Expanding Into a New Market? Audit the Journey Before Your Customers Do

by Braden Kelley and Art Inteligencia

Market expansion plans get validated within an inch of their life on almost everything except the one thing that determines whether customers actually stay once they arrive: whether the experience you’re bringing with you was ever built for the people you’re about to bring it to. Product-market fit gets tested. Go-to-market strategy gets modeled and re-modeled. The actual customer journey — the thing a real person will move through from first contact to renewal — usually just comes along for the ride, unexamined, on the assumption that if it worked here, it’ll work there too.

The assumption that quietly undermines expansion

That assumption is rarely stated out loud, which is part of why it survives so many planning cycles unchallenged. Nobody in an expansion planning meeting says “we’re assuming our existing journey translates perfectly to this new market.” They just don’t say anything about the journey at all, because it’s not the part of the plan anyone’s job is to stress-test. The financial model gets scrutiny. The competitive landscape gets scrutiny. The experience a new-market customer will actually have moving through your funnel, your onboarding, your support process — that gets inherited wholesale from whatever already exists, on the theory that if the core offering is sound, the wrapper around it doesn’t need a second look.

Where the existing journey actually breaks

It rarely breaks in the obvious place — translated marketing copy, currency formatting, the things everyone remembers to check. It breaks in the places built around assumptions nobody remembers making. A support response-time standard that felt generous in your home market can feel slow against a new market’s expectations, if the incumbents there have trained customers to expect faster. A sales cycle built around how your existing buyers evaluate a purchase can stall completely against a new segment’s actual buying committee structure, if nobody mapped how decisions really get made there before launch. Payment norms, preferred channels, even how directly or indirectly customers expect to be communicated with — all of it can differ in ways that don’t show up as an error, just as a slightly worse experience that a new-market customer has nothing to compare it to except the local alternative they almost chose instead.

Why waiting for complaints is the expensive option

By the time these gaps show up as customer complaints, you’ve usually already spent a meaningful share of the expansion budget acquiring the customers who are now quietly churning, and you’re competing for the next wave of customers in a market where your early reputation is already partly set by the people who had the rough experience first. Word of mouth in a new market works both directions faster than people expect — the same network effects that could help an expansion take off quickly can just as easily spread a “their onboarding is confusing” reputation before you’ve had a chance to fix it.

Auditing before launch instead of diagnosing after

The alternative is treating the new-market journey as something to validate deliberately before launch, not something to inherit by default. That means building out validated personas for the new market specifically, rather than assuming your existing personas translate — buying committees, decision criteria, and expectations can differ enough that a persona built for one market actively misleads you in another. It means walking the journey the way a new-market customer actually would, ideally with someone unfamiliar with your existing assumptions doing the walking, so the things your team has stopped noticing about your own process get caught before a real customer catches them instead. And it means benchmarking specifically against the local incumbents and best-in-class examples a new-market customer will actually be comparing you to, not against your existing competitors back home, since “good enough” is set by whoever they’re used to, not by whoever you’re used to competing against.

Where to start

If you’re heading into a market or segment expansion and want to validate the journey before your launch budget is already spent finding out the hard way, a Customer Experience Audit scoped to the new market specifically is built for exactly this. And if you want a rough sense of what an undiagnosed gap could cost in early churn before you scope that engagement, the CX ROI Calculator is a fast place to start putting a number on 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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We Must Rethink the Myth of Xerox PARC and the Macintosh

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We Must Rethink the Myth of Xerox PARC and the Macintosh

GUEST POST from Greg Satell

When people like to tell stories of historic corporate missteps, the story of Xerox and the Macintosh is near the top of the list. As the tale goes, the corporate giant spent a fortune to create all the technology that the famous computer was based on, but failed to market it and let Steve Jobs steal it out from under them.

But that version leaves out important context. Yes, Xerox did create the technology. It was also true that Steve Jobs, while touring the company’s research facility, understood that he could use it to make a revolutionary consumer product. But it wasn’t a blunder. Steve Jobs was there because Xerox had invested in Apple at bargain prices, not because they were tricked in some way.

The story has deeper implications, because the myth of Xerox’s blunder influences how firms invest in technology. The truth is that Xerox’s research strategy was visionary and incredibly successful. In fact, it likely saved the company. So rather than looking at the story of Xerox and the Macintosh as a cautionary tale, we should see it as a model to replicate.

The Xerox PARC Strategy

When Peter McColough took the helm of Xerox in 1968, it was at the top of American industry. An incredibly profitable business, it had a culture devoted to technical excellence and produced the world’s best performing copiers. Over the years it also developed a great sales and service organization that built strong relationships with its customers.

But it was becoming clear that trouble was looming. Japanese competitors like Canon and Ricoh started selling simpler, cheaper copiers, based on 20-year-old technology, that were easier to use and needed less maintenance. Rather than staffing a “copy room,” companies could place these smaller, less expensive units on every floor.

Xerox was getting disrupted. It continued to innovate, but most of those efforts were going toward making its copiers better and better at things people cared less and less about. McColough saw the nascent computer industry as an opportunity and sought to control the “architecture of information.

“It was a great phrase,” someone would later say, “because no one knew exactly what it meant.” To that end, he created the Palo Alto Research Center (PARC) and located it 3000 miles from Xerox’s headquarters. He hired Bob Taylor, already considered a visionary for his work on ARPANET and told him to staff it with the best minds in the emerging field of computer science.

The Incredible Success Of The Laser Printer

It was around this time that the company hired a young engineer named Gary Starkweather. He was a guy with big ideas, but soon found he didn’t fit in well at Xerox. Part of the problem probably had to do with his background. Copiers were largely based on chemistry and Gary’s interest was optics. In particular, he was excited about lasers.

But it was more than that. Gary wanted to build something outside the copier business and the higher-ups just didn’t see how it fit in with their business. In fact, his boss actually threatened to fire anyone who worked with Starkweather on the project.

Eventually, he had enough. He marched into the Vice President’s office and asked, “Do you want me to do this for you or for someone else?” In the business culture at the time, this was considered unheard of behavior, clearly a firing offense. Yet fate intervened and destiny had something very different in store for Gary Starkweather.

As luck would have it,news of Gary’s work made it across the country to PARC, the fledgling computer lab that Xerox had recently established in California. The researchers there had developed a graphical technology called bitmapping, but had no way to print the images out until he showed up. His development of the laser printer was not only a breakthrough in its own right, but with the decline of Xerox’s copier business, it actually saved the company.

Leveraging PARC Technology

No one disputes that the number of groundbreaking technologies created at PARC was astounding. The graphical user interface, networked computing, object oriented programing, the list goes on. Virtually everything that we came to know as “personal computing” had its roots in the work done at PARC in the 1970s.

Yet Xerox never became successful in the computer industry, which is why so many question the strategy. That’s the wrong way to look at the investment, however. You wouldn’t evaluate a stock portfolio against all the companies you could have bought, but on how the portfolio performed and by that measure PARC was a magnificent investment.

Clearly, the development of the laser printer paid for the investment of PARC many times over. It also mildly profited on its shares in Apple (although not nearly as much as it could have if it held on to them longer). Xerox PARC technology also led to a number of spinoffs, including 3Com, Adobe and Synoptics, just to name a few. Xerox had shares in many of them.

Many look at these companies and see lost opportunities for Xerox, but that’s a red herring. There is no way that any company could have pursued all of those opportunities. As Henry Chesbrough put it in Open Innovation, “The success of some of these departing spinoffs was largely unforeseen—and unforeseeable. When they left, these spin-offs were more like ugly ducklings than elegant swans.

When you add it all up, even the supposed “failures” of PARC provided enormous value for Xerox.

If You Don’t Explore, You Won’t Invent And You Will Be Disrupted

In the late 1960s, Xerox faced a problem without a clear solution. With many of its key patents expiring, it was losing its chokehold on the industry it had created. That’s what led its visionary CEO, Peter McColough, to create PARC, which invented breakthrough technologies, incredible profits and saved the company.

Yet many see it as a cautionary tale because of all the possibilities it wasn’t able to pursue. Steve Jobs once said that “Xerox could have owned the entire computer industry, could have been the IBM of the nineties, could have been the Microsoft of the nineties.”

Maybe it could have. But you don’t judge a strategy on what could have been, but on whether it solved the problem it was designed to solve. Xerox was facing irrelevance and extinction. Its copier business was dying and it desperately needed technologies that would provide new sources of revenue for a company that was quickly becoming irrelevant. In that context, Xerox PARC succeeded enormously.

In researching my book, Mapping Innovation, I found that the most important thing that great innovators do differently is that they are actively seeking out new problems. In other words, they not only continue to hone their existing processes and practices, they go actively look for areas where they can make an impact.

The truth is that innovation needs exploration and that’s never efficient nor can it be optimized, but it can be done cheaply enough to be sustainable. For large enterprises that usually means investing in labs, but even small business can access world class research by connecting to larger institutions, such as government labs and universities.

It’s a fairly simple equation. If you don’t explore, you won’t discover. If you don’t discover you won’t invent. And if you don’t invent, you will be disrupted.

— Article courtesy of the Digital Tonto blog
— Image credit: 1 of 1,550+ FREE quote slides available at http://misterinnovation.com

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The 8 Best Teamwork Books That Actually Improve Collaboration

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The 8 Best Teamwork Books That Actually Improve Collaboration

GUEST POST from David Burkus

Most leaders know teamwork matters. But few take the time to study what actually makes teams work.

We rely on instinct, gut feel, or experience from past teams—good or bad—and assume it’ll all work out. But in today’s complex, collaborative, cross-functional world, those assumptions often lead us astray.

If you want to lead high-performing teams consistently, you need better inputs. And for me, some of the best inputs have come from books—not just books about leadership, but books specifically about teamwork. The kind that change how you think about team culture, team dynamics, and the actual work of working together.

These aren’t just good reads. They’re the best teamwork books I’ve found, presented in no particular order because this isn’t a ranking… it’s a resource.

📚 Best Teamwork Books

1. The Five Dysfunctions of a Team by Patrick Lencioni

Probably the first one you thought of too. This is a business fable that somehow nails real team dysfunctions with precision. Lencioni’s model—from lack of trust to inattention to results—offers a practical, memorable way to diagnose and address breakdowns. I’ve referenced this book with dozens of leaders because it gets past the surface and straight to the relational heart of team performance.

2. The Culture Code by Daniel Coyle

Coyle makes the case that talent alone doesn’t create great teams—culture does. He distills team culture into three key behaviors: build safety, share vulnerability, and establish purpose. What stood out to me is how much of team excellence is invisible, built in the small moments. It’s a great reminder that culture is less about slogans and more about habits.

3. Dream Teams by Shane Snow

This one turns a lot of conventional wisdom upside down. Shane Snow argues that the best teams aren’t the most harmonious—they’re the ones that learn how to disagree productively. With examples from science, sports, and even organized crime, he shows that friction (when handled well) makes teams smarter. A great read for leaders dealing with complexity and cognitive diversity.

4. Team of Teams by Stanley McChrystal

McChrystal’s story of transforming Joint Special Operations Command is a masterclass in breaking down silos. His team couldn’t win modern battles using traditional hierarchy. So they built a “team of teams” built on shared consciousness and empowered execution. It’s a great reminder that agility isn’t just for startups—it’s a requirement in any complex environment.

5. Collaboration by Morten Hansen

Here’s the uncomfortable truth: not all collaboration is good. Hansen shows that ineffective collaboration can actually make performance worse. His concept of disciplined collaboration—knowing when and how to collaborate—helped me see the difference between helpful alignment and wasted effort. Especially valuable for leaders in large or matrixed organizations.

6. Tribal Leadership by Dave Logan, John King & Halee Fischer-Wright

This book maps out five stages of team culture, from toxic to world-changing. What makes it stand out is the focus on language—how the words people use reveal the tribe they belong to and the performance they can unlock. It gave me a new lens for diagnosing team dynamics and helping leaders upgrade their team’s shared narrative.

7. Teaming by Amy Edmondson

Unlike most books that treat teams as fixed entities, Teaming focuses on teamwork as a dynamic, ever-evolving process. Edmondson shows how people must learn to collaborate quickly across boundaries, roles, and expertise areas—especially in fast-moving environments. It reframed for me how learning and collaboration go hand in hand, especially when teams are temporary or fluid.

8. Best Team Ever by David Burkus

Yes, it’s mine. But if you noticed, a lot of these other books are pretty old. They’ve stood the test of time, but research is always evolving. And so I wanted to understand what consistently sets high-performing teams apart—based on the latest evidence. What I found were three repeatable habits: common understanding, psychological safety, and prosocial purpose. Best Team Ever is a blueprint for building teams where people do their best work—and genuinely want to.

Final Thoughts

You don’t have to read every book on teams to lead a great one. But you do need to rethink how you approach teamwork—what you reward, what you reinforce, and how you respond when things go sideways. These are the best teamwork books that helped me do that. They changed how I build, lead, and coach teams—and they might do the same for you.

Because great teams aren’t just born.

They’re built — habit by habit, choice by choice, leader by leader.

Image credit: David Burkus

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Turning Foresight Into an Actual Strategic Roadmap

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From “What If” to “What Now”

Turning Foresight Into an Actual Strategic Roadmap

by Braden Kelley and Art Inteligencia

I have seen more beautiful scenario maps gather dust than I can count. Gorgeous work — well-researched signals, genuinely thoughtful trends, three or four possible futures mapped out with real rigor, presented to an engaged room that nodded along and felt smarter walking out than they did walking in. And then, six months later, nothing about how the organization actually operates has changed. The binder is still on someone’s shelf. This is the single most common way foresight work dies, and it has nothing to do with the quality of the thinking.

“What if” is the fun part. That’s the problem.

Exploring possible futures is genuinely engaging work. There’s something almost playful about it — imagining how the market could shift, what technology could disrupt, which future would be exciting to build toward. Rooms come alive during this part of a planning session in a way they rarely do during budget reviews. That energy is valuable, and I’d never want to dampen it. But it’s also exactly why so much foresight work stalls right there: the “what if” conversation is satisfying enough on its own that it can feel like the destination, when it was only ever supposed to be the on-ramp.

Nobody’s job is the translation

Here’s the structural reason this happens, and it’s not about laziness or lack of follow-through. In most organizations, nobody’s actual job description includes turning a set of possible futures into this year’s specific strategic priorities. The strategy team’s job, as usually defined, ends at “here are the scenarios and here’s the probable one.” Execution teams’ jobs start from wherever the strategic plan already tells them to begin. The translation step — the actual “so given all that, what do we do differently starting Monday” — falls into a gap between two functions that both assume the other one owns it.

What “what now” actually requires

Turning a preferable future into a real roadmap means doing something more specific than restating the future in more urgent language. It means identifying the near-term moves that make sense regardless of exactly how the future unfolds — the no-regret moves, the ones that pay off whether your probable future arrives on schedule, arrives differently than expected, or gets overtaken by one of the other futures you mapped. It means sequencing those moves against a realistic timeline instead of listing them all as equally urgent. And it means naming specific owners for each one, because a strategic priority without a named owner is, in practice, nobody’s priority at all.

It also means building in signposts — specific, observable things you’ll watch for that would tell you your probable future is actually arriving, or that one of the other futures is starting to look more likely instead. Without signposts, “what now” plans quietly calcify into exactly the single-future thinking foresight was supposed to prevent in the first place. With them, the roadmap stays genuinely responsive instead of becoming next year’s version of the plan that goes stale by Q2.

This is exactly why I built a specific step for it

This translation gap is precisely why FutureHacking™ doesn’t stop at mapping possible futures. The methodology includes a dedicated step — I call it NowBuilder™ — built specifically to take the output of the “what if” work and force the room through the “what now” conversation before anyone leaves: near-term moves, sequencing, ownership, and the signposts that tell you when to adjust course. It exists because I watched too many organizations do genuinely excellent scenario work and then have no structured way to turn it into anything that showed up on an actual roadmap the following Monday.

You can learn more about the full methodology at FutureHacking™ — it’s the art and science of getting to the future first, and getting there requires both halves: the imagination to genuinely hold multiple futures, and the discipline to convert that thinking into something your organization actually does.

Where to start

The free FutureHacking Signal Picker is the right first step if your team hasn’t run a structured foresight exercise yet — it walks you through identifying and prioritizing real signals, the foundation everything else in the methodology builds on, at no cost.

Something new I’m building

I’m also finishing a second tool — the FutureCanvas Picker — that carries a planning team through the full arc in one sitting: from signals, to the trends they suggest, to a genuine set of possible futures, narrowing to your most probable future, and mapping the path to your preferable one. It’s the closest thing I’ve built yet to running a complete FutureHacking™ session on your own.

I’m opening early access to a select group first — strategic planners, CSOs, and leaders actively running planning processes right now — because I want real feedback from people using it under real deadline pressure before it’s available more broadly. If that’s you, and you’d like to be considered for early access, reach out and let me know — I’ll be following up personally with the first few who get in.

A scenario map tells you what could happen. It’s still just a very well-organized guess until something in your organization actually moves because of it.

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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Trust the Test, Not Your Gut

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Trust the Test, Not Your Gut

GUEST POST from Mike Shipulski

At first glance, it seems easy to run a good test, but nothing can be further from the truth.

The first step is to define the idea/concept you want to validate or invalidate. The best way is to complete one of these two sentences: I want to learn that [type your idea here] is true. Or, I want to learn that [enter your idea here] is false.

Next, ask yourself this question: What information do I need to validate (or invalidate) [type your idea here]? Write down the information you need. In the engineering domain, this is straightforward: I need the temperature of this, the pressure of that, the force generated on part xyz or the time (in seconds) before the system catches fire. But for people-related ideas, things aren’t so straightforward. Some things you may want to know are: how much will you pay for this new thing, how many will you buy, on a scale of 1 to 5 how much do you like it?

Now the tough part – how will you judge pass or fail? What is the maximum acceptable temperature? What is the minimum pressure? What is the maximum force that can be tolerated? How many seconds must the system survive before catching fire? And for people: What is the minimum price that can support a viable business? How many must they buy before the company can prosper? And if they like it at level 3, it’s a go. And here’s the most importance sentence of the entire post:

The decision criteria must be defined BEFORE running the test.

If you wait to define the go/no-go criteria until after you run the test and review the data, you’ll adjust the decision criteria so you make the decision you wanted to make before running the test. If you’re not going to define the decision criteria before running the test, don’t bother running the test and follow your gut. Your decision will be a bad one, but at least you’ll save the time and money associated with the test.

And before running the test, define the test protocol. Think recipe in a cookbook: a pinch of this, a quart of that, mix it together and bake at 350 degrees Fahrenheit for 40 minutes. The best protocols are simple and clear and result in the same sequence of events regardless of who runs the test. And make sure the measurement method is part of the protocol – use this thermocouple, use that pressure gauge, use the script to ask the questions about price and the number they’d buy.

And even with all this rigor, good judgement is still part of the equation. But the judgment is limited to questions like: did we follow the protocol? Did the measurement system function properly? Do the initial assumptions still hold? Did anything change since we defined the learning objective and defined the test protocol?

To create formal learning objectives, to write well-defined test protocols and to formalize the decision criteria before running the test require rigor, discipline, time and money. But, because the cost of making a bad decision is so high, the cost of running good tests is a bargain at twice the price.

Image credits: NASA Goddard Flight Center

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Lack of Feedback is a Gift

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Lack of Feedback is a Gift

GUEST POST from Shep Hyken

This article answers the question: Are organizations paying attention to the feedback customers give and also to the feedback they unintentionally withhold?

There’s an old expression: Feedback is a gift. Whenever a customer is willing to take time to share feedback by talking to you, emailing you, or leaving an online comment, it’s a gift. It either validates that what you’re doing is working or points out opportunities to improve. Companies obsess over survey scores and online reviews, and that’s smart. But if you only focus on what customers are telling you, you might miss important feedback that’s unintentionally hidden in what they don’t mention.

With that said, I’m going to flip this around and say that sometimes the lack of feedback can be as important as the specific feedback a customer shares.

It’s best if I share an example, and this comes from when I was 12 years old and had a birthday party magic show business. On a good week, I was performing at eight to 10 parties. About a week after every show, I would call the parents who hired me to perform for their child to thank them again and ask, “How did you like the show?” The answer was almost always positive. My father suggested I take it a step further and follow up with, “What magic tricks did you like the best?”

My father told me that over time, I would hear the same tricks mentioned. While that’s nice, what’s just as important, if not more so, are the tricks that weren’t mentioned. This unintentional silence can be a signal. His point was that if people aren’t talking about the tricks, replace them with tricks they will talk about. At the age of 12, I was learning an important tenet of customer service and experience, which is not only to ask for feedback, but also to operationalize it. In this case, it was to create and deliver a better magic show by paying attention to what my customers weren’t saying.

In any business, there are processes and experiences that customers encounter. If we’re customer-focused, we assume that what we’ve created delivers a good experience. Often, we’ll ask for feedback. Sometimes it’s about the overall experience, and other times it’s about something specific. All of that feedback should be appreciated, and while we need to pay attention to what customers say, we should also diligently pay attention to what they don’t say. That unspoken feedback may be the best feedback, and what’s so cool about this is that the customer doesn’t even know they are doing it.

Silence can equal insight. When customers seldom or never mention parts of your experience, ask yourself why. Whatever the reason, that silence may lead to your next big improvement. Look for the feedback customers offer, and look even harder for the feedback they don’t.

Image Credits: Pixabay

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AI ROI and Market Valuations

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AI ROI and Market Valuations

GUEST POST from Geoffrey Moore

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

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

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

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

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

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

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

Gartner Hype Cycle

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

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

— Image credit: Gemini

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Auditing Customer Experience Before and After a Systems Migration

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Auditing Customer Experience Before and After a Systems Migration

by Braden Kelley and Art Inteligencia

Every systems migration I’ve ever seen gets sold internally with some version of the same reassurance: “nothing will change for the customer.” I understand why people say it — it’s meant to calm nerves and keep the project moving. It’s also almost never entirely true, and the gap between that promise and reality is exactly where a lot of quiet, expensive customer experience damage happens.

Migrations are measured by the wrong success criteria

A CRM cutover, a new billing platform, a support tooling replacement — these get judged by IT and project management on a specific set of criteria: did the data migrate correctly, is uptime where it should be, did the go-live happen on schedule. Those are the right questions for a systems team to ask. They’re the wrong questions, or at least an incomplete set, for understanding whether the customer experience survived the transition intact. A migration can hit every technical success metric on the project plan and still quietly degrade the actual experience a customer has, because nobody on the technical side was specifically measuring for that.

The silent regression problem

The most expensive migration failures I’ve seen aren’t the dramatic ones — the outage, the data loss, the system that won’t come back up. Those get noticed immediately and fixed fast, precisely because they’re loud. The expensive ones are silent regressions: a new billing platform that technically works but changes the invoice format customers had built internal processes around, so now their AP department has to redo reconciliation manually every month. A new CRM that migrates the data correctly but changes how support reps see a customer’s history, so reps start asking questions customers have already answered before, over and over, without anyone flagging it as a problem because each individual instance looks like a minor inconvenience rather than a pattern.

None of that shows up in a migration status report. All of it shows up, eventually, in retention numbers that took a hit for reasons nobody traced back to the systems change that happened two quarters earlier.

Why you need a real baseline before you touch anything

You can’t know what changed for customers after a migration if you don’t have an honest picture of what their experience actually looked like before it — not the technical specs of the old system, but the lived experience of using it. That means validated personas, a current journey map across the specific touchpoints the migration will touch, and a firsthand walkthrough of those touchpoints as they exist today. This is the step migrations skip most often, because the old system is about to be replaced anyway and it feels like wasted effort to study something on its way out. It isn’t wasted — it’s the only way to tell the difference between “the migration caused this” and “this was already broken and we just never noticed.”

What to actually check after cutover

Once the new system is live, the audit isn’t finished — it shifts to verification. This means walking the same touchpoints again, deliberately, rather than assuming success because no one’s complained yet. Complaints are a lagging indicator; most customers adapt to a worse experience quietly before they ever formally report it. It also means comparing the same data points — response times, resolution rates, whatever mattered in the baseline — against the pre-migration numbers specifically, not just against general benchmarks, since the question that matters is whether this specific change made things better, worse, or invisible.

The teams that get this right treat it as one audit, not two

The most effective version of this isn’t a rushed pre-migration check plus a separate, disconnected post-migration review commissioned only if something goes wrong. It’s a single audit designed from the start to bracket the migration — same personas, same touchpoints, same evaluation criteria, measured before and after, so the comparison is actually apples to apples. That structure is also what makes the business case afterward credible: “here’s exactly what improved, what regressed, and what stayed flat” is a far stronger position than a vague sense that the migration “went fine.”

Where to start

If you have a systems migration coming up and want a credible before-picture while there’s still time to establish one, a Customer Experience Audit scoped to bracket the migration is exactly the right tool — and if you’re trying to build the case for why that baseline is worth the investment before the project timeline locks in, the CX ROI Calculator is a fast way to put a number on what a silent regression could actually cost you.

Download the Customer Experience Audit Checklist as a PDF

Image Credits: Pixabay

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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4 Things to Learn About Change From the Orange Revolution

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4 Things to Learn About Change From the Orange Revolution

GUEST POST from Greg Satell

In 2004, I joined a KP Media, the leading news organization in Ukraine, where I would become Co-CEO. In the runup to the presidential election that year, the opposition candidate for president, a technocratic reformer named Viktor Yushchenko, was poisoned by pro-Russian agents. He survived, but his face was permanently disfigured.

In that moment, the once mild-mannered banker was transformed into an inspirational leader. His opponent, an almost cartoonish thug named Viktor Yanukovych, tried to falsify the election. Hundreds of thousands of Ukrainians poured into the streets to protest in what came to be known as the Orange Revolution.

It was very much an awakening. That was the moment when Ukraine, although not yet ready to turn away from Russia, began to insist on its independence and freedom, something that Vladimir Putin wasn’t willing to respect. It was also a turning point for me. It would permanently change how I saw the world and how it works. Here are four things I learned.

1. Power Doesn’t Work Like We Typically Think It Does

One of the first things I noticed about the Orange Revolution was that nobody with any conventional form of power had any ability to shape events at all. In fact, nobody seemed to understand what was going on or what would happen next—not the journalists I would speak to in the newsroom everyday, not the other business leaders and certainly not the political leaders.

We all grow up amidst power structures. We have parents who make the rules at home, teachers and administrators who do so at school. When we enter adulthood and begin to work for a living, we have managers and customers to attend to. Political leaders call the shots in public life. Everywhere we go, there is some authority guiding us.

But in the Orange Revolution nobody seemed to be directing the action. There was just a mysterious force, which nobody could describe but nobody could deny, that was moving things along. It almost felt like we were all characters in a play, there to play a part but without any real agency over events.

In Martin Heidegger’s essay, The Question Concerning Technology, he described technology as akin to art, in that it reveals truths about the nature of the world, brings them forth and puts them to some specific use. I learned that power is something akin to that, a force that we can shape and direct, but not anything we can actually possess.

2. The Power Of Cascades

One of the first things I discovered on my journey to understand the events during the Orange Revolution of 2004 is that scientists had known for centuries about the mysterious force I encountered on the streets of Kyiv and even had a name for it. It’s called a network cascade or a viral cascade.

Imagine thousands of protesters rushing to flood the streets of Kyiv, or Cairo — or Washington D.C. Countless fireflies blinking on and off in nearly perfect unison. Sports fans doing the “wave” at a stadium. These are all examples of information cascading through a network and they have the power to create rapid, transformational change.

When ideas are held by individuals or small groups, they are negligible. However, when groups connect and synchronize their collective behavior, they can become immensely powerful. An industry is remade, a country overthrown, entire societies evolve and change, the preceding order is transcended and the world is transformed.

The key to understanding cascades is that they are driven by small groups, loosely connected, but united by a shared purpose. This is true for any network, whether the nodes are people, proteins or computers. To drive transformation, change leaders need to help those groups to connect and inspire them with purpose.

3. You Have To Prepare To Survive Victory From The Start

I still remember the feeling in the winter of 2005. We were triumphant. We had taken to the streets and, seemingly against all odds, we won—or at least we thought we did. But those who opposed our cause, they didn’t just melt away and go home. No, they redoubled their efforts to undermine us and were able to reverse what we had achieved.

Our mistake was that we failed to understand that you can never bet your transformation on any particular person, policy, program or technology. It always needs to be rooted in shared values. That was what we got wrong. The Orange Revolution got its name because orange was the campaign color of the opposition candidate, Viktor Yushchenko. Its purpose was to propel him into office.

Yet once we won and Yushchenko rose to the presidency, we lost our purpose and everyone went home. And it turned out that Yushchenko wasn’t a magician or a god who could make the country’s deep problems disappear. He was just a man, who was fallible, made mistakes and couldn’t achieve all that we wanted. That’s why we failed to survive victory.

When the global financial crisis hit, Ukraine once again descended into crisis and whatever support Yushchenko still had quickly dissipated. Yanukovych, the thuggish candidate backed by Russia, had his image rehabilitated by American campaign consultant Paul Manafort, who ran a polished campaign predicated on re-establishing order. He won in an election international observers deemed to be legitimate.

He was even worse than we had feared and in 2013, the new protests broke out. But this time they got it right. These protests were called Euromaidan because they were about values, specifically European values and as things progressed the events became known as the Revolution of Dignity, because they were fighting for their basic identity and sovereign status.

4. There Is A Limit To How Much Impropriety People Will Accept

When I first arrived in Ukraine it was a cynical place. After 80 years of communism and then 10 more under kleptocratic rule, few thought change was possible. So why worry or complain about things that you couldn’t do anything about anyway? It seemed better to focus on things close to you; your family, your work, your friends.

The Orange Revolution in 2004 changed that. It turned out that there was a limit to what people could accept. It wasn’t so much about policy, capitalism or even democracy, it was about dignity, the most basic yearning for people to be treated as ends in themselves, not means to the ends of a corrupt elite.

The original intention of the Ukrainian people was not to pull away from Russia, which many, if not most, considered a “brother” country, but to pursue a so-called “Finnish model” that would maintain good relations with both Russia and the west. Yet Putin could not bring himself to recognize the Ukrainians’ desire for a separate and distinct identity.

That’s an important lesson for anyone seeking to bring about transformation. When leaders set out to pursue change we must do it in ways that allow others to adopt in ways that reinforce, rather than undermine, their identity. That’s why the second Ukrainian revolution continues even now, because it was about dignity.

Lasting change is always built on common ground.

— Article courtesy of the Digital Tonto blog
— Image credit: Dall-E

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Leading to the Future of Work

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GUEST POST from David Burkus

If you led a team through 2025, you probably felt like you were trapped in a never-ending game of workplace buzzword bingo. Coffee badging. Quiet quitting. Quiet firing. Corporate catfishing. Every week came with a new term, a new “trend,” and a new reason to wonder whether you were falling behind in the future of work—or just drowning in noise.

That’s the challenge for leaders right now: not “keeping up,” but getting clear on what actually changed. Because some of what we saw in 2025 wasn’t a fad. It was a preview. And 2026 won’t be a clean break from the past; it’ll be the year those shifts harden into the new defaults.

Here’s what I mean. In hindsight, 2025 wasn’t a year of brand-new problems. It was a year when familiar tensions finally became impossible to ignore: autonomy versus control, speed versus security, loyalty versus flexibility, experience versus reinvention. The labels made it feel new. The underlying issues weren’t.

And the way most organizations responded? Predictable. Also, mostly wrong.

When leaders face uncertainty, they usually reach for what worked before. They tighten policies. They standardize schedules. They add approvals. They try to “fix” culture with perks. It’s a natural reflex—especially when performance pressure is high. But it assumes the problem is that people need more rules, more oversight, or more incentives.

In reality, the lesson of 2025 is simpler: the rules of work didn’t just bend. They rewired. And if you keep trying to manage 2026 with a 2016 playbook, your team won’t just feel frustrated—they’ll quietly build workarounds. That’s where the real risk lives.

The Future of Work Is Structured Flexibility, Not a Return to the Old Office

Take the return-to-office story. Early 2025 looked like a full-scale push back to “normal,” with big-name companies raising the stakes and making attendance a mandate instead of a suggestion. Plenty of leaders braced for a rebellion.

But the more interesting story was what happened after the headlines: most people returned. Not necessarily happily. Not necessarily five days a week. But they showed up — often because they felt they didn’t have a better option.

And then something else happened. Leaders started realizing that office time isn’t a strategy. It’s a setting. You don’t get collaboration just because you share a zip code.

In the data, the pattern has been consistent: the best outcomes show up when people have flexibility—and when that flexibility is designed with intention. Hybrid can work. Remote can work. In-office can work. What doesn’t work is forcing one model without explaining the purpose or giving people any control.

That’s why I don’t think 2026 will be the year “RTO wins” or “remote wins.” I think 2026 will be the year structured flexibility becomes the default. You’ll see more anchor days, where teams coordinate time together for the work that benefits from face-to-face interaction. You’ll see more meeting-free focus time. You’ll see more experiments that look like “summer Fridays,” only year-round.

The real debate was never about office chairs. It was always about autonomy.

And autonomy doesn’t mean chaos. It means leaders stop managing presence and start managing performance.

AI Didn’t Replace Your Team. It Joined It.

The second shift is happening just as fast, but with more confusion: AI stopped being a novelty and started becoming a coworker.

In 2025, a lot of organizations rolled out enterprise AI tools — often with the best of intentions. But leaders ran straight into a new kind of friction: security guardrails that made the tools less useful than the consumer versions people were already using.

So employees did what humans always do when systems slow them down: they routed around them. They used personal accounts. They used personal devices. They found ways to keep moving.

From a productivity standpoint, that makes sense. From a risk standpoint, it’s terrifying.

And it creates a trust problem on both sides. Leaders worry about data leakage and compliance. Employees feel like the company is handing them a power tool…then wrapping it in bubble wrap and asking why the job isn’t done faster.

Here’s what 2026 is likely to bring: less debate about whether AI belongs at work, and more expectation that everyone knows how to use it responsibly. Senior leaders will lean on it for strategic thinking and competitive analysis. Frontline employees will use it to automate the “gray work” that clogs calendars—summaries, drafts, templates, first passes—so they can spend more time doing what humans still do best: judgment, creativity, and problem-solving.

The winners won’t be the companies with the fanciest AI license. They’ll be the ones that treat AI like a teammate who needs onboarding, training, and clear norms.

Your Team Is Becoming a Network

Meanwhile, another major shift has been quietly becoming permanent: freelancing is no longer a side hustle. It’s becoming infrastructure.

Layoffs, hiring freezes, and reorganizations pushed a lot of talented people out of traditional roles. At the same time, organizations started pulling contractors in—not just for gig work, but for high-skill roles: finance, HR, operations, even fractional executives.

This blurs the line between “employee” and “teammate.” And in 2026, that line will blur even more.

That creates a leadership challenge most managers weren’t trained for. Traditional management assumes stable teams: the same people, the same roles, the same reporting lines. But the emerging reality looks more like a project-based network, where talent plugs in, contributes, and moves on—sometimes to your competitor, sometimes back to you, sometimes to three other clients at the same time.

If you keep treating contractors like “temporary labor,” you’ll get temporary commitment. But if you start treating them like part of the mission, you’ll get better work, better collaboration, and—over time—a stronger talent ecosystem around your team.

In the future of work, leadership is less about supervising a roster and more about stewarding a network.

Generational Change Isn’t a Culture War. It’s a Coordination Problem.

Finally, 2025 made one more shift impossible to ignore: the workforce mix is changing fast.

Millennials have moved into the largest share of management roles, Gen Z is stepping into leadership earlier than many expected, and boomers are retiring—but unevenly, and often later than planned. That means many teams now include people in radically different life stages, with different expectations about careers, technology, and what work is “for.”

A lot of leaders respond to this by stereotyping. “Gen Z doesn’t want to work.” “Millennials are entitled.” “Boomers won’t let go.” That framing is easy…and lazy.

The better framing is this: cross-generational teams aren’t doomed. They’re just harder to coordinate. And coordination improves with curiosity.

If you want the brilliance without the conflict, you can’t lead by assumption. You have to lead by inquiry.

What to Do Next: 5 Moves to Lead the Future of Work

The point of all these trends isn’t to predict the next buzzword. It’s to build a team that can adapt when the next shift arrives — because it will.

Start here.

First, design autonomy on purpose. Don’t “allow flexibility” as a perk; build it as a system. Get clear on which work truly benefits from synchronous time together — onboarding, creative collisions, complex problem-solving—and which work benefits from uninterrupted focus. Then build schedules around those moments. When people understand the “why,” they’ll stop treating policies like arbitrary rules and start treating them like shared commitments.

Second, upgrade accountability from monitoring to check-ins. If your instinct is to measure hours, logins, or keystrokes, you’re solving the wrong problem. Accountability works best when it’s social and meaningful: clear goals, frequent progress conversations, and regular moments where people can ask for help before they’re stuck. When teammates feel counted on—not watched—they show up differently.

Third, onboard AI like you would a new hire. Make training normal, not optional and not reserved for “the tech people.” Give your team safe ways to experiment. Define guardrails in plain language: what data can be used, what outputs must be verified, what decisions require human judgment. And partner with IT and compliance to make policies usable in real work, not just defensible in audits.

Fourth, lead your contractors like they belong. Invite them into the rhythms that create cohesion: the kickoff, the retro, the celebration, the shared language of “what good looks like.” Show them how their work connects to outcomes, not just tasks. You don’t have to pretend they’re full-time. But you do need them to feel like insiders while they’re with you—or you’ll pay the tax later in misalignment and rework.

Fifth, build reverse mentoring into the culture. Don’t wait for it to happen organically. Create opportunities where Gen Z can teach tools and trends, while more experienced employees share context, judgment, and the “why” behind how the organization works. The goal isn’t to make everyone the same. The goal is to help people value what the others bring.

If 2025 taught us anything, it’s that change isn’t a season anymore. It’s the operating environment. High-performing teams aren’t the ones that finally “figure it out.” They’re the ones that keep learning faster than the world keeps shifting.

That’s what the future of work will reward in 2026: leaders who can replace control with clarity, replace policies with purpose, and replace rigid teams with resilient networks.

Image credit: Pixabay

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