Author Archives: Braden Kelley

About Braden Kelley

Braden Kelley is a Human-Centered Experience, Innovation and Transformation practice lead at HCL Technologies, a popular innovation speaker, and creator of the FutureHacking™ and Human-Centered Change™ methodologies. He is the author of Stoking Your Innovation Bonfire from John Wiley & Sons and Charting Change (Second Edition) from Palgrave Macmillan. Braden is a US Navy veteran and earned his MBA from top-rated London Business School. Follow him on Linkedin, Twitter, Facebook, or Instagram.

Running the Fandom Playbook to Increase Your Business Success

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Exclusive Interview with David Meerman Scott

In an era dominated by algorithmic feeds, automated messaging, and relentless digital noise, capturing basic customer attention has never been harder — or less valuable. According to renowned marketing strategist and bestselling author David Meerman Scott, the traditional playbook of closing transactions and optimizing sales funnels is no longer enough to build a lasting brand. Today’s most resilient and successful organizations don’t just accumulate followers; they cultivate passionate, loyal fans who advocate for them in a chaotic marketplace.

In his latest book, The Fandom Playbook: How to Create Passionate Fans in a World of Online Chaos, Scott offers actionable, field-tested strategies to help businesses transition from transactional relationships to genuine human connection. We sat down with David to discuss how leaders can cut through the online clutter, harness the power of shared passion, and build an unstoppable fan base from the ground up.

Running the Fandom Playbook to Increase Your Business Success

David Meerman Scott is an internationally acclaimed marketing strategist, business growth advisor, and bestselling author who has shaped how modern organizations generate attention and build human connections. Known globally for his groundbreaking book The New Rules of Marketing & PR — which has sold over 500,000 copies in nearly 30 languages — David serves as a HubSpot advisor, venture capital strategic partner, and keynote speaker who has delivered talks across 47 countries and on all seven continents. A former Wall Street Journal bestselling co-author of Fanocracy, he combines his professional expertise in real-time marketing with a personal obsession for everything from the Grateful Dead and the Apollo lunar program to swing dancing and surfing — offering a unique, hands-on perspective on how shared passion builds unstoppable brand momentum.

Below is the text of my interview with David and a preview of the kinds of insights you’ll find in The Fandom Playbook: How to Create Passionate Fans in a World of Online Chaos presented in a Q&A format:

1. Why is fandom so important now?

Fandom is powerful because it turns transactions into relationships. In a noisy marketplace, fans don’t just buy, they tell friends, show up again and again, and offer feedback that shapes future success. When fans gather and share in your story, your brand no longer has to fight for attention; you become part of your audience’s identity. In an age of infinite choice and instant digital reach, real fans are the difference between fleeting clicks and lasting growth.

2. Why do so many things today feel fake and unsatisfying?

Because so much of what we’re exposed to is manufactured to be “safe” and generic rather than real. When brands strip away personality and rely on polished buzzwords or bland “content strategies,” they may avoid controversy, but they also lose what makes them human. People are craving authenticity and a sense of genuine connection, what’s often missing is passion, individuality, and honesty.

3. What impact have social media algorithms had on marketers?

Algorithms promised us reach, but at a heavy cost. They reward sameness and safe bets, nudging everyone toward the lowest common denominator. Marketers often end up optimizing for what the algorithm wants instead of what their audience truly needs or wants to talk about. The irony is, the more we try to “hack” the feed, the less distinctive and memorable we become, blending into a sea of manufactured relevance instead of standing out as truly remarkable.

4. Social media made humans more connected but more lonely? Will AI make this better or worse?

Social media put us in touch with billions, but filtered connection through screens and highlight reels. We’re more aware of each other than ever, but real belonging can get lost in the algorithmic shuffle. AI has the potential to assist, but not to replace genuine human connection. If we use AI to automate or impersonate what should be real relationships, loneliness will get worse. The antidote? Double down on empathy, storytelling, and actual human moments, supported by AI, not replaced by it.

5. What is your perspective on how (if at all) content creators should think about using AI in their operations?

I encourage creators to experiment with AI, but to use it in service of their vision, not as a replacement for it. AI is a powerful assistant: it can help with research, drafting, and even helping find new creative directions. But it’s your passion, your weirdness, and your unique take that sets you apart. Use AI to remove drudgery, not to erase what makes you distinct.

6. Can you think of any organization that shouldn’t try to build fans?

Every organization, no matter the field, can tap into fandom. If you have a mission, a story, and a group of people who care, you can build real human connections. Organizations that operate in highly regulated or sensitive environments, the “how” might look different, but the principle holds. Even serious B2B businesses or nonprofits can unlock magic by bringing people together around what matters to them.

Heck, NASA created fans of government employees, the Apollo astronauts, which I wrote about in my book “Marketing the Moon: The Selling of the Apollo Lunar Program” and which was used as inspiration for the Hollywood romcom “Fly Me to the Moon”.

7. Are there any prerequisites before trying to build fans?

The only real prerequisite is a human connection. You’ve got to know what you, and your team, genuinely care about. Fans can spot a fake from a mile away. If you’re willing to show passion, listen to your community, and share your story, you’re ready to build fans. But if you see fandom as just another trick or hack, you’ll come up short every time.

8. What is one thing about The Fandom Playbook — the official companion to your bestselling book Fanocracy — that people would be surprised by, but absolutely must know about?

People might be surprised to find that The Fandom Playbook isn’t just theory, it’s a step-by-step action guide. It gives you plays to run in ninety days (or even less) tailored to your business, your strengths, and your natural community. It’s about showing exactly how to invite people in, nurture them, and grow enduring relationships, no matter your industry or company size.

Conclusion

Thank you for the great conversation David!

I hope everyone has enjoyed this peek into the mind of one of the women behind the insightful new title The Fandom Playbook: How to Create Passionate Fans in a World of Online Chaos!

Image credits: David Meerman Scott, Pixabay

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Does Hybrid Intelligence Solve Our Compute Shortage?

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Does Hybrid Intelligence Solve Our Compute Shortage?

by Braden Kelley and Art Inteligencia

Here’s a mental image I can’t shake. Somewhere right now, a very large, very expensive, very thirsty computer in a very large building is being asked to figure out whether a customer’s email is angry or merely brisk.

That’s the compute shortage in one sentence. We have built an astonishing capability, and then we’ve gotten into the habit of using it for everything, including the tasks a much smaller tool could handle without breaking a sweat.

So let’s ask the obvious question. If we stopped sending every request to the biggest brain in the cloud, could we ease the shortage? Does hybrid intelligence solve it? My honest answer is: it helps a great deal, it won’t solve it alone, and the most interesting part is what the fix might do to the software we use every day.

The shape of the shortage

First, the problem. The International Energy Agency reports that electricity demand from data centers rose 17% in 2025, far outpacing 3% growth in global power demand overall, and it expects data center consumption to roughly double by 2030, with AI-specific demand growing even faster. Gartner warned back in 2024 that power shortages could constrain 40% of existing AI data centers by 2027. And industry reports describe waits of two to six years or more in some regions to connect a new large facility to the grid. (Some of those figures come from industry sources and older forecasts, so treat the specifics as directional. The direction itself is not in dispute.)

Notice what that tells you. The bottleneck isn’t only chips. It’s electricity, transformers, cooling and the time it takes to build all of it. You can’t download a substation.

When supply can’t expand quickly, the remaining lever is demand. Which brings us to a very old idea wearing new clothes: don’t use more than you need.

Hybrid intelligence: the right brain for the job

Hybrid intelligence is a simple idea. Run small language models (SLMs) on the device in your hand or on your desk for most everyday intelligence needs, and call on large models in the cloud only when a request truly needs the extra horsepower.

Think of it the way a hospital works. You don’t send every patient to the specialist surgeon. Most needs are handled by a nurse, a general practitioner, or a pharmacist, and the surgeon gets the cases that require one. The system works because someone, or something, decides where each case goes.

The pieces for this are arriving. Neural processing units are becoming standard in new phones and laptops. Operating systems are beginning to expose local models to developers, so a small team can use on-device intelligence without paying a per-request bill. Small open models are getting better at doing more with less, and with modern compression techniques, a capable model of several billion parameters can fit in a few gigabytes of memory. And the routing pattern already exists in the wild: Apple’s Private Cloud Compute, for instance, is built around handling what it can locally and sending only what it must to a larger remote model.

What would run locally? Summarizing a document, drafting a reply, sorting email, transcribing a call, classifying a support ticket, cleaning up an image, answering a question about the file you have open. What goes to the cloud? Long, complex reasoning, large-scale analysis, tasks that need broad world knowledge or the most capable model available.

If most requests are the first kind, and I suspect most are, the cloud’s workload shrinks by a lot. Every request that stays on your device is one fewer asking a data center for electricity.

Hybrid software: let each tool do what it’s good at

Here’s the companion idea, and I think it’s the underrated one. Hybrid intelligence is about where AI runs. Hybrid software is about when to use AI at all.

For decades, software has been written as explicit instructions: if this, then that. It’s predictable, fast, cheap and auditable. Language models are the opposite: flexible, adaptable and capable of handling the messy, ambiguous inputs that rigid code chokes on, but slower, costlier and occasionally creative in ways you’d rather they weren’t.

The wasteful approach is to use the flexible tool for everything. Asking a language model to add up a column of numbers is like hiring a poet to do your taxes: possible, expensive and slightly alarming. A better approach combines the two:

Traditional code handles what’s well-defined: calculations, rules, validations, database lookups, anything where the answer should be the same every time.
A small local model handles the fuzzy interpretation: understanding what the user meant, classifying, summarizing, extracting meaning from messy text.
A large cloud model is called only when the task is complex enough to justify the cost.

The result is software that’s smarter, more capable and more adaptable without being bigger. It’s smarter where intelligence helps, lean where it doesn’t, and cheap enough to run that you can afford to give it to everyone. That last part matters, and I’ll come back to it.

Adaptive interfaces: software that gets out of your way

Now for the part I find most exciting, because it changes how software feels to use.

Look at the menus of any mature application. They’re cathedrals of features, built up over decades. Hundreds of commands, nested menus, toolbars within toolbars. Most users touch a tiny fraction of them. Some features, in any given person’s hands, are never used once. And yet they sit there, every day, making the useful ones harder to find.

We’ve lived with this because the alternatives were bad. Hiding features meant frustrating people who needed them. Showing everything meant overwhelming everyone else.

AI changes that tradeoff. With a local model that understands how you work, an application can learn which features you actually use, surface those, and quietly tuck the rest out of sight. And, here’s the key, still make everything reachable. Ask for the hidden feature in plain language (“make this table a chart,” “remove the background,” “merge these two documents”) and the software finds it, or does the job for you. The capability never left. It just stopped getting in the way.

What this means in practice:

Simpler by default. A beginner sees an uncluttered tool. A power user sees their tools.
Nothing is locked away. A rarely used feature appears the moment you need it.
Interfaces that evolve. As your work changes, so does what the software puts in front of you.
Less to learn. You don’t need to memorize where things live. You describe what you want.

And there’s a compute angle too. An interface that adapts using a small, local model, with no round trip to a data center, is exactly the kind of lightweight intelligence that hybrid design makes affordable. The thing that makes software easier to use also happens to be easy on the grid.

There’s a caution worth saying out loud, though. Adaptive interfaces must be predictable enough that people don’t feel the ground shifting under them, and they should always let users see and control what’s been hidden. A menu that rearranges itself without warning is a terrible experience. The goal is to simplify, not to surprise.

Other technologies that can ease the squeeze

Hybrid intelligence isn’t the only tool in the box. Several others work alongside it.

Smaller, smarter models. Techniques like quantization (storing a model’s numbers with less precision) and distillation (teaching a small model to imitate a larger one) shrink the cost of running a model dramatically. Models that once needed a server rack increasingly fit on a laptop.
Architectures that don’t use everything at once. Some model designs activate only the portions of a network relevant to a given request, rather than firing every parameter every time. Less computing per answer.
Caching and reuse. If thousands of people ask nearly the same question, the system shouldn’t recompute the answer from scratch each time. Smart caching is a quiet and surprisingly large efficiency win.
Specialized chips. Hardware designed specifically for AI workloads can do more work per watt than general-purpose processors. The efficiency per unit of electricity keeps improving.
Better data center design. More efficient cooling, smarter power management and facilities built near abundant energy all reduce the electricity cost of each unit of computing.
Flexible scheduling. Not every AI job is urgent. Training runs and batch processing can be timed to match when power is cheap and plentiful, easing peak demand on the grid.
More power, built faster. Ultimately, supply matters too. New generation, grid upgrades and faster permitting are part of the answer, even if they take longer to arrive than software can.

None of these is a silver bullet. Together, they form something more like a portfolio.

The caveat I owe you: efficiency can backfire

Here’s the part that keeps me honest. When something gets dramatically cheaper to use, people tend to use a lot more of it. Economists have a name for it, the Jevons paradox, and it’s been observed with everything from coal to computing.

If hybrid intelligence makes AI cheap enough to put into every application, every device and every workflow, total demand could grow even as the cost of each task falls. I made a related argument recently about software itself: cheaper development means far more software gets built, not less. It’s the same pattern, and it cuts both ways. Efficiency is how we make wonderful things affordable, but it doesn’t automatically mean we use less in total.

So I wouldn’t promise that hybrid intelligence will shrink data center demand. I’d say it lets us do far more with whatever capacity we have, which is arguably the more useful outcome, and it buys time while supply catches up. Whether total consumption falls is a different question, and the honest answer is that we don’t know yet.

There are other limits too. Local models are less capable than the largest cloud models, so the routing has to be good. On-device AI uses battery, memory and heat. Different devices have different capabilities, which complicates life for developers. And keeping data on the device helps with privacy, but it doesn’t make an application automatically compliant.

What the future might hold

Let me make a few predictions, and mark them clearly as predictions.

Intelligence becomes a utility you don’t notice. Today, using AI feels like an event: you open a chat window, you type, you wait. I suspect that within a few years, most of it will disappear into the background. Your software will quietly handle small intelligent tasks locally, the way it quietly handles spell checking now, and you’ll hardly think of it as AI.
The cloud becomes the specialist. The big models won’t go away. They’ll become the surgeons, reserved for the hardest problems, which may make them more valuable, not less.
Software gets smaller and smarter at the same time. The old trend was that capability meant bloat. Hybrid design offers a different path: applications that are lean at rest, capable on demand and shaped around each user.
Compute becomes a design constraint, not an afterthought. Product teams will start asking, “where should this intelligence run?” alongside “what should it do?” That discipline, designing for efficiency from the start, may turn out to be one of the more important skills of the next decade.
The real shortage may be imagination. If we get better at doing more with less, the question shifts from “do we have enough compute?” to “what do we want to build with it?” I’d rather be wrestling with that one.

So, does it solve it?

Not alone. The shortage is a physical problem of power, hardware and time, and no software trick makes a transformer appear. But hybrid intelligence and hybrid software attack it from the other side, by making sure the biggest resources are used only when they’re needed, and by building software that’s capable without being bloated.

That’s the kind of solution I like best: not bigger, just smarter about where the work happens. And the next time a very large computer is asked whether an email is angry or merely brisk, I’d like a small, quiet model on a laptop somewhere to say, “I’ve got this one.”

Planning for the Future

If your team hasn’t yet pulled together the signals behind your current view of the future, the free FutureHacking Signal Picker helps you identify and prioritize the signals worth watching, which gives you the evidence to point to when the board asks where your view of the future comes from.

I’m also finishing the FutureCanvas Picker, which takes planners through the whole arc, from signals to trends to possible futures to your most probable future and the path toward the one you’d prefer. I’m opening early access to a select group first: Chief Strategy Officers, strategic planners and leaders preparing for planning cycles and board conversations right now, who can tell me honestly what works under real deadline pressure. If that’s you, reach out and let me know and I’ll follow up personally with the select few who get in first.

Frequently Asked Questions

What is hybrid intelligence?

Hybrid intelligence is an approach to AI in which small language models run locally on a device for most everyday tasks, and large cloud-based models are used only for complex requests that need more power. Because most requests stay on the device, less work lands on data centers.

Does hybrid intelligence solve the AI compute shortage?

It helps, but it can’t solve the shortage alone. Hybrid intelligence reduces how much demand reaches data centers, which buys time while power and hardware supply catch up. But the shortage is also a physical problem of electricity, equipment and construction time, and cheaper AI can lead people to use more of it, so total demand may not fall.

What is hybrid software?

Hybrid software combines traditional programming with AI. Standard code handles tasks that should give the same answer every time, such as calculations and rules. A small local model handles fuzzy interpretation, such as understanding what a user meant. A large cloud model is called only when a task is complex enough to justify it. The result is software that is smarter and more adaptable without being bigger.

Sources

Image Credits: m365 CoPilot

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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How to Present Strategic Uncertainty to Your Board Without Looking Indecisive

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How to Present Strategic Uncertainty to Your Board Without Looking Indecisive

by Braden Kelley and Art Inteligencia

There’s a particular silence that falls over a boardroom when a strategy leader says the words “we’re not certain.”

It lasts about two seconds. In those two seconds, directors are doing math. Is this person hedging? Do they know what they’re doing? Why am I paying for a plan that isn’t sure of itself? By the time the silence breaks, you’ve either lost the room or you’ve earned something rarer than confidence, which is credibility.

Which one you get depends almost entirely on how you present the uncertainty. If you’re a CSO or strategy leader heading into the 2027 planning cycle, here’s how I’d approach it.

Why the instinct to hide uncertainty backfires

The temptation is understandable. Boards want decisions. Executives are rewarded for sounding sure. So the natural move is to smooth over the unknowns and present a single confident number, a single confident forecast and a single confident plan.

The trouble is that directors aren’t naive. Most of them have sat through enough planning cycles to know that a forecast with no stated uncertainty is either a forecast nobody questioned or one somebody hid the doubt inside. When reality drifts from the plan, as it always does, the board doesn’t just see a miss. It sees a team that never told them the miss was possible.

Hidden uncertainty feels safe on the day you present. It’s expensive on the day you’re wrong.

What boards actually worry about

When directors push back on uncertainty, it’s rarely because they dislike uncertainty. It’s because they’re asking three more specific questions:

Does management understand the risks? Not “are there risks,” which everyone knows, but “have you thought about the right ones?”
Is there a plan if things go differently? A board doesn’t need certainty. It needs to know you won’t be paralyzed.
Will we find out in time? The real fear isn’t that the future is unknown. It’s learning about a problem late.

Notice that none of these require you to pretend to know the future. They require you to show you’ve thought about it. That’s a much easier thing to be good at.

The structure that works: firm on action, explicit on uncertainty

The reframe that has served me best is this: be firm about what you’re doing, and be explicit about what you don’t know. These are different things, and mixing them up is what makes uncertainty sound like indecision.

Here’s a structure you can use.

Start with the decision, not the doubt. Lead with what you’re recommending. “We recommend investing in X, Y and Z in 2027.” Boards relax when they hear a position before they hear a caveat.
Name the few uncertainties that actually matter. Not twenty. Three or four. Pick the ones that, if they break differently than expected, would change your strategy. If an uncertainty wouldn’t change a decision, leave it out. It’s noise, and it dilutes the ones that count.
Show the futures, not just the risks. Instead of a list of risks, describe a small set of genuinely different ways the next few years could unfold, each traced back to signals the board can see. It’s more concrete than “risks,” and it lets directors engage with the world you’re describing instead of with an abstract worry.
Separate no-regret moves from bets. Tell the board which parts of the plan hold up in every future you described, and which depend on one future arriving. Directors love this, because it shows you know exactly where the real exposure is.
Show your tripwires. For each major uncertainty, say what you’d have to see to conclude things are going the other way, who is watching for it, and what you’d do. This answers the board’s third question directly. You won’t be the last to know.
Close on how you’ll report back. Tell them when and how they’ll hear about changes. A predictable rhythm of updates turns uncertainty from a source of anxiety into a managed process.

Language that helps, and language that hurts

Small word choices matter more than you’d expect in this setting.

Instead of “we don’t know,” try “here’s what we know, here’s what we’re watching, and here’s what we’d do if it changes.”

Instead of “it depends,” try “it depends on two things, and here’s how we’ll know which way each is going.”

Instead of “we think maybe,” try “our current view, based on these signals, is…” Then give it a rough confidence level. “We’re fairly confident in this one and much less confident in that one” is a perfectly respectable thing to say to a board.

Instead of a single number, give a range and the assumption it rests on. A range with a stated assumption signals competence. A single number with no assumptions signals either naivety or a hidden agenda.

And avoid the two extremes: false precision, which gets punished later, and endless hedging, which gets punished now.

Use a picture

One practical suggestion. Directors take in far more from a single well-built visual than from twenty slides of text. If you can put the signals, the trends and the possible futures onto one shared page, with the most probable future and your preferred path marked, the board can see the whole argument at once and point at the part they’d like to discuss. That’s a much better conversation than one where everyone is holding a slightly different mental picture of what you meant.

That’s the reasoning behind the visual approach in FutureHacking™: it moves a leadership team from signals, through trends, to a set of possible futures and a preferred path, on a page everyone can see. A team that has worked that way arrives at the boardroom with the thinking already organized.

Rehearse the hard question

Before the meeting, find the toughest director and ask yourself what they’d say. Usually it’s some version of “so which is it?” Have a short, clear answer: “Our most likely future is this one, and here’s why. We’ve built the plan so it works if we’re wrong.” If you can say that without flinching, you’ve already won the room.

A way to get started

I’m also finishing the FutureCanvas Picker, which takes planners through the whole arc, from signals to trends to possible futures to your most probable future and the path toward the one you’d prefer. I’m opening early access to a select group first: CSOs, strategic planners and leaders preparing for planning cycles and board conversations right now, who can tell me honestly what works under real deadline pressure. If that’s you, reach out and let me know and I’ll follow up personally with the select few who get in first.

The credibility dividend

Here’s the long game. The leaders who present uncertainty well don’t just survive the meeting. They build a reputation for telling the board the truth early, and that reputation is worth more than any single forecast. The next time something goes sideways, the board remembers who warned them and who didn’t.

Present the uncertainty. Just make sure the plan looks like it was built by someone who knew it was there.

Image Credits: Unsplash

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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Three Audits Before You Finalize the 2027 Plan: Customer, Employee and Partner Experience

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Three Audits Before You Finalize the 2027 Plan: Customer, Employee and Partner Experience

by Braden Kelley and Art Inteligencia

It’s October, which means somewhere in your organization a spreadsheet called something like “FY27 Investment Requests” is quietly filling up. Every department has a list. Every list has a champion. Every champion has a slide explaining why their item is the most important thing the company could possibly fund.

And every one of those slides is built on the same raw material: what that department already believes.

That’s the problem with most annual planning. The requests that win aren’t the ones backed by the best evidence. They’re the ones presented by the most persuasive person, in the room, on the day the budget gets cut. If you’d like to change that for 2027, there’s still time, and the most useful thing you can do with it is look at your organization from three directions you probably haven’t looked at together: through your customers’ eyes, your employees’ eyes and your partners’ eyes.

Why now, specifically

Timing matters here for two reasons.

First, the planning window is open and it won’t be for long. Once the 2027 budget is locked, findings from an audit become next year’s problem, and next year’s problem has to compete for money that’s already been spoken for. An audit finished in November can change the plan. An audit finished in February can only decorate it.

Second, the money is under scrutiny. Headline inflation hit 3.7% in September, and the Fed raised rates last month for the first time in three years, with more possible. When capital gets more expensive, every line item has to justify itself harder. That’s a good time to swap “we believe” for “we found.”

Why three audits and not one

Most organizations that take experience seriously look at one of the three, usually the customer. That’s a good instinct. It’s also an incomplete one, because the three experiences are links in the same chain.

Your employees create your customer experience. A frustrated, under-equipped frontline team can’t deliver a good one, however polished the strategy deck. Your partners, whether resellers, suppliers, integrators, agencies or distributors, deliver pieces of your customer experience too, and when they stumble, your customer blames you, not them. Fix only the customer-facing symptom and you’ll often be treating a cause that sits in one of the other two.

Here’s the practical test. Pick any customer complaint you’ve heard this year and ask: was the root cause in our product or process, in the way our people are set up to work, or in something a partner handles for us? In my experience, the honest answer is “all three, depending on the complaint.” One audit sees one third of the picture.

The customer experience audit

This is the one most teams already understand, and the case for doing it before planning is straightforward. Surveys and dashboards tell you what customers have been willing to score. They’re far less good at telling you about the silent downgrades, the workarounds and the quiet frustrations that never show up as a number until renewal.

An independent audit walks the journey the way a customer does, not the way your internal process diagram says it should work, and finds the friction your own team has stopped noticing. Planning-wise, it gives you:

A ranked list of where customers actually struggle, instead of where the loudest customer complained last.
A way to separate friction that costs you money from friction that’s merely annoying.
Evidence for the investments that prevent churn and protect revenue, which are the hardest to defend when budgets tighten.

If you haven’t put a number on what that friction costs, the CX ROI Calculator is a good place to start, and the Customer Experience Audit page explains what an independent audit covers.

The employee experience audit

Here’s where most plans have a blind spot. We budget for tools, systems and programs for employees without ever asking whether the people using them find them workable.

An employee experience audit looks at how work actually gets done: the tools that slow people down, the approvals that add nothing, the handoffs where things fall through, the manual workarounds that exist because the official process doesn’t fit reality. It also listens to the frontline, who know exactly where customers get frustrated and are often the least consulted people in the building.

For planning, it tells you:

Which internal friction is quietly capping your customer experience, since your people can only deliver what their systems and processes let them.
Where the hidden costs are: time lost to rework, turnover in critical roles, and talented people spending their days on tasks nobody should be doing by hand.
Which investments in tooling and process will pay back, and which are expensive ways to rearrange the same frustration.

An employee-side finding is often the cheapest fix on the whole list, which makes it one of the best candidates for a budget under pressure.

The partner experience audit

This is the audit almost nobody does, which is exactly why it’s worth doing.

Partners are an extension of your organization from the customer’s point of view. The reseller who onboards your customers, the distributor who handles delivery, the integrator who implements your product: when they have a bad experience working with you, they pass it along. Slow payments, confusing onboarding, unclear support channels, a portal that fights them at every step. None of it appears in your customer data, but all of it surfaces eventually as a customer problem.

A partner experience audit looks at what it’s like to work with you: how easy it is to get started, get support, get paid and get answers. For planning, it helps you:

Find the places where partner friction is leaking into customer experience before it becomes a customer complaint.
Decide where investing in partner enablement, portals, documentation or support will do more for customers than investing in the customer-facing side directly.
Spot partners who are quietly disengaging, a risk that never appears on a revenue dashboard until it’s too late.

How the three audits help you prioritize

Collecting findings is only half the job. The value comes from using them to rank the investment list. Here’s the approach I’d take.

1. Tag every finding with its evidence. For each issue, note whether it came from observation, direct customer, employee or partner input, or data. Findings that show up in more than one audit move to the top, because they’re real.

2. Look for the cross-audit patterns. A customer complaint about slow resolution, plus an employee finding about a clunky internal system, plus a partner finding about unclear escalation paths: that’s not three problems. It’s one problem seen from three angles, and fixing it pays off three times.

3. Score each candidate investment on three things. Impact on customers and revenue, effort and cost, and confidence in the evidence. Be honest about the third one. A confident hunch is still a hunch.

4. Sort into three piles. Quick fixes that cost little and should happen early in the year. Major investments that deserve a place in the 2027 budget on the strength of the evidence. And items to hold or drop, because the audits showed the problem lives somewhere else.

5. Write down what you expect to change. Attach a measure to each funded item, so that next October you’re reviewing results, not rebuilding the case from scratch.

A realistic timeline

If you want findings in hand before the budget closes, you don’t have long, so here’s a workable sequence:

Now through early November: scope the three audits and line up access to customers, frontline staff and partners. Start with the customer audit, since it usually has the most evidence behind it, and run the other two in parallel at a lighter scale if resources are tight.
Mid-November: consolidate the findings, look for the cross-audit patterns and score the candidate investments.
Before budgets lock: bring the ranked list to planning with the evidence attached, so the conversation is about findings, not about who argues best.

Even a lighter version of all three beats a deep dive into one, because the point of this exercise is the combination.

What I’d say to a skeptical CFO

You’ve probably already thought of the objection: another set of studies, in a year when everyone is cutting. Here’s the answer I’d give. The audits aren’t an additional line on the investment list. They’re how you decide which of the existing lines deserve to survive. If an audit helps you avoid even one major investment aimed at the wrong problem, it has paid for itself, and in my experience, there’s usually more than one on the list.

Start with the one that’s closest

If you can only do one audit before the plan closes, make it the customer experience audit, and treat the other two as lenses to add as soon as you can. The Customer Experience Audit page explains what an independent audit covers and how it works, and the Experience Design Glossary is useful for getting your team using the same words before the conversation starts.

The 2027 plan is going to get written either way. The only question is whether it’s built on what your organization believes or on what it’s actually like to be a customer, an employee or a partner. I know which one I’d bet my budget on.

Download the Customer Experience Audit Checklist as a PDF

Image Credits: ChatGPT

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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Compliance-Driven Customer Experience

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What Financial Services and Healthcare Teams Need to Know

Compliance-Driven Customer Experience

by Braden Kelley and Art Inteligencia

In most industries, a bad customer experience (CX) costs you a customer. In financial services and healthcare, it can also cost you an exam finding, a complaint to a regulator, or a line item in an audit report that someone on your compliance team now has to explain. That’s a meaningfully different kind of pressure, and it changes what a customer experience audit is actually worth to these two industries specifically — not just as a commercial tool, but as something that can double as genuine regulatory evidence.

A caveat before I go further: I’m not a lawyer or a compliance officer, and nothing here is regulatory advice. What I am is someone who’s watched CX and compliance teams operate as two separate functions inside the same organization, often documenting the same customer touchpoints in two different ways, for two different audiences, without either one fully benefiting from what the other already knows.

The silo that’s costing you twice

Here’s the pattern I see constantly in regulated industries: compliance runs its own fair-treatment reviews, complaint trend analysis, and documentation processes, built specifically to satisfy an examiner or auditor. CX, separately, runs its own journey mapping and improvement work, built to satisfy customers and the business. The two teams are frequently looking at the exact same touchpoints — the same loan application flow, the same patient intake process, the same claims experience — and producing two different, uncoordinated pictures of it, often without either team knowing in detail what the other found.

That duplication isn’t just inefficient. It means your compliance documentation and your actual customer experience improvement work rarely inform each other, and the organization effectively pays twice for insight into the same process it could have gotten once, more thoroughly, if the two efforts were designed together from the start.

What regulators in these industries are actually looking for

Across financial services and healthcare, the regulatory expectation increasingly isn’t just “treat customers fairly” as an outcome — it’s evidence of a systematic, ongoing process for finding and fixing where you aren’t. Fair lending and UDAAP-related reviews in financial services look for documented, proactive self-assessment, not just a clean complaint log. Patient experience standards in healthcare increasingly tie directly to things like CAHPS-style measurement and documented quality improvement processes, not just satisfaction scores reported upward once a year. In both industries, accessibility requirements for digital experiences — online banking, patient portals — are under increasing scrutiny on their own. What all of this adds up to is a consistent theme: proof that you looked, systematically and on a defensible cadence, matters as much as what you found.

Why a well-run CX audit produces exactly this kind of evidence

This is where the overlap gets genuinely useful instead of just theoretically interesting. A properly structured audit — validated personas, real journey mapping, an honest evaluation of existing complaint and service data, firsthand walkthroughs of the actual touchpoints, and benchmarking against what good looks like elsewhere — is, almost incidentally, precisely the kind of systematic, documented, dated process that compliance functions in these industries need to be able to point to. The commercial insight and the compliance evidence aren’t two different projects. They’re two outputs of the same rigorous process, if that process is designed with both audiences’ needs in mind from the outset rather than retrofitted afterward.

Timing matters more here than almost anywhere else

In most industries, a CX audit can happen whenever budget and bandwidth allow. In financial services and healthcare, timing it ahead of a scheduled exam or review changes its character entirely — it becomes proactive evidence of a mature process instead of a reactive scramble after a finding already landed. I’ve seen the difference in posture this creates firsthand: walking into a regulatory conversation with “here’s the systematic process we run and here’s what we found and fixed” is an entirely different position than walking in after a finding, trying to explain what you’re doing about it now that someone else noticed first.

Where to start

If your organization is in financial services or healthcare and hasn’t yet connected your CX and compliance efforts into a single, well-documented process, a Customer Experience Audit designed with both your commercial goals and your documentation needs in mind can serve both functions at once — worth coordinating with your compliance and legal teams on scope from the start. And if you want a sense of what unresolved experience gaps could be costing commercially, separate from the compliance conversation entirely, the CX ROI Calculator is a good place to start putting a number on it.

Download the Customer Experience Audit Checklist as a PDF

Image Credits: ChatGPT

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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Is Your pDoom Less Than Zero Yet?

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Is Your pDoom Less Than Zero Yet?

by Braden Kelley and Art Inteligencia

Every statistician who just read that headline winced. Probabilities don’t go below zero, and they’re right. Hold that wince for a moment, because it points at something the whole conversation about superintelligence keeps skipping.

If you haven’t run into the term, pDoom is the probability you personally assign to artificial intelligence ending badly for humanity. People trade their pDoom numbers at dinner parties now, the way people used to trade fantasy football picks. It’s a useful little conversation starter. It’s also a lopsided one, because it only counts one column of the ledger.

Nobody asks for your pDoom of inaction.

Press play first

I built this article around a music video, because some arguments land better with a hook in them. Here it is: Nothing Went Foom! from Bright Mirror.

That title is a wink at one of the great nerd arguments of the last twenty years. “Foom” is the sound an intelligence explosion makes in the fast-takeoff story: the machine gets a little smarter, uses that to get a lot smarter, and before anyone finishes the meeting it has left us standing on the platform. The idea was batted around in the Yudkowsky and Hanson debates back in 2008 and has been living rent-free in our collective imagination ever since.

I love that the title makes the point that nothing went foom. Technology almost never works that way. Electricity took decades to rewire the world. The internet took decades more, and we are still arguing about what it did to us. Transformations arrive in increments, which sounds boring until you realize what increments give you: time to watch, time to measure, time to adjust, and time to build the guardrails while the thing is still being built. Gradual is where safeguards live.

What we’re actually talking about building

I get tired of conversations where superintelligence is only ever the villain, so let me describe the other movie.

We already have a preview. AlphaFold predicted the structures of hundreds of millions of proteins, a job that used to cost a doctoral student years per protein, and its creators shared a Nobel Prize in Chemistry in 2024 for it. That is what narrow, early, imperfect AI did to one corner of biology. Now scale the ambition.

A superintelligence, pointed carefully at our hardest problems, could plausibly help us:

Compress drug discovery from a decade to a few years, and take a serious run at diseases we’ve written off as incurable.
Design new materials for batteries, solar cells and carbon capture that human intuition would never stumble across.
Help tame fusion, where the physics is understood but the control problem is brutal.
Put a patient, personalized tutor in front of every child on earth, including the ones who live hours from the nearest decent school.
Bring specialist-level diagnostics to places that have never had a specialist.
Speed up science itself, which is the part that makes my futurist heart skip. When the engine of discovery accelerates, every other list gets longer.

I’m not promising any of it will happen. I’m saying it could, and that “could” is worth a lot of human lives and a lot of human potential.

The risk we never put on the spreadsheet

Here’s where the less-than-zero joke earns its keep. The standard pDoom calculation asks how likely it is that we build this and it goes wrong. Fair question. But look at what’s missing: the cost of every year we don’t build it, or build it late, or build it only in the places that don’t care about safeguards.

A disease that goes uncured for another decade is a cost. A grid that stays dirty for another decade is a cost. A child who never gets the tutor is a cost. Those costs are quieter than a robot uprising, so they never show up in the headline, but they are real people. If you only count the risks of acting and never the risks of not acting, you haven’t done risk analysis. You’ve done fear analysis.

So here’s my proposal for a better number. Take your pDoom from moving forward, then subtract the doom you are already accepting by standing still. I suspect that for a lot of people the net figure is a lot lower than they thought, and for some of us it’s flirting with the territory of the headline. Statisticians, you may now exhale.

Safeguards, of course

Now let me be extremely clear, because I know how this paragraph gets quoted without its neighbors: I am not arguing for recklessness. Brakes are what let you drive fast, and seatbelts didn’t kill the automobile, they saved the automobile.

Responsible innovation means staged deployment instead of big-bang launches. It means independent testing and red-teaming, transparency about what these systems can and can’t do, real accountability when they fail, and international cooperation, because a safeguard that only half the world adopts is a suggestion. It means keeping humans in the loop for the decisions that matter, and building systems whose values we can inspect rather than hope for.

And I’ll say this plainly: thoughtful, serious people believe the danger is real, and some put meaningful odds on catastrophic outcomes. They might be right. That’s exactly why the safeguards have to be real and not decorative, and why dismissing the worriers is as unhelpful as dismissing the optimists. The goal isn’t to win the argument. It’s to build the thing well.

Ask a better question

The debate usually gets stuck on “should we build superintelligence?” Treat it as a yes-or-no switch and one side has to lose. Ask instead: what would we need to see in place to say yes? What safeguards, what evidence, what governance, what tripwires that tell us to slow down?

That question has answers. It also has owners and deadlines, which a vibe never does.

So, how’s your pDoom?

Mine is not zero, and I would be suspicious of anyone whose is. But when I count both columns, including the diseases, the climate, the classrooms and the discoveries waiting on the other side of responsible progress, I find the biggest risk we face isn’t that we build this thing carefully. It’s that we let fear stop us from building it at all, and hand the future to whoever was less cautious.

Hit play on the video again. Then ask yourself what future you’d rather get to first.

OR, is it all an EVIL PLAN?

Image Credits: m365 CoPilot

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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When a Bad Review Goes Viral

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Turning a PR Moment Into a Real Fix

When a Bad Review Goes Viral

by Braden Kelley and Art Inteligencia

The moment a bad customer experience goes viral, an organization snaps into a very specific and very well-rehearsed mode: crisis response. Legal gets looped in. Communications drafts a statement. Someone reaches out to make the customer whole, quickly and visibly. All of that is necessary, and none of it answers the only question that actually matters once the immediate fire is out: was this a genuine one-off, or is it the first time anyone’s noticed something that’s been happening quietly for a while?

The PR response and the real diagnosis are two different jobs

Here’s the trap I’ve watched organizations fall into repeatedly: the PR response feels like it resolved the problem, because from the outside, it did. The story dies down. The affected customer is satisfied, or at least quiet. Leadership moves on, understandably relieved. But making one visible customer whole and understanding whether the underlying failure is systemic are two completely different exercises, and only one of them actually happened. The apology addressed the symptom that became public. It said nothing about whether the same failure is quietly happening to other customers who simply didn’t have a large enough platform for anyone to notice.

The customer who went viral is rarely the only one it happened to

This is the part that gets lost in the relief of a crisis passing: the customer whose complaint went viral usually isn’t unique in what happened to them. What’s unique is that they had an audience willing to amplify it. For every customer with the platform and inclination to make a bad experience public, there are likely dozens or hundreds who had the same experience, said nothing beyond a quiet complaint or no complaint at all, and simply left — or stayed, resentfully, waiting for a better alternative to come along. A viral incident isn’t the problem. It’s a visibility event for a problem that was probably already there, silently costing you customers who never made noise about it.

Why this moment is actually a rare opportunity, if you use it right

There’s an uncomfortable but useful truth about the period right after a public incident: it’s often the single easiest moment to get real budget and leadership attention for a genuine diagnostic effort. The urgency that made the crisis painful is the same urgency that can get a proper audit approved in days instead of the usual months of internal advocacy. Most organizations waste this window by spending it entirely on the public-facing fix and the internal postmortem meeting, then let the appetite for deeper investigation fade along with the news cycle. The smarter move is using that same window to actually find out whether this was a true outlier or a known category of failure hiding in plain sight.

What the diagnosis actually needs to answer

A proper look at this needs to walk the exact touchpoint that failed, the way the customer who went viral actually experienced it — not the documented process, the real one, with whatever gaps and workarounds have accumulated around it. It needs to check the existing data for any pattern of similar complaints that never individually rose to leadership’s attention, because a string of quiet, unconnected complaints about the same root issue often exists well before one of them goes public. And it needs a clear-eyed read on whether this really was a rare edge case, because overcorrecting an entire process based on a single dramatic incident carries its own cost — reacting to n=1 with organization-wide policy changes can create new friction for the vast majority of customers who never experienced the original problem at all.

Turning a bad week into a real fix

The organizations that come out of a viral moment genuinely stronger aren’t the ones with the best-worded apology. They’re the ones that used the moment’s unusual clarity and unusual leadership attention to actually find out whether they had a true one-off or a systemic gap, and fixed the right thing instead of just the visible thing.

If you’re in or just past this kind of moment and want an honest answer to which one you’re actually dealing with, a Customer Experience Audit scoped to the failed touchpoint is built for exactly this kind of diagnosis. And if you want a fast sense of what a silent, unaddressed version of this problem could be costing beyond the one visible incident, the CX ROI Calculator is a good place to start.

Download the Customer Experience Audit Checklist as a PDF

Image Credits: Pexels

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

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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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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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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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