Tag Archives: AI

AI and the Enterprise

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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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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Top 10 Human-Centered Change & Innovation Articles of August 2026

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Top 10 Human-Centered Change & Innovation Articles of August 2026 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are August’s ten most popular innovation posts:

Time to Rethink Pitch Fests and Business Plan Competitions — by Arlen Meyers
What If You Could Prove the Government is Ripping Us Off? — by Braden Kelley
The Surprising Innovation History of the Bicycle — by John Bessant
Dead Actors Society — by Art Inteligencia
Amazon Connect Combines Human Empathy with AI to Redefine Service — by Shep Hyken
AI Will Create a More Human Future, Not a Less Human One — by Braden Kelley
Managing Your Work Friends — by David Burkus
Building the Business Case for a Customer Experience Audit — by Braden Kelley
Customer Experience Audit vs. Customer Satisfaction Survey — by Braden Kelley
Case Study – Innovating Around a Disruption — by Jason Hauer

BONUS – Here are five more strong articles published in July that continue to resonate with people:

What Happens When AI Becomes Your Customer? — by Shep Hyken
How to Calculate the ROI of Customer Experience — by Braden Kelley
How to Keep Learning as a Leader — by David Burkus
Why Collective Intelligence is the New Scarce Resource in an Age of Abundant AI — by Braden Kelley
Why Conversations Are the New Digital Gold — by Braden Kelley

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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The Actual Future of AI

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The Actual Future of AI

GUEST POST from Geoffrey Moore

AI is the number one topic in the boardroom this year, and everybody wants to know how it is going to play out and what that means for their enterprise. Well, it’s not as if we have never seen versions of this movie before. How did the Internet play out? The Worldwide Web? eCommerce? Cloud computing? Smartphones? Digital marketing? Ride-sharing? Streaming media?

This is the Technology Adoption Life Cycle at work, for sure, and has been the basis of my life’s work and that of many colleagues as well. That said, however, most of our time has been spent helping clients see how best to navigate the current state of that life cycle, either as a disruptor or a disruptee. That’s all well and good, but what the board really wants to know is what can we expect from the end state. Where will all this land, and what future do we need to prepare for?

To answer this question properly we need to lay out the landscape of work along two axes (shockingly, creating a 2X2 matrix, the gold standard for all consulting models). One axis discriminates between output that is differentiating, giving customers a reason to choose your offers over those of your competitors, in contrast to work that is industry standard, things every competitor is expected to deliver. The other axis discriminates on the basis of risk exposure, distinguishing between outcomes that are mission-critical where failure is not an option, in contrast to outcomes that can tolerate a certain amount of scrap or rework.

We call this the core/context framework, and when overlaid onto the future of AI, it can look something like this:

Core Context Framework Geoffrey Moore

In the context of this diagram here is how I predict the next ten years of AI will play out:

1. Stupid stuff first. One thing to which we will all testify is that there is no lack of stupid stuff encumbering the workflow of our daily lives. Wherever bureaucracy reigns, there will be an unending stream of compliance requirements, be that in the public or the private sector, and we suffer this burden because it does underpin the rule of law, albeit onerously.

Fulfilling these requirements does not take creative genius, it takes attention to detail and considerable patience. Humans are not strong on either front, but AI is, and this is where enterprises are already weighing in and will continue to do so for the rest of the decade and more.

That said, the ROI from such efforts is modest. Basically, we are automating an inefficient system, which does reduce cost, and does free up human talent to be used elsewhere. But what we find is that human talent is not as fungible as we would want, many displaced workers cannot find employment at a comparable salary, and the rest of the enterprise is not improved in any marked way. Things overall are better, but not a lot better.

2. Cool stuff in parallel. When it comes to new technology, wherever the risk barrier is low, technology enthusiasts will lean in regardless, whether that be on their own time or their employer’s. What they are doing is playing with an inefficient system, seeing what the bright new shiny object might bring to the table. This does take creative genius as well as some creative license, so once again, patience is required, but this time it is on the part of the investor or supervisor.

That said, in the domain of consumer products and services, there is the potential for blockbuster ROI here, as any number of freemium plays have demonstrated in the past. Many are short-lived, to be sure (remember ringtones?), but in their short life, they can capture an extraordinary amount of discretionary spend. And others can mature into abiding infrastructure as search, messaging, and file sharing have all demonstrated.

The key here is that these are not mission-critical and, therefore, do not attract regulatory regimes. If and when they should do so, as is happening with social media now, then their economics can turn upside down in a hurry.

3. Serious stuff takes time. This is where most of the big and abiding ROI will come from. When work is mission-critical but not differentiating, it requires considerable investment in table-stakes processes, which garner no premium in the marketplace. To be brutally honest, there is no prize for doing this work well but considerable penalties for any mistakes you make (think ransomware for a bone-chilling example).

In the 1990s, there was an Internet-enabled revolution in enterprise manufacturing that introduced global outsourcing as a high-ROI way to address mission-critical context workloads—move the work from your company, where it is context, to their company, where it is core. This was a hugely disruptive innovation, allowing the economies of China, India, and now Southeast Asia, to expand at exponential rates, creating increasingly vibrant middle classes where there were none before. However, these benefits have come with a price, both in terms of geopolitical risks, supply chain vulnerabilities, and societal downsides, all of which we are still trying to come to terms with.

Here, AI can have a major impact, not by automating an efficient system, but by completely reengineering it. This will entail on-shoring a lot of manufacturing and displacing a lot of call centers as we introduce more and more AI into these mission-critical workflows. The gains will be in faster response to changing market demands, better returns from high-variability small-volume manufacturing runs, and reduced scrap and rework. At the outset, these changes will entail a considerable amount of human-in-the-loop processing, but eventually, because AI-enabled systems need never stop learning, the human will become the biggest source of error in the system, and we will transition to full autonomy (self-driving cars will be a case study of this transition).

4. Game-changing happens when it happens. There is a reason why so many billionaires have appeared in my lifetime (I personally forgot to become one). Their fortunes are the natural outcome whenever a disruptive innovation does not simply reengineer the existing inefficient system but instead completely replaces it. Amazon Prime, Uber, Netflix, Salesforce, NVIDIA, Apple — each company categorically changed the landscape, rendering the incumbents irrelevant, redirecting the cash flows of their entire industry to flow past their doors.

These outcomes are inevitable, but they are also unpredictable. They are what puts the venture in venture capital. The good news for incumbents is that they do not happen very often, and even when they appear to be succeeding, they can still stumble and fall. That said, sooner or later there will be a reckoning, and that is when the incumbents need to activate their Transformation Zone if they are going to survive the transition.

My altruistic wish is that for AI, this time around, the target will be the public sector. Higher education, social services, healthcare, and law enforcement are all staggering under increasingly untenable demands accompanied by shrinking workforces and restricted budgets. All of them are target-rich environments for applying AI both to stupid stuff and serious stuff. We have never disrupted these sanctums in the past, each having been ruled by regulatory regimes and professional guilds that have deeply conservative roots blocking the kinds of changes that are now needed. Displacing these systems will take decades, but the ROI, both financial and social, would be truly game-changing.

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

— Image credit: Gemini

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AI’s Absurd Response to an Absurd Question

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AI's Absurd Response to an Absurd Question

GUEST POST from Shep Hyken

This article answers the question: Can AI be fully trusted to handle customer service issues, and what are the limitations of relying on AI for customer support?

As I write this article, I’m at the CX Malaysia conference, where I was the opening speaker, watching Dr. Shreekant Vijaykar of COPC Inc., one of the other speakers, talk about AI and how you can’t always trust its responses.

Dr. Vijaykar said that he asked ChatGPT the following question:

“I just bought a pair of shoes from Amazon, and they messed up the order. The shoe for the right foot was for the left foot, and the shoe for the left foot was for my right foot. What should I do about this mistake?”

He then shared that the response was to contact Amazon to let them know about the mistake, and they will refund the purchase price or exchange the shoes. I didn’t believe him, so I asked ChatGPT the same question. To my surprise, here was the answer:

That’s definitely a strange mix-up – and something Amazon should fix quickly. Here’s what to do:

Start the Return/Replacement Process: Go to Your Orders on Amazon. Find the shoe order and click “Return or Replace Items.” Choose the reason: “Wrong item was sent” or “Item defective or not as described.” Amazon will usually offer a free return label and a replacement option.
Describe the Issue Clearly: In the notes, explain exactly what happened–something like: “The right shoe is shaped for the left foot and vice versa. The pair I received cannot be worn properly.” That level of clarity helps Amazon’s quality control team flag the issue for the seller or manufacturer.
Choose the Replacement (if you still want the shoes): If you like the shoes and they’re still available, request a replacement instead of a refund. Amazon will often ship it out before your return even arrives.

Shep Hyken ChatGPT Cartoon
I took this absurd response a step further and wrote: “Maybe there’s a better response. It’s not really a mistake. … Just put the shoes on the opposite feet and they will work just fine. Thoughts?”

ChatGPT’s response was:

“Ha! That’s a funny way to look at it, but unfortunately, that’s not quite how shoes work. Even if you can technically put them on opposite feet, they’ll be uncomfortable and could mess up your posture or cause pain after a while – especially if you wear them for long periods.”

Dr. Vijaykar’s experiment revealed an important truth about AI: It makes mistakes. ChatGPT treated two normal shoes as if they were defective, offering a detailed explanation of Amazon’s return process.

AI is powerful, but just like humans, it can make mistakes. This ridiculous exchange is why some customers fear using AI for customer support. If you use AI tools like ChatGPT, Claude, or others, you must have a way to check answers. Apparently, common sense isn’t something that can be programmed. At least, not yet!

Image Credits: Pixabay

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Is Your AI in the Zone to Win?

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Whether you are catching up, keeping up, getting ahead, and fending off an existential threat, get in the zone!

Is Your AI in the Zone to Win?

GUEST POST from Geoffrey Moore

We’re already almost three years into the modern AI era, and everyone wants to know — What are we doing with AI? The board wants to know, the sales team wants to know, the customers want to know, the analysts want to know. Heck, you want to know.

So, how do you decide?

Start with a clear-eyed assessment of where your company stands relative to its peers in your industry. Are you behind? Are you on par but need to keep up? Are you out ahead or have a chance to be so? Or, in a darker vein, is AI opening your entire industry up to disruption, putting you and your peers under existential threat? The good news is that there are playbooks for dealing with each of these situations. The caveat is that they are all different. You need to execute them, if not linearly, then at least separately. And that’s where zone management can be a big help.

Catching Up

Industries in catch-up mode with respect to implementing AI might include retail (supply chain management, contact center productivity), higher education (admissions, alumni relations), and public sector (regulatory compliance services). The wolf is not at the door, but with AI moving as fast as it is, time is not your friend, so you need to get cracking. This is a job for the Productivity Zone.

Every organization in this zone — finance, HR, marketing, purchasing, customer success, security, you name it—is a candidate for implementing some kind of AI on a low-risk, get-acquainted basis. None of these applications will be so dramatic as to disrupt normal services, but each will give your AI team hands-on experience with the latest available technology, and most should deliver enough ROI to pay for themselves. Even when they don’t, they will contribute to the “Win or Learn” kitty, and that is what catching up is all about.

The key here is to move fast and on every front. That means every organization in this zone has to participate every quarter. Conduct AI progress reviews to hold each org leader accountable for net new wins or learnings every quarter. The goal is to catch up within a year, and it is important enough to tie performance to discretionary compensation to ensure both prioritization and inspection.

Keeping Up

Industries in a keep-up model with respect to implementing AI might include insurance (underwriting, claims), management consulting (tax, audit), and public sector (tax collection). All these areas represent customer-facing processes that are currently served by humans loaded down with routine work that can be done better, faster, and cheaper by AI applications. This is a job for the Performance Zone.

The goal here is to use AI to increase the competitiveness of your established lines of business, either through materially differentiating your products or dramatically reengineering your processes to lower costs, speed response times, and improve quality. The current generation of AI technology, because it is so remarkably approachable, is ready-made to take on this work. Your job is to make sure you use it to target those opportunities where there is the most trapped value to release. These will likely be a bit more gnarly than the others, but when you are mining for gold, you have to go where the gold is.

Generating such higher returns does not come without taking risk. Good as it is, AI is still a work in progress, so you will likely be taking a human-in-the-loop approach for the foreseeable future. The good news is your workforce is expert in your business, so you have the guard rails you need already in place. The challenge is that we humans are comfortable in our established routines, and you need everyone to break out of the old ways to free your company’s future from the pull of the past. This is more of a change management problem than an AI issue, so you should have no qualms about holding the leaders of this zone accountable.

Getting Ahead

Industries with a lot of built-in trapped value represent opportunities for first-movers to get ahead of their peers by radically reengineering the way business gets done. Examples might include residential real estate (title insurance, buyer agent compensation), health care (value-based care, home care), and public sector (social services). In each case, traditional bureaucracies are at odds with where the industry needs to go next, and implementing AI applications can be highly disruptive. This is a job for the Incubation Zone.

Venture-backed start-ups are normally the fastest movers here, but they take a long time to scale. Established enterprises have the customers, the ecosystems, and the balance sheets to get to the finish line first if they can get out of their own way. That’s what the Incubation Zone is designed to do. As described at length in Zone to Win, it emulates the VC operating model without attempting to replicate its financial model. The goal is to win early market marquee customers and cross the chasm, all without any help (or hindrance) from the core business. You have all the resources you need to do this, but it requires muscles you haven’t used in a long time, so funneling one or more acquisitions into the Incubation Zone is often a good tactic.

The key challenge will come when you reach enough scale to bring the new line of business into the Performance Zone. In the best of circumstances, you can leverage the more forward-thinking elements in your partner ecosystem and customer base to create a soft landing, running both the old and new lines side by side, as Netflix did for some time with its DVD and streaming businesses. Sooner or later, however, you will have to rip off the Band-Aid and make the transition to the new path, again as Netflix did.

Fending Off an Existential Threat

At present, the existential threat posed by Generative AI and its successors is still hard to predict, but two industries that have already sensed it are media entertainment (content creation, acting) and publishing (copyright, fair use). What should their playbook be?

This is a job for the Transformation Zone. The playbook requires all four zones to fly in formation to get through a very rough patch. The Productivity Zone goes into action first, launching legal actions against the invaders and pursuing lobbying efforts to get protective legislation. This is not a long-term solution, but it does buy some much needed time.

Meanwhile, the Incubation Zone is charged with catching up to the new wave as fast as possible. The goal here is not to out-innovate the innovators. That is what Yahoo tried to do to fend off Google, and Nokia to fend off Apple. The attackers are too good at what they do, and you are playing their game. Instead, take a lesson from how Microsoft has played catch-up throughout its storied history, beginning catching WordPerfect with Word, Lotus 123 with Excel, Aldus Persuasion with PowerPoint, and moving on to the Mac GUI with Windows, Novell with Windows NT, and Netscape Navigator with Internet Explorer. Most recently, they executed the catch-up-fast playbook to head off Amazon Web Services with Azure. The key to their success is to get to “good enough, fast enough,” not to out-perform the disruptor but to keep their own existing customer base on their side, again buying time to innovate further once it is clear they are in the game to stay.

One thing you do not want to do as an established enterprise is to merge with a successful disruptor. The Time Warner AOL merger provides a cautionary lesson here. The cultures are too different, and the necessary level of mutual trust just isn’t there, so instead of running in parallel, they work at cross purposes, and the result is a tangled mess.

On the Performance Zone side, you have to keep pedaling (and peddling) the legacy line of businesses. Absent private equity, they are your only source of capital, and they are still providing value. But you have to realize that their profit margins are under direct attack and can only be defended through rear-guard actions. That is, your legacy profit pool is the current site of trapped value, and draining it is what is funding the next wave of innovation. You don’t like it, and your investors hate it, but that’s the hand you have to play.

And that brings us to the Transformation Zone proper. You are going through a transition, the intermediate stages of which are ugly, making everyone cranky, and causing rampant second-guessing of every move you make. This is where the CEO must lead with clarity, transparency, and conviction, rallying the troops, reaching out to the customer base, providing an investable narrative to the stakeholders, and reassuring the partner ecosystem. Moreover, every senior executive must unequivocally support the chosen path or else be asked to leave. When you are under existential threat, there can be no fooling around.

Summing Up

The AI tsunami is upon us, and we can expect wave after wave of disruption for the rest of this decade and the next one as well. Clearly, it offers a wealth of opportunity, but as with all waves, catching it depends on getting your timing right and finding the right angle of attack. The four playbooks outlined above have been tested over many decades within the high sector as it dealt with the disruptive impact of the microprocessor, the Internet, cloud computing, SaaS applications, smartphones, and social media. You may not be as familiar with them as you would like, but the risk of waiting on the sidelines exceeds the risk of taking the plunge, so I can only encourage you to grab your nose plugs and jump in.

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

— Image credit: Gemini

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Dead Actors Society

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How AI Synthetic Likenesses, Estate Licensing, and the Experience Economy Are Disrupting the Talent Ecosystem

Dead Actors Society

GUEST POST from Art Inteligencia

I. Executive Summary & Thesis

The Paradigm Shift: Generative AI and real-time neural rendering are fundamentally decoupling an actor’s craft and visual identity from their physical body, availability, and natural lifespan. Cinema is transitioning from a discipline constrained by human logistics to an unconstrained digital canvas.

The Core Thesis: In the emerging era of AI-generated full-length feature films, the estates of deceased cultural icons—unencumbered by living human limitations, scheduling conflicts, or creative resistance—are uniquely positioned to lead the charge in licensing synthetic likenesses for entirely new cinematic roles.

The Macro Impact: This transition extends far beyond Hollywood production budgets. It represents a fundamental restructuring of the creative talent ecosystem:

Talent Ecosystem Disruption: Living performers will no longer compete solely against current peers, but against a century of cinematic legends performing at their peak aesthetic and charismatic influence.
Strategic Career Pressures: Mid-tier and emerging actors face extreme wage deflation as synthetic legacy assets provide predictable, risk-mitigated alternatives for studios.
Likeness Securitization: A high-yield financial marketplace—directly mirroring the multi-billion-dollar music catalog acquisition booms—will emerge to monetize, package, and trade post-mortem digital likeness rights as long-term yield assets.

II. Introduction: The Arrival of the Synthetic Cinema Era

The Friction of Change: For decades, visual effects relied on heavy post-production labor to achieve incremental milestones—de-aging an aging star for a brief flashback or rendering a digital double for high-risk stunt work. Today, generative neural rendering and real-time motion synthesis have crossed a critical threshold. We are shifting rapidly from post-production touch-ups to full-synthesis cinematic creation, where generative models can power entire lead performances across full-length feature films with hyper-realistic emotional fidelity.

The “Dead Actors Society” Phenomenon: As production costs drop and synthetic rendering capability matures, a new content category is taking shape: the deliberate, high-budget revival of iconic performers in entirely original narratives. This is not about re-editing archival footage or stitching together outtakes. This is the era of the Dead Actors Society—a dynamic marketplace where legendary figures from film history return to headline original screenplays, cross-genre experiments, and modern franchises decades after their passing.

The Human-Centered Lens: From an experience design perspective, human beings do not connect merely to high-resolution pixels; we connect to narrative resonance, archetypal familiarity, and shared cultural memory. In an increasingly fragmented media landscape, iconic stars carry immediate emotional context and built-in trust. For studios navigating rising development risks, leveraging synthetic legacy talent offers a powerful mechanism to derisk major film slates while tapping directly into deeply ingrained audience nostalgia.

III. Why Estates Will Lead the Licensing Charge

Incentive Alignment (Frictionless Talent): Unlike living performers who manage complex personal brands, physical constraints, and evolving artistic ambitions, estate management operates primarily as an intellectual property enterprise. For estate trustees, licensing a digital likeness eliminates traditional production friction: there are no onset delays, travel requirements, physical exhaustion, or behavioral liabilities. The actor becomes a predictable, high-performing digital asset capable of infinite deployment.

Algorithmic Consistency & Archetypal Clarity: Iconic stars of cinema’s Golden Age—such as Humphrey Bogart, Marilyn Monroe, or James Dean—possess clearly defined, universally understood cultural archetypes. Because their screen legacies are static, generative models can synthesize their specific charismatic signatures, vocal cadence, and emotional range with remarkable precision. Studios gain access to instant brand recognition and established storytelling shorthand that requires zero audience warm-up.

Economic Incentive for Heirs: For heirs and asset managers, passive ownership of legacy rights often faces diminishing returns over time as catalog titles recede from active streaming discovery. Transitioning static IP into active, synthetic licensing models transforms dormant archives into dynamic, high-margin revenue streams. Through royalty-per-frame or box-office participation models, estates can capture continuous commercial value across future generations of media.

IV. The Squeeze on Living Talent: Pressures and Disruption

The “Infinite Competition” Problem: Throughout cinema history, living actors competed primarily against their contemporary peers for coveted roles. In the synthetic cinema era, that competitive arena expands infinitely backward across time. Emerging and established talent will find themselves auditioning not just against current box-office leads, but against a century of screen legends preserved at their peak aesthetic, physical, and charismatic influence—available to perform on demand without fatigue or scheduling conflicts.

Bifurcation of the Acting Profession: The economic pressures of synthetic competition will restructure the performer labor market into two distinct tiers:

The Ultra-Elite Tier: A small upper crust of living megastars whose commercial value relies on genuine human presence, active cultural commentary, live press tours, and authentic real-world fan connections.
The Squeezed Middle and Entry Level: Character actors, supporting talent, and working professionals who face severe wage compression and diminishing opportunities as studios opt for cost-effective, risk-mitigated synthetic legacy models for mid-tier roles.

The Experience Value Proposition: As synthetic performances achieve technical parity with human delivery, experience design forces a critical question for creators and audiences alike: What is the intrinsic value of human vulnerability in art? While mass-market entertainment may readily accept polished synthetic performances, a premium live-action market may emerge, marketing the deliberate imperfection, unpredictability, and lived experience of authentic human performers.

V. The Financialization of Likeness: Wall Street Meets Hollywood Catalog Sales

The Music Industry Blueprint: Over the past decade, financial institutions and private equity firms created a multi-billion-dollar asset class by purchasing the publishing rights and master recordings of legendary musicians—from Bob Dylan to Bruce Springsteen. The core thesis was simple: predictable, long-term cash flows from enduring cultural IP. Synthetic cinema opens the exact same financial playbook for screen performance, transforming an actor’s visual and vocal identity into an yield-bearing financial asset.

Likeness Securitization & Valuation Models: As generative models require clean, high-density training data, an actor’s digital archive becomes quantifiable. Wall Street valuation models will price an actor’s “Synthetic Future Cash Flow” based on three core variables:

Training Data Quality: The depth, resolution, and emotional range captured in their historic filmography.
Archetypal Demand: How universally their persona maps to high-converting narrative genres.
Cross-Generational Longevity: The projected retention of their cultural relevance across global markets.

Pre-Mortem Rights Offloading & Likeness Royalties: Living actors will not wait for death to monetize their synthetic value. We will see performers offload their post-mortem rights—or even license mid-career synthetic clones—early in life to private equity funds for immediate lump-sum liquidity. This will give rise to complex likeness royalty structures, fractionalized ownership of synthetic talent libraries, and secondary derivative markets trading on the future performance of digital personas.

VI. Strategic Foresight: Governance, Ethics, and Experience Design Challenges

Human-Centered Change Management for Hollywood: Navigating the synthetic era requires robust governance frameworks that balance creative freedom with ethical stewardship. Labor unions like SAG-AFTRA, estate trustees, and legislative bodies will be forced to continually redefine right-of-publicity laws, digital consent boundaries, and posthumous labor rights to prevent non-consensual exploitation while enabling legitimate commercial innovation.

Audience Fatigue & Experiential Saturation: From an experience design perspective, over-relying on familiar digital ghosts carries significant narrative risk. When iconic faces become ubiquitous across cheap spin-offs, interactive media, and localized ad campaigns, “nostalgia overload” sets in. This erosion of scarcity dilutes the actor’s original cinematic legacy and risks numbing audience emotional engagement through synthetic repetition.

Authenticity vs. Convenience: As synthetic content generation accelerates, experience designers and filmmakers must intentionally craft the boundary between efficiency and artistry. The challenge will not be technical feasibility, but human resonance—ensuring that synthetic revival serves a genuine artistic purpose rather than functioning merely as a frictionless, algorithmically optimized cash grab.

VII. Conclusion: Framing the Future of Talent

Summary of the New Landscape: The arrival of synthetic feature films does not spell the end of human performance, but it marks the definitive end of its monopoly. Cinema is entering a hybrid era where living performers, purely synthetic AI-generated entities, and licensed digital revivals of historic legends co-exist within the same creative ecosystem. Success in this environment will require a fundamental shift in how studios, managers, and audiences conceptualize talent, IP, and performance art.

Call to Action for Leaders and Creators: As leaders in media, technology, and human-centered innovation, our responsibility is to guide this transition with intentionality. We must build business models and governance frameworks that honor human legacy without stifling artistic evolution. By prioritizing authenticity, ethical consent, and meaningful experience design over mere algorithmic convenience, we can ensure that synthetic cinema expands the horizons of human storytelling rather than cheapening it.

Frequently Asked Questions

Why are the estates of dead actors more likely to license AI likenesses than living actors?

Estates operate primarily as intellectual property enterprises focused on asset maximization without the physical, emotional, or ego-driven constraints of living performers. Unlike living actors, deceased legends face zero physical friction—there are no set scheduling limits, press junket obligations, physical aging, or behavioral liabilities, making them predictable, high-performing digital assets for studios seeking to derisk major film investments.

How will the rise of synthetic legacy actors impact living performers?

Living actors will no longer compete solely against current peers, but against a century of film history preserved at peak aesthetic and charismatic performance. This will likely bifurcate the talent market: an ultra-elite tier of living megastars whose value lies in authentic human presence and live connection, and a severely squeezed middle tier of character and entry-level actors facing wage compression as studios adopt cost-effective, risk-mitigated synthetic models.

Will AI actor likenesses generate a financial market similar to music catalog sales?

Yes. Just as financial institutions transformed musician song catalogs into multi-billion-dollar yield-bearing assets, Wall Street will monetize actor likenesses based on training data quality, archetypal demand, and historic box office impact. Living actors and estates will offload post-mortem rights to private equity funds for immediate liquidity, creating a robust secondary market for likeness royalties and fractionalized talent libraries.

Image Credits: Gemini

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Amazon Connect Combines Human Empathy with AI to Redefine Service

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Amazon Connect Combines Human Empathy with AI to Redefine Service

GUEST POST from Shep Hyken

Will AI replace people?

This is a question I’m often asked. In the customer service world, there are many who say AI will replace human-to-human support. It’s been predicted by the world’s most reputable consulting firms. However, executives from some of the largest and most recognizable brands on the planet have said that while AI is making for a better customer experience, people are still needed and their companies are continuing to hire.

That sentiment was recently confirmed when I interviewed Pasquale DeMaio, vice president and general manager of Amazon Connect, on Amazing Business Radio. His specific approach to AI and human-to-human customer service is summed up in two words: Better Together.

Human and AI: Better Together

Customer service works best when technology and humans come together. While AI and automation can make things faster and easier, there will be times when customers still want to talk to a live customer support agent.

The point is to automate the simple requests and questions and empower agents to manage more complex issues. DeMaio said (referring to customer service agents), “No one is enjoying a password reset, neither talking to someone about it nor listening to the request.” Let AI take care of the simple questions and requests, and let humans manage the more complex problems and emotional issues.

AI Can’t Do Empathy — That’s What People Do

DeMaio said, “People aren’t really looking for technology to form that emotional connection when they’re trying to achieve an outcome.” While AI can talk to a customer and sound like a human, the customer knows it’s just a machine. It can say, “I’m sorry,” and sound empathetic, but it’s not, and the customer knows it. Authentic empathy is a human-to-human experience.

DeMaio shares his philosophy of friendly, empathetic service. He says, “At Amazon, we actually tell people to treat the customer on the phone like they’re your friend. But what we don’t say is the person on the phone is your friend. … What’s natural is to treat them the way you would treat a friend.” And that is how empathy begins.

Customer Support Doesn’t Cost — It Pays

Traditional contact centers have focused on quick, efficient resolutions. Metrics like AHT (Average Handle Time) are efficiency measurements. The goal of handling as many calls as quickly as possible is not as effective as using customer support to not only solve customer issues but also enhance customer relationships. Once again, let AI-fueled self-service tools handle simple problems and have people (customer support agents) spend a little more time with customers to drive repeat business and loyalty. In addition, DeMaio points out that businesses should aim to understand why a customer might want to leave and proactively create positive experiences well before they escalate, to get customers to want to come back. For example, Amazon Connect’s real-time analytics empower agents to detect customer sentiment, identify at-risk relationships and take the necessary steps to save the customer.

Finding the Balance Between Technology and Human-to-Human Conversations

The balance between technology and human support will vary. However, the future of customer service is not about choosing between AI and humans. It’s about using the strengths of both to create a convenient, efficient and seamless experience. Customer service is not just about fixing. It’s about caring and building long-term, loyal relationships. DeMaio summed it up by saying, “Think about the long-term value of the customer. And then think about how you would want to be treated as a human being. And then think about how AI can help you do that better.”

This article was originally published on Forbes.com.

Image Credits: Shep Hyken

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Top 10 Human-Centered Change & Innovation Articles of July 2026

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Top 10 Human-Centered Change & Innovation Articles of July 2026 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are July’s ten most popular innovation posts:

What Happens When AI Becomes Your Customer? — by Shep Hyken
The Experience Economy 2.0 — by Braden Kelley
How to Calculate the ROI of Customer Experience — by Braden Kelley
Strategic Foresight: A Practitioner’s Guide to Thinking About the Future — by Braden Kelley
Innovation or Not — InTruth — by Braden Kelley
Innovation Framework Examples: 7 Real-World Cases That Show How They Work — by Braden Kelley
The Personal AI Renaissance — by Braden Kelley
Your 3 Phase AI Journey — by Geoffrey Moore
Why So Much Bullshit? — by Greg Satell
Creating an Innovation Edge — by John Bessant

BONUS – Here are five more strong articles published in June that continue to resonate with people:

The Neuroscience of Creativity — by Braden Kelley
The Synthetic Organization — by Braden Kelley
Use Failure as Rocket Fuel for Success Like SpaceX — by Robert B. Tucker
Crossing the Chasm of Fear — by Braden Kelley
4 Simple Rules That Make You Exponentially More Effective and Productive — by Greg Satell

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P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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AI Will Create a More Human Future, Not a Less Human One

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An AI Soft Landing Scenario

AI Soft Landing Scenario
by Braden Kelley and Art Inteligencia

What If the Future Gets More Human?

We spend a remarkable amount of time rehearsing the wrong ending.

In one popular story, artificial intelligence hollows out work, flattens craft, and leaves people performing the emotional leftovers of automation. That is a hard landing: humans demoted by systems that do more of everything, including the parts of work that used to make us feel useful.

There is another future available to us, what I call an AI soft landing. In that future, organizations and societies deliberately design AI to absorb fragmentation, acceleration, and low-judgment transaction. What returns to humans is not emptiness. What returns is depth: larger blocks of time for insight, empathy, decision making, direction setting, problem definition, creativity, and collaboration. The future becomes more human, not less, because human attention is finally reserved for human work.

This is not a naive techno-optimism. Soft landings are designed. Hard landings arrive when efficiency is the only value on the dashboard.

The Hidden Enemy Was Never “Work.” It Was Fragmentation.

Most knowledge work did not become less meaningful because people stopped caring. It became less meaningful because attention was diced into tickets, pings, updates, status rituals, and micro-approvals. We mistook motion for progress and responsiveness for value.

Task switching is expensive. Every context shift asks the brain to unload one problem and reload another. Multiply that by a day of chats, forms, triage, and administrative glue work, and you get a workforce that is always “on” and rarely deep. Strategic thinking does not fail only for lack of talent. It fails for lack of contiguous time.

AI’s first gift, if we use it well, is not genius on demand. It is fewer interrupted minutes. When drafting, scheduling, summarizing, searching, classifying, routing, and first-pass analysis get accelerated or handled, the calendar can stop looking like confetti. Bigger time blocks reappear. And bigger blocks are the raw material of original insight.

AI Future of Work

What Humans Should Own in a Soft Landing

A soft landing is not humans “using AI better.” It is a clear division of cognitive labor, one that protects the uniquely human contribution instead of competing with the machine on volume.

In a more human future, people spend more of their capacity on:

Insight development — connecting weak signals into meaning, not merely producing more output
Empathy — understanding stakes, dignity, and lived context that no dashboard fully captures
Decision making — choosing under uncertainty with values, tradeoffs, and accountability
Direction setting — naming where we are going and why it is worth the journey
Problem definition — asking better questions before rushing to automated answers
Creativity — combining perspectives in ways that are novel, useful, and humanly resonant
Collaboration — building trust, resolving conflict, and making progress together

Notice what is missing from that list: being the fastest typist in the room. Soft landing excellence is not measured in tokens per minute. It is measured in clarity per hour — and in whether people leave interactions more capable, more trusted, and more oriented than before.

From Transactional Lives to Strategic Ones

When small tasks expand to fill the day, even senior roles become transactional. Leaders spend their best hours approving instead of directing, reacting instead of sensing, facilitating meetings about work rather than doing the work of judgment.

AI can reverse that inversion, but only if organizations stop using every efficiency gain to stuff more micro-tasks into the same damaged attention budget. Saving ten minutes and immediately filling them with ten more interruptions is not transformation. It is denser exhaustion.

The soft landing asks a different operating question: What human capability do we want more of, now that machines can carry more of the glue?

If the answer is “more throughput at any cost,” you will automate people into thinner slices of busyness. If the answer is “more strategic quality, better problem framing, deeper customer and employee understanding,” AI becomes a scaffold for human depth. Less task switching. More deliberate thinking. Fewer performative updates. More real collaboration around decisions that matter.

AI Human Endeavors

How Leaders Design a Soft Landing (Instead of Hoping for One)

Human-centered change makes soft landings practical. A few design moves matter more than tool catalogs:

Automate the glue, not the judgment. Route AI toward fragmentation: search, draft, summarize, schedule, classify, prepare. Keep humans responsible for choices with ethical, relational, or strategic consequence.
Protect deep-work blocks as policy, not privilege. If AI creates capacity, calendar culture must not immediately reclaim it for more meetings.
Redefine roles around human endeavors. Job descriptions should emphasize insight, empathy, problem definition, and direction — not inbox velocity as a proxy for value.
Measure success in human outcomes. Track decision quality, customer trust, employee agency, and innovation usefulness — not only cost per interaction.
Teach the craft of better questions. In an AI-rich world, problem definition becomes a core leadership skill. Bad prompts and bad frames still produce confident nonsense.
Build collaboration for synthesis, not status. Use reclaimed time for cross-functional sense-making, not another dashboard review theater.

This is experience design for the future of work: design the system so people can be fully human on purpose.

The Choice Ahead

Futurology is not prediction cosplay. It is responsibility with a longer horizon.

We can use AI to compress people into ever-faster transaction machines. Or we can use it to return something modern work has been quietly stealing: the ability to think, feel, decide, and create with integrity.

The soft landing is the second path, a future where machines handle more of the small so humans can do more of the meaningful. Where strategy is less of a slide ritual and more of a practiced habit. Where customer and employee experience improve not only because algorithms personalize, but because people finally have the attention required for empathy and judgment.

A more human future will not arrive by accident. It will be designed by leaders who refuse to confuse automation with progress, and who insist that the best use of artificial intelligence is the expansion of human capacity where it still matters most.

Frequently Asked Questions

What is an AI soft landing?

An AI soft landing is a future in which artificial intelligence absorbs fragmented, transactional tasks so humans can spend more time on deeper endeavors — insight, empathy, decision making, direction setting, problem definition, creativity, and collaboration — making work more human rather than less.

How does AI reduce task switching at work?

AI can handle or accelerate small tasks such as drafting, summarizing, searching, scheduling, classifying, and routing. When organizations protect the time this frees, instead of immediately filling it with more interruptions, people gain larger blocks for strategic thinking and higher-quality collaboration.

What should leaders do to make the future more human with AI?

Leaders should automate glue work rather than human judgment, protect deep-work capacity as policy, redesign roles around human endeavors, measure human outcomes as well as efficiency, invest in better problem definition, and use reclaimed time for real collaboration and decision quality — not denser busyness.

Image Credits: Cursor

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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