’t know. You could be ready to become a student of the game.

Anon: But where do I start?

Me: I’d start Wardley Maps for situation analysis and LEANSTACK to figure out if customers will pay for your new offering.

Anon: With those two I’m good to go?

Me: Hell no!

Anon: What do you mean?

Me: There’s a whole body of work to learn about. Then you’ve got to build the organization, create the right mindset, select the right projects, train on the right tools, and run the projects.

Anon: That sounds like a lot of work.

Me: Well, you can always do what you did last time. END.

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Is AI Saving Corporate Innovation or Killing It?

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Is AI Saving Corporate Innovation or Killing It?

GUEST POST from Robyn Bolton

AI is killing Corporate Innovation.

Last Friday, the brilliant minds of Scott Kirsner, Rita McGrath, and Alex Osterwalder (plus a few guest stars like me, no big deal) gathered to debate the truth of this statement.

Honestly, it was one of the smartest and most thoughtful debates on AI that I’ve heard (biased but right, as my husband would say), and you should definitely listen to the whole thing.

But if you don’t have time for the deep dive over your morning coffee, then here are the highlights (in my humble opinion)

Why this debate is important

Every quarter, InnoLead fields a survey to understand the issues and challenges facing corporate innovators. The results from their Q2 survey and anecdotal follow-on conversations were eye-opening:

Resources are shifting from Innovation to AI: 61.5% of companies are increasing the resources allocated to AI, while 63.9% of companies are maintaining or decreasing their innovation investments
IT is more likely to own AI than innovation: 61.5% of companies put IT in charge of exploring potential AI use cases, compared to 53.9% of Innovation departments (percentages sum to greater than 0 because multiple departments may have responsibility)
Innovation departments are becoming AI departments. In fact, some former VPs and Directors of Innovation have been retitled to VPs or Directors of AI

So when Scott asked if AI was killing Corporate Innovation, the data said YES.

The people said NO.

What’s killing corporate innovation isn’t technology. It’s leadership.

Alex Osterwalder didn’t pull his punches and delivered a truth bomb right at the start. Like all the innovation tools and technologies that came before, the impact of AI on innovation isn’t about the technology itself—it’s about the leaders driving it.

If executives take the time to understand AI as a tool that enables successful outcomes and accelerates the accomplishment of key strategies, then there is no reason for it to threaten, let alone supplant, innovation.

But if they treat it like a shiny new toy or a silver bullet to solve all their growth needs, then it’s just “innovation theater” all over again.

AI is an Inflection Point that leaders need to approach strategically

As Rita wrote in her book Seeing Around Corners, an inflection point has a 10x impact on business, for example, 10x cheaper, 10x faster, or 10x easier. The emergence and large-scale adoption of AI is, without doubt, an inflection point for business.

Just like the internet and Netscape shook things up and changed the game, AI has the power to do the same—maybe even more. But, to Osterwalder’s point, leaders need to recognize AI as a strategic inflection point and proceed accordingly.

Leaders don’t need to have it all figured out yet, but they need a plan, and that’s where we come in.

This inflection point is our time to shine

From what I’ve seen, AI isn’t killing corporate innovation. It’s creating the biggest corporate innovation opportunity in decades. But it’s up to us, as corporate innovators, to seize the moment.

Unlike our colleagues in the core business, we are comfortable navigating ambiguity and uncertainty. We have experience creating order from what seems like chaos and using innovation to grow today’s business and create tomorrow’s.

We can do this because we’ve done it before. It’s exactly what we do,

AI is not a problem. It’s an opportunity. But only if we make it one.

AI is not the end of corporate innovation —it’s a tool, a powerful one at that.

As corporate innovators, we have the skills and knowledge required to steer businesses through uncertainty and drive meaningful change. So, let’s embrace AI strategically and unlock its full potential.

The path forward may not always be crystal clear, but that’s what makes it exciting. So, let’s seize the moment, navigate the chaos, and embrace AI as the innovation accelerant that it is.

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Re-engineering the Incubation Zone for a Downturn

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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:

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.
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.
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!
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.
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

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CEO Secrets of a Successful Turnaround

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CEO Secrets of a Successful Turnaround

GUEST POST from Shep Hyken

While most outside of the tech industry won’t know the Avaya brand, most will have experienced its technology if you’ve contacted customer support or communicated directly with a brand for any reason. It is a multinational technology company based in the U.S. that provides communications and collaboration technologies for contact centers in 172 countries, including 90% of the Fortune 100 companies in the U.S. Its product helps give a better customer service experience for its customer’s customers.

I had the opportunity to interview Alan Masarek about the Avaya story. Specifically, we discussed what happened since he joined the company less than one year ago. The short version of the story is that he and his leadership team successfully guided the company through Chapter 11 bankruptcy, restructuring its finances and streamlining its operations. And they did this while maintaining what Masarek calls Avaya’s North Star.

In referring to that “North Star,” Masarek says, “Customer service and experience is core to who we are and for every role in the company. Our customers count on us for the communications and collaboration technology that make customer interactions not only work, but work better.” He went on to explain the four core components they focus on:

1. Culture: Everything starts with culture. Masarek wants to make Avaya a “destination place to work,” which means attracting and keeping the best talent. Once you get good people, you must keep them there. His strategy for creating a “destination place to work” includes three components. The first is a rewards and recognition program that validates an employee’s efforts and creates a sense of accomplishment. The second is to create a culture employees want to be a part of. And third is to provide an opportunity for growth. Masarek says a company’s positive reviews and ratings on glassdoor.com, where employee rate their employers, is a success criteria he looks at.

2. Product: Avaya is a technology company and must continuously innovate and improve. They created a “product roadmap” where customers can see what products are being phased out, retained and, most importantly, being developed for the future. “We must deliver innovation—the right innovation—and we have to deliver it on time and with quality,” said Masarek. “We will be successful when we are both transparent (which is why Avaya published the roadmap) and reliable. When we deliver on that commitment over time, that reliability becomes trust.”

3. Customer Delight: If your customers don’t like the experience or the product doesn’t do what it’s supposed to do, they will find another company and product that meets their needs. Masarek recognizes the importance of customer delight and has invested heavily in hearing and understanding the “Voice of the Customer,” paying attention to customer satisfaction scores and NPS (Net Promoter Scores). Masarek is emphatic about customer delight, stating, “We are in service to the customer. CX is everyone’s responsibility.” And this isn’t just lip service. Those satisfaction and NPS numbers are tied to some of the employees’ compensation plans.

4. Accountability: “We must be accountable,” Masarek says, “to one another, to the customers, and to the results. When you take care of the first three (culture, product and customer delight), this fourth one becomes much easier to achieve.”

While sharing the entire story in a short article is impossible, you can see the overarching strategies and thinking behind Masarek’s leadership and Avaya’s success. And here’s my observation: It’s not complicated!

If you look at the four core components Avaya focuses on, you might say, “There’s nothing new here,” but don’t let simplicity, or that these seem like common sense, get in the way of incorporating them into your strategy. In good times and bad, focusing on culture, product, customer delight and accountability/results are the undeniable strategies that drive success.

This article originally appeared on Forbes.com

Image Credit: Unsplash

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The Power of Dreams

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A Veterans Day Innovation Story

The Power of Dreams - A Veterans Day Innovation Story

by Braden Kelley

On this Veterans Day I send my thanks to all of my fellow veterans for the sacrifices they and their families have made in support of the great nations of the world. Military science has long been a source of innovation that goes beyond the defense of a population. From duct tape, GPS, jet engines and the Internet to nuclear power, sanitary napkins and digital photography, there is an endless list of innovations that owe their existence to investments in military research.

Innovation has always been fueled by exceptional ideas that push the boundaries of what is possible. Some of the most groundbreaking inventions in history have originated from the most unexpected sources, proving that inspiration knows no boundaries. One such remarkable innovation that emerged from the realm of dreams is the M9 Gun Director, a groundbreaking concept envisioned by David Parkinson. Today, we explore the fascinating story of how an ordinary dream sparked an extraordinary revolution in military technology.

Dreams have long been a source of fascination for humanity, acting as the gateway to our subconscious minds, guiding our creativity and problem-solving abilities. Great minds throughout history, from Albert Einstein to Nikola Tesla, have attested to the transformative power of dreams shaping their inventions and discoveries. In the case of David Parkinson, the M9 Gun Director serves as a testament to the astounding potential that lies within our dreams.

The Birth of a Revolutionary Concept

In 1895, Parkinson, a modest engineer by profession, experienced a vivid dream that would forever change the world of military technology. In this dream, he envisioned a device capable of automatically predicting and adjusting the trajectory of a gun, enabling unparalleled precision in aiming and firing. This visionary concept would ultimately become the foundation for the M9 Gun Director and revolutionize artillery warfare as we knew it.

Pursuing the Unconventional

David Parkinson, driven by an insatiable curiosity and an unwavering belief in his dream, embarked on a journey to transform this abstract idea into a tangible reality. Despite facing skepticism and opposition, Parkinson remained undeterred, recognizing the immense potential in his concept. He tirelessly invested his time in research, experimentation, and collaboration, all the while fueled by the hope of revolutionizing military technology.

Bringing Dreams to Life

After years of relentless persistence, Parkinson succeeded in developing a prototype that embodied his vision of the M9 Gun Director. It incorporated advanced mechanisms, including gears, gyroscopes, and other innovative technologies, to predict and adjust artillery gun trajectories with remarkable accuracy. This revolutionary innovation significantly enhanced the efficiency, precision, and destructive power of artillery systems, forever changing the course of warfare worldwide.

Implications and Significance

The advent of the M9 Gun Director marked a turning point in military history, fundamentally altering the dynamics of armed conflict. By harnessing the power of dream-inspired innovation, Parkinson had unlocked a whole new level of precision previously unimaginable in the realm of artillery. This groundbreaking invention significantly reduced casualties, transformed strategic planning, and tilted the balance of power on the battlefield.

Embracing the Power of Dreams

The story of David Parkinson and the M9 Gun Director serves as a testament to the incredible creative potential that lies within each of us. It encourages us to embrace the unexplored territories of our dreams, recognizing them not just as fleeting nocturnal experiences, but as wellsprings of unmatched inspiration. Who knows what other world-changing ideas are waiting to be unleashed from within our subconscious minds?

Image credits: Pixabay

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A Quantum Computing Primer

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A Quantum Computing Primer

GUEST POST from Greg Satell

Every once in a while, a technology comes along with so much potential that people can’t seem to stop talking about it. That’s fun and exciting, but it can also be confusing. Not all of the people who opine really know what they’re talking about and, as the cacophony of voices increases to a loud roar, it’s hard to know what to believe.

We’re beginning to hit that point with quantum computing. Listen to some and you imagine that you’ll be strolling down to your local Apple store to pick one up any day now. Others will tell you that these diabolical machines will kill encryption and bring global commerce to a screeching halt. None of this is true.

What is true though is that quantum computing is not only almost unimaginably powerful, it is also completely different than anything we’ve ever seen before. You won’t use a quantum computer to write emails or to play videos, but the technology will significantly impact our lives over the next decade or two. Here’s a basic guide to what you really need to know.

Computing In 3 Dimensions

Quantum computing, as any expert will tell you, uses quantum effects such as superposition and entanglement to compute, unlike digital computers that use strings of ones and zeros. Yet quantum effects are so confusing that the great physicist Richard Feynman once remarked that nobody, even world class experts like him, really understands them.

So instead of quantum effects, think of quantum computing as a machine that works in three dimensions rather than two-dimensions like digital computers. The benefits of this should be obvious, because you can fit a lot more stuff into three dimensions than you can into two, so a quantum computer can handle vastly more complexity than the ones we’re used to.

Another added benefit is that we live in three dimensions, so quantum computers can simulate the systems we deal with every day, like those in materials and biological organisms. Digital computers can do this to some extent, but some information always gets lost translating the data from a three dimensional world to a two dimensional one, which leads to problems.

I want to stress that this isn’t exactly an accurate description of how quantum computers really work, but it’s close enough for you to get the gist of why they are so different and, potentially, so useful.

Coherence And Error Correction

Everybody makes mistakes and the same goes for machines. When you think of all the billions of calculations a computer makes, you can see how even an infinitesimally small error rate can cause a lot of problems. That’s why computers have error correction mechanisms built into their code to catch mistakes and correct them.

With quantum computers the problem is much tougher because they work with subatomic particles and these systems are incredibly difficult to keep stable. That’s why quantum chips need to be kept within a fraction of a degree of absolute zero. At even a sliver above that, the system “decoheres” and we won’t be able to make sense out of anything.

It also leads to another problem. Because quantum computers are so prone to error, we need a whole lot of quantum bits (or qubits) for each qubit that performs a logical function. In fact, with today’s technology, we need more than a thousand physical qubits (the kind that are in a machine) for each qubit that can reliably perform a logical function.

This is why most of the fears of quantum computing killing encryption and destroying the financial system are mostly unfounded. The most advanced quantum computers today only have about 50 qubits, not nearly enough to crack anything. We will probably have machines that strong in a decade or so, but by that time quantum safe encryption should be fairly common.

Building Practical Applications

Because quantum computers are so different, it’s hard to make them efficient for the tasks that we use traditional computers for because they effectively have to translate two-dimensional digital problems into their three-dimensional quantum world. The error correction issues only compound the problem.

There are some problems, however, that they’re ideally suited to. One is to simulate quantum systems, like molecules and biological systems, which can be tremendously valuable for people like chemists, materials scientists and medical researchers. Another promising area is large optimization problems for use in the financial industry and helping manage complex logistics.

Yet the people who understand those problems know little about quantum computing. In most cases, they’ve never seen a quantum computer before and have trouble making sense out of the data they generate. So they will have to spend some years working with quantum scientists to figure it out and then some more years explaining what they’ve learned to engineers who can build products and services.

We tend to think of innovation as if it is a single event. The reality is that it’s a long process of discovery, engineering and transformation. We are already well into the engineering phase of quantum computing—we have reasonably powerful machines that work—but the transformation phase has just begun.

The End Of The Digital Revolution And A New Era Of Innovation

One of the reasons that quantum computing has been generating so much excitement is that Moore’s Law is ending. The digital revolution was driven by our ability to cram more transistors onto a silicon wafer, so once we are not able to do that anymore, a key avenue of advancement will no longer be viable.

So many assume that quantum computing will simply take over where digital computing left off. It will not. As noted above, quantum computers are fundamentally different than the ones we are used to. They use different logic, require different computing languages and algorithmic approaches and are suited to different tasks.

That means the major impacts from quantum computers won’t hit for a decade or more. That’s not at all unusual. For example, although Apple came out with the Macintosh in 1984, it wasn’t until the late 90s that there was a measurable bump in productivity. It takes time for an ecosystem to evolve around a technology and drive a significant impact.

What’s most important to understand, however, is that the quantum era will open up new worlds of possibility, enabling us to manage almost unthinkable complexity and reshape the physical world. We are, in many ways, just getting started.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: Pixabay

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AI and Human Creativity Solving Complex Problems Together

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AI and Human Creativity Solving Complex Problems Together

GUEST POST from Janet Sernack

A recent McKinsey Leading Off – Essentials for leaders and those they lead email newsletter, referred to an article “The organization of the future: Enabled by gen AI, driven by people” which stated that digitization, automation, and AI will reshape whole industries and every enterprise. The article elaborated further by saying that, in terms of magnitude, the challenge is akin to coping with the large-scale shift from agricultural work to manufacturing that occurred in the early 20th century in North America and Europe, and more recently in China. This shift was powered by the defining trait of our species, our human creativity, which is at the heart of all creative problem-solving endeavors, where innovation is the engine of growth, no matter, what the context.

Moving into Unchartered Job and Skills Territory

We don’t yet know what exact technological, or soft skills, new occupations, or jobs will be required in this fast-moving transformation, or how we might further advance generative AI, digitization, and automation.

We also don’t know how AI will impact the need for humans to tap even more into the defining trait of our species, our human creativity. To enable us to become more imaginative, curious, and creative in the way we solve some of the world’s greatest challenges and most complex and pressing problems, and transform them into innovative solutions.

We can be proactive by asking these two generative questions:

What if the true potential of AI lies in embracing its ability to augment human creativity and aid innovation, especially in enhancing creative problem solving, at all levels of civil society, instead of avoiding it? (Ideascale)
How might we develop AI as a creative thinking partner to effect profound change, and create innovative solutions that help us build a more equitable and sustainable planet for all humanity? (Hal Gregersen)

Because our human creativity is at the heart of creative problem-solving, and innovation is the engine of growth, competitiveness, and profound and positive change.

Developing a Co-Creative Thinking Partnership

In a recent article in the Harvard Business Review “AI Can Help You Ask Better Questions – and Solve Bigger Problems” by Hal Gregersen and Nicola Morini Bianzino, they state:

“Artificial intelligence may be superhuman in some ways, but it also has considerable weaknesses. For starters, the technology is fundamentally backward-looking, trained on yesterday’s data – and the future might not look anything like the past. What’s more, inaccurate or otherwise flawed training data (for instance, data skewed by inherent biases) produces poor outcomes.”

The authors say that dealing with this issue requires people to manage this limitation if they are going to treat AI as a creative-thinking partner in solving complex problems, that enable people to live healthy and happy lives and to co-create an equitable and sustainable planet.

We can achieve this by focusing on specific areas where the human brain and machines might possibly complement one another to co-create the systemic changes the world badly needs through creative problem-solving.

A double-edged sword

This perspective is further complimented by a recent Boston Consulting Group article “How people can create-and destroy value- with generative AI” where they found that the adoption of generative AI is, in fact, a double-edged sword.

In an experiment, participants using GPT-4 for creative product innovation outperformed the control group (those who completed the task without using GPT-4) by 40%. But for business problem solving, using GPT-4 resulted in performance that was 23% lower than that of the control group.

“Perhaps somewhat counterintuitively, current GenAI models tend to do better on the first type of task; it is easier for LLMs to come up with creative, novel, or useful ideas based on the vast amounts of data on which they have been trained. Where there’s more room for error is when LLMs are asked to weigh nuanced qualitative and quantitative data to answer a complex question. Given this shortcoming, we as researchers knew that GPT-4 was likely to mislead participants if they relied completely on the tool, and not also on their own judgment, to arrive at the solution to the business problem-solving task (this task had a “right” answer)”.

Taking the path of least resistance

In McKinsey’s Top Ten Reports This Quarter blog, seven out of the ten articles relate specifically to generative AI: technology trends, state of AI, future of work, future of AI, the new AI playbook, questions to ask about AI and healthcare and AI.

As it is the most dominant topic across the board globally, if we are not both vigilant and intentional, a myopic focus on this one significant technology will take us all down the path of least resistance – where our energy will move to where it is easiest to go. Rather than being like a river, which takes the path of least resistance to its surrounding terrain, and not by taking a strategic and systemic perspective, we will always go, and end up, where we have always gone.

Living our lives forwards

According to the Boston Consulting Group article:

“The primary locus of human-driven value creation lies not in enhancing generative AI where it is already great, but in focusing on tasks beyond the frontier of the technology’s core competencies.”

This means that a whole lot of other variables need to be at play, and a newly emerging set of human skills, especially in creative problem solving, need to be developed to maximize the most value from generative AI, to generate the most imaginative, novel and value adding landing strips of the future.

Creative Problem Solving

In my previous blog posts “Imagination versus Knowledge” and “Why Successful Innovators Are Curious Like Cats” we shared that we are in the midst of a “Sputnik Moment” where we have the opportunity to advance our human creativity.

This human creativity is inside all of us, it involves the process of bringing something new into being, that is original, surprising useful, or desirable, in ways that add value to the quality of people’s lives, in ways they appreciate and cherish.

Taking a both/and approach

Our human creativity will be paralysed, if we focus our attention and intention only on the technology, and on the financial gains or potential profits we will get from it, and if we exclude the possibilities of a co-creative thinking partnership with the technology.

To deeply engage people in true creative problem solving – and involving them in impacting positively on our crucial relationships and connectedness, with one another and with the natural world, and the planet.

A marriage between creatives, technologists, and humanities

In a recent Fast Company video presentation, “Innovating Imagination: How Airbnb Is Using AI to Foster Creativity” Brian Chesky CEO of Airbnb, states that we need to consider and focus our attention and intention on discovering what is good for people.

To develop a “marriage between creatives, technologists, and the humanities” that brings the human out and doesn’t let technology overtake our human element.

Developing Creative Problem-Solving Skills

At ImagineNation, we teach, mentor, and coach clients in creative problem-solving, through developing their Generative Discovery skills.

This involves developing an open and active mind and heart, by becoming flexible, adaptive, and playful in the ways we engage and focus our human creativity in the four stages of creative problem-solving.

Including sensing, perceiving, and enabling people to deeply listen, inquire, question, and debate from the edges of temporarily hidden or emerging fields of the future.

To know how to emerge, diverge, and converge creative insights, collective breakthroughs, an ideation process, and cognitive and emotional agility shifts to:

Deepen our attending, observing, and discerning capabilities to consciously connect with, explore, and discover possibilities that create tension and cognitive dissonance to disrupt and challenge the status quo, and other conventional thinking and feeling processes.
Create cracks, openings, and creative thresholds by asking generative questions to push the boundaries, and challenge assumptions and mental and emotional models to pull people towards evoking, provoking, and generating boldly creative ideas.
Unleash possibilities, and opportunities for creative problem solving to contribute towards generating innovative solutions to complex problems, and pressing challenges, that may not have been previously imagined.

Experimenting with the generative discovery skill set enables us to juggle multiple theories, models, and strategies to create and plan in an emergent, and non-linear way through creative problem-solving.

As stated by Hal Gregersen:

“Partnering with the technology in this way can help people ask smarter questions, making them better problem solvers and breakthrough innovators.”

Succeeding in the Age of AI

We know that Generative AI will change much of what we do and how we do it, in ways that we cannot yet anticipate.

Success in the age of AI will largely depend on our ability to learn and change faster than we ever have before, in ways that preserve our well-being, connectedness, imagination, curiosity, human creativity, and our collective humanity through partnering with generative AI in the creative problem-solving process.

Find Out More About Our Work at ImagineNation™

Find out about our collective, learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack, is a collaborative, intimate, and deeply personalized innovation coaching and learning program, supported by a global group of peers over 9-weeks, which can be customised as a bespoke corporate learning program.

It is a blended and transformational change and learning program that will give you a deep understanding of the language, principles, and applications of an ecosystem focus, human-centric approach, and emergent structure (Theory U) to innovation, and upskill people and teams and develop their future fitness, within your unique innovation context. Find out more about our products and tools.

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