You Need to Know What Your Customers Think of AI

GUEST POST from Shep Hyken

Ten years ago, only the most technologically advanced companies used AI — although it barely resembled what companies use today when communicating with customers — and it was very, very expensive. But not anymore. Today, any company can implement an AI strategy using ChatGPT-type technologies, often creating experiences that give customers what they want. But not always, which is why the information below is important.

The 2025 Findings

My annual customer service and customer experience (CX) research study surveys more than 1,000 U.S. consumers weighted to the population’s demographics of age, gender, ethnicity and geography. It included an entire group of questions focused on how customers react to and accept (or don’t accept) AI options to ask questions, resolve problems and communicate with a company or brand. Consider the following findings:

AI Success: Half of U.S. customers (50%) said they have successfully resolved a customer service issue using AI or ChatGPT-type technologies without needing human assistance. In 2024, only three out of 10 customers (32%) did so. That’s great news, but it’s important to point out that age makes a difference. Six out of 10 Gen-Z customers (61%) successfully used AI support versus just 32% of Boomers.
AI Is Far From Perfect: Half of U.S. customers (51%) said they received incorrect information from an AI self-service bot. Even with incredible improvement in AI’s capabilities, it still serves up wrong information. That destroys trust, not only in the company but also in the technology as a whole. A few bad answers and customers will be reluctant, at least in the near term, to choose self-service over the traditional mode of communication, the phone.
Still, Customers Believe: Four out of 10 customers (42%) believe AI and ChatGPT can handle complex customer service inquiries as effectively as humans. Even with the mistakes, customers believe AI solutions work. However, 86% of customers think companies using AI should always provide an option to speak or text with a real person.
The Phone Still Rules: It’s still too early to throw away phone support. My prediction is that it will be years, if ever, that human-to-human interactions completely disappear, which was proven when we asked, “When you have a problem or issue with a company, which solution do you prefer to use: phone or digital self-service?” The answer is that 68% of customers will still choose the phone over digital self-service. That number is highly influenced by the 82% of Baby Boomers who choose to call a company over any other type of digital support.
The Future Looks Strong For AI Customer Support: Six out of 10 customers (63%) expect AI-fueled technologies to become the primary mode of customer support. We asked the same question in 2021, and only 21% of customers felt this way.

The Strategy Behind Using AI For CX

Age Matters: As you can see from some of the above findings, there is a big generational gap between younger and older customers. Gen-Z customers are more comfortable, have had more success, and want more digital/AI interactions compared to older customers. Know your customer demographics and provide the appropriate support and communication options based on their age. Recognize you may need to provide different support options if your customer base is “everyone.”
Trust Is a Factor: Seven out of 10 customers (70%) have concerns about privacy and security when interacting with AI. Once again, age makes a difference. Trust and confidence with AI consistently decrease with age.

The Future of AI

As AI continues to evolve, especially in the customer service and experience world, companies and brands must find a balance between technology and the human touch. While customers are becoming more comfortable and finding success with AI, we can’t become so enamored with it that we abandon what many of our customers expect. The future of AI isn’t a choice between technology and humans. It’s about creating a blended experience that plays to the technology’s strengths and still gives customers the choice.

Furthermore, if every business had a 100% digital experience, what would be a competitive differentiator? Unless you are the only company that sells a specific product, everything becomes a commodity. Again, I emphasize that there must be a balance. I’ll close with something I’ve written before, but bears repeating:

The greatest technology in the world can’t replace the ultimate relationship-building tool between a customer and a business: the human touch.

This article was originally published on Forbes.com.

Image Credits: Google Gemini

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Building a Learning Organization

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Building a Learning Organization

GUEST POST from Stefan Lindegaard

Building a learning organization goes beyond adopting new methods or tools. At its core, it’s about fostering a culture where continuous growth, adaptability, and shared learning are prioritized at every level.

Creating this culture requires a top-down commitment led by leadership and management teams who embody a growth mindset, promote psychological safety, and actively engage in building a learning-focused environment.

Without this dedication, organizations miss a crucial opportunity to develop the capabilities essential for innovation and future-readiness.

Why is this important? Well, in today’s unpredictable and rapidly evolving landscape, a learning organization isn’t just a “nice-to-have” – it’s an imperative. While a company may excel in current operations, failing to invest in learning and adaptability poses significant risks to long-term success. Can any organization truly afford to ignore the need to shape its future?

Three Key Pillars

The foundation of a strong learning organization rests on three pillars:

A growth mindset,
psychological safety,
and an unwavering commitment to fostering a culture of learning.

Leaders must first embody these values to inspire the entire organization to follow. It starts with self-reflection: How can leaders upgrade their mindset, skills, and tools to champion this change? How can they be supported in making it happen?

Only when leaders truly commit to this journey can we build a resilient organization where people and teams possess the adaptability, skills, and mindset needed to innovate, grow, and thrive.

Image Credit: Stefan Lindegaard

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The Great American Contraction

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Population, Scarcity, and the New Era of Human Value

The Great American Contraction - Population, Scarcity, and the New Era of Human Value

GUEST POST from Art Inteligencia

We stand at a unique crossroads in human history. For centuries, the American story has been a tale of growth and expansion. We built an empire on a relentless increase in population and labor, a constant flow of people and ideas fueling ever-greater economic output. But what happens when that foundational assumption is not just inverted, but rendered obsolete? What happens when a country built on the idea of more hands and more minds needing more work suddenly finds itself with a shrinking demand for both, thanks to the exponential rise of artificial intelligence and robotics?

The Old Equation: A Sinking Ship

The traditional narrative of immigration as an economic engine is now a relic of a bygone era. For decades, we debated whether immigrants filled low-skilled labor gaps or competed for high-skilled jobs. That entire argument is now moot. Robotics and autonomous systems are already replacing a vast swath of low-skilled labor, from agriculture to logistics, with greater speed and efficiency than any human ever could. This is not a future possibility; it’s a current reality accelerating at an exponential pace. The need for a large population to perform physical tasks is over.

But the disruption is far more profound. While we were arguing about factory floors and farm fields, Artificial Intelligence (AI) has quietly become a peer-level, and in many cases, superior, knowledge worker. AI can now draft legal briefs, write code, analyze complex data sets, and even generate creative content with a level of precision and speed no human can match. The very “high-skilled” jobs we once championed as the future — the jobs we sought to fill with the world’s brightest minds — are now on the chopping block. The traditional value chain of human labor, from manual to cognitive, is being dismantled from both ends simultaneously.

“The question is no longer ‘What can humans do?’ but ‘What can only a human do?'”

The New Paradigm: Radical Scarcity

This creates a terrifying and necessary paradox. The scarcity we must now manage is not one of labor or even of minds, but of human relevance. The old model of a growing population fueling a growing economy is not just inefficient; it is a direct path to social and economic collapse. A population designed for a labor-based economy is fundamentally misaligned with a future where labor is a non-human commodity. The only logical conclusion is a Great Contraction — a deliberate and necessary reduction of our population to a size that can be sustained by a radically transformed economy.

This reality demands a ruthless re-evaluation of our immigration policy. We can no longer afford to see immigrants as a source of labor, knowledge, or even general innovation. The only value that matters now is singular, irreplaceable talent. We must shift our focus from mass immigration to an ultra-selective, curated approach. The goal is no longer to bring in more people, but to attract and retain the handful of individuals whose unique genius and creativity are so rare that AI can’t replicate them. These are the truly exceptional minds who will pioneer new frontiers, not just execute existing tasks.

The future of innovation lies not in the crowd, but in the individual who can forge a new path where none existed before. We must build a system that only allows for the kind of talent that is a true outlier — the Einstein, the Tesla, the Brin, but with the understanding that even a hundred of them will not be enough to employ millions. We are not looking for a workforce; we are looking for a new type of human capital that can justify its existence in a world of automated plenty. This is a cold and pragmatic reality, but it is the only path forward.

Human-Centered Value in a Post-Labor World

My core philosophy has always been about human-centered innovation. In this new world, that means understanding that the purpose of innovation is not just about efficiency or profit. It’s about preserving and cultivating the rare human qualities that still hold value. The purpose of immigration, therefore, must shift. It is not about filling jobs, but about adding the spark of genius that can redefine what is possible for a smaller, more focused society. We must recognize that the most valuable immigrants are not those who can fill our knowledge economy, but those who can help us build a new economy based on a new, more profound understanding of what it means to be human.

The political and social challenges of this transition are immense. But the choice is clear. We can either cling to a growth-based model and face the inevitable social and economic fallout, or we can embrace this new reality. We can choose to see this moment not as a failure, but as an opportunity to become a smaller, more resilient, and more truly innovative nation. The future isn’t about fewer robots and more people. It’s about robots designing, building and repairing other robots. And, it’s about fewer people, but with more brilliant, diverse, and human ideas.

This may sound like a dystopia to some people, but to others it will sound like the future is finally arriving. If you’re still not quite sure what this future might look like and why fewer humans will be needed in America, here are a couple of videos from the present that will give you a glimpse of why this may be the future of America:

Image credit: Google Gemini

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Don’t Fall for the Design Squiggle Lie

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Don't Fall for the Design Squiggle Lie

GUEST POST from Robyn Bolton

Last night, I lied to a room full of MBA students. I showed them the Design Squiggle, and explained that innovation starts with (what feels like) chaos and ends with certainty.

The chaos part? Absolutely true.

The certainty part? A complete lie.

Nothing is Ever Certain (including death and taxes)

Last week I wrote about the different between risk and uncertainty. Uncertainty occurs when we cannot predict what will happen when acting or not acting. It can also be broken down into Unknown uncertainty (resolved with more data) and Unknowable uncertainty (which persists despite more data).

But no matter how we slice, dice, and define uncertainty, it never goes away.

It may be higher or lower at different times,

More importantly, it changes focus.

Four Dimensions of Uncertainty

Something new that creates value (i.e. an innovation) is multi-faceted and dynamic. Treating uncertainty as a single “thing” therefore clouds our understanding and ability to find and addresses root causes.

That’s why we need to look at different dimensions of uncertainty.

Thankfully, the ivory tower gives us a starting point.

WHAT: Content uncertainty relates to the outcome or goal of the innovation process. To minimize it, we must address what we want to make, what we want the results to be, and what our goals are for the endeavor.

WHO: Participation uncertainty relates to the people, partners, and relationships active at various points in the process. It requires constant re-assessment of expertise and capabilities required and the people who need to be involved.

HOW: Procedure uncertainty focuses on the process, methods, and tools required to make progress. Again, it requires constant re-assessment of how we progress towards our goals.

WHERE: Time-space uncertainty focuses on the fact that the work may need to occur in different locations and on different timelines, requiring us to figure out when to start and where to work.

It’s tempting to think each of these are resolved in an orderly fashion, by clear decisions made at the start of a project, but when has a decision made on Day 1 ever held to launch day?

Uncertainty in Pharmaceutical Development

Let’s take the case of NatureComp, a mid-sized company pharmaceutical company and the uncertainties they navigated while working to replicate, develop, and commercialize a natural substance to target and treat heart disease.

What molecule should the biochemists research?
How should the molecule be produced?
Who has the expertise and capability to synthetically poduce the selected molecule because NatureComp doesn’t have the experience required internally?
Where to produce that meets the synthesization criteria and could produce cost-effectively at low volume?
What target disease specifically should the molecule target so that initial clincial trials can be developed and run?
Who will finance the initial trials and, hopefully, become a commercialization partner?
Where would the final commercial entity exist (e.g. stay in NatureComp, move to partner, stand-alone startup) and the molecule produced?

And those are just the highlights.

It’s all a bit squiggly

The knotty, scribbly mess at the start of the Design Squiggle is true. The line at the end is a lie because uncertainty never goes away. Instead, we learn and adapt until it feels manageable.

Next week, you’ll learn how.

Image credit: The Process of Design Squiggle by Damien Newman, thedesignsquiggle.com

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What We See Influences How We’ll Act

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What We See Influences How We'll Act

GUEST POST from Greg Satell

“Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually slaves of some defunct economist,” John Maynard Keynes, himself a long dead economist, once wrote. We are, much more than we’d like to admit, creatures of our own age, taking our cues from our environment.

That’s why we need to be on the lookout for our own biases. The truth, as we see it, is often more of a personalized manifestation of the zeitgeist than it is the product of any real insight or reflection. As Richard Feynman put it, “The first principle is that you must not fool yourself—and you are the easiest person to fool. So you have to be very careful about that.”

We can’t believe everything we think. We often seize upon the most easily available information, rather than the most reliable sources. We then seek out information that confirms those beliefs and reject evidence that contradicts existing paradigms. That’s what leads to bad decisions. If what we see determines how we act, we need to look carefully.

The Rise And Fall Of Social Darwinism

In the 1860s, in response to Darwin’s ideas, Herbert Spencer and others began promoting the theory of Social Darwinism. The basic idea was that “survival of the fittest” meant that society should reflect a Hobbesian state of nature, in which most can expect a life that is “nasty, brutish and short,” while an exalted few enjoy the benefits of their superiority.

This was, of course, a gross misunderstanding of Darwin’s work. First, Darwin never used the term, “survival of the fittest,” which was actually coined by Spencer himself. Secondly, Darwin never meant to suggest that there are certain innate qualities that make one individual better than others, but that as the environment changes, certain traits tend to be propagated which, over time, can lead to a new species.

Still, if you see the world as a contest for individual survival, you will act accordingly. You will favor a laissez-faire approach to society, punishing the poor and unfortunate and rewarding the rich and powerful. In some cases, such as Nazi Germany and in the late Ottoman empire, Social Darwinism was used as a justification for genocide.

While some strains of Social Darwinism still exist, for the most part it has been discredited, partly because of excesses such as racism, eugenics and social inequality, but also because more rigorous approaches, such as evolutionary psychology, show that altruism and collaboration can themselves be adaptive traits.

The Making Of The Modern Organization

When Alfred Sloan created the modern corporation at General Motors in the early 20th century, what he really did was create a new type of organization. It had centralized management, far flung divisions and was exponentially more efficient at moving around men and material than anything that had come before.

He called it “federal decentralization.” Management would create operating principles, set goals and develop overall strategy, while day-to-day decisions were performed by people lower down in the structure. While there was some autonomy, it was more like an orchestra than a jazz band, with the CEO as conductor.

Here again, what people saw determined how they acted. Many believed that a basic set of management principles, if conceived and applied correctly, could be adapted to any kind of business, which culminated in the “Nifty Fifty” conglomerates of the 60’s and 70’s. It was, in some sense, an idea akin to Social Darwinism, implying that there are certain innate traits that make an organization more competitive.

Yet business environments change and, while larger organizations may be able to drive efficiencies, they often find it hard to adapt to changing conditions. When the economy hit hard times in the 1970s, the “Nifty Fifty” stocks vastly under-performed the market. By the time the 80s rolled around, conglomerates had fallen out of fashion.

Industries and Value Chains

In 1985, a relatively unknown professor at Harvard Business School named Michael Porter published a book called Competitive Advantage, which explained that by optimizing every facet of the value chain, a firm could consistently outperform its competitors. The book was an immediate success and made Porter a management superstar.

Key to Porter’s view was that firms compete in industries that are shaped by five forces: competitors, customers, suppliers, substitutes, and new market entrants. So he advised leaders to build and leverage bargaining power in each of those directions to create a sustainable competitive advantage for the long term.

If you see your business environment as being neatly organized in specific industries, everybody is a potential rival. Even your allies need to be viewed with suspicion. So, for example, when a new open source operating system called Linux appeared, Microsoft CEO Steve Ballmer considered it to be a threat and immediately attacked, calling it a cancer.

Yet even as Ballmer went on the attack, the business environment was changing. As the internet made the world more connected, technology companies found that leveraging that connectivity through open source communities was a winning strategy. Microsoft’s current CEO, Satya Nadella, says that the company loves Linux. Ultimately, it recognized that it couldn’t continue to shut itself out and compete effectively.

Looking To The Future

Take a moment to think about what the world must have looked like to J.P. Morgan a century ago, in 1922. The disruptive technologies of the day, electricity and internal combustion, were already almost 40 years old, but had little measurable economic impact. Life largely went on as it always had and the legendary financier lorded over his domain of corporate barons.

That would quickly change over the next decade when those technologies would gain traction, form ecosystems and drive a 50-year boom. The great “trusts” that he built would get broken up and by 1930 virtually all of them would be dropped as components of the Dow Jones Industrial average. Every face of life would be completely transformed.

We’re at a similar point today, on the brink of enormous transformation. The recent string of calamities, including a financial meltdown, a pandemic and the deadliest war in Europe in 80 years, demand that we take a new path. Powerful shifts in technology, demographics, resources and migration, suggest that even more disruption may be in our future.

The course we take from here will be determined by how we see the world we live in. Do we see our fellow citizens as a burden or an asset? Are new technologies a blessing or a threat? Is the world full of opportunities to be embraced or dangers we need to protect ourselves from? These are questions we need to think seriously about.

How we answer them will determine what comes next.

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

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Journeying Through the Technology Adoption Lifecycle

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Journeying Through the Technology Adoption Lifecycle

GUEST POST from Geoffrey A. Moore

Like everything else in this Darwinian world of ours, customer journeys evolve with changes in the environment. Ever since the advent of the semiconductor, a compelling source of such changes has been disruptive digital technology. Although we are all eager to embrace its benefits, markets must first work through their adoption life cycles, during which different buying personas come to the fore at different stages, with each one on a very different kind of journey. So, if you plan to catch the next wave and sell the next big thing, you’re going to need to adjust your customer journey playbook as you go along. Here’s a recap of what is in store for you.

Customer Journeys in the Early Market

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

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

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

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

Customer Journeys to Cross the Chasm

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

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

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

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

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

Customer Journeys Inside the Tornado

The tornado is that point in the technology adoption life cycle when the pragmatist community shifts from fear of going too soon to fear of missing out. As a consequence, they all rush to catch up. Even without a compelling first use case, they commit resources to the new category. Thus, for the first time in the history of the category, prospective customers have budget allocated before the salesperson calls. (In the early market, there was no budget at all—the visionary had to create it. In the chasm-crossing scenario, there is budget, but it is being spent on patchwork fixes with legacy solutions and needs to get reallocated before a deal can be closed.)

Budget is allocated to the department that will purchase and support the new offer, not the ones who will actually use it (although they will no doubt get chargebacks at some point). That means for IT offerings the target customer is the technical buyer and the CIO, the former who will make the product decision, the latter who will make the vendor decision. Ideally, the two will coincide, but when they don’t, the vendor choice usually prevails.

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

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

Customer Journeys on Main Street

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

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

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

Summing Up

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

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

Image Credit: Pexels

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Augmented Expertise

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How XR is Redefining “In-the-Flow” Training

Augmented Expertise XR

GUEST POST from Art Inteligencia

In our relentless pursuit of innovation and efficiency, we often talk about automation, AI, and the promise of a future where machines handle the heavy lifting. But what about the human element? How do we empower our greatest asset – our people – to perform at their peak, adapt to rapid change, and master increasingly complex tasks without being overwhelmed? The answer, increasingly, lies not in replacing humans, but in augmenting human expertise through sophisticated, intuitive technologies.

One of the most compelling frontiers in this space is Extended Reality (XR) for “in-the-flow” training. This isn’t about traditional classroom learning or even simulated environments that mimic reality. This is about bringing learning directly into the operational context, providing real-time, context-aware guidance that enhances performance precisely when and where it’s needed. Imagine a technician performing a complex repair, seeing holographic instructions overlaid directly onto the machinery. Or a surgeon practicing a new procedure with anatomical data projected onto a mannequin. This is the promise of XR in-the-flow training: learning by doing, with intelligence baked into the environment itself.

Beyond Simulation: The Power of Contextual Learning

For decades, training has largely been a pull-based system: individuals seek out knowledge, or organizations push it through scheduled courses. While effective for foundational understanding, this model struggles in dynamic environments where information decays rapidly, and complexity demands immediate, precise application. The “forgetting curve” is a well-documented phenomenon; we lose a significant portion of what we learn very quickly if it’s not applied.

XR in-the-flow training flips this script. It leverages augmented reality (AR) and mixed reality (MR) to provide just-in-time, just-enough, just-for-me information. Instead of abstract concepts, learners engage with real-world problems, receiving immediate feedback and instruction that is directly relevant to their current task. This approach drastically improves retention, reduces errors, and accelerates skill acquisition because the learning is deeply embedded in the context of action.

“The future of work isn’t about replacing humans with machines; it’s about seamlessly augmenting human capabilities with intelligent tools that empower us to achieve more.”

This paradigm shift has profound implications for human-centered design. We’re moving from designing for a user who consumes information to designing for a user who *interacts* with information as an integral part of their physical workflow. The interface becomes the environment, and the learning experience is woven into the fabric of the task itself.

Case Study 1: Transforming Aerospace Manufacturing

Consider the aerospace industry, where precision, safety, and efficiency are paramount. As aircraft become more sophisticated, the complexity of assembly and maintenance tasks escalates, leading to longer training cycles and higher potential for human error. One leading aerospace manufacturer faced challenges with new hires in assembly operations, particularly with intricate wiring harnesses and component installation.

They deployed an AR-based in-the-flow training system using smart glasses. When a technician dons the headset, holographic overlays guide them through each step of the assembly process. Arrows point to specific components, digital models show correct placement, and textual instructions appear precisely where needed. The system can even detect if a step is performed incorrectly and provide immediate corrective feedback. The results were dramatic: training time for complex tasks was reduced by 30%, and error rates plummeted by 40% in pilot programs. More importantly, new employees felt more confident and productive much faster, leading to higher job satisfaction and retention.

Case Study 2: Revolutionizing Healthcare Procedures

In healthcare, the stakes are even higher. Doctors, nurses, and technicians constantly need to learn new procedures, operate complex medical equipment, and adapt to evolving protocols. Traditional methods often involve classroom sessions, practice on mannequins (away from the real patient context), or observation, which can be time-consuming and resource-intensive.

A major hospital network implemented a mixed reality training solution for surgical residents learning a minimally invasive procedure. Using an MR headset, residents could visualize a patient’s internal anatomy (from MRI or CT scans) as a 3D hologram directly superimposed onto a high-fidelity surgical mannequin. The system provided real-time guidance on instrument placement, incision angles, and potential risks, all without obscuring the physical tools or the training environment. This allowed residents to practice repeatedly in a highly realistic yet safe environment, receiving immediate visual and auditory feedback. The program demonstrated a significant increase in procedural proficiency and a reduction in the learning curve, leading to better patient outcomes and increased surgeon confidence.

The Ecosystem of Augmented Expertise

The innovation in this space is fueled by a dynamic ecosystem of companies and startups. Microsoft with its HoloLens continues to be a leader, providing a robust platform for mixed reality applications in enterprise. Magic Leap is also making strides with its advanced optical technology. Specialized software providers like PTC (Vuforia), Scope AR, and Librestream are developing powerful authoring tools and platforms that enable companies to create their own AR work instructions and remote assistance solutions without extensive coding. Startups like DAQRI (though recently restructured) have pushed the boundaries of industrial smart glasses, while others focus on specific verticals, offering tailored solutions for manufacturing, logistics, and healthcare. The competition is fierce, driving rapid advancements in hardware form factors, content creation tools, and AI integration for more intelligent guidance.

The Path Forward: Designing for Human Potential

The shift towards XR in-the-flow training is more than just a technological upgrade; it’s a fundamental rethinking of how we empower the human workforce. It’s about recognizing that expertise isn’t just accumulated knowledge, but the ability to apply that knowledge effectively in complex, dynamic situations. By integrating learning directly into the flow of work, we unlock unprecedented levels of productivity, safety, and human potential.

For leaders in human-centered change, innovation, and experience design, this presents a massive opportunity. We must move beyond simply adopting technology and focus on designing holistic systems where the technology seamlessly serves the human. This means:

Empathy Mapping: Truly understanding the challenges, cognitive loads, and pain points of front-line workers.
Iterative Design: Prototyping and testing XR solutions directly with users to ensure they are intuitive, non-intrusive, and genuinely helpful.
Ethical Considerations: Addressing concerns around data privacy, cognitive overload, and the psychological impact of constant augmentation.
Integration Strategy: Ensuring XR training solutions are integrated with existing learning management systems and operational data streams.

The future of work is not just augmented reality; it’s augmented human capability. By embracing XR for in-the-flow training, we are not just making tasks easier; we are making our people smarter, more adaptable, and ultimately, more valuable. This is true innovation, designed with humanity at its core.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credit: Pexels

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Do You Have Gumption?

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Do You Have Gumption?

GUEST POST from Mike Shipulski

Doing new work takes gumption. But there are two problems with gumption. One, you’ve got to create it from within. Two, it takes a lot of energy to generate the gumption and to do that you’ve got to be physically fit and mentally grounded. Here are some words that may help.

Move from self-judging to self-loving. It makes a difference.

It’s never enough until you decide it’s enough. And when you do, you can be more beholden to yourself.

You already have what you’re looking for. Look inside.

Taking care of yourself isn’t selfish, it’s self-ful.

When in doubt, go outside.

You can’t believe in yourself without your consent.

Your well-being is your responsibility. And it’s time to be responsible.

When you move your body, your mind smiles.

With selfish, you take care of yourself at another’s expense. With self-ful, you take care of yourself because you’re full of self-love.

When in doubt, feel the doubt and do it anyway.

If you’re not taking care of yourself, understand what you’re putting in the way and then don’t do that anymore.

You can’t help others if you don’t take care of yourself.

If you struggle with taking care of yourself, pretend you’re someone else and do it for them.

Image credit: Unsplash

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Customer Experience is Changing

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If You Don’t Like Change, You’re Going to Hate Extinction

Customer Experience is Changing

GUEST POST from Shep Hyken

Depending on which studies and articles you read, customer service and customer experience (CX) are getting better … or they’re getting worse. Our customer service and CX research found that 60% of consumers had better customer service experiences than last year, and in general, 82% are happy with the customer service they receive from the companies and brands with which they do business.

Yet, some studies claim customer service is worse than ever. Regardless, more companies than ever are investing in improving CX. Some nail it, but even with an investment, some still struggle. Another telling stat is the growing number of companies attending CX conferences.

Last month, more than 5,000 people representing 1,382 companies attended and participated in Contact Center Week (CCW), the world’s largest conference dedicated to customer service and customer experience. This was the largest attendance to date, representing a 25% growth over last year.

Many recognized brands and CX leaders attended and shared their wisdom from the main stage and breakout rooms. The expo hall featured demonstrations of the latest and greatest solutions to create more effective customer support experiences.

The primary reason I attend conferences like CCW is to stay current with the latest advancements and solutions in CX and to gain insight into how industry leaders think. AI took center stage for most of the presentations. No doubt, it continues to improve and gain acceptance. With that in mind, here are some of my favorite takeaways with my commentary from the sessions I attended:

AI for Training

Becky Ploeger, global head of reservations and customer care at Hilton, uses AI to create micro-lessons for employee training. Hilton is using Centrical’s platform to take various topics and turn them into coaching modules. Employees participate in simulations that replicate customer issues.

Can We Trust AI?

As excited as Ploeger is about AI (and agentic AI), there is still trepidation. CX leaders must recognize that AI is not yet perfect and will occasionally provide inaccurate information. Ploeger said, “We have years and years of experience with agents. We only have six months of experience with agentic AI.”

Wrong Information from AI Costs a Company Money—or Does it?

Gadi Shamia, CEO of Replicant, an AI voice technology company, commented about the mistakes AI makes. In general, CX leaders are complaining that going digital is costing the company money because of the bad information customers receive. Shamia asks, “How much are you losing?” While bad information can cause a customer to defect to a competitor, so does a bad experience with a live customer service rep. So, how often does AI provide incorrect information? How many of those customers leave versus trying to connect with an agent? The metrics you choose to define success with a digital self-service experience need to include more than measuring bad experiences. Mark Killick, SVP of experiential operations at Shipt, weighed in on this topic, saying, “If we don’t fix the problems of providing bad information, we’ll just deliver bad information faster.”

Making the Case to Invest in AI

Mariano Tan, president and CEO of Prosodica says, “Nothing gets funded without a clear business case.” The person in charge of the budget for customer service and CX initiatives (typically the CFO in larger companies) won’t “open the wallet” without proof that the expenditure will yield a return on investment (ROI). People in charge of budgets like numbers, so when you create your “clear business case,” be sure to include the numbers that make a compelling reason to invest in CX. Simply saying, “We’ll reduce churn,” isn’t enough. How much churn—that’s a number. How much does it mean to the bottom line—another number. Numbers sell!

Final Words: Love Change, or Else

Neil Gibson, SVP of CX at FedEx, was part of a panel and shared a quote that is the perfect way to end the article. AI is rapidly changing the way we do business. We must keep up, or else. Gibson quoted Fred Smith, the first CEO and founder of FedEx, who said, “If you don’t like change, you’re going to hate extinction.” In other words, keep up or watch your competition blow past you.

This article was originally published on Forbes.com.

Image Credits: Pixabay

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Top 10 Human-Centered Change & Innovation Articles of August 2025

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Top 10 Human-Centered Change & Innovation Articles of August 2025 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:

The Nordic Way of Leadership in Business — by Stefan Lindegaard
Science Says You Shouldn’t Waste Too Much Time Trying to Convince People — by Greg Satell
A Manager’s Guide to Employee Engagement — by David Burkus
Decoding the Code of Life – Human-Centered Innovation in Synthetic Biology — by Art Inteligencia
Why Innovators Can’t Ignore the Quantum Revolution — by Art Inteligencia
Performance Reviews Don’t Have to Suck — by David Burkus
Why Explainable AI is the Key to Our Future – The Unseen Imperative — by Art Inteligencia
Goals Require Belief to be Achievable — by Mike Shipulski
The Future is Rotary – Human-Centered Innovation in Rotating Detonation Engines — by Art Inteligencia
The Killer Strategic Concept You’ve Never Heard Of – You Really Need to Know About Schwerpunkt! — by Greg Satell

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

Do We Really Need Managers? — by David Burkus
Building Psychological Safety – Team Dynamics Explained — by Stefan Lindegaard
3 Reasons Why Bad Business Thinking Exists — by Greg Satell
Turning Around Declining Customer Satisfaction — by Shep Hyken
Three Executive Decisions for Strategic Foresight Success or Failure — by Robyn Bolton

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 four years:

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