Author Archives: Chateau G Pato

About Chateau G Pato

Chateau G Pato is a senior futurist at Inteligencia Ltd. She is passionate about content creation and thinks about it as more science than art. Chateau travels the world at the speed of light, over mountains and under oceans. Her favorite numbers are one and zero. Content Authenticity Statement: If it wasn't clear, any articles under Chateau's byline have been written by OpenAI Playground or Gemini using Braden Kelley and public content as inspiration.

8 Signals You’re Preparing for the Wrong Future of Work

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8 Signals You’re Preparing for the Wrong Future of Work

by Braden Kelley and Chateau G Pato

How Do You Know You’re Preparing for the Wrong Future of Work? (Short Answer)

You are preparing for the wrong future of work when your AI and “future of work” investments optimize efficiency and throughput while shrinking human agency, judgment, and contiguous time — and nobody can say what stays human-accountable on Tuesday. Eight signals: headcount math before job design, saved time refilled as denser busyness, agents with mandate and humans with monitoring, volume metrics in a judgment era, prompt training without practice, “keeping up” as strategy, frontline power shrinking while AI slides expand, and a tool stack you can demo but a landing you cannot describe.

The corrective habit is not “move faster on AI.” It is designing the landing — what machines absorb, what stays human-accountable, and whose attention gets protected when the efficiency gains arrive.

The Wrong Future Looks Like Progress

We rehearse the wrong ending when we confuse activity with direction. The board deck shows copilots. The roadmap has agents. Someone declares that “the future of work is here.” And yet the calendar still looks like confetti, the front line still cannot recover a bad moment, and the business case still opens with subtraction before anyone maps what work becomes.

That is not falling behind on technology. That is building a hard landing — machines doing more of everything, including the human parts of work, while people inherit leftovers, interruptions, and less authority. A soft landing, by contrast, protects insight, empathy, decision making, direction, problem definition, creativity, and collaboration. The signals below tell you which landing you are actually buying.

Signal	Hard landing	Right future
1. Headcount math first	Hollow roles; volume competition with the model	Map cognitive labor before subtraction
2. Denser busyness	More task switching; no deep work	Reclaimed time funds depth
3. Agents mandated; humans monitored	Loop traps; rubber-stamp people	Delegated action with undo and handoff
4. Volume metrics win	Faster at the wrong things	Dual scorecard: reliability + judgment
5. Prompts, not practice	Tool fluency in broken jobs	Managers develop judgment on live work
6. Keeping up as strategy	Random automation; no coherent landing	Name the soft landing you refuse to miss
7. Frontline power shrinks	Attrition; failure demand; brand damage	Fund authority at the moment of truth
8. Stack demo, no human contract	Unowned decisions; no landing owner	Division of cognitive labor on one page

1. What Signal Shows You’re Optimizing for Headcount Before Job Design?

The signal: Every AI business case opens with FTE reduction, cost takeout, or “do more with less” — before anyone maps what work becomes, who decides, and what capability must grow.

Why it seduces leaders: Finance understands subtraction. Job redesign sounds slow, political, and annoyingly specific about power.

Hard landing: Humans compete with the model on volume. Roles hollow out. Judgment work never earns protected blocks because nobody was asked to protect it.

Right future: Start with cognitive labor — what machines absorb, what stays human-accountable, what managers must develop. Subtraction may follow. It should not lead.

2. What Happens When Saved Time Becomes Denser Busyness?

The signal: Efficiency gains from AI, automation, or self-service are immediately reinvested as more tickets, more pings, more micro-approvals — not protected deep-work blocks.

Why it seduces leaders: “We’re getting more done.” Utilization dashboards stay green. Motion still masquerades as progress.

Hard landing: Task switching accelerates. Strategic thinking never gets contiguous minutes. Burnout wears a productivity costume.

Right future: Explicit policy: a defined share of reclaimed time funds depth, not density. Calendar design is part of the AI bet — not an afterthought for people who “find time.”

3. Why Is It a Bad Sign When Agents Get Mandate and Humans Get Monitoring?

The signal: Autonomy ships for bots — refund, route, decide — while people get tighter scripts, scorecards, and surveillance, not undo, escalation, or recovery power.

Why it seduces leaders: Agents scale. Humans are framed as “the risk.” Containment metrics improve on slides that never show the trapped customer.

Hard landing: Customers stuck in loops. Employees rubber-stamp the model. Trust erodes on both sides of the glass.

Right future: Clarity, competence, control, care — delegated action with human handoff and authority at the moment of truth. Scale the routine. Protect the exception.

4. How Do Volume Metrics Reveal the Wrong Future of Work?

The signal: Handle time, tickets closed, tokens generated, outputs per hour — still the hero numbers — while insight, quality of decision, and human success stay soft or unmeasured.

Why it seduces leaders: Old scorecards are auditable. Judgment is harder to metricize. Volume is easy to put on a quarterly review.

Hard landing: People optimize what gets measured. The organization gets faster at the wrong things — including automating work that should have stayed human.

Right future: A dual scorecard — reliability plus human success. A few judgment metrics with owners. Experience-led management applied to work itself, not only customer journeys.

5. Why Does Prompt Training Without Practice Signal the Wrong Future?

The signal: Future-of-work readiness equals tool training, certification, and prompt libraries — not spaced practice on real work, manager coaching, or redesigned workflows where the new way is the easy way.

Why it seduces leaders: Training is procurable, completable, and reportable. You can count completions. You cannot count Tuesday.

Hard landing: Prompt-fluent people in broken jobs. Capability theater. Adoption without transformation.

Right future: Managers as developers of judgment. Practice on live work. Enablement tied to decision rights — not a badge for attending the copilot webinar.

6. What Does It Mean When “Future of Work” Means Keeping Up?

The signal: Strategy is reactive — vendor roadmaps, competitor panic, “we need an AI strategy by Q3” — with no articulation of whose attention gets protected or what human endeavor should grow if this works.

Why it seduces leaders: Urgency feels like leadership. Naming tradeoffs feels like delay. “We’re not falling behind” is a comforting story.

Hard landing: Random automation. No coherent landing. Every team improvises a different future while the operating model stays frozen.

Right future: Foresight with constraints — name the soft landing, the hard landing you refuse, and who owns the design. The future is not what gets pitched. It is what you design the landing to be.

7. Why Is Shrinking Frontline Power While AI Slides Expand a Warning Sign?

The signal: Customer-facing and operational roles lose staffing, recovery budget, and decision authority — while executive decks celebrate “AI-powered experience” and agentic service.

Why it seduces leaders: Automation is cheaper at the point of contact. Slides scale faster than enablement. Containment looks like efficiency until the humans leave.

Hard landing: I know what they deserve; I am not allowed to deliver it. Regrettable attrition. Failure demand. Brand damage that no agent can recover because recovery power was the first thing cut.

Right future: Fund authority where the moment of truth lives. Agents handle routine multi-step work. Humans own exception, dignity, and the judgment call that saves the relationship.

8. What If You Can Demo the Stack but Not Name What Stays Human?

The signal: Leaders can walk through copilots, agents, and platforms — but stumble when asked: What decisions must remain human-accountable? What gets undone? What would a hard landing feel like for employees first?

Why it seduces leaders: Demos photograph well. Philosophy sounds like foot-dragging. Procurement has a date.

Hard landing: Unowned decisions. Humans as exception handlers for the model. No one responsible for the landing after the pilot party.

Right future: Before the next pilot — division of cognitive labor on one page, a review date, and a named owner after go-live. If you cannot describe the landing, you are not ready to buy the stack.

How Should Leaders Test Future-of-Work Readiness?

Before the next AI or “ways of working” investment, run five go/no-go questions. If you cannot answer them, you are building a hard landing while calling it transformation:

Whose contiguous time are we protecting — and what policy enforces that?
What metric still punishes judgment — and who owns changing it?
What can an employee or customer undo when the system gets it wrong?
What frontline power are we funding, not only automating?
What human endeavor grows if this works — and who owns that outcome on Tuesday?

If you want the designed alternative spelled out, read The AI Soft Landing — and for ten futures being sold right now, each with a hard and human-centered landing, see 10 Futures in 2026: Soft Landing vs Hard Landing.

The wrong future of work is not falling behind. It is building a hard landing while calling it transformation. Spot the signals early, and you still have time to design a future where work gets more human — not less.

Frequently Asked Questions

What is the wrong future of work?

The wrong future of work is one where efficiency and throughput are the only values on the dashboard — AI and automation absorb more of the human parts of work, saved time becomes denser busyness, and people lose agency, judgment, and contiguous time. It is a hard landing disguised as progress.

How do you know if your AI strategy is wrong?

Warning signs include business cases that start with headcount reduction before job redesign, agents with autonomy while humans get tighter monitoring, volume metrics that still dominate, and leaders who can demo tools but cannot name what stays human-accountable or what can be undone when the system fails.

What is the difference between a soft landing and hard landing at work?

A hard landing gives machines more of everything — including tasks that require judgment — and leaves people with leftovers and interruptions. A soft landing deliberately automates fragmentation and low-judgment transaction so humans can spend larger blocks on insight, empathy, decision making, creativity, and collaboration.

Why does AI sometimes make work worse?

AI makes work worse when efficiency gains are reinvested as more tickets and pings instead of protected deep work, when roles are hollowed out without redesign, when frontline recovery power shrinks, and when organizations measure volume instead of judgment. The tool works; the landing was never designed.

How should leaders prepare for the future of work?

Leaders should map cognitive labor before subtraction, protect reclaimed time for depth, give agents delegated action with human undo and escalation, align metrics with judgment, fund frontline authority, and name the soft landing they want — with a division of cognitive labor, review date, and owner after go-live.

Image credits: Google Gemini

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

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11 Signs Your CX Program is Scorekeeping, Not Sense-Making

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11 Signs Your CX Program is Scorekeeping, Not Sense-Making

by Braden Kelley and Chateau G Pato

How Do You Know Your CX Program Is Scorekeeping, Not Sense-Making? (Short Answer)

Your CX program is scorekeeping, not sense-making, when governance energy goes to NPS, CSAT, CES, and dashboard hygiene while nobody can explain — in a customer’s words — why the number moved or what journey you actually changed. Eleven signs: the CX review is a score review; people are paid for the number; driver models replace talking to humans; surveys serve the calendar not the moment; closed loop is a ticket not a redesign; competitive NPS theater; channel scores with orphaned seams; capture volume as the KPI; comments in a dump; sentiment AI as costume; and a green score with a red Tuesday you are not allowed to name.

A score asks whether they would recommend you. Sense-making asks what happened to them — and what you will stop doing because of it.

The Number Is Not Understanding

I have sat in CX reviews that felt like a weather report. Promoters up. Detractors down. Traffic lights polite. Someone asked what we learned about humans. The room reached for a driver model the way a drowning person reaches for a brochure.

That is scorekeeping: treating the number as the product of the program. Sense-making demotes the score to a signal. It funds contact with real Tuesdays. It judges CX by journeys changed — not points harvested.

Sign	Scorekeeping	Sense-making
1. Score review as CX review	Traffic lights vs last quarter	One journey, one story, one decision
2. Paid for the number	Beg for tens; hide detractors	Incentives on outcomes and loops
3. Models replace humans	Word cloud as root cause	Contact evidence required
4. Calendar surveys	Tool default / ticket+24h	Listen at the hot moment
5. Loop = case closed	SLA on the detractor call	Person recovered; system owned
6. NPS theater	Beat the category chart	Your Tuesday, your economics
7. Channel scores	Leaderboards by org chart	Seam owners and journey scores
8. Capture as KPI	We listened to N people	What got redesigned or stopped
9. Comments in a dump	Verbatim “available”	One decision, owner, date
10. Sentiment costume	The tag is the insight	AI sorts; humans make meaning
11. Green score, red Tuesday	Official story is the dashboard	Dual truth; red journey can stop the review

1. Why Is a CX Review That Is Only a Score Review a Warning Sign?

The sign: The monthly CX meeting is a walk through NPS, CSAT, CES, traffic lights, and “up or down versus last quarter.” Stories, seams, and decisions are leftover time — if they get time at all.

Why it seduces: Numbers look like management. A chart is faster than a human.

Sense-making instead: Lead with one journey, one human story, one decision. The score is a footnote, not the agenda. If the meeting could run without a customer’s words, it was scorekeeping with catering.

2. What Happens When People Are Paid or Punished for the CX Score?

The sign: Bonuses, vendor scorecards, or site rankings hang on survey points. Teams beg for tens, suppress detractors, or time outreach to harvest promoters.

Why it seduces: “What gets measured gets done.” True. It also gets gamed.

Sense-making instead: Incentives on closed loops and journey outcomes — effort down, repeat contact down, recovery power used — not on extracting a 9. If the front line is coached to hunt scores, you taught them the customer is a ballot box.

3. Why Can’t Driver Models and Word Clouds Replace Talking to Humans?

The sign: “Root cause” is a regression, a theme cluster, or a word cloud. Nobody has to sit with a customer or a frontline person this month to keep the program green.

Why it seduces: Analytics look like insight. You can screenshot a driver model. You cannot screenshot someone’s third explanation of the same billing break.

Sense-making instead: Every priority theme requires contact evidence — quotes, recordings, ride-alongs — that could not have been invented from the dashboard. If it could have been written without leaving the building, it is not sense. It is interior decorating with data.

4. Why Is Surveying on the Program’s Calendar Instead of the Customer’s Moment a Problem?

The sign: Cadence is quarterly, or twenty-four hours after a ticket close, or whenever the tool’s default fires — not at the emotional peak or the stalled wait.

Why it seduces: A program needs a calendar. Tools need a trigger. Neither is a moment of truth.

Sense-making instead: Listening designed for the moment — conversational, in-channel, after the cliff. Scores are a pulse, not a ritual. If you always ask when it is convenient for you, you will always hear a polite version of Tuesday.

5. How Is a Closed Loop That Only Closes Cases Still Scorekeeping?

The sign: Detractor follow-up is a SLA: call them, code them, close them. The same break happens next week. The loop never climbs from person to pattern to policy.

Why it seduces: “We close the loop” photographs well. Cured systems do not fit in a weekly metric as neatly.

Sense-making instead: Person-level recovery and a named system owner when the same friction repeats. Closed is not cured. A recovered human and an unchanged process is hospitality theater.

6. What Is Competitive NPS Theater in a CX Program?

The sign: The north star is beating a category benchmark or a rival’s published score. Your own journeys, effort, and failure demand are secondary.

Why it seduces: Boards like relative rankings. A league table feels like strategy.

Sense-making instead: Your economics and your Tuesday — retained humans, repeat contacts, dignity costs — beat a borrowed chart. If you cannot name the friction you removed in this book of business, you are competing at poster height.

7. Why Do Channel Scores With Orphaned Seams Mean You Are Scorekeeping?

The sign: Phone, chat, web, and store each have a score. The handoff between them has no owner and no metric. Customers live in the seam; the program lives in the channel.

Why it seduces: Channel dashboards map cleanly to org charts. Seams do not.

Sense-making instead: Journey and seam scores with owners — not only channel leaderboards. If the customer fell between teams and every channel still looks “green,” you measured the boxes, not the human path.

8. Why Is Capture Volume a Weak CX KPI?

The sign: Success is response rate, comments captured, tickets tagged, “we listened to N customers.” Action rate and journey change are unmeasured or someone else’s job.

Why it seduces: Listening is visible. Changing the work is political.

Sense-making instead: Judge the program by what got redesigned or stopped — not by how much you collected. Volume without action is a warehouse of other people’s pain.

9. What Does It Mean When CX Comments Live in a Dump?

The sign: Verbatim sit in a portal. Nobody has to translate them into a decision in human language. Qualitative is “available” and unused.

Why it seduces: You can say you “have the voice of the customer.” Storage is cheaper than courage.

Sense-making instead: A standing ritual: this month’s comments must produce one decision, one owner, one date — or they were storage, not listening. Available is not the same as heard.

10. When Is Sentiment AI Just a Sense-Making Costume?

The sign: Models tag emotion and topics at scale. The readout is the insight. No one checks whether the tag matches the job the human was trying to do.

Why it seduces: AI looks like understanding at volume. Speed flatters the wrong conclusion.

Sense-making instead: AI as a sorter, humans as meaning-makers. If a frontline person would not recognize the “theme,” it is not sense. It is labeling. Use machines to find the pile. Use people to say what the pile is.

11. Why Is a Green CX Score With a Hard Tuesday Still Failure?

The sign: NPS or CSAT hold or rise while effort, workarounds, and “I know what they deserve” attrition stay ugly. Naming the gap is treated as disloyalty to the program.

Why it seduces: The dashboard is the official story. Official stories like to stay employed.

Sense-making instead: Dual truth — score and lived experience. A green number cannot close the review if the journey is still red. If you cannot say that out loud, you are not running CX. You are running a reputation program for a metric.

How Do You Test Scorekeeping Versus Sense-Making in a CX Review?

Before the next VoC or CX steering meeting, run five questions. If you cannot answer them, you are managing a score, not making sense of humans:

What human story led this meeting — in their words, not in a theme label?
What did we change in a journey — not in a survey or a dashboard tile?
Who is paid for the number versus the outcome?
Where does the seam live that no channel score owns?
Can a frontline person recognize our “why” — or would they laugh?

If scores have become the strategy because nothing else gets funded, that is a budget problem as much as a program problem — see 9 Reasons Companies Underinvest in CX. If the listening layer is still a dead form, Conversational and Agentic VoC is the method shift. If the number never had to name a behavior, 7 Ways to Calculate CX ROI Without Hope-Based Slideware is how you price the work. And if the map is still late to the pain, start with 12 Friction Points Customers Feel Before Your Journey Map Does.

Scorekeeping asks how we did. Sense-making asks what happened to them — and what we will stop pretending is fine.

Frequently Asked Questions

What is the difference between CX scorekeeping and sense-making?

Scorekeeping treats NPS, CSAT, or CES as the product of the CX program — optimizing surveys, samples, and dashboards. Sense-making uses those scores as signals, requires contact with real journeys, and judges success by what you changed for humans, not by points gained.

How do you know if your CX program is just managing NPS?

Warning signs include CX meetings that are only score reviews, incentives tied to survey points, root cause that never leaves a driver model, closed loops that close cases but not systems, channel scores with no seam owner, and a green NPS while Tuesday is still hard.

Why is NPS not enough for customer experience?

NPS is a signal, not a strategy. It does not name the job the customer was trying to do, the seam that failed, or the behavior you must change. Without that translation — and without owners, recovery power, and journey redesign — a rising score can coexist with rising effort and workarounds.

What should a CX review meeting cover?

Lead with one journey, one customer or frontline story in their language, and one decision — what to fix, stop, fund, or own. Scores, themes, and AI tags can inform. They should not consume the agenda. End with an owner and a date, not only a traffic light.

How do you stop survey gaming in CX programs?

Stop paying people for the score. Tie incentives to journey outcomes and closed-loop system changes. Sample in ways that cannot hide detractors. Treat begging for tens as a control failure. If the number is the bonus, the number will be the work.

Image credits: Unsplash

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9 Reasons Companies Underinvest in CX

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(Even When They “Believe” in It)

9 Reasons Companies Underinvest in CX

by Braden Kelley and Chateau G Pato

Why Do Companies Underinvest in CX If They Believe in It? (Short Answer)

Companies underinvest in customer experience even when they “believe” in it because belief is not a budget line. CX often arrives at the funding table with scores and journey maps; competing asks arrive with dollars. Leaders nod at loyalty, then fund what they can defend on a spreadsheet.

Nine common reasons: (1) nobody can price it, (2) scores are treated as the strategy, (3) CX is a department not an operating system, (4) the front line is accountable without authority, (5) cost cuts win because loyalty lags, (6) green SLAs hide red experiences, (7) no one owns the journey after the workshop, (8) listening is cheap and acting gets cut, and (9) the truth would require tradeoffs nobody wants.

Belief Is Not a Budget Line

I have sat in enough leadership rooms to recognize the pattern by the coffee cups. Someone says customer experience is a priority. Heads nod. A journey map appears that could win a design award. Then finance asks the only question that counts in that room: What is this worth?

The conversation that felt strategic starts to sound like a philosophy elective. Marketing shows pipeline. Product shows release velocity. Sales shows bookings. CX shows a score — and hopes the room will emotionally translate “up and to the right” into money.

Hope is not a business case. Companies rarely underinvest because they hate customers. They underinvest because experience is treated as a belief, a metric, or a department — not as a priced operating system with owners, incentives, and frontline power.

Reason	What “belief” looks like	What to do instead
1. Unpriced impact	NPS slides, no dollars	Metric → behavior → money → intervention
2. Score as strategy	Managing the number	Fund the why and the loop
3. Department sidecar	CX team, no levers	Journey owners with rights
4. Accountable, powerless	Front line absorbs anger	Fund enablement at the moment of truth
5. Loyalty lags	Cuts win this quarter	Show the lag; protect key journeys
6. SLA-green theater	Uptime hides struggle	Human success on the scorecard
7. Maps without owners	Workshop, then drift	Name the Tuesday operator
8. Listen, don’t act	VoC tools survive; recovery doesn’t	Budget hear → act → confirm
9. Tradeoff avoidance	Symbolic CX everywhere	Fund fewer journeys fully

1. Nobody Can Price It

What it is: Underinvestment because CX impact never gets translated into finance-ready estimates of retained revenue, avoided acquisition cost, or lower cost-to-serve.

The pattern: Customer experience speaks NPS, CSAT, and CES. The budget speaks dollars. The proposal that speaks fluent finance gets funded. The proposal that speaks fluent satisfaction waits another year.

What to do instead: Use a simple value chain — experience metric → customer behavior → financial outcome → named intervention. Start with your churn, revenue per customer, and cost per contact. Model a conservative-to-optimistic range. Say what is estimated. Transparency survives a CFO. False precision does not.

2. Scores Are Treated as the Strategy

What it is: Managing the survey number as if it were the customer relationship.

The pattern: Teams chase points on a dashboard while the journeys that produce loyalty stay underfunded. Scorekeeping without sense-making. When response rates collapse, the “strategy” gets thinner and the belief gets louder.

What to do instead: Demote scores to signals. Fund the why — conversational listening, journey repair, closed loops — not another instrument that asks people to perform unpaid labor for a brand that will not change.

3. CX Is a Department, Not an Operating System

What it is: Belief lives on the org chart. Investment does not follow the work.

The pattern: A CX team owns maps, research, and the annual summit. It does not own policy, product sequencing, staffing, or incentives. So “we believe in experience” becomes a sidecar to the real operating model.

What to do instead: Give journey owners decision rights. Treat customer experience as how the enterprise runs — handoffs, recovery, promises — not as a department that comments after the fact.

4. The Front Line Is Accountable Without Authority

What it is: Employees are held responsible for customer outcomes they lack the staffing, tools, or permission to deliver.

The pattern: Your best people feel the underinvestment before the dashboard does. Gallup has found staffing is often the top barrier employees name to delivering exceptional service. Qualtrics has found understaffed frontline teams far more likely to think about quitting. The quiet line I keep hearing: I know what the customer deserves. I am not allowed to deliver it.

What to do instead: Fund enablement where the moment of truth lives — judgment, recovery budget, time, and a path to fix the system, not just absorb the anger. Underinvestment in CX is also an employee-experience strategy, whether you meant it or not.

5. Cost Cuts Win Because Loyalty Lags

What it is: Savings show up this quarter. Churn, effort, and brand damage show up later — so the impatient P&L always has the better story.

The pattern: Headcount, knowledge, and recovery get trimmed because the harm is delayed. By the time loyalty moves, the cut already looks like “discipline.”

What to do instead: Make the lag visible. Protect a few high-leverage journeys from efficiency that is actually extraction. Pair any cost takeout with the human behaviors you are betting will not change — and watch those behaviors like a hawk.

6. Green SLAs Hide Red Experiences

What it is: Managing by cold service metrics while humans still fail the job.

The pattern: Uptime, response time, and handle time look healthy. Customers repeat their story. Employees run workarounds. The dashboard is green; the relationship is not. Belief in “service excellence” funds the SLA stack and starves experience-level measures.

What to do instead: Put human success on the same review as reliability. A green SLA should not close the meeting if effort is high, trust is low, or people cannot finish in one attempt. SLAs keep the lights on. They should not get to pretend they are the whole truth.

7. No One Owns the Journey After the Workshop

What it is: Maps and research get funded. The operating owner of a broken handoff does not.

The pattern: Belief photographs well in a workshop. Then Monday returns the work to silos. Nobody is empowered to change the seam between “marketing promised” and “operations delivered.”

What to do instead: Name a durable journey owner before the next offsite — someone who can change the path, not only present it. If the map has no Tuesday operator, you funded theater.

8. Listening Is Cheap; Acting Is What Gets Cut

What it is: Voice-of-customer tools, surveys, and dashboards survive budget cycles. Recovery staffing, redesign, and closed loops do not.

The pattern: Organizations get very good at collecting opinions and very timid about spending to change the thing people complained about. Customers notice. So do employees who asked for input and watched nothing move.

What to do instead: Budget the loop — hear, understand, act, confirm — as one investment, not a listening layer plus a vague “we’ll take it back to the business.” Reciprocity is what makes the next conversation possible.

9. The Truth Would Require Tradeoffs Nobody Wants

What it is: Real CX investment would expose turf, pet channels, understaffing, and brand promises operations cannot keep — so “belief” stays safer than choice.

The pattern: Symbolic experience everywhere; funded experience nowhere that hurts. Another journey map. Another principle. No decision about what you will stop doing so a few moments can actually work.

What to do instead: Surface winners and losers. Fund fewer journeys fully rather than every journey symbolically. Belief that refuses tradeoffs is not strategy. It is branding for the annual report.

How Do You Get Customer Experience Funded?

Turn belief into an investable ask. Do not take the next CX proposal to finance until you can answer these:

What is this worth in our numbers — retained revenue, cost-to-serve, regrettable attrition — not an industry slogan?
Which customer or employee behavior must change for the investment to count as success?
Who owns this journey on a normal Tuesday after the workshop ends?
What old path or SLA-only definition of winning will we stop treating as success?
What frontline power — staffing, tools, recovery authority — are we actually funding?

Belief gets you the slide. A priced human outcome gets you the line item. If you want companies to stop underinvesting in customer experience, stop asking leaders to have more faith — and start giving them a case the budget can recognize without translating it into a foreign language.

Frequently Asked Questions

Why do companies underinvest in CX even when they believe in it?

Because belief is not a budget line. Customer experience often arrives with scores and maps while competing investments arrive with dollars. Underinvestment follows when impact is unpriced, scores substitute for strategy, CX lacks operating power, the front line has accountability without authority, cost cuts beat lagging loyalty, SLAs hide poor experience, journeys lack owners, listening is funded without action, and real tradeoffs are avoided.

Why isn’t believing in customer experience enough?

Belief produces slides, principles, and workshops. Funding requires a priced outcome, a named owner, incentives that match the new behavior, and frontline power to deliver the promise. Without those, “we believe in CX” is branding, not investment.

How do you get CX funded in a budget meeting?

Translate an experience metric into a customer behavior and a dollar outcome tied to a specific intervention. Use internal numbers where possible, show a range, name the journey owner, and include the frontline enablement required to make the change real.

What are the main reasons companies underinvest in customer experience?

Nine common reasons are unpriced impact, treating scores as strategy, CX as a powerless department, frontline accountability without authority, short-term cost cuts beating lagging loyalty, green SLAs hiding red experiences, maps without journey owners, listening without funded action, and unwillingness to make tradeoffs.

How does underinvesting in CX affect employees?

When experience is underfunded, frontline people are often still held accountable for customer outcomes they cannot deliver. That gap drives frustration, burnout, and regrettable attrition — so the company pays twice: weaker customer loyalty and higher cost to replace the humans who knew how to save the moment.

Image credits: Google Gemini

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7 Ways Design Thinking Gets Misused

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(And How to Course-Correct)

7 Ways Design Thinking Gets Misused

by Braden Kelley and Chateau G Pato

Why Does Design Thinking Get Misused? (Short Answer)

Design thinking gets misused when organizations treat it as a workshop format instead of a discipline for understanding humans and making better choices under constraint. Seven common misuses are: empathy theater without real people, ideation without mandate, prototyping without a behavior to test, journey maps without owners, endless discover as schedule padding, facilitators without power, and “we design think” as brand instead of outcomes.

Course correction means restoring jobs-to-be-done, real evidence, decision rights, behavior-based tests, journey ownership, time boxes with kill criteria, sponsors with levers, and success measured by adoption — not sticky-note acreage.

The Method Is Not the Costume

I have walked into rooms that smell like fresh markers and old coffee and felt the familiar mix of hope and dread. Hope because someone finally said “human-centered.” Dread because the wall is already filling with personas nobody met and “How might we…” statements nobody is allowed to answer.

Design thinking is not the problem. Misuse is. When empathy becomes a poster, ideation a substitute for power, and prototypes a way to delay a decision, you get innovation cosplay with better stationery. The method still works — when it is tied to real humans, real constraints, and a Tuesday someone can actually succeed on.

Misuse	Why it happens	Course-correct
1. Empathy theater	Faster, no access politics	Talk to humans doing the job
2. Ideation without mandate	Performance over power	Match method to decision rights
3. Prototyping without behavior	Demos photograph well	Test one falsifiable behavior
4. Journey maps without owners	Maps are deliverables	Name owner + one seam to fix
5. Schedule padding	Ambiguity feels safe	Time-box; define the decision
6. Facilitators without power	DT outsourced	Sponsor with levers in room
7. DT as brand	Credibility borrowing	Judge by behavior and adoption

1. Empathy Theater — Post-Its Without People

The misuse: Personas, empathy maps, and “user needs” built from conference-room imagination. No customers in the room. No frontline staff. No evidence beyond what someone senior once said at an offsite.

Why it happens: It is faster. It avoids access politics. It lets the team feel virtuous without scheduling the awkward conversation where reality contradicts the roadmap.

Course-correct: Talk to humans doing the job — customers, employees, partners. Capture jobs-to-be-done, friction, and dignity costs in their language. If your empathy artifact could have been written without leaving the building, it is fiction with better fonts.

2. Ideation Without Mandate — Brainstorming What You Cannot Change

The misuse: “How might we…” on problems policy, budget, incentives, or turf forbid anyone to fix. The wall fills with clever ideas that die the moment the workshop ends.

Why it happens: Design thinking becomes performance. Brainstorming feels like progress without naming decision rights or making enemies.

Course-correct: Before the workshop, ask: What are we empowered to decide, ship, or stop? Match the method to the mandate. If the honest answer is “nothing structural,” fix the mandate — or cancel the sticky notes and stop charging the room rent for theater.

3. Prototyping Without a Behavior — Demo Is Not Learning

The misuse: Clickable mockups, storyboards, or AI demos that never test a named human behavior or success signal. The prototype exists to impress, not to falsify.

Why it happens: Prototypes photograph well. Measurement is harder. Applause is immediate; adoption is delayed and therefore ignorable.

Course-correct: Prototype to test one behavior hypothesis — completion in one attempt, abandoned workaround, time-to-confidence, effort reduced. Measure what people do, not what they say in the debrief. A beautiful demo that does not change a behavior is a portfolio piece, not design.

4. Journey Maps Without Owners — Beautiful Maps, Orphaned Seams

The misuse: Journey maps that end at the workshop wall. Broken handoffs identified, celebrated, and then left without an operator in business-as-usual.

Why it happens: Maps are deliverables. Operating-model change is political. Printing is easier than owning.

Course-correct: Name a journey owner with decision rights before you print the poster. Pick one seam — one handoff, one wait, one moment of shame — and fund its fix. A map without an owner is wallpaper that teaches the organization how to look empathetic while staying unchanged.

5. Design Thinking as Schedule Padding — “We’re Still in Discover”

The misuse: Endless discover and define to avoid a decision, a vendor choice, a kill, or a tradeoff someone does not want to make.

Why it happens: Ambiguity feels like safety. Without kill criteria, “we’re learning” becomes a luxury hobby with catering.

Course-correct: Time-box phases. Define what decision the sprint must produce — proceed, pivot, or stop. Learning without a decision date is tourism. Design thinking should reduce uncertainty, not indefinitely postpone it.

6. Facilitators Without Power — Great Session, No Tuesday

The misuse: External or internal facilitators who run excellent sessions but cannot move budget, policy, metrics, or the workaround everyone still uses on Monday.

Why it happens: Design thinking is outsourced to people without levers. Sponsors attend the readout and disappear into the next crisis.

Course-correct: Put a sponsor with decision rights in the room for the whole arc — not only the applause at the end. The facilitator serves evidence and structure; the sponsor owns tradeoffs. If nobody with power was present when the uncomfortable insight appeared, the insight will not survive contact with the calendar.

7. “We Design Think” as Brand — Method as Moral License

The misuse: The label slapped on unchanged process. Design thinking badge replaces human-centered outcomes. Workshop count substitutes for adoption.

Why it happens: Credibility borrowing. Innovation theater with better vocabulary. Easier to claim the method than to change the system.

Course-correct: Judge by behavior change and adopted outcomes on a named journey — not wall art, not certificate count, not how many times someone said “empathy” in a steering committee. Design thinking earns its keep when humans can succeed on Tuesday. Everything else is branding.

How Do You Fix Misused Design Thinking?

Before the next design thinking workshop, run five go/no-go questions. If you cannot answer them, you are buying costume, not method:

Who did we talk to — and did we hear jobs and friction in their words?
What can we change if the evidence says we should?
What behavior are we testing — not what demo are we showing?
Who owns the journey after the markers dry?
What decision must this sprint produce — by when?

Design thinking is still one of the most practical ways to put humans back at the center of innovation — when it is used as a discipline for contact with reality, not as a sticker on the same broken Tuesday. Course-correct the misuse, and the method does what it always promised: better questions, better choices, and work that lands where people actually live it.

Frequently Asked Questions

What are common design thinking mistakes?

Common mistakes include empathy theater without real users, ideation without decision rights, prototypes that never test behavior, journey maps without owners, endless discover phases, facilitators without power to implement, and treating design thinking as brand instead of measuring adoption and outcomes.

Why does design thinking fail in organizations?

It often fails when used as a workshop format rather than a discipline tied to real humans, real constraints, and decision rights. Teams produce personas and prototypes but cannot change policy, incentives, or broken handoffs — so the operating model stays the same and the method gets blamed.

How do you course-correct misused design thinking?

Talk to people doing the work, match workshops to what you are empowered to change, prototype to test specific behaviors, name journey owners, time-box phases with kill criteria, keep sponsors with levers in the room, and measure success by behavior change and adoption — not workshop output.

Is design thinking the same as brainstorming?

No. Brainstorming is one activity. Design thinking is an end-to-end discipline: understand humans and jobs-to-be-done, define the right problem, ideate within mandate, prototype to learn, and test behavior — with owners who can change the system after the session.

What should you ask before a design thinking workshop?

Ask who you will talk to, what you can change if evidence requires it, what behavior you will test, who owns the journey afterward, and what decision the sprint must produce by when. Without those answers, you are likely funding empathy theater.

Image credits: Braden Kelley from a Pixabay base image

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11 Signs Your Transformation Is Managing the Deck, Not the Work

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11 Signs Your Transformation Is Managing the Deck, Not the Work

by Braden Kelley and Chateau G Pato

What Does It Mean When a Transformation Manages the Deck, Not the Work? (Short Answer)

Your transformation is managing the deck, not the work, when governance energy goes to RAG colors, narrative polish, and workstream theater while the median employee’s Tuesday — tools, handoffs, incentives, and authority — stays the same. Eleven signs: RAG greener than reality, slide time over floor time, status agendas without decisions, activity reporting instead of behavior, risks that only live in registers, change as cascade and completions, dependencies as Gantt arrows, problems parked until next steering, go-live as finish line on the roadmap, vendor demos owning the room, and shadow process “managed” as residual risk.

A deck is a mirror. When the program starts performing for the mirror, the work has already left the room.

Green Decks, Red Tuesdays

I have sat in enough steering rooms to recognize the smell of a program that has become a reporting product. The slides are crisp. The RAG is mostly green. Workstreams have spoken. Risks have been “mitigated.” Someone thanks the PMO for the pack.

Then you walk the floor — or talk to the median person trying to do the new job with the old incentives — and Tuesday is still red.

That gap is not a communication problem. It is governance theater: managing the artifacts of transformation as if they are the transformation. The work — redesigned jobs, owned handoffs, killed shadow processes, practiced capability, and behavior after go-live — never gets the same energy as the deck.

Sign	Deck reality	Work reality
1. RAG greener than Tuesday	Status looks healthy	Workarounds still run the day
2. Slides over floor time	Pack is polished	Nobody watched the median user
3. Status, not decisions	Tour complete	Nothing killed or funded
4. Activity, not behavior	Milestones hit	Old path still preferred
5. Risks in the register	Rated and reviewed	Broken handoff still daily
6. Cascade = change	Completions high	Capability thin
7. Dependencies as arrows	Gantt looks integrated	Seams have no owner
8. Problems parked	On next month’s agenda	Blocker lives this week
9. Go-live as climax	Roadmap peaks at cutover	Adoption is a footnote
10. Vendor demos own the room	Product theater	Adopters absent
11. Shadow process “managed”	Labeled residual / out of scope	Old path still easy

1. Why Is a Green RAG a Warning Sign When Tuesday Is Still Red?

The sign: Status is amber or green while frontline effort, workarounds, and adoption quality are clearly red.

Why it seduces: Color coding is legible in fifty minutes. Lived experience is not. Leaders can leave the room feeling informed.

Manage the work instead: Dual truth — system status and human success. A green RAG cannot close the review if the experience is red. If status and Tuesday disagree, Tuesday wins.

2. What Does It Mean When Slide Production Outruns Floor Time?

The sign: The team’s scarce hours go to deck assembly, appendix hygiene, and “story alignment” — not ride-alongs, floor walks, or watching the median user struggle.

Why it seduces: The pack is a visible deliverable. Floor time looks soft and does not photograph in the steering invite.

Manage the work instead: Cap slide hours. Require evidence from contact with the work in every steering pack — a quote, a timed task, a workaround still in use. No floor signal, no green story.

3. How Do You Spot a Steering Agenda That Is Status, Not Decisions?

The sign: Steering meetings are tour updates. Few tradeoffs named. Few things stopped. Few owners assigned for next Tuesday.

Why it seduces: Status feels safe. Decisions create losers, vendors, and uncomfortable clarity.

Manage the work instead: Decision-first agenda — what must we choose, kill, fund, or unblock before we leave? If the meeting ends with “thanks for the update,” you managed the deck.

4. Why Is Reporting Activity Instead of Behavior a Transformation Trap?

The sign: Workstreams celebrate milestones, tickets closed, training launched, and “progress against plan” — not whether humans stopped the old path or succeeded on the new one.

Why it seduces: Activity is countable. Behavior change is political and harder to put in a chart.

Manage the work instead: One behavior metric per workstream — adoption quality, workaround retired, time-to-confidence. Progress that cannot name a human behavior is still theater.

5. When Do Risk Registers Become Governance Theater?

The sign: Risks are rated, mitigated on paper, and reviewed monthly — while the broken handoff still happens daily.

Why it seduces: Registers look like governance. Fixing the workflow looks like operations, which someone else is supposed to own.

Manage the work instead: Every top risk must map to an owned workflow change this sprint — or it is a slide, not a control. Mitigation without a Tuesday owner is fiction.

6. Why Isn’t Cascade Decks and Training Completions Real Change Management?

The sign: Comms plans, town halls, and training completion percentages are the change scorecard. Practice, coaching, and permission to stop the workaround are missing.

Why it seduces: Completions are procurable and reportable. You can screenshot the cascade. You cannot screenshot someone’s third Tuesday failing with the old incentives still in force.

Manage the work instead: An enablement scorecard — practice on real work, manager coaching, protected learning time. Informed is not enabled.

7. What Goes Wrong When Dependencies Are Arrows Instead of Owned Handoffs?

The sign: The Gantt shows neat dependencies. Nobody can name who owns the seam between teams when it breaks on Tuesday.

Why it seduces: Planning software creates the illusion of integration. Arrows feel like ownership.

Manage the work instead: Name seam owners. Design the handoff. Test the seam before you celebrate the milestone. Integration that only exists in the plan does not exist.

8. Why Is Parking Problems Until the Next Steering Pack a Red Flag?

The sign: Issues escalate into “for discussion next month” rather than a 48-hour fix with an owner. The deck becomes a waiting room.

Why it seduces: Cadence protects calendars. Urgency threatens the narrative and the RAG.

Manage the work instead: Fast-path ownership for anything blocking the median user’s success this week. If it can wait a month, it was never blocking the work — only the story.

9. Why Shouldn’t Go-Live Be the Finish Line on the Roadmap Slide?

The sign: The visual climax of the program is cutover. Hypercare is a thin bar. Adoption and BAU ownership are footnotes.

Why it seduces: Vendors and PMOs get paid at go-live. Steering committees want a date they can declare. Adoption is someone else’s Tuesday.

Manage the work instead: Make the roadmap climax a durable new way of working. Go-live is a technical event. Transformation is behavior that survives after the PMO leaves.

10. What Does It Mean When Vendor Demos Own the Room and Adopters Don’t?

The sign: Steering time goes to product walkthroughs and roadmap theater. The people who must live the change are absent, summarized, or reduced to a survey chart.

Why it seduces: Demos photograph well. Adopters complicate the story with workarounds, missing authority, and truths that do not fit the narrative.

Manage the work instead: Adopter voice as a standing agenda item — what broke, what workaround returned, what decision rights are missing. If adopters are not in the room, you are managing a brochure.

11. Why Is “Managing” Shadow Process as Residual Risk Still Failure?

The sign: The old spreadsheet, parallel path, or “just in case” process is acknowledged on a slide and left alive — draining adoption while status stays green.

Why it seduces: Killing the old way is political. Labeling it residual or out of scope feels tidy and keeps the RAG calm.

Manage the work instead: Explicit kill date and owner for the shadow path. Make the new way the only easy way. A residual risk that still runs the business is not residual. It is the operating model.

How Do You Test Whether You’re Managing the Deck or the Work?

Before the next RAG review, run five go/no-go questions. If you cannot answer them, you are performing transformation for an audience that can leave the room:

What evidence from the floor is in this pack — not only from the plan?
What decision will we make today — choose, kill, fund, or unblock?
What behavior changed since last month for the median person?
What will we stop — not only mitigate on a register?
Who owns the median user’s Tuesday after go-live when the PMO leaves?

If the beliefs underneath the theater need naming, see 8 Change Myths Leaders Still Believe in 2026. If the pilot worked and scale did not, use 9 Reasons Digital Transformations Stall After the Pilot. Before the next funded experiment, run 11 Questions Before Funding Any Innovation Pilot.

If the deck is healthier than the work, you are not transforming. You are managing the mirror. Put the energy back into Tuesday — and let the slides catch up to reality, not the other way around.

Frequently Asked Questions

What does it mean when a transformation manages the deck, not the work?

It means the program optimizes RAG status, narrative polish, and workstream reporting while the median person’s tools, handoffs, incentives, and authority stay unchanged. Governance energy goes to slides instead of redesigned work, owned seams, killed shadow processes, and behavior after go-live.

How do you know a transformation is failing?

Warning signs include green status with red frontline experience, steering agendas that never make decisions, activity metrics instead of behavior change, risks that only live in registers, go-live treated as victory, and old shadow processes left alive while labeled “mitigated.”

What should a transformation steering committee focus on?

Focus on decisions — what to choose, kill, fund, or unblock — plus floor evidence, behavior change since last month, and who owns the median user’s Tuesday after go-live. Status tours and vendor demos are insufficient if adopters and seams have no voice or owner.

Why are transformation RAG statuses misleading?

RAG colors often track plan, milestones, and system readiness — not human success, workaround retirement, or adoption quality. A green RAG can coexist with a red Tuesday when the scorecard rewards narrative hygiene over lived experience.

How do you fix transformation governance theater?

Require floor evidence in every pack, run decision-first steering, add one behavior metric per workstream, map top risks to owned workflow changes, fund enablement over cascade completions, name seam owners, kill shadow paths on a date, and treat go-live as a technical event — not the finish line.

Image credits: Google Gemini

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9 Reasons Digital Transformations Stall After the Pilot

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9 Reasons Digital Transformations Stall After the Pilot

by Braden Kelley and Chateau G Pato

Why Do Digital Transformations Stall After the Pilot? (Short Answer)

Most digital transformations stall after the pilot because leaders confuse technical proof with organizational readiness. A successful demo shows something can work. Scaling requires named owners, redesigned work, aligned incentives, data and integration, change capacity, the courage to kill shadow processes — and the political will to live differently. The pilot was never the hard part.

The nine common reasons transformations stall are: (1) proof mistaken for readiness, (2) no named owner of the new way of working, (3) work redesign skipped, (4) incentives still reward old metrics, (5) data and integration debt, (6) change treated as communications, (7) shadow processes never killed, (8) pilot heroes ≠ scale reality, and (9) fear of the political cost of winning.

The Pilot Was Never the Hard Part

I have watched too many rooms celebrate a green pilot as if the transformation were already over. The interface worked. The select users smiled. The slide said “validated.” Then Monday arrived for everyone else — average managers, messy data, conflicting KPIs, and a legacy workaround that refused to die.

Human-centered change treats that gap as designable, not mysterious. Below are nine stall patterns I see repeatedly, what each looks like, and what real transformers do instead.

Reason	Common sign	What to do instead
1. Proof ≠ readiness	“It worked” used as scale approval	Require an adoption/scale scorecard
2. No durable owner	Project ends; BAU orphaned	Name a workflow owner with rights
3. No work redesign	New tool, old handoffs	Redesign jobs and decisions first
4. Old incentives	KPIs punish new behavior	Align metrics to transformed outcomes
5. Integration debt	Pilot used clean workarounds	Fund data/integration as scale work
6. Change = decks	Training without practice time	Build change muscle and coaching
7. Shadow process lives	Old path still easiest	Kill legacy paths on purpose
8. Heroics don’t scale	A-team pilot, average-team crash	Design for the median user
9. Political avoidance	Endless “learning”	Govern winners/losers in the open

1. Proof Mistaken for Readiness

What it is: Treating a successful pilot as proof the organization is ready to scale.

The stall: “It worked” becomes a funding sentence. Feasibility gets confused with operability. Leaders skip the boring questions about volume, exceptions, support load, and who changes how they spend Tuesday.

What to do instead: Separate technical validation from operating-model readiness. Before scale money, require an adoption and scale scorecard: owners, workflows, incentives, data path, support model, and stop rules for the old way.

2. No Named Owner of the New Way of Working

What it is: The transformation has a project manager — and no durable operator once the project ends.

The stall: Sponsorship was energetic in kickoff season. Then the program office demobilizes, and business-as-usual inherits a half-changed process with no one empowered to keep improving it.

What to do instead: Assign a named workflow owner with decision rights before scale funding. If nobody owns the new way after applause, you do not have a transformation. You have a temporary exhibit.

3. Work Redesign Skipped — Tool Bolted onto Old Process

What it is: Installing new technology on top of unchanged handoffs, policies, and approvals.

The stall: Same friction, prettier screens. People invent local workarounds because the system still asks them to do impossible coordination. Digitization accelerates the old mess.

What to do instead: Redesign jobs, handoffs, and decisions first. Technology should serve the human system you intend to run — not freeze the system you meant to leave.

4. Incentives Still Reward the Old Metrics

What it is: Asking people to adopt new behaviors while KPIs, bonuses, and recognition still pay for the old ones.

The stall: Managers optimize what still gets measured — speed-to-close, local utilization, ticket deflection theater — even when those metrics punish quality, collaboration, or customer effort in the new model.

What to do instead: Align metrics and recognition to transformed outcomes: adoption quality, end-to-end cycle time, customer/employee effort, first-time resolution, revenue or risk outcomes tied to the journey — not activity that photographs well.

5. Data and Integration Debt Comes Due

What it is: Pilots that succeed on clean samples, manual stitches, or heroic data wrangling collapse under production reality.

The stall: Scale exposes fragmented systems, inconsistent definitions, and brittle interfaces. The “digital” experience becomes a queue of exceptions.

What to do instead: Fund data quality and integration as first-class scale work — not a vague phase two. If the pilot needed spreadsheet glue, assume production needs architecture, not optimism.

6. Change Capacity Treated as Communications

What it is: Substituting town halls, slide decks, and one-time training for the real work of building new skills and habits.

The stall: People were informed. They were not enabled. Managers never got coaching time. Role anxiety filled the gap that practice should have filled.

What to do instead: Build change muscle: champions, spaced practice, protected learning time, manager enablement, and clear stories about how roles evolve. Communication is necessary. It is not capacity.

7. Shadow Process Never Gets Killed

What it is: Leaving the old spreadsheet, email path, or “temporary” workaround alive “just in case.”

The stall: Adoption stays optional. The easy path is still the legacy path. Leaders wonder why usage is soft while the organization quietly runs two operating systems.

What to do instead: Write stop criteria for shadow processes. Make the new way the only easy way. If you cannot turn something off, you have not finished the design of scale.

8. Pilot Team ≠ Scale Team — Heroics Don’t Industrialize

What it is: A hand-picked A-team succeeds in the pilot; average teams inherit complexity without the same support.

The stall: What worked for enthusiasts fails for the median user and median manager. Support tickets spike. Leaders blame “resistance” instead of design for the middle of the bell curve.

What to do instead: Design for the median. Staff enablement and operations for steady state. If only heroes can run it, it is not a transformation asset — it is a dependency on burnout.

9. Fear of the Political Cost of Winning

What it is: Avoiding scale because success would force real tradeoffs — turf, vendors, pet projects, or status stories that cannot survive daylight.

The stall: The organization stays in luxurious “learning mode.” Another pilot. Another vendor bake-off. Another steering committee. Progress becomes a lifestyle of almost.

What to do instead: Surface winners and losers early. Govern tradeoffs in the open. Use kill criteria as leadership hygiene. Transformation is not only a technology journey. It is a power redesign with manners.

How Do You Scale a Digital Pilot Without Stalling?

Before you fund the next wave, run a soft-landing go/no-go. Say no — or not yet — unless you can answer yes to these:

Who owns the new workflow after the program ends?
What human behavior must change for scale to count as success?
What old path will we turn off, and when?
Is integration and data readiness funded as core work, not hope?
Do incentives and manager routines reinforce the new way?

A pilot proves possibility. Transformation proves the organization can live differently. If you want digital change that lands with customers and employees, stop celebrating demos as destinations — and start designing the human system that has to carry the future on a normal Tuesday.

Frequently Asked Questions

Why do digital transformations stall after the pilot?

They stall when leaders treat technical pilot success as organizational readiness. Scaling fails without durable owners, redesigned work, aligned incentives, data/integration, change capacity, retired shadow processes, design for average users, and political willingness to make tradeoffs.

What is the difference between a successful pilot and a scalable transformation?

A successful pilot proves something can work under controlled conditions. A scalable transformation proves the organization can run the new way of working in production — with owners, incentives, data plumbing, adoption support, and the old path intentionally shut down.

How do you scale a digital pilot successfully?

Name a workflow owner, redesign jobs and handoffs, align metrics, fund integration and data quality, build real change capacity, kill shadow processes, design for the median user, and govern political tradeoffs openly before expanding volume.

What are common reasons digital transformation fails after a pilot?

Nine common reasons include mistaking proof for readiness, lacking a durable owner, skipping work redesign, keeping old incentives, hitting data/integration debt, treating change as communications, leaving shadow processes alive, relying on pilot heroics, and avoiding the politics of winning.

What should leaders check before funding digital transformation scale-up?

Leaders should confirm ownership after the program, the human behavior that defines success, which legacy paths will be turned off, whether integration/data work is funded, and whether incentives and manager routines reinforce the new operating model.

Image credits: Google Gemini

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5 Reasons Innovation Labs Fail — and 5 Replacements That Work

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5 Reasons Innovation Labs Fail — and 5 Replacements That Work

by Braden Kelley and Chateau G Pato

Why Do Innovation Labs Fail — and What Replaces Them? (Short Answer)

Innovation labs fail for five structural reasons: they sit outside power, they score activity not adoption, they confuse a place with a capability, they have no Tuesday owner or transfer path, and they run on tourist talent and hero myths. Five replacements that work: embed bets with P&L or journey owners; judge adopted human outcomes; build capability next to real constraints (not a lounge); build-operate-transfer with a named BAU operator before you build; treat innovation as a team sport with dual recognition for insight and change.

A lab is a costume if the core cannot be changed. A lab is a vehicle if bets are designed to leave the building.

The Lab Was Never the Innovation

I have toured labs that smelled like new furniture and old fear. The wall was photogenic. The coffee was better than the core’s. Someone said, “This is where innovation happens.”

That sentence is usually the tell. Innovation that can only happen “over there” is not allowed to happen where the P&L, the policy, and the median manager live. Labs rarely fail because people lack ideas. They fail because a lab is treated as a destination — space, brand, career parking, demo theater — instead of a temporary vehicle for bets that must land in a named journey with decision rights.

The five replacements are not a nicer beanbag. They are operating-model moves.

Why labs fail	Replacement that works
1. Isolated from power	Embed bets with journey / P&L owners
2. Activity as scoreboard	Adopted outcomes in a named journey
3. Place pretends to be capability	Innovate next to real constraints
4. No path to Tuesday	Build-operate-transfer; named BAU operator
5. Tourist talent and hero inventors	Team sport; dual credit for insight and change

1. Why Do Innovation Labs Fail When They’re Isolated From Power?

Why labs fail: The lab reports to “innovation” or a brand story. It cannot change policy, incentives, staffing, or the P&L journey it claims to disrupt. The core treats it as a zoo — interesting, contained, not allowed to bite.

Why it seduces: Distance feels safe. You can look innovative without threatening the machine. Executives get a tour. The operating model keeps its week.

Replacement that works: Embed each bet next to a named journey or P&L owner with decision rights. If a lab still exists, it is a small enablement core — facilitation, methods, a shared kill board — not a parallel company that never gets to touch the real one. Mandate first. Furniture later.

2. Why Is Activity a Fatal Scoreboard for Innovation Labs?

Why labs fail: KPIs are ideas generated, prototypes shown, visitors hosted, press hits, “innovation NPS.” Nothing has to be lived by the median employee or customer. The lab can be busy and the enterprise unchanged.

Why it seduces: Activity photographs. Adoption is political and slow. A visitor log is easier to defend than a retired workaround.

Replacement that works: Score the lab — or whatever replaces it — on behavior and adoption in a named journey: workaround retired, time-to-confidence, retained revenue, cost-to-serve, a Tuesday that got easier. If it cannot name a human outcome, it is a studio. Studios are allowed. Do not call them an innovation function.

3. Why Does Treating the Lab as a Place (Not a Capability) Cause Failure?

Why labs fail: Square footage, furniture, logo walls, and guest speakers stand in for skill, mandate, and contact with reality. Innovation happens “over there.” The rest of the company is excused from practicing it. (If you have seen Lab as Lounge theater, this is that failure as an org chart.)

Why it seduces: Real estate is procurable. Capability is practice. You can open a lab in a quarter. You cannot fake judgment in a quarter.

Replacement that works: Innovate next to real constraints — customers, the front line, regulators, the ugly system of record. Measure how bets are chosen, killed, and transferred — not Instagram tours. You can keep a room. You cannot let the room be the strategy.

4. What Replacement Works When Labs Have No Path to Tuesday?

Why labs fail: Demos never industrialize. Scale is “later.” Business-as-usual never asked for the baby. Pilot purgatory with better lighting. The lab claims success at prototype; operations claims the idea wasn’t ready. Everyone is green. Nobody is responsible.

Why it seduces: Split ownership protects both sides. The lab never has to operationalize. The core never has to adopt.

Replacement that works: Before you build, name the operator who will run it in BAU, the kill-or-scale date, and what old path dies. Build-operate-transfer — or embed from day one — so the lab is a runway, not a forever home. If there is no named operator, you are not funding innovation. You are funding a demo with a lease.

5. Why Do Tourist Talent and Hero Inventors Sink Innovation Labs?

Why labs fail: Rotations of clever people who do not have to live the change. A spotlighted visionary. The people who adopt, fix, and scale stay invisible — then the idea dies on contact with the median manager.

Why it seduces: Heroes are fundable. Operators are boring. A keynote about the inventor photographs better than a quiet transfer to a team that will still be there in November.

Replacement that works: Dual recognition for insight and change. Second frontline and operations into the bet. Co-create with the people whose Tuesday must change. If they are not on the team, you are prototyping for a museum.

Should You Keep, Convert, or Close the Innovation Lab?

Before the next lab budget, run five questions. Convert if you can answer them. Close if you cannot and will not. Keep only if the lab is already a vehicle — enablement plus transfer — not a lounge:

Who can we change if we learn something uncomfortable — policy, metric, staffing, or only the slide?
What adopted outcome counts as winning — in a named journey, for a named human?
Where does this sit relative to a real constraint and a P&L — not relative to a floor plan?
Who operates it after applause — name, role, decision rights?
Who besides the inventor gets credit if it lands?

For the broader costume patterns labs often wear, see 7 Types of Innovation Theater. Before you fund the next bet the lab wants to run, use 11 Questions Before Funding Any Innovation Pilot. If the prototype “worked” and the enterprise did not, 9 Reasons Digital Transformations Stall After the Pilot is the scale anatomy. The habits that still matter when the room is gone are in 9 Habits of Human-Centered Innovators.

Don’t fund a nicer lounge. Fund a vehicle that is allowed to arrive.

Frequently Asked Questions

Why do corporate innovation labs fail?

They fail structurally more than creatively: isolation from decision rights, activity metrics instead of adoption, a place standing in for capability, no operator or transfer path into business-as-usual, and tourist talent plus hero myths. The core stays frozen while the lab performs innovation.

What should replace an innovation lab?

Replace isolation with bets embedded next to P&L or journey owners; replace activity scores with adopted human outcomes; replace the lounge with work next to real constraints; replace forever-demos with build-operate-transfer and a named BAU operator; replace hero inventors with a team sport that credits insight and change.

How do you know if an innovation lab is theater?

Theater shows up as tours, logo walls, and prototype applause with no mandate to change the core, no adopted outcome, and no named operator after the demo. If innovation can only happen in the lab, it is not allowed to happen in the business.

What is build-operate-transfer for innovation?

Build-operate-transfer means a bet is designed to leave the lab: you name who will operate it in business-as-usual, when you will scale or kill, and what old path dies — before you build. The lab is a runway. It is not a forever home for demos.

Should companies shut down their innovation labs?

Shut down a lab that is a lounge with no power, no transfer path, and no adopted outcomes. Convert one that can become a small enablement core for embedded bets. Keep one only if it is already a vehicle — mandate, operators, kill criteria — not a destination.

Image credits: Google Gemini

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Is OpenAI About to Go Bankrupt?

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LAST UPDATED: June 14, 2026 at 4:05 PM

Is OpenAI About to Go Bankrupt?

GUEST POST from Chateau G Pato

The innovation landscape is shifting, and the tremors are strongest in the artificial intelligence (AI) sector. For a moment, OpenAI felt like an impenetrable fortress, the company that cracked the code and opened the floodgates of generative AI to the world. But now, as a thought leader focused on Human-Centered Innovation, I see the classic signs of disruption: a growing competitive field, a relentless cash burn, and a core product advantage that is rapidly eroding. The question of whether OpenAI is on the brink of bankruptcy isn’t just about sensational headlines — it’s about the fundamental sustainability of a business model built on unprecedented scale and staggering cost.

The “Code Red” announcement from OpenAI, ostensibly about maintaining product quality, was a subtle but profound concession. It was an acknowledgment that the days of unchallenged superiority are over. This came as competitors like Google’s Gemini and Anthropic’s Claude are not just keeping pace, but in many key performance metrics, they are reportedly surpassing OpenAI’s flagship models. Performance parity, or even outperformance, is a killer in the technology adoption curve. When the superior tool is also dramatically cheaper, the choice for enterprises and developers — the folks who pay the real money — becomes obvious.

Update — May 2026

Since this article was first published in December 2025, the financial pressures on OpenAI have continued to evolve. The company has pursued additional fundraising rounds and its transition from a nonprofit to a for-profit structure has accelerated — a move widely interpreted as necessary to sustain its capital requirements. Meanwhile competition from Anthropic, Google DeepMind, Meta AI, and a wave of open-source models has intensified, compressing the window in which OpenAI can convert its brand leadership into durable revenue. The core question this article raises — whether OpenAI’s cost structure is sustainable at scale — remains as relevant today as when it was written.

The Inevitable Crunch: Performance and Price

The competitive pressure is coming from two key vectors: performance and cost-efficiency. While the public often focuses on benchmark scores like MMLU or coding abilities — where models like Gemini and Claude are now trading blows or pulling ahead — the real differentiator for business users is price. New models, including the China-based Deepseek, are entering the market with reported capabilities approaching the frontier models but at a fraction of the development and inference cost. Deepseek’s reportedly low development cost highlights that the efficiency of model creation is also improving outside of OpenAI’s immediate sphere.

Crucially, the open-source movement, championed by models like Meta’s Llama family, introduces a zero-cost baseline that fundamentally caps the premium OpenAI can charge. Llama, and the rapidly improving ecosystem around it, means that a good-enough, customizable, and completely free model is always an option for businesses. This open-source competition bypasses the high-cost API revenue model entirely, forcing closed-source providers to offer a quantum leap in utility to justify the expenditure. This dynamic accelerates the commoditization of foundational model technology, turning OpenAI’s once-unique selling proposition into a mere feature.

OpenAI’s models, for all their power, have been famously expensive to run — a cost that gets passed on through their API. The rise of sophisticated, cheaper alternatives — many of which employ highly efficient architectures like Mixture-of-Experts (MoE) — means the competitive edge of sheer scale is being neutralized by engineering breakthroughs in efficiency. If the next step in AI on its way to artificial general intelligence (AGI) is a choice between a 10% performance increase and a 10x cost reduction for 90% of the performance, the market will inevitably choose the latter. This is a structural pricing challenge that erodes one of OpenAI’s core revenue streams: API usage.

The Financial Chasm: Burn Rate vs. Reserves

The financial situation is where the “bankruptcy” narrative gains traction. Developing and running frontier AI models is perhaps the most capital-intensive venture in corporate history. Reports — which are often conflicting and subject to interpretation — paint a picture of a company with an astronomical cash burn rate. Estimates for annual operational and development expenses are in the billions of dollars, resulting in a net loss measured in the billions.

This reality must be contrasted with the position of their main rivals. While OpenAI is heavily reliant on Microsoft’s monumental investment — a complex deal involving cash and Azure cloud compute credits — Microsoft’s exposure is structured as a strategic infrastructure play. The real financial behemoth is Alphabet (Google), which can afford to aggressively subsidize its Gemini division almost indefinitely. Alphabet’s near-monopoly on global search engine advertising generates profits in the tens of billions of dollars every quarter. This virtually limitless reservoir of cash allows Google to cross-subsidize Gemini’s massive research, development, and inference costs, effectively enabling them to engage in a high-stakes price war that smaller, loss-making entities like OpenAI cannot truly win on a level playing field. Alphabet’s strategy is to capture market share first, using the profit engine of search to buy time and scale, a luxury OpenAI simply does not have without a continuous cash injection from a partner.

The question is not whether OpenAI has money now, but whether their revenue growth can finally eclipse their accelerating costs before their massive reserve is depleted. Their long-term financial projections, which foresee profitability and revenues in the hundreds of billions by the end of the decade, require not just growth, but a sustained, near-monopolistic capture of the new AI-driven knowledge economy. That becomes increasingly difficult when competitors are faster, cheaper, and arguably better, and have access to deeper, more sustainable profit engines for cross-subsidization.

The Future Outlook: Change or Consequence

OpenAI’s future is not doomed, but the company must initiate a rapid, human-centered transformation. The current trajectory — relying on unprecedented capital expenditure to maintain a shrinking lead in model performance — is structurally unsustainable in the face of faster, cheaper, and increasingly open-source models like Meta’s Llama. The next frontier isn’t just AGI; it’s AGI at scale, delivered efficiently and affordably.

OpenAI must pivot from a model of monolithic, expensive black-box development to one that prioritizes efficiency, modularity, and a true ecosystem approach. This means a rapid shift to MoE architectures, aggressive cost-cutting in inference, and a clear, compelling value proposition beyond just “we were first.” Human-Centered Innovation principles dictate that a company must listen to the market — and the market is shouting for price, performance, and flexibility. If OpenAI fails to execute this transformation and remains an expensive, marginal performer, its incredible cash reserves will serve only as a countdown timer to a necessary and painful restructuring.

Frequently Asked Questions (FAQ)

Is OpenAI currently profitable?
OpenAI is currently operating at a significant net loss. Its annual cash burn rate, driven by high R&D and inference costs, reportedly exceeds its annual revenue, meaning it relies heavily on its massive cash reserves and the strategic investment from Microsoft to sustain operations.
How are Gemini and Claude competing against OpenAI on cost and performance?
Competitors like Google’s Gemini and Anthropic’s Claude are achieving performance parity or superiority on key benchmarks. Furthermore, they are often cheaper to use (lower inference cost) due to more efficient architectures (like MoE) and the ability of their parent companies (Alphabet and Google) to cross-subsidize their AI divisions with enormous profits from other revenue streams, such as search engine advertising.
What was the purpose of OpenAI’s “Code Red” announcement?
The “Code Red” was an internal or public acknowledgment by OpenAI that its models were facing performance and reliability degradation in the face of intense, high-quality competition from rivals. It signaled a necessary, urgent, company-wide focus on addressing these issues to restore and maintain a technological lead.

UPDATE: Just found on X that HSBC has said that OpenAI is going to have nearly a half trillion in operating losses until 2030, per Financial Times (FT). Here is the chart of their $100 Billion in projected losses in 2029. With the success of Gemini, Claude, Deep Seek, Llama and competitors yet to emerge, the revenue piece may be overstated:

OpenAI estimated 2029 financials

Bring This Thinking to Your Next Event

Braden Kelley is a LinkedIn Top Voice, bestselling author, and innovation keynote speaker who helps organizations get to the future first and build sustainable innovation cultures.

Book Braden as a Keynote Speaker →

Image credits: Google Gemini, Financial Times

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Harnessing the Scarcity Principle: Driving Innovation through Consumer Psychology

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Harnessing the Scarcity Principle: Driving Innovation through Consumer Psychology

GUEST POST from Chateau G Pato

In today’s fast-evolving business landscape, innovation has become the lifeblood of success. In order to stay ahead of the competition, companies must continuously find new ways to captivate consumers and create lasting impact. One powerful strategy that businesses can employ is leveraging consumer psychology, specifically the scarcity principle. By understanding and harnessing this principle, companies can drive innovation and maintain a competitive edge. This article will explore the scarcity principle and its application in two compelling case studies, highlighting how it can be effectively utilized to spur innovation.

The Scarcity Principle:

The scarcity principle, rooted in human psychology, states that people perceive scarce resources as being more valuable and desirable. When a product or service is scarce or perceived as limited, it creates a sense of urgency and triggers a fear of missing out (FOMO). This psychological phenomenon drives consumers to take immediate action, leading to increased demand and a willingness to pay a premium.

Case Study 1: Apple and Limited Edition Products

Apple Inc. has mastered the art of harnessing the scarcity principle to drive innovation and maintain a fiercely dedicated consumer base. Their approach revolves around the strategic release of limited edition products. For instance, they frequently launch new iPhone models with specific color variations, available in limited quantities. This scarcity tactic generates enormous buzz and compels consumers to line up outside Apple stores, eager to get their hands on the exclusive product. By leveraging the scarcity principle, Apple continues to innovate and maintain remarkable consumer loyalty.

Case Study 2: Supreme and Streetwear Hype

Supreme, the iconic streetwear brand, has garnered a cult-like following by skillfully exploiting the scarcity principle. Their business model revolves around producing limited quantities of products and maintaining an aura of exclusivity. Supreme creates an air of frenzy through limited drops of apparel items and accessories, coupled with secretive release information. This meticulously crafted approach creates scarcity, leading to long queues outside their stores and an immediate sell-out of their products. The brand’s masterful utilization of the scarcity principle fuels innovation in every collection release.

Harnessing the Scarcity Principle for Innovation:

The scarcity principle can be harnessed beyond the release of limited edition products. Companies can tap into this psychological phenomenon to drive innovation across various aspects of their business.

1. Limited Time Offers: Implementing time-limited promotions or discounts can be an effective strategy to create a sense of urgency and drive sales. Businesses can offer exclusive deals to a limited number of customers or for a specific timeframe, leveraging scarcity to spur innovation in marketing tactics.

2. Membership Programs: Implementing a membership-based model with exclusive benefits can tap into consumers’ desire for exclusivity. By offering limited spots or restricted access to events, content, or perks, companies can foster innovation by continuously enhancing the membership experience.

Conclusion

Innovation is critical for businesses to thrive in the competitive marketplace. By understanding and harnessing the scarcity principle, companies can drive innovation through consumer psychology. The strategic application of scarcity can create a sense of urgency, trigger FOMO, and lead to increased demand and loyalty. Through case studies on Apple and Supreme, we observed how brands effectively employed the scarcity principle to maintain their competitive edge and inspire innovation. By implementing limited-time offers and membership programs, businesses can successfully leverage scarcity, fostering innovation across various facets of their operations. Embracing the scarcity principle allows companies to tap into the power of consumer psychology and take their innovation game to new heights.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pexels

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Ten Reasons Your Next Innovation Speaker Should Be Braden Kelley

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Top Ten Reasons Your Next Innovation Speaker Should Be Braden Kelley

GUEST POST from Chateau G Pato

If you’re looking for an innovation speaker who can captivate your audience and inspire them to think differently, look no further than Braden Kelley. With his wealth of knowledge and experience in the field of innovation, he’s the perfect choice to help you and your team uncover new ideas and drive meaningful change. I’ve had the privilege of being trained and inspired by Braden Kelley, and so I thought I would share with you the top ten reasons to hire him as your next innovation speaker:

1. Expertise: Braden is a renowned innovation expert, having advised countless organizations on how to embrace innovation and stay ahead of the competition. His deep understanding of the subject makes him a valuable resource for any audience.

2. Engaging Storytelling: Mr. Kelley is a natural storyteller who knows how to captivate an audience. He weaves personal anecdotes and real-world examples into his talks to make his message relatable and impactful.

3. Customized Content: Braden takes the time to understand your organization’s specific needs and challenges, tailoring his content to address them directly. This ensures that his presentations resonate with your team and provide actionable insights.

4. Action-Oriented Approach: MisterInnovation goes beyond theory and focuses on providing practical strategies and tactics that participants can immediately implement. He empowers individuals and teams to take action and start innovating right away.

5. Interactive Presentations: Braden’s talks are highly interactive, with plenty of opportunities for audience participation. Through thought-provoking exercises and group discussions, he encourages attendees to actively engage with the content and collaborate with their peers.

6. Provocative Thinking: Mr. Kelley challenges conventional ways of thinking and encourages participants to step outside their comfort zones. By provoking new perspectives and questioning the status quo, he helps spark innovation within your organization.

7. Adaptability: Braden’s flexible speaking style allows him to adjust his delivery based on the needs of the audience. Whether you have a small team or a large conference, he has the expertise to deliver a memorable and impactful presentation.

8. Diverse Industry Experience: MisterInnovation has worked with organizations across various industries, including healthcare, technology, finance, and consumer goods. His broad experience allows him to draw relevant insights for any audience, regardless of the sector.

9. Thought Leadership: Braden is a recognized thought leader in the field of innovation, contributing regularly to prominent publications and speaking at prestigious industry events. By hiring him as your speaker, you’re gaining access to cutting-edge knowledge and the latest industry trends.

10. Lasting Impact: Ultimately, Mr. Kelley’s goal is to leave a lasting impact on your organization. By challenging the status quo and pushing boundaries, he inspires teams to embrace innovation on an ongoing basis, driving continuous improvement and growth.

Braden Kelley is much more than just your average innovation speaker. With his expertise, engaging style, and thought leadership, he can help your organization unlock its full innovative potential. So, if you’re looking to inspire your team and drive meaningful change, consider hiring Braden Kelley as your next innovation speaker.

Click here to download his speaker sheet, or click this other link for testimonials, sample videos, or to Book Innovation Keynote Speaker Braden Kelley.

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