Do Your Innovation Words Match Your Actions?

Do Your Innovation Words Match Your Actions?

GUEST POST from Mike Shipulski

Innovation isn’t a thing in itself. Companies need to meet their growth objectives and innovation is the word experts use to describe the practices and behaviors they think will maximize the likelihood of meeting those growth objectives. Innovation is a catchword phrase that has little to no meaning. Don’t ask about innovation, ask how to meet your business objectives. Don’t ask about best practices, ask how has your company been successful and how to build on that success. Don’t ask how the big companies have done it – you’re not them. And, the behaviors of the successful companies are the same behaviors of the unsuccessful companies. The business books suffer from selection bias. You can’t copy another company’s innovation approach. You’re not them. And your project is different and so is the context.

With innovation, the biggest waste of emotional energy is quest for (and arguments around) best practices. Because innovation is done in domains of high ambiguity, there can be no best practices. Your project has no similarity with your previous projects or the tightest case studies in the literature. There may be good practice or emergent practice, but there can be no best practice. When there is no uncertainty and no ambiguity, a project can use best practices. But, that’s not innovation. If best practices are a strong tenant of your innovation program, run away.

The front end of the innovation process is all about choosing projects. If you want to be more innovative, choose to work on different projects. It’s that simple. But, make no mistake, the principle may be simple the practice is not. Though there’s no acid test for innovation, here are three rules to get you started. (And if you pass these three tests, you’re on your way.)

  1. If you’ve done it before, it’s not innovation.
  2. If you know how it will turn out, it’s not innovation.
  3. If it doesn’t scare the hell out of you, it’s not innovation.

Once a project is selected, the next cataclysmic waste of time is the construction of a detailed project plan. With a well-defined project, a well-defined project plan is a reasonable request. But, for an innovation project with a high degree of ambiguity, a well-defined project plan is impossible. If your innovation leader demands a detailed project plan, it’s usually because they are used running to well-defined continuous improvement projects. If for your innovation projects you’re asked for a detailed project plan, run away.

With innovation projects, you can define step 1. And step 2? It depends. If step 1 works, modify step 2 based on the learning and try step 2. And if step 1 doesn’t work, reformulate step 1 and try again. Repeat this process until the project is complete. One step at a time until you’re done.

Innovation projects are unpredictable. If your innovation projects require hard completion dates, run away.

Innovation projects are all about learning and they are best defined and managed using Learning Objectives (LOs). Instead of step 1 and step 2, think LO1 and LO2. Though there’s little written about LOs, there’s not much to them. Here’s the taxonomy of a LO: We want to learn if [enter what you want to learn]. Innovation projects are nothing more than a series of interconnected LOs. LO2 may require the completion of LO1 or L1 and LO2 could be done in parallel, but that’s your call. Your project plan can be nothing more than a precedence diagram of the Learning Objectives. There’s no need for a detailed Gantt chart. If you’re asked for a detailed Gantt chart, you guessed it – run away.

The Learning Objective defines what you learn, how you want to learn, who will do the learning and when they want to do it. The best way to track LOs is with an Excel spreadsheet with one tab for each LO. For each LO tab, there’s a table that defines the actions, who will do them, what they’ll measure and when they plan to get the actions done. Since the tasks are tightly defined, it’s possible to define reasonable dates. But, since there can be a precedence to the LOs (LO2 depends on the successful completion of LO1), LO2 can be thought of a sequence of events that start when LO1 is completed. In that way, an innovation project can be defined with a single LO spreadsheet that defines the LOs, the tasks to achieve the LOs, who will do the tasks, how success will be determined and when the work will be done. If you want to learn how to do innovation, learn how to use Learning Objectives.

There are more element of innovation to discuss, for example how to define customer segments, how to identify the most important problems, how to create creative solutions, how to estimate financial value of a project and how to go to market. But, those are for another post.

Until then, why not choose a project that scares you, define a small set of Learning Objectives and get going?

Image credits: Pixabay

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AI’s Absurd Response to an Absurd Question

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?

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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How Long Does a Customer Experience Audit Take?

A Realistic Timeline

How Long Does a Customer Experience Audit Take?

by Braden Kelley and Art Inteligencia

The honest answer to “how long will this take” is almost always more useful than a vague reassurance that it “won’t take too long.” Vagueness here is what makes people hesitate — not the actual time commitment, which is usually more manageable than they expect once they can see it laid out phase by phase.

Why duration varies more than people assume

A Customer Experience Audit runs through five integrated activities, and each one has its own natural pace. The total timeline isn’t one number so much as it’s the sum of how much depth each activity needs for your specific journey — which is why a single-persona, single-channel audit might wrap in a few weeks, while a multi-segment, omnichannel engagement with full competitive benchmarking runs considerably longer. Here’s roughly what each phase involves.

Phase 1: Audience research and persona validation

This phase moves relatively quickly when personas already exist and mostly need validation against current reality, rather than being built from nothing. It slows down considerably when there’s no existing research to validate against — in that case, this phase does double duty as both discovery and validation, which naturally extends it.

Phase 2: Journey mapping and touchpoint analysis

This phase’s pace tracks directly with touchpoint count and channel count. Mapping a single-channel journey with a handful of touchpoints is straightforward. Mapping an omnichannel journey — where a customer might start on a website, continue on a phone call, and finish in person — takes meaningfully longer, because each channel handoff needs its own attention, not just each channel in isolation.

Phase 3: Existing data evaluation

This can run partly in parallel with the phases around it, which is one of the easiest ways to compress a timeline without cutting corners. The pace here depends less on data volume and more on data organization — data spread across disconnected systems that were never designed to talk to each other takes real time to reconcile, even when there isn’t much of it.

Phase 4: Walking the journey firsthand

This is usually the phase people underestimate, because it’s genuine fieldwork rather than analysis — retail visits, live service calls, full sales cycles observed end to end, sometimes across both B2B and B2C contexts in the same engagement. It’s also, consistently, where an audit turns up its most valuable and most surprising findings, which makes it a poor candidate for compressing even when the calendar is tight.

Phase 5: Competitive benchmarking

This phase can run largely independent of the others and is one of the more schedule-flexible components — it’s also the one most commonly trimmed or skipped entirely when timeline pressure is real, since it looks outward at competitors rather than inward at your own journey.

What actually extends a timeline

In practice, three things push a timeline out further than people expect: internal scheduling delays (getting the right stakeholders and frontline staff available for interviews and shadowing), data that’s harder to access or reconcile than anticipated, and scope that expands mid-engagement as new touchpoints or segments turn out to matter more than originally assumed. None of these are about the audit process itself moving slowly — they’re about the realities of coordinating across an organization, and they’re worth planning around rather than being surprised by.

What compresses a timeline

Running the data evaluation phase in parallel with early journey mapping, having stakeholder availability locked in before the engagement starts rather than scheduled reactively, and scoping deliberately — one primary journey and persona rather than every segment at once — are the three most reliable ways to bring a timeline in faster without sacrificing the depth that makes the findings useful.

Getting a real number for your situation

Because the actual timeline depends on your specific journey — how many personas, how many touchpoints, whether benchmarking is in scope — a general range is only ever a starting point for the conversation, not a substitute for it. If you’re building a case internally and need a realistic figure to bring to your own leadership, the audit page has the full detail on how an engagement runs, and I’m glad to talk through a specific timeline for your situation directly — the same way I would the disruption and cost questions that usually come up alongside it.

Download the Customer Experience Audit Checklist as a PDF
Image Credits: Gemini

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

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Time to Finally Kill the Idea of Leaderless Organizations

Time to Finally Kill the Idea of Leaderless Organizations

GUEST POST from Greg Satell

About a decade ago, the management guru Gary Hamel wrote a highly cited article in Harvard Business Review entitled ‘First, Let’s Fire All the Managers’. He analyzed the success of Morningstar, a leading manufacturer of tomato products that operates with a flat management structure and called for other corporations to follow its lead.

“A hierarchy of managers exacts a hefty tax on any organization,” he wrote. “This levy comes in several forms. First, managers add overhead and, as an organization grows, the costs of management rise in both absolute and relative terms.” The article created a lot of buzz and helped bolster other flat models, such as Holacracy.

Yet the “flat organization” idea hasn’t caught on. “Since 1983, the size of the bureaucratic class — the number of managers and administrators in the US workforce — has more than doubled, while employment in other categories has grown by only 40%,” Hamil recently wrote. The truth is that we need managers and trying to eliminate them is a waste of time.

Planning A Spontaneous Revolution

In the early 2000s, a series of color revolutions spread across Eastern Europe sweeping away the authoritarian remnants of post-communist governments in Serbia, the Georgian Republic and Ukraine. These would prove to other revolutionary waves such as the Arab Spring. Old-style hierarchies suddenly seemed out of date.

I experienced some of these events first-hand. I was living in Ukraine during the Orange Revolution and managing the leading news organization in the country. I also spent some time in the Georgian Republic and got to see many of the reforms take place. When Hamel’s article came out, I had already begun the research that would lead to my book Cascades and I found his ideas about flat organizations not only persuasive, but inspiring.

I shouldn’t have. Even at the time, it had become clear that the revolutions weren’t as successful and many of us had hoped. In Ukraine, Viktor Yanukovych had already come to power and it would take another revolution to dislodge him. In other countries, such as Egypt, new authoritarians would soon take the place of those who had been overthrown.

Yet even more importantly, I would later get to know one of the chief architects of the color revolutions, my friend Srdja Popović, and would learn that the revolutions weren’t leaderless at all. In fact, much of what I had experienced as spontaneous and organic was actually very much planned, engineered and organized.

As I continued to research supposedly “leaderless” organizations this would be a recurring theme. Either their success was either not genuine or ephemeral, or that there was a less obvious, informal hierarchy at work.

The Truth About The Orpheus Orchestra

One of the most cited examples of successful leaderless organizations is the Orpheus Chamber Orchestra in New York, which has been operating without a conductor since 1972. They not only regularly play at top venues like Carnegie Hall and Lincoln Center, but have won multiple Grammy awards.

An orchestra concert is a highly coordinated event, with many different musicians needing to coordinate their efforts to play music according to a specific vision. If everyone applies their own interpretation, what should be a symphony would end up as a cacophony. So how does Orpheus manage to not only survive, but thrive?

The truth is that Orpheus is not really a purely leaderless organization. It would be more accurate to say that the members trade off leadership, with one member leading one particular collection and then a different member leading another. So while it is true that the Orchestra as a whole is leaderless, each concert is leaderful.

That’s quite a big difference. If you would believe that an entire orchestra could conduct itself, you might go and try to run your organization with no direction at all, which would be a disaster. However, if you would follow the direction of the Orpheus Chamber Orchestra, you would appoint a particular team member to run each project, which would be so utterly conventional that it wouldn’t even seem worth mentioning.

The Open Source Pecking Order

Another favorite that advocates of “leaderless” organizations like to point to are open-source software communities. Yet once again, when you take a closer look, these communities are not some free-for-all, with everybody chiming in and making changes at will. In fact, in successful communities take governance very seriously.

Some projects, like Android and WordPress, are tightly controlled by the companies that originated them, Google and Automattic, respectively. They manage the community fairly tightly, accepting patches, revisions and improvements as they see fit and providing a vision for where they think the technology should go.

Open source foundations, like Linux and Apache provide more intricate governance structures. They don’t have much in the way of formal leadership, but in practice each project has informal leaders who drive the direction of the technology. In fact, competition for clout within those communities can be very stiff.

There’s a reason why some of the world’s most valuable companies pay people well to contribute to open-source software communities and it’s not altruism. They want to shape how crucial technologies will develop to benefit their business. To do that, talented people need to spend time building the trust and reputation that will enable them to lead.

Let’s Not Fire All The Managers

For a while now, management gurus such as Gary Hamel have been advocating for flatter organizations, yet there is little evidence that eliminating leaders is a viable model. In fact, when Wharton Professor Ronnie Lee took a close look at game software developers, he actually found that the number of levels of bureaucracy increased significantly, not decreased, over the last 50 years.

There are several reasons that this is true. The first is that, while having a flatter structure leads to more innovation and creativity, you need good leadership and governance to execute well. As an industry matures and becomes more complex, more levels of hierarchy are needed to manage it effectively.

Another important factor to consider is that even without a formal hierarchy, leaders will tend to emerge. Which is why when you take a closer look at often cited examples of “leaderless organizations,” there is much more hierarchy that it would at first seem. Just because there isn’t an organization chart doesn’t mean there isn’t a pecking order.

We need to stop thinking in terms of how many levels of bureaucracy there are and start working to network our organizations. We don’t need to eliminate managers — or anyone else for that matter — but to widen and deepen connections within and without our enterprise. We need to lead and to do it more effectively.

The role of leadership in organizations has changed. It is no longer merely to plan and direct work, but to inspire meaning and empower belief. As I wrote in Cascades, the key to transformational change is small groups, loosely connected by united by a shared purpose. The job of leaders today is to help those groups connect and forge a common purpose.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Unleashing Your Innovation Potential

Unleashing Your Innovation Potential

GUEST POST from Janet Sernack

It is not unusual, especially in the corporate world, to label someone who disrupts and challenges the status quo, or what others say, as argumentative and “oppositional”. According to Human Synergistics, the Oppositional Style, in their circumplex, when measured as a dominant leadership style, is summarized in their 360-degree Leadership Styles Inventory (LSI) by two items: “usually against things” and “opposes new ideas.” It labels the person, or leader as being aggressively defensive and causes them to disengage by avoiding engaging in any kind of conflict, dissent and disagreement. This inhibits and prevents them from unleashing their creative thinking and their innovation potential.

In my many years as a corporate trainer presenting high-performance cultures and transformational leadership programs, I learned to apply my imagination, curiosity and inquiry skills to be attentively present, listen deeply and ask generative questions. This helped me develop strategies to unleash a person’s innovation potential by encouraging and enabling thoughtful dissent and disagreement.  I learned to uncover and recognize a leader’s energy and positive intent behind many of the scary, argumentative, hostile, contrary, critical, and aggressive behaviors directed at me, especially in my years upfront as a female corporate trainer in many toxic organizational cultures.

I often found that they were compromised in some way by their personal and family needs and values, their assumptions and beliefs about themselves and their performance at work, poor physical and mental health, and/or a lack of emotional well-being. Any one of these factors made them self-protective, leading to either overt aggression toward others, passive avoidance of conflict, or an unconscious refusal to engage in thoughtful dissent and disagreement because they did not want to be perceived as hostile and argumentative.

All of these unconscious reactive responses inhibited them from unleashing their creative thinking and innovative potential. 

This blog explores the power of unleashing creative thinking and innovation potential through supporting people to thoughtfully dissent and disagree – by using conflict as a catalyst for better decision-making, problem-solving, and strengthened collaboration and innovation – not as a barrier.

Unconscious reactive responses

This manifests as a neurologically challenging reactive response, as people’s brains are wired to defend and protect them to ensure their survival. These defensive responses are composed of fight/flight/freeze/fawn reactions and arise when people face what their neurology (the limbic system) perceives as a threat to their security, safety, or survival.

These reactions stem from past experiences and traumas that are often stored in people’s somatic memories. They become neurologically wired and embedded in people’s habitual ways of being unless they are noticed, labelled, accepted, acknowledged and owned. Until there is a safe holding space for this to happen, it will be impossible to unleash their creative thinking and innovation potential.

Safe holding spaces

Once a safe holding space is established, an oppositional person can be carefully and empathetically challenged, disputed, deviated from, and redirected towards more self-regulated, resourceful, and emotionally healthy states and new approaches. This involves bravely engaging them in thoughtful dissent and disagreement, despite being encased in a toxic organizational culture. Its about building permission, safety and trust to encourage creative thinking that unleashes a persons innovation potential.

Fight-flight-freeze and fawn responses

The amygdala is designed to detect threats and trigger the fight-flight-freeze response to help people survive

  •  A fight response involves standing our ground when we feel threatened, driven by anger, fear, and anxiety.
  •  A flight response entails moving away from or avoiding a situation to reduce painful feelings.
  •  A freeze response involves shutting down, feeling numb, or experiencing paralysis to reduce painful feelings, resulting in immobility.
  • A fawn response involves appeasing and placating others or seeking approval while neglecting the need to feel safe.  

Role of smart conflict, thoughtful dissent and disagreement

This is particularly evident in political and organizational contexts, where people are suffering from change fatigue and overwork. As a result, they unconsciously disengage from their work, families, and daily lives through a flight-or-freeze response, driven by burnout, the pursuit of external validation (a fawn response), or frustration and anger (usually a fight response). A combination of any or all of these largely unconscious but fully present reactive responses creates toxic organizational cultures, in which leaders’ behaviors are either passively or aggressively defensive in their attempts to meet people’s core security, safety, and survival needs. Leaders avoid encouraging or engaging in thoughtful dissent and disagreement that mobilizes people’s creative and critical thinking and unleashes their innovation potential.

The constraints and challenges of the pandemic, coupled with threats to people’s stability and the desire for certainty, also led many people in organizations to fear that their needs for security, safety, and survival would not, and could not, be met. These fears unconsciously intensified people’s defensiveness and reactivity, leading to immobilization through passivity, helplessness, hopelessness, powerlessness, disengagement, and detachment. This indicates a lack of emotional intelligence, largely due to the absence of initial self-regulation and self-leadership strategies.  It is easier and sometimes safer to be a victim and blame others for any negative, pessimistic, or painful feelings of shame, guilt, and embarrassment, resulting from competing commitments, value violations, thought distortions, and emotional dissociation.

These emotionally overwhelming and cognitively overloading factors are also accepted inhibitors to unleashing a person’s innovative potential, especially when they are unable to engage in thoughtful dissent and disagreement under the influence of a toxic organizational culture.

Making the shift

To support people to effectively shift this way of being and safely unleash their creative thinking and innovation potential:

It is crucial to develop and apply the generative discovery skillset to welcome dissent and thoughtful disagreement:

  • Notice: by being aware and attentively present to what is happening to, and within the person, neurologically and physiologically, emotionally, cognitively and physically.
  • Disrupt: by being curious and safely asking open, explorative discovery questions, and by listening carefully to hear and notice what is really going on for them.
  • Dispute: by safely summarizing and challenging their assumptions, perceptions and perspectives, asking evocative and provocative questions that encourage contrary thinking, diversity of thought, differences and constructive disagreement.
  • Deviate: by safely summarizing and eliciting a mindset flip or an agility shift in the oppositional person, directing their creative energy towards constructive behaviors that deliver a positive outcome.

This enables people to redirect their emotional energy and move away from unresourceful internal thought patterns and distortions. It also encourages them to open their minds to thoughtful dissent and disagreement, allowing them to think differently and enter creative and innovative realms. 

This is impossible when toxic organizational cultures force people to hide behind conventional, rigid, and closed minds that won’t allow them to rock the boat and be, think and act differently by intentionally using conflict constructively by embracing dissent and thoughtful disagreement 

At the same time, it’s crucial to safely, empathically and compassionately evoke and provoke an opening of people’s hearts by purposefully and meaningfully aligning their needs and values, so that the change motivates them to change meaningfully and purposefully. This creates the safe holding space that allows them to let go of the need to be in control and be open-willed, unleashing their creative thinking and innovation potential to make the shift to the creative and innovative realms.

Empowering people and teams to redirect their emotional energy towards opening their minds to thoughtful dissent and disagreement allows them to handle hard conversations with constructive and creative intent.

Welcoming dissent and thoughtful disagreement involves daring yourself and others to be and think differently. Being able to think creatively maximize a person or a teams innovation potential, unleashes their human ingenuity in the age of AI, and mobilizes their collective intelligence to lead, manage, or implement constructive and sustainable change.

Leveraging constructive conflict, dissent and thoughtful disagreement unleashes people’s creative thinking and innovation potential and is a necessary part of enabling AI, digital transformation and innovation initiatives to succeed in uncertain and disruptive times.

Find out more about our work at ImagineNation™ and The Start-Up Game.™ Discover our collective learning products and tools that can be customized as playful bespoke corporate learning programs. Our blended and transformational change and learning programs provide a deep understanding of the language, principles, and applications of an ecosystem-focused, human-centric approach and emergent structure (Theory U) to innovation and entrepreneurship. It will also upskill people and teams, developing their future fitness within your unique digital transformation and innovation initiatives. Please find out more about our products and tools.

Image Credit: Gemini

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Customer Experience Audit Checklist

What a Professional Auditor Actually Looks For

Customer Experience Audit Checklist

by Braden Kelley and Art Inteligencia

People often picture a customer experience audit as something closer to inspiration than inspection — a few interviews, some good ideas, a workshop. It isn’t. A real audit runs a specific, repeatable checklist across five activities, and knowing what’s actually on that checklist is useful even if you’re not commissioning a full audit yet — it tells you exactly where your own internal review is likely leaving gaps.

1. Audience research and persona validation

  • Have you named your distinct audience segments explicitly, rather than treating “the customer” as one undifferentiated group?
  • Are your existing personas based on validated research, or on assumptions that were accurate once and never rechecked?
  • Do you know which needs, expectations, and pain points are consistent across segments, and which ones genuinely differ?
  • Has anyone recently asked customers directly what they expect, rather than inferring it from internal debate?

Most organizations have personas. Far fewer have validated them against current reality in the last two years — and a persona built on outdated assumptions can misdirect an entire audit before it starts.

2. Journey mapping and touchpoint analysis

  • Is there a current journey map for each validated persona, or one generic map applied to everyone?
  • Are all relevant channels represented, not just the primary one your team happens to work in?
  • Does the map capture emotional highs and lows at each touchpoint, or only the functional steps?
  • Have improvement opportunities been identified at the touchpoint level, rather than as vague, journey-wide observations?

A journey map that only lists steps, without the emotional experience at each one, tells you what happens without telling you what it’s like — and “what it’s like” is usually where the real problem lives.

3. Existing data evaluation

  • Are your KPIs, NPS, CSAT, and sentiment data being analyzed together, or read in isolation by different teams?
  • Have you looked for patterns across data sources that no single dashboard would surface on its own?
  • Is there a gap between what your data says and what your frontline teams say — and if so, has anyone reconciled it?
  • What insight is your current reporting structurally unable to produce, no matter how closely you look at it?

This step exists because most organizations already have more data than they’ve actually used. The checklist item isn’t “collect more data” — it’s “extract what’s already sitting there unexamined.”

4. Walking the journey firsthand

  • Has anyone on your team experienced the journey as a customer would — not reviewed it on paper, but actually gone through it?
  • Does that include the channels that are hardest to observe from a desk — a retail visit, a live service call, a full sales cycle?
  • For B2B experiences specifically, has the buying-committee journey been walked, not just the end-user journey?
  • What did that firsthand pass turn up that no dashboard or survey had previously flagged?

This is consistently where an audit finds its most valuable insights, and it’s also the step most internal reviews skip entirely, because it requires time in the field rather than time in a meeting.

5. Competitive benchmarking

  • Do you know how your experience compares to your closest competitors at the touchpoints that matter most, not just anecdotally?
  • Have you looked at best-in-class examples outside your own industry, where customer expectations are quietly being reset?
  • Is “good enough” being judged against your own history, or against where your customers’ expectations actually sit today?

Benchmarking is the item most often skipped to save time — understandably, since it’s the one activity that looks outward instead of inward. It’s also the one that answers the question your leadership will eventually ask: not “are we good,” but “are we good enough relative to the alternative.”

Using this checklist honestly

If you walk through these five sections and find real gaps — persona research from three years ago, a journey map from before a major process change, data that’s never been cross-analyzed, no one who’s actually walked the journey firsthand, no benchmarking at all — that’s not a failure. It’s an accurate picture of where a professional audit would start, and it’s useful information either way.

If the gaps are small, a lighter internal review might close them. If the gaps run through several of these five, it’s usually a sign that a proper Customer Experience Audit is worth the investment rather than another attempt to patch it internally — and if you want a sense of what closing those gaps could be worth before you commit, the CX ROI Calculator is the fastest way to find out.

Download the Customer Experience Audit Checklist as a PDF

Image Credits: Gemini

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

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Mapping the Future with Wardley Maps

Mapping the Future with Wardley Maps

GUEST POST from Mike Shipulski

How do you know when it’s time to reinvent your product, service or business model? If you add ten units of energy and you get less in return than last time, it’s time to work in new design space. If improvement in customer goodness (e.g., miles per gallon in a car) has slowed or stopped, it’s time to seek a new fuel source. If recent patent filings are trivial enhancements that can be measured only with a large sample sizes and statistical analysis, the party is over.

When there’s so many new things to work on, how do you choose the next project? When you’re lost, you look at a map. And when there is no map, you make one. The first bit of work is defined by the holes in the first revision of your map. And once the holes are filled and patched, the next work emerges from the map itself. And, in a self-similar way, the next work continually emerges from the previous work until the project finishes.

Simon Wardley Map

But with so much new territory, how do you choose the right new territory to map? You don’t. Before there’s a need to map new territory, you must map the current territory. What you’ll learn is there are immature areas that, when made mature, will deliver new value to customers. And you’ll also learn the mature areas that must be blown up and replaced with infant solutions that will ultimately create the next evolution of your business. And as you run thought experiments on your map – projecting advancements on the various elements – the right new territory will emerge. And here’s a hint – the right new solutions will be enabled by the newly matured elements of the map.

But how do you predict where the right new solutions will emerge? I can’t tell you that. You are the experts, not me. All I can say is, make the maps and you’ll know.

And when I say maps, I mean Wardely Maps – here’s a short video (go to 4:13 for the juicy bits).

Image credits: Pixabay, Simon Wardley

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Dead Actors Society

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

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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