Category Archives: Change

Your Feelings Are Often Triggers That Mislead You

Your Feelings Are Often Triggers That Mislead You

GUEST POST from Greg Satell

The social psychologist Jonathan Haidt developed the metaphor of the Elephant and the Rider to describe the relationship between our emotional and cognitive brains. While the rider (representing our cognitive brain) may feel in control, it is the elephant (our emotions) that is more likely to determine which direction we will go.

That’s why it feels so good to act on our emotions. Rather than struggling with the reins to get the elephant to go where we want it to, we can just give in and race with abandon towards our destination. It’s usually not until we’ve run off a cliff that we realize that we should have exercised more restraint. By that time, it’s often too late to undo the damage.

The truth is that our brains are wired for survival, not to make rational decisions for a modern, industrialized economy. That’s why we shouldn’t blindly trust our feelings. We should see them as warning signs to proceed with caution because, while they can alert us to unseen dangers, they can also be triggers that others use to manipulate us.

The Thrill Of The Shift & Pivot

As Eric Ries explained in The Startup Way, when General Electric CEO Jeffrey Immelt wanted to implement a more entrepreneurial approach he asked Ries to help him implement “Lean Startup” methods at the company. The resulting program, called Fastworks, trained 80 coaches and launched a hundred projects in its first year. Pretty soon, Immelt was calling his company a 124 year-old startup.

A key ambition was the development of Predix, an industrial software platform. No longer would GE be a boring old manufacturing company, but would make a “pivot” to the digital age. It did not go well. During Immelt’s tenure, the company’s value would fall by 30%, while the broader maker more than doubled. Eventually the firm would collapse altogether.

Pundits love to tout the change gospel, but there’s little evidence that “pivots” are necessarily a good idea. Look at the world’s most valuable companies, Apple still makes most of its money on iPhones, Microsoft’s success is still rooted in business software, Alphabet’s profits come from search and so on. There are exceptions, of course, but most organizations become and stay successful by deepening their capabilities in a few key areas.

But that’s boring. Journalists rarely write cover stories about it. Business school professors don’t get tenure for writing case studies about how Procter & Gamble stuck with soap for more than a century or how Coke continues to make money off of sugary water. “Pivots,” on the other hand, are thrilling and fun. They get people talking. They feel good. That’s why they’re so popular.

The Eden Myth

Watch pundits on cable news or on stage at conferences and you may begin to notice a familiar pattern. They tell us that once there was a period when everything was pure and good, but then we—or the organization we work for—were corrupted in some way and cast out. So to return to the good times, we need to eliminate that corrupting influence.

This Eden myth is as old as history itself and it continues to thrive because it works so well.. We’re constantly inundated with scapegoats— the government, big business, tech giants, the “billionaire” class, immigrants, “woke” society—to blame for our fall from grace. The story feeds our anger and, much like the “thrill of the pivot,” makes us want to act.

Perhaps most importantly, the Eden myth makes us feel good. The outrage it triggers stimulates the release of the neurotransmitter dopamine which affects the pleasure centers in our brain. Our adrenal glands then begin to produce cortisol, which initiates a “fight or flight” response. Our senses get heightened. We feel motivated and alive.

Who wouldn’t want to feel like that? That’s why we can become addicted to the outrage-dopamine response machine and continually look for new opportunities to get our fix. We begin to need it and tune in every night, doom scroll on social media and seek out social connections that promote it. Ultimately, we’re going to want to act on it.

People who seek to manipulate us know all about this and design their approach to trigger an emotional response.

Creating An Echo Chamber

Once our neurons are primed and our senses are tuned to respond to specific stimuli, we will begin to frame what we experience in terms that reinforce those biases. Psychologists have found that we tend to overweight information that is most easily accessible and then look for information to confirm those early impressions and ignore evidence to the contrary.

These effects are multiplied by tribal tendencies. We form group identities easily, and groups tend to develop into echo chambers, which amplify common beliefs and minimize contrary information. We also tend to share more actively with people who agree with us and, without fear of questioning or rebuke, we are less likely to check that information for accuracy.

We are highly affected by what those around us think. In fact, a series of famous experiments first performed in the 1950’s, and confirmed many times since then, showed that we will conform to the opinions of those around us even if they are obviously wrong. More recent research has found that the effect extends to three degrees of social distance.

It’s likely that some version of this is what doomed Jeffrey Immelt at General Electric. When he took over as CEO in 2001, Silicon Valley was in a process of renewal after the dotcom crash. As the startup boom gathered steam, it captured the imagination of business journalists. He brought in Ries to “cast out” the old ways of plodding, industrial firms and surrounded himself with people who believed similar things. Everything must have felt right.

The elephant was in full control and the rider just went along—all the way off the cliff.

Don’t Believe Everything You Feel

The neuroscientist Antonio Damasio believes we encode experiences in our bodies as somatic markers and that our emotions often alert us to things that our brains aren’t aware of. Another researcher, Joseph Ledoux, had similar findings. He pointed out that our body reacts much faster than our mind, such as when we jump out of the way of an oncoming object and only seconds later realize what happened.

Nobel Laureate Daniel Kahneman suggests that we have two modes of thinking. The first is emotive, intuitive and fast. The second is rational, deliberative and slow. Our bodies evolved to make decisions quickly in life or death situations. Our rational minds came much later and don’t automatically engage. It takes effort to bring in the second system.

There are some contexts in which we should favor system one over system two. Certain professions, such as surgeons and pilots, train for years to hone their instincts so that they will be able to react quickly and appropriately in an emergency. When we have a bad feeling about a situation, we should take it seriously and proceed with caution.

However, our feelings need to be interrogated, especially in areas for which we do not have specific training or relevant expertise. We need to gain insight into what exactly our feelings are alerting us to and that requires us to engage our rational brain.

Yes, feelings should be taken seriously. They are often telling us that something is amiss. But they are much more reliable when they are alerting us to danger than when they are pushing us to overlook pertinent facts and proceed with a course of action. When we go with our gut, we need to make sure it’s not just because we had a bad lunch.

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

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8 Roles That Make or Break Enterprise Transformation

8 Roles That Make or Break Enterprise Transformation

by Braden Kelley and Chateau G Pato


Which Roles Make or Break Enterprise Transformation? (Short Answer)

Eight roles that make or break enterprise transformation: (1) Sponsor with Levers, (2) Work-Not-Deck Program Lead, (3) Line Manager as Local Change Leader, (4) BAU Receiving Owner, (5) Old-Path Kill Owner, (6) Enablement / Practice Designer, (7) Portfolio Capacity Steward, and (8) Floor Truth Partner. Soft landings staff these jobs — full-time, part-time, or embedded. Hard landings staff titles and still leave the human work vacant.

Enterprise transformation makes or breaks on ownership of the human system — not on another workstream lead with a better title.

Why Don’t Transformation Titles Guarantee These Jobs Are Done?

“Transformation Officer,” “Change Manager,” and “PMO Lead” on an org chart do not prove these eight jobs are done. Soft landings check coverage of mandate, enablement, local coaching, BAU receipt, old-path kill, capacity, and floor truth — regardless of badge.

I have watched programs hire the titles and still manage the deck while the median person’s work never changed. For the method spine behind Leadership, Management, Maintenance, and Portfolio, see 5 Steps of Human-Centered Change. For innovation’s different team-sport catalog, see 10 Innovation Roles Every Enterprise Needs — adjacent ownership, different domain.

Role Owns Break tell
1. Sponsor with Levers Budget/policy/metric/stop power Logo sponsorship; cascades only
2. Work-Not-Deck Lead Behavior + decisions in governance RAG theater; status tours
3. Line Manager Coach Local practice and permission Cascade middle; no coaching time
4. BAU Receiving Owner New way after applause “Project owns it” forever
5. Old-Path Kill Owner Dual-run and shadow death Kindness without a kill date
6. Enablement Designer Practice on real work Completions as capability
7. Capacity Steward What we stop so change can live Stacked initiatives; “and also”
8. Floor Truth Partner Contact evidence in the room Deck greener than the floor

If you cannot name who owns it, the landing already broke.

2. Why Does a Work-Not-Deck Program Lead Make or Break Transformation?

Owns: Program governance that forces decisions, behavior metrics, and tradeoffs — not slide hygiene as the product.

Costume (breaks): Transformation as a reporting factory; green decks, red work.

Make: Decision-first agendas; dual truth (system + human success); cap hours on deck production.

Tell missing: Steering is a tour; few things stop; few owners leave with a real next action. For the diagnostic pattern, see 11 Signs Your Transformation Is Managing the Deck, Not the Work.

3. How Do Line Managers Make or Break Local Change?

Owns: Local permission, coaching, practice time, and “what we stop doing here.”

Costume (breaks): Middle management as cascade amplifiers and attendance police.

Make: Managers develop judgment; protected practice; honest answers to quiet questions.

Tell missing: Town halls loud; team huddles empty of how work actually changes. The hallway questions managers must be ready for live in 10 Questions Employees Quietly Ask During Every Transformation.

4. Who Is the BAU Receiving Owner — and Why Does It Matter?

Owns: The workflow or journey in BAU — handoffs, support, reinforcement, and success behavior after the PMO leaves.

Costume (breaks): “Project owns it” forever; orphan process after cutover cake.

Make: Named receiving owner before go-live; signed transfer; reinforcement rituals.

Tell missing: Hypercare ends; relapse begins; nobody to escalate drift to. Many stalls after the pilot are vacant receiving ownership with a different name — see 9 Reasons Digital Transformations Stall After the Pilot.

5. What Does an Old-Path Kill Owner Do?

Owns: Kill-date honor for legacy process, shadow tools, and “just in case” dual paths.

Costume (breaks): Endless coexistence celebrated as kindness; immortal workarounds.

Make: Time-boxed dual-run; retirement volume tracked; incentives stop rewarding the old way.

Tell missing: New system “up”; median person still lives on the spreadsheet.

6. Why Is an Enablement / Practice Designer Different From “Change Communications”?

Owns: Practice on real work, redesigned jobs, and coaching design — not only comms and e-learning.

Costume (breaks): Completions, town halls, and awareness scores as “change management.”

Make: Time-to-confidence by role; managers as practice partners; enablement equal to the build backlog.

Tell missing: Training green; behavior flat; “resistance” blamed on character. Informed is not enabled.

7. What Does a Portfolio Capacity Steward Protect?

Owns: Organizational load — what initiatives pause, kill, or sequence so humans can succeed.

Costume (breaks): “And also” portfolio; every bet gets a workstream; capacity is someone else’s problem.

Make: Visible stop/start list; change load as a steering metric; permission to refuse new starts.

Tell missing: The quiet question “What will I stop doing?” never gets an answer.

8. Why Does Every Transformation Need a Floor Truth Partner?

Owns: Ride-alongs, verbatim jobs, adoption quality, and the story the deck cannot invent.

Costume (breaks): Sentiment pulses and status narratives substitute for contact.

Make: Every steering pack includes floor evidence; dual truth is allowed in the room.

Tell missing: RAG greener than reality; nobody has watched the median person struggle this month.

How Do You Check Role Coverage Before the Next Steering Committee?

Eight questions:

  1. Who has levers — and used them this month?
  2. Who owns decisions vs deck polish?
  3. Which line managers are coaching practice?
  4. Who receives the work in BAU?
  5. Who owns the old-path kill date?
  6. Who designs enablement as practice?
  7. What did we stop to create capacity?
  8. Where is this month’s floor truth?

Mantra: Staff the ownership. Titles without these eight jobs are just a better org chart for a hard landing.

FAQ: Roles That Make or Break Enterprise Transformation

What roles are needed for enterprise transformation?

Enterprise transformation needs a sponsor with levers, a work-not-deck program lead, line managers as local change leaders, a BAU receiving owner, an old-path kill owner, an enablement/practice designer, a portfolio capacity steward, and a floor truth partner — whether those jobs are full-time titles or embedded responsibilities.

Who owns transformation success?

Transformation success is owned across mandate (sponsor), governance of the work (program lead), local coaching (line managers), BAU receipt and reinforcement, old-path retirement, enablement as practice, portfolio capacity, and floor truth — not by a single Transformation Officer badge alone.

What is a transformation sponsor with levers?

A transformation sponsor with levers holds budget, policy, metric, and stop/start power for the change arc — not only a logo on the cascade deck. If they cannot change the system, they are a mascot.

Why do transformations fail without BAU owners?

Transformations fail without BAU owners because after hypercare the PMO leaves, reinforcement stops, relapse begins, and nobody owns the new way of working — so go-live photographs success while behavior returns to the old path.

What is the difference between a change manager and an enablement designer?

A costume “change manager” often owns communications and training completions. An enablement/practice designer owns practice on real work, redesigned jobs, coaching design, and time-to-confidence — so people can succeed, not only stay informed.

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 Google Gemini and Cursor to clean up the article, add images and create infographics.

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6 Metrics That Prove Change Worked

Beyond Go-Live Day

6 Metrics That Prove Change Worked

by Braden Kelley and Art Inteligencia


What Metrics Prove Change Worked Beyond Go-Live? (Short Answer)

Six metrics that prove change worked beyond go-live day: (1) named behavior adoption, (2) old-path / workaround retirement, (3) time-to-confidence, (4) post-hypercare relapse rate, (5) BAU reinforcement coverage, and (6) outcome movement on the funded human or business result. Completions, cake, and “system up” photograph the project. These metrics score whether the new way of working survived contact with real work.

Go-live proves the system shipped. These six metrics prove humans changed — and stayed changed.

Why Isn’t a Green Cutover Proof That Change Worked?

I have watched rooms celebrate a green cutover while the median person still lived on the spreadsheet, the shadow path stayed open “just in case,” and nobody in BAU owned reinforcement after the PMO left. Cutover is a technical event. Proof that change worked is behavioral and durable.

Anti-metrics that can inform but do not prove change worked: training completions, town-hall attendance, cutover RAG, license/login occupancy, and “awareness” pulse scores. Soft landings score maintenance and behavior — not cake.

Leaders who still treat go-live as transformation should pair this scorecard with 8 Change Myths Leaders Still Believe in 2026. For the method spine behind Maintenance, see 5 Steps of Human-Centered Change.

Metric Replaces Proves
1. Named behavior adoption Training completion %; awareness scores The success behavior is happening in BAU
2. Old-path retirement Endless dual-run celebrated as kindness The easy old way actually died
3. Time-to-confidence Speed-to-cutover; e-learning hours Median person succeeds without heroics
4. Post-hypercare relapse Hypercare ticket volume alone Behavior stuck after applause
5. BAU reinforcement coverage “Project closed”; RAG with no BAU owner Someone still owns the new way
6. Outcome movement Milestone burn-down; “launched on time” The funded human/business result moved

1. How Do You Measure Named Behavior Adoption After Go-Live?

Proves: The success behavior written into the change plan is happening in BAU — not only in training.

Replaces: Training completion % and “aware / informed” pulse scores.

Measure: One named behavior per change — for example, complete in the new system without a shadow spreadsheet, use the new handoff, or stop dual entry. Sample or use system logs. Trend after day one, day thirty, and day ninety.

Soft-landing use: If behavior is flat, diagnose design and incentives — not “resistance.” Adoption without a named behavior is theater with better attendance.

2. How Do You Prove Old-Path and Workaround Retirement?

Proves: Adoption was not optional because the shadow path stayed open forever.

Replaces: “Go-live” with dual-running celebrated as kindness with no kill date.

Measure: Kill-date honor rate; volume on the legacy process toward zero or a hard floor; count of retired workarounds. Immortal shadows turn good tools into shelfware — see 12 Adoption Mistakes That Turn Good Tools Into Shelfware.

Soft-landing use: Time-boxed coexistence with an explicit stop. Retirement is part of value, not cruelty.

3. What Is Time-to-Confidence — and Why Does It Matter?

Proves: Enablement worked — practice and redesigned work, not only broadcasts.

Replaces: Speed-to-cutover and hours of e-learning.

Measure: Time from first exposure to unsupervised successful completion — or self-reported confidence paired with observed success. Cohort by role. The median matters more than the hero cohort.

Soft-landing use: Long tails mean managers and jobs need redesign, not another cascade deck.

4. How Do You Track Post-Hypercare Relapse Rate?

Proves: Maintenance is real — the change did not relapse when the PMO left.

Replaces: Hypercare ticket volume as the only post-launch dashboard.

Measure: Return-to-old-path rate; reopened shadow tools; drop-off in new-way usage thirty to ninety days after hypercare ends. Lower is better.

Soft-landing use: Relapse triggers reinforcement, incentive checks, and owner escalation — not surprise at day ninety. Many “stalls after the pilot” are relapse with a different name; see 9 Reasons Digital Transformations Stall After the Pilot.

5. What Is BAU Reinforcement Coverage?

Proves: Change maintenance has an operator — rituals, coaching, and drift response are running.

Replaces: “Project closed” and RAG green with nobody in BAU accountable.

Measure: Named owner exists; reinforcement rituals held as planned; drift signals reviewed; percent of sites or teams with active reinforcement.

Soft-landing use: No owner = no proof. Coverage is a leading indicator of sustained adoption. Maintenance is a step in human-centered change, not a footnote after cake.

6. How Do You Measure Outcome Movement on the Funded Result?

Proves: The change delivered the human or business outcome it was sold on — effort, quality, safety, cycle time, customer or employee success — not only a new system.

Replaces: Milestone burn-down, vendor acceptance, and “we launched on time.”

Measure: Baseline → after on one primary outcome tied to the original case for change. Ranges are fine. Fake precision is not required. (Innovation portfolios need a different scoreboard — see 12 Metrics That Actually Measure Innovation Value — this metric is the change-funded result.)

Soft-landing use: Behavior without outcome invites a harder question — wrong behavior or wrong bet. Outcome without behavior invites theater and luck.

What Should You Ask in the Next Steering Review After Cutover?

Five questions before the next steering review after go-live:

  1. Which named behavior moved — and how do we know?
  2. Is the old path actually dead on a date?
  3. What is time-to-confidence by role?
  4. What is the relapse rate since hypercare ended?
  5. Who owns reinforcement in BAU — and did the funded outcome move?

Mantra: Stop proving you launched. Start proving humans changed — and stayed changed.

FAQ: Change Management Metrics Beyond Go-Live

How do you measure if change management worked?

Measure whether change management worked with behavior adoption, old-path retirement, time-to-confidence, post-hypercare relapse, BAU reinforcement coverage, and movement on the funded outcome — not training completions, town halls, or a green cutover alone.

What metrics prove change adoption?

Metrics that prove change adoption include a named success behavior trending in BAU, legacy and workaround volume falling after a kill date, and low relapse after hypercare — paired with a BAU owner still reinforcing the new way.

Why is go-live not enough to prove change worked?

Go-live proves the system shipped and cutover ran. It does not prove the median person adopted the new behavior, retired the old path, reached confidence without heroics, or sustained the change after the project team left.

What is change maintenance measurement?

Change maintenance measurement tracks whether a named BAU owner runs reinforcement rituals, reviews drift, and keeps relapse low after hypercare — so the new way of working survives when the PMO closes the project.

How do you measure behavior change after go-live?

Measure behavior change after go-live by naming one success behavior, sampling or logging it at day 1 / 30 / 90, tracking return to the old path after hypercare, and confirming time-to-confidence for the median person in each role — not awareness scores alone.

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 Google Gemini and Cursor to clean up the article, add images and create infographics.

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12 Adoption Mistakes That Turn Good Tools into Shelfware

12 Adoption Mistakes That Turn Good Tools into Shelfware

by Braden Kelley and Chateau G Pato


What Adoption Mistakes Turn Good Tools Into Shelfware? (Short Answer)

Shelfware is a tool that is licensed, launched, and often “adopted” on a dashboard — while the median person still uses the old path, a workaround, or nothing. Twelve adoption mistakes that create it: (1) job-blind purchase, (2) go-live as the finish line, (3) comms and training mistaken for enablement, (4) orphan after hypercare, (5) immortal shadow path, (6) login metrics as “adoption,” (7) tool bolted onto unreformed work, (8) incentives that punish the new way, (9) manager blackout, (10) big-bang feature dump, (11) change capacity ignored, and (12) no reinforcement after applause.

Shelfware is what you get when the organization buys a capability and funds a go-live — but never designs a new way of working humans can succeed at.

Why Isn’t the Tool Usually the Failure?

I have watched too many “successful” implementations end with renewals, green occupancy dashboards, and a quiet return to the spreadsheet. The product worked in the demo. The vendor delivered. The adoption design never did.

Soft landings for tools require treating adoption as the product — not as a training workstream bolted on after procurement. For the beliefs underneath these mistakes, see 8 Change Myths Leaders Still Believe in 2026.

Mistake Feels smart Shelfware path
1. Job-blind purchase Best-of-breed checklist No job to hire the tool for
2. Go-live as finish Clean PMO exit System up; behavior never sticks
3. Comms = enablement Visible completions Informed people keep old path
4. Orphan after hypercare Clear program RACI Drift back to workarounds
5. Immortal shadow path “Just in case” kindness Adoption stays optional
6. Login metrics Green occupancy Click theater; jobs unfinished
7. Unreformed work Faster deployment Friction in a shinier UI
8. Bad incentives Fix metrics later Rational avoidance of the tool
9. Manager blackout Fewer champions to train Daily work stays legacy
10. Feature dump Full-value launch story Overwhelm; retreat
11. No capacity Standalone business case Survival mode wins
12. No reinforcement Project “done” Relapse with renewals

1. How Does a Job-Blind Purchase Create Shelfware?

The mistake: Select the tool for a vendor demo, category checklist, or peer parity — not a struggling moment and success behavior.

Why it feels smart: Procurement is clear. “Best of breed” feels safe.

Shelfware path: People cannot hire the tool for a job they don’t recognize. Workarounds win.

Fix: One sentence before the contract: who, what stuck job, what behavior proves success.

2. Why Is Go-Live as the Finish Line an Adoption Mistake?

The mistake: Fund and celebrate go-live; treat adoption as leftover time.

Why it feels smart: Programs are dated. Vendors invoice. The PMO exits cleanly.

Shelfware path: The system is “up.” The new way of working never sticks.

Fix: Behavior metrics after day one. An owner after the PMO leaves. For the scale anatomy when pilots work and landings don’t, see 9 Reasons Digital Transformations Stall After the Pilot.

3. How Do Comms and Training Get Mistaken for Enablement?

The mistake: Cascade decks, e-learning completions, and town halls stand in for practice on real work.

Why it feels smart: Visible, reportable, leader-friendly.

Shelfware path: Informed people return to the easy old path under load.

Fix: Time-boxed practice, coaching, and permission to stop the workaround.

4. What Happens When the Tool Is Orphaned After Hypercare?

The mistake: Project and vendor own the tool until hypercare ends; then nobody owns the operating path.

Why it feels smart: Clear RACI during the program.

Shelfware path: Drift, ticket piles, quiet return to spreadsheets.

Fix: Named workflow owner and success behaviors before go-live.

5. How Does an Immortal Shadow Path Kill Adoption?

The mistake: Keep the legacy process “just in case” with no kill date.

Why it feels smart: Feels kind. Avoids political cost.

Shelfware path: Adoption stays optional. The easy path wins.

Fix: Time-boxed coexistence. Explicit retirement of the old path.

6. Why Are Login Metrics a Dangerous Definition of Adoption?

The mistake: Score success as licenses, logins, seats, or “% active users” without a behavior theory.

Why it feels smart: Dashboards look green. Vendor QBRs feel good.

Shelfware path: Click theater. Jobs still unfinished. A ghost town with traffic.

Fix: Measure completed jobs, abandoned workarounds, and time-to-confidence — not only occupancy.

7. What Happens When You Bolt a Tool Onto Unreformed Work?

The mistake: Same handoffs, approvals, and policies — shinier interface.

Why it feels smart: Faster deployment. Less politics.

Shelfware path: Friction moves into the new UI. People escape to shadow tools.

Fix: Redesign jobs and decisions first. Technology follows the human system.

8. How Do Incentives That Punish the New Way Create Shelfware?

The mistake: Leave KPIs and recognition aligned to old speed, volume, or local efficiency.

Why it feels smart: “We’ll fix metrics after adoption stabilizes.”

Shelfware path: Rational people avoid the tool that makes them look worse.

Fix: Align a few critical measures with the change, not after it.

9. Why Does Manager Blackout Turn Good Tools Into Shelfware?

The mistake: Enable end users and executives; skip managers as coaches of judgment and practice.

Why it feels smart: Fewer “change champions” to train.

Shelfware path: Daily huddles still run on the old way. The tool never becomes how work is done.

Fix: Managers as developers of the new behavior — with time and talking points that match real work.

10. How Does a Big-Bang Feature Dump Create Shelfware?

The mistake: Turn on everything day one; “full value” as a launch slogan.

Why it feels smart: Maximizes the ROI narrative. One cutover.

Shelfware path: Cognitive overload. Retreat to familiar fragments of the tool — or none.

Fix: Progressive adoption — few critical jobs first; expand when confidence is real.

11. Why Does Ignoring Change Capacity Produce Shelfware?

The mistake: Launch another platform into a portfolio already overloaded with initiatives.

Why it feels smart: Each business case stands alone.

Shelfware path: Attention is the scarce resource. The newest tool loses to survival mode.

Fix: Portfolio stop/start. Protect capacity. Refuse launch without what you will stop.

12. How Does No Reinforcement After Applause Create Shelfware?

The mistake: End interest after launch cake; no rituals, coaching, or drift response.

Why it feels smart: The project is “done.” The budget closed.

Shelfware path: Relapse with better branding. Licenses renew. Behavior doesn’t.

Fix: Post-go-live reinforcement owner. Celebrate retired workarounds. Instrument for drift. That is the maintenance key in 5 Steps of Human-Centered Change — and when AI tools densify the change, see 7 Ways AI Changes Change Management.

How Do You Run a Pre-Launch Adoption Audit Before the Next Go-Live?

Before the next tool go-live, run five go/no-go questions:

  1. What human job and success behavior did we buy this for?
  2. Who owns BAU after hypercare?
  3. What old path dies on a date?
  4. Which metric could show green while the job still fails?
  5. Who coaches managers — and what will we stop so people have capacity to learn?

Stop launching tools. Start launching new ways of working — or expect shelfware with a ribbon.

Frequently Asked Questions

What is shelfware?

Shelfware is a tool that is licensed, launched, and often marked “adopted” on a dashboard while the median person still uses the old path, a workaround, or nothing. The product may work. The new way of working was never designed or owned.

Why do employees not adopt new software?

Employees often don’t adopt new software because the job was never named, enablement was only training, the old path stayed easy, incentives punished the new way, managers weren’t coached, features overwhelmed, or nobody owned reinforcement after go-live. Much “resistance” is rational response to bad adoption design.

How do you prevent shelfware?

Prevent shelfware by buying for a named human job, measuring behavior after go-live, enabling practice not only completions, naming a BAU owner, killing the old path on a date, redesigning work and incentives with the tool, coaching managers, rolling out progressively, protecting change capacity, and funding reinforcement after applause.

What are common software adoption mistakes?

Common software adoption mistakes include job-blind purchase, treating go-live as the finish line, mistaking comms for enablement, orphaning the tool after hypercare, leaving shadow paths forever, scoring logins as adoption, bolting tools onto unreformed work, misaligned incentives, skipping managers, big-bang feature dumps, ignoring portfolio capacity, and skipping reinforcement.

How do you measure tool adoption beyond logins?

Measure tool adoption beyond logins with completed jobs, abandoned workarounds, time-to-confidence, repeat use on critical tasks, and whether the old path is actually retired — outcomes that show the median person succeeded, not only that seats were occupied.

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 Google Gemini and Cursor to clean up the article, add images and create infographics.

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7 Ways AI Changes Change Management

(Without Replacing It)

7 Ways AI Changes Change Management

by Braden Kelley and Art Inteligencia


How Does AI Change Change Management Without Replacing It? (Short Answer)

Seven ways AI changes change management without replacing it: (1) sensing replaces guessing, (2) enablement can become personal, (3) rehearsal before cutover, (4) portfolio load becomes visible, (5) message volume stops being the bottleneck, (6) AI densifies the change — roles and decision rights — and (7) maintenance gets instrumented. AI drafts, clusters, simulates, and surfaces patterns at machine speed. Humans still own mandate, dignity, practice design, kill dates for the old path, and stop/start honesty.

AI can speed the artifacts of change. It cannot own the human adoption challenge — and pretending it can is how organizations buy a harder landing.

Why Doesn’t Faster Change Artifact Work Mean Less Change Management?

I keep hearing the same executive shortcut: the tools feel intuitive, the demo looks easy, so we can “do less change management this time.” That is how you buy denser busywork with a friendlier UI.

AI accelerates sensing, drafting, personalization, simulation, and portfolio visibility. That does not shrink the need for change management. It densifies the work: more roles rewritten, more decision rights in play, more temptation to confuse message volume with enablement. Soft landings treat AI as a change amplifier designed with humans — not as a substitute for sponsorship, practice, maintenance, and portfolio honesty.

Way What AI changes Still human
1. Sensing Earlier resistance and confusion patterns Meaning, dignity, and who can act
2. Personal enablement Role- and journey-aware learning paths Practice, coaching, redesigned work
3. Rehearsal Cheaper scenario practice before go-live Success behavior and sponsors with levers
4. Portfolio visibility Collision maps and capacity signals Stop/start honesty and tradeoffs
5. Message volume Nearly free cascade drafts Capacity — enablement, not broadcast
6. Densified change Rewritten tasks and decision rights Job redesign and accountable judgment
7. Instrumented maintenance Drift and relapse sensing after applause BAU ownership and old-path kill dates

1. How Does Sensing Replace Guessing — Without Replacing Judgment?

What AI changes: Continuous signal from tickets, chat, surveys, usage, and search — resistance, confusion, and workaround patterns can surface earlier than a quarterly pulse.

Costume temptation: Dashboard theater. “Sentiment” as a green RAG without contact or a response anyone will fund.

Still human: Meaning, stakes, and dignity — deciding what the pattern asks of leaders, not only that a score moved.

Human-centered move: Pair sensing with a named owner who can change work, policy, or enablement — not only refresh a slide.

2. Can Enablement Become Personal While Capability Still Needs Practice?

What AI changes: Role-, journey-, and skill-aware learning paths; adaptive practice prompts instead of one cascade deck for thousands.

Costume temptation: Completions and prompt workshops mistaken for competence. Personalized spam that still leaves the old path easy.

Still human: Coaching, redesigned work, permission to stop the workaround, manager judgment.

Human-centered move: Treat personalization as a path to practice on real work — measure behavior, not module finishes. That is the enablement half of change management in 5 Steps of Human-Centered Change — sped up by AI, not retired by it.

3. Why Is Rehearsal Before Cutover Not the Same as Sponsorship?

What AI changes: Scenario practice, agent-assisted dry runs, and “what if” walkthroughs before go-live — cheaper rehearsal at scale.

Costume temptation: Beautiful simulations that never touch floor constraints. Rehearsal theater while sponsors lack levers.

Still human: Defining success behavior, psychological safety to fail in practice, and sponsors who can move budget, policy, and metrics.

Human-centered move: Rehearse the median user’s moment of truth — then fund the system changes the rehearsal exposed. Simulation without levers is still a costume.

4. How Does Portfolio Visibility Change Change Management?

What AI changes: Collision maps, capacity estimates, and initiative overlap become easier to surface across the change portfolio.

Costume temptation: Pretty heatmaps while every pet project remains “priority.” Visibility without stop authority.

Still human: Portfolio honesty — stop/start rules, protecting frontline capacity, naming what the organization will not absorb.

Human-centered move: Treat AI portfolio views as evidence for tradeoffs, not as permission to stack more change. Seeing the load is not the same as choosing what to stop.

5. When Message Volume Stops Being the Bottleneck, What Becomes the Test?

What AI changes: Drafting cascade emails, FAQs, talking points, and “personalized” announcements is nearly free.

Costume temptation: More communication mistaken for more change. AI as an infinite broadcast engine.

Still human: Capacity — time, coaching, redesigned incentives, retirement of the old way.

Human-centered move: Raise the bar. If enablement is not funded, do not celebrate message velocity. Informed is still not enabled — a truth that survived every new channel, including this one. For the belief patterns underneath the costume, see 8 Change Myths Leaders Still Believe in 2026.

6. How Does AI Densify Change — Roles, Judgment, and Decision Rights?

What AI changes: Introducing AI rewrites tasks, who decides, who is accountable, and where human judgment must sit. That is denser change than “a new tool.”

Costume temptation: “Intuitive agents adopt themselves.” Change-management budgets cut because the vendor demo felt easy.

Still human: Job redesign, accountability for model-assisted decisions, deep-work policy, and recovery power when the agent fails.

Human-centered move: Package every material AI bet with work redesign and change muscle — not a go-live date alone. For the designed split of cognitive labor, see The AI Soft Landing and 11 Human Endeavors AI Should Free (Not Replace).

7. How Does Instrumented Maintenance Still Need Human Owners?

What AI changes: Post-go-live drift detection — usage drop-off, shadow-process return, help-desk themes — can be sensed continuously after applause.

Costume temptation: Hypercare dashboards with nobody in BAU responsible for reinforcement or killing the old path.

Still human: Maintenance ownership, celebration of retired workarounds, and metric realignment that makes the new way the easy way.

Human-centered move: Instrument for relapse and name the BAU owner before cutover cake. Launch without maintenance is still a relapse with better branding. The full visual method behind planning, enablement, and maintenance lives in Charting Change.

How Do You Split Change Labor Between AI and Humans Before the Next Review?

Before the next AI-assisted transformation review, run five go/no-go questions:

  1. What are we sensing — and who can act on it?
  2. Are we personalizing practice or only broadcasting faster?
  3. What did rehearsal force us to redesign?
  4. What will we stop so portfolio load is real?
  5. Who owns reinforcement after go-live — and what old path dies on a date?

AI changes the speed of change artifacts. Humans still own whether the median person can succeed — and whether the organization will let them.

Frequently Asked Questions

Does AI replace change management?

No. AI can accelerate sensing, drafting, personalization, simulation, and portfolio visibility, but it cannot own mandate, dignity, practice design, sponsorship with levers, or stop/start honesty. Treating AI as a substitute for change management usually densifies role and decision-rights change while starving enablement.

How does AI change change management?

AI changes change management by making sensing continuous, enablement more personalizable, rehearsal cheaper before cutover, portfolio collisions more visible, broadcast nearly free, AI itself a denser work redesign, and post-go-live drift easier to instrument. The human job shifts toward judgment, redesign, and ownership — not away from change management.

Why do organizations still need change management with AI?

Organizations still need change management with AI because introducing models rewrites tasks, accountability, and judgment seams. People still need practice, permission to stop workarounds, aligned metrics, sponsors who can change the system, and BAU owners after go-live. Intuitive demos do not equal adopted behavior.

What is AI-assisted change management?

AI-assisted change management uses models to draft communications, cluster employee signals, personalize learning paths, simulate scenarios, and surface portfolio load — while humans own interpretation, enablement design, tradeoffs, sponsorship, and reinforcement. AI speeds the artifacts; humans own the adoption challenge.

How do you soft-land an AI transformation?

Soft-land an AI transformation by pairing every material AI bet with work redesign and change muscle: named owners who can act on signals, practice over completions, rehearsal that funds redesign, portfolio stop/start honesty, enablement funded beyond broadcast, clear decision rights for model-assisted work, and instrumented maintenance with a kill date for the old path.

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 Google Gemini and Cursor to clean up the article, add images and create infographics.

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10 Questions Employees Quietly Ask During Every Transformation

10 Questions Employees Quietly Ask During Every Transformation

by Braden Kelley and Chateau G Pato


What Questions Do Employees Quietly Ask During Transformation? (Short Answer)

During every transformation, employees quietly ask ten questions: Will my job still exist — and in what form? What will I stop doing? Who actually has the power to change this? Is this real, or another initiative costume? What happens if I speak up? Will I get time and practice — or a mandatory video? Who loses if this succeeds? How will you measure us when the old metrics still run promotions? What happens after go-live when things break? Why should I believe you this time? Silence is not buy-in. It is risk management.

People don’t withhold questions because they lack curiosity. They withhold them when honesty has a career cost.

Why Is the Hallway the Real Town Hall?

I have watched town halls end in applause and hallways begin in whispers. Official questions go on sticky notes. Real questions travel in chats, parking lots, and the careful silence of people who have learned what gets rewarded.

Leaders who treat quiet questions as “resistance” miss the brief. These ten questions are how employees test whether the transformation is a new way of working — or another costume with better branding. Soft landings are designed. Answering these questions honestly is part of the design.

Quiet question Non-answer signals Better answer
1. Will my job still exist? Vague “higher-value work” Named roles, timelines, transition paths
2. What will I stop doing? “In addition to your day job” Explicit stop/start list
3. Who has the power? “Everyone owns change” Named decision rights
4. Is this real? Branding louder than the work Dated changes to tools, handoffs, authority
5. If I speak up? Open-door theater Protected dissent; visible learning from bad news
6. Time and practice? Completions as capability Practice time, coaching, behavior measures
7. Who loses? Fake win-win Honest tradeoffs, managed in the open
8. How will you measure us? New KPIs; old reviews Retired metrics; dual scorecard for managers
9. After go-live breaks? Cutover party; no owners Hypercare, escalation, undo, accountability
10. Why believe this time? A better slogan Receipts and verifiable governance changes

1. Will My Job Still Exist — and in What Form?

Why they ask quietly: Fear of looking “not a team player.” Rumors fill every vacuum leaders leave.

Non-answer signal: Vague promises that AI or the new platform will “free you for higher-value work” with no role redesign.

Human-centered answer: Named role changes, timelines, and transition paths — including who is protected, retrained, or exited with dignity. Ambiguity is not kindness. It is fuel for fear.

2. What Will I Stop Doing?

Why they ask quietly: Initiatives already stack. Capacity is already gone. Another “and” feels like contempt for their calendar.

Non-answer signal: “In addition to your day job.”

Human-centered answer: An explicit stop/start list. What dies so the new way can live. If nothing stops, nothing new is real — it is only additional load wearing a transformation badge.

3. Who Actually Has the Power to Change This?

Why they ask quietly: They have watched sponsors clap while middle managers freeze the work. Applause is not authority.

Non-answer signal: “Everyone owns change.”

Human-centered answer: Named decision rights — who can change policy, incentives, staffing, and the system of record. Methods without power are cosplay. For leader myths that hide this gap, see 8 Change Myths Leaders Still Believe in 2026.

4. Is This Real, or Another Initiative Costume?

Why they ask quietly: Change fatigue. They have seen logo launches and quiet deaths.

Non-answer signal: Branding louder than operating-model moves.

Human-centered answer: What will look different in the work within a dated window — tools, handoffs, authority — not only in the deck. If the only new thing is the name, employees already know. For governance that optimizes slides over work, see 11 Signs Your Transformation Is Managing the Deck, Not the Work.

5. What Happens If I Speak Up — Especially About What’s Broken?

Why they ask quietly: Psychological safety is uneven. Messenger-shooting is remembered longer than the kickoff video.

Non-answer signal: “Open door” theater with no visible reward for bad news.

Human-centered answer: How dissent is protected. Examples of changes made because someone told the truth. If honesty is career-expensive, the hallway will always be smarter than the town hall.

6. Will I Get Time and Practice — or a Mandatory Video and a Quiz?

Why they ask quietly: Training that does not build skill feels like compliance. Completions do not make people competent.

Non-answer signal: Completions as the capability metric.

Human-centered answer: Contiguous practice time, coaching, and permission to be slow while learning — measured by behavior, not video minutes. Enablement is practice. A quiz is a receipt.

7. Who Loses If This Succeeds — and Are We Allowed to Say So?

Why they ask quietly: Power, status, and shadow processes have winners today. Pretending otherwise sends politics underground.

Non-answer signal: “Win-win for everyone.”

Human-centered answer: Honest tradeoffs. Who loses status or control. How conflict will be managed in the open. Transformations that cannot name losers cannot manage them.

8. How Will You Measure Us When the Old Metrics Still Run Promotions?

Why they ask quietly: Employees know what actually gets rewarded. They watch managers, not posters.

Non-answer signal: New KPIs on the wall; old scorecards in the review.

Human-centered answer: Which metrics retire. What the dual scorecard is. What managers will be judged on after go-live. If promotion still runs on the old path, the old path wins.

9. What Happens After Go-Live When Things Break?

Why they ask quietly: Go-live often ends sponsorship. The frontline inherits the mess while the program celebrates cutover.

Non-answer signal: Cutover party; no recovery owners.

Human-centered answer: Named hypercare, escalation paths, undo options, and who stays accountable when the cake is gone. Adoption begins after go-live — it does not end there. For why pilots stall when operating readiness is missing, see 9 Reasons Digital Transformations Stall After the Pilot.

10. Why Should I Believe You This Time?

Why they ask quietly: Trust is a ledger. Prior transformations overdrew it. A better slogan does not reset the balance.

Non-answer signal: A bigger kickoff; a fresher narrative.

Human-centered answer: Receipts — what you stopped, funded, and kept promising last time; what is different in governance this time; how employees will verify progress without waiting for another town hall. Belief is earned in the work. For a method path that survives contact with reality, read 5 Steps of Human-Centered Change.

How Do Leaders Check Quiet Questions Before the Next Town Hall?

Before the next town hall, run five questions. If you cannot answer them in writing, you are asking employees to trust branding:

  1. Which of the ten have we answered — with names and dates, not slogans?
  2. Where are we still asking people to trust branding instead of operating-model proof?
  3. What stop list accompanies the start list?
  4. Who is safe to bring bad news — and how would employees know?
  5. How will employees verify that old metrics and old paths actually retired?

They’re not resisting. They’re waiting for answers that make honesty safer than silence.

Frequently Asked Questions

What questions do employees ask during transformation?

Employees quietly ask whether their job will exist and in what form, what they will stop doing, who has real power to change the system, whether the initiative is real, what happens if they speak up, whether they will get practice time, who loses if the change succeeds, how they will be measured, what happens after go-live when things break, and why they should believe leaders this time.

Why don’t employees speak up during change?

They often stay quiet when honesty has a career cost — uneven psychological safety, messenger-shooting, and open-door theater with no visible reward for bad news. Silence is usually risk management, not a lack of curiosity or buy-in.

How should leaders answer employee concerns about transformation?

Answer with names, dates, and operating-model proof: role paths, stop/start lists, decision rights, dated changes to work, protected dissent, practice time, honest tradeoffs, retired metrics, post-go-live recovery owners, and receipts that rebuild trust. Avoid slogans that leave the hallway smarter than the town hall.

What is change fatigue?

Change fatigue is the exhaustion that comes from stacked initiatives, branding louder than work redesign, and repeated launches that quietly die. Employees ask “is this real?” because they have lived costume transformations before — and capacity is already gone.

How do you rebuild trust during transformation?

Rebuild trust with receipts, not kickoffs: show what you stopped and funded, change governance so employees can verify progress, protect people who bring bad news, retire old metrics that contradict the new story, and keep sponsorship after go-live when things break. Trust is a ledger. Slogans do not reset it.

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 Google Gemini and Cursor to clean up the article, add images and create infographics.

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

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:

  1. What evidence from the floor is in this pack — not only from the plan?
  2. What decision will we make today — choose, kill, fund, or unblock?
  3. What behavior changed since last month for the median person?
  4. What will we stop — not only mitigate on a register?
  5. 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

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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Three Myths That Kill Change and Transformation

Three Myths That Kill Change and Transformation

GUEST POST from Greg Satell

In 1975, more than 80% of US corporate assets were tangible assets, things like factories, equipment and real estate. When leaders in an organization made decisions about change, they tended to involve tangible, strategic assets, such as building a new factory, entering a new market or launching a new line of products.

So when the modern practice of change management arose in the 1980s, that’s what it was designed to address. Managers began to recognize the need to communicate changes to the rank and file, so that they could better understand it and contribute to its success. An entire cottage industry of consultants arose to fill that need.

But now that situation has flipped and more than 80% of corporate assets are intangible. When we talk about change today we are usually talking about changes in people themselves, in how they think and how they act. Clearly, that’s a very different type of thing and we need to approach change differently. Unfortunately, too many people are mired in the past.

Myth #1: If People Understand Change, They Will Embrace It

Leaders like to be seen at the cutting edge and, to be effective, they need to believe in themselves. That’s what makes transformational initiatives so attractive. They’re much more fun than the more mundane aspects of managing an enterprise, like improving operations or cutting costs. Change gives leaders a chance to dream.

That’s what the practice of change management was designed to support. Someone high up in an organization would get an idea to, say, launch a new product line for a new market and the consultants would be brought in to help communicate the idea so that everyone could understand just how brilliant the idea was.

Of course, even if employees thought the idea was stupid there wasn’t much they could do about it. If a CEO wants to launch a new product line, invest in new factories and equipment and hire new people, there’s nothing the rank and file can do about it. Leadership has full control over tangible, strategic assets.

But today, when the vast majority of corporate assets are intangible, transformation initiatives involve changes in how people think and what they do, which leadership does not control. People have the power to resist and you can be sure they will. That’s why change fails, not because people don’t understand it, but because they don’t like it and actively sabotage it.

The truth is that humans form attachments to other people, ideas and things. When they feel those attachments are threatened, they will often lash out. That’s why when you ask people to change how they think or what they do, you will invariably offend some people’s identity, dignity and sense of self and they will act out in ways that are dishonest, underhanded and deceptive. That doesn’t make them bad people—we all do it—it just makes them human.

Myth #2: You Have To Convince The Skeptics

There is something baffling about human nature. Whenever we have an idea we are passionately about we feel intense desire to convince skeptics. Our inner marketers want to identify specific objections and then devise airtight arguments to counter them. We envision ourselves being dazzlingly persuasive and making our case.

Change management consultants encourage this type of thinking. They advise us to “provide simple, clear choices and consequences” and “show the benefits in a real and tangible way.” They also suggest that we have “open and honest conversations” and “even make a personal appeal” in order to “convert the strongest dissenters.”

This may make sense if the objections are rational, but often they are not. In fact, the most visceral dissent almost invariably has more to do with how people see themselves. That’s why change so often offends people’s dignity, because their identity is so often wrapped up in what they think and what they do. You can’t ask people to stop being who they think they are.

The good news is that you don’t have to. Consider the scientific evidence:

  • Sociologist Everett Rogers‘ “S-curve” research estimated that it takes only 10%-20% of a system to adopt an innovation for rapid acceptance by the majority to follow.
  • Professor Erica Chenoweth’s analysis of over 300 political revolutions in the past century finds that it only took 3.5% of active participation in a society to succeed, and many campaigns prevailed with less.
  • Recent research by sociologist Damon Centola at the University of Pennsylvania suggests that the tipping point for change is getting 25% of people in an organization on board.

There’s no need to waste time trying to convince people who hate your idea and want to undermine it in any way they can. Any engagement is very unlikely to be successful and very likely to frustrate and exhaust you. You are much better off focusing your energies on empowering those who are enthusiastic about change to succeed, so that they can bring in others who can bring in others still. That’s how you build traction.

Myth #3: Things Will Get Easier After A “Quick & Easy” Win

Change management pioneer John Kotter, who first started writing books about organizational transformation in the 1970s, has long advised to establish short-term wins. He stressed that these must be unambiguously successful, visible throughout the organization and clearly related to the change effort.

The concept is problematic for a number of reasons. First, and this isn’t really Kotter’s fault, but the idea of a “short-term win” is often understood to be a “quick and easy win,” which can backfire. If a change isn’t meaningful and relevant, then touting it can make a leader seem out of touch, discrediting the transformation effort.

More problematic is the idea that we should be shooting for projects that are unambiguously successful. That level of success is exceedingly rare. If we are going to wait for perfect projects, we may be waiting a long time. What we want to do is start with a Keystone Change and then learn from whatever successes and failures we encounter on the way.

Perhaps most dangerous of all is the notion that early projects should be visible to large numbers of people. Remember, if a change is significant and has the potential for impact, there will always be people who want to undermine it in ways that are dishonest, underhanded and deceptive. Why would we want to broadcast early efforts so they can knock them down?

The truth is that things don’t get easier after initial successes. They often get harder because those who oppose change now see it is really possible. That’s why you need to build a plan to anticipate resistance and Survive Victory from the start.

Change for the World We Live In

In the early 20th century, the great sociologist Max Weber noted that the sweeping industrialization taking place would lead to a change in organization. As cottage industries were replaced by large enterprises, leadership would have to become less traditional and charismatic and more organized and rational.

He also foresaw that jobs would need to be broken down into small, specific tasks and be governed by a system of hierarchy, authority and responsibility. This would require a more formal mode of organization—a bureaucracy—in which roles and responsibilities were clearly defined. Weber’s model reigned for a full century.

Over the past few decades we’ve undergone a similar shift from bureaucratic hierarchies to connected ecosystems and that affects how we need to approach transformation. The changes we need to implement today have less to do with decisions made about strategic, tangible assets and more to do with how people think and act. That presents a very different set of challenges and we need to adapt.

What we can’t do is pretend that the world is the same as it was 30 or 40 years ago and continue with practices that are so obviously failing. Just as Weber dispelled myths about infallible leaders a century ago, we need to break free of outdated concepts that have led to unacceptably poor results.

It’s time to leave myths behind and take a more clear-eyed approach to leading change.

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

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8 Change Myths Leaders Still Believe in 2026

8 Change Myths Leaders Still Believe in 2026

by Braden Kelley and Art Inteligencia


What Change Myths Do Leaders Still Believe in 2026? (Short Answer)

In 2026, leaders still believe eight change myths: that a compelling story is enough, that resistance means the people are the problem, that go-live equals transformation, that AI reduces the need for change management, that you can run the new way and the old way until people are ready, that training equals capability, that incentives can wait, and that you can transform without anyone losing. Each myth feels efficient. Each one underwrites a hard landing — stalled adoption, burnt-out employees, and technology that never becomes a new way of working.

Human-centered change replaces those beliefs with enablement, work redesign, post-go-live ownership, job and decision-rights redesign for AI, explicit kill dates for shadow processes, practice over course completions, aligned metrics from day one, and honest tradeoffs.

New Tools, Old Myths

2026 did not retire the old change myths. It gave them better costumes. We now have agents, copilots, and “transformation offices” — and still the same comforting stories: If we message it well, they will come. If they don’t, they are resistant. If we went live, we transformed. If we bought AI, we need less change work.

Those stories survive because they are leader-friendly. Communication is visible. Blaming people is cheaper than redesigning power. Go-live is a date finance can calendar. Training is procurable. Dual systems feel kind. Leaving incentives untouched feels like peace. Pretending nobody loses feels like culture.

Humans still pay the invoice. Below are eight myths I still hear in rooms that should know better — why they feel true, and what to do instead.

Myth Human-centered truth What to do instead
1. Comms = change Informed ≠ enabled Fund practice, time, coaching
2. Resistance = people Often rational design failure Diagnose work and power first
3. Go-live = transformed Cutover is a technical event Measure behavior after day one
4. AI = less change work AI changes more roles and rights Redesign jobs and accountability
5. Dual systems until ready Shadow path makes adoption optional Time-box and kill the old way
6. Training = capability Completions ≠ competence Practice on real work; enable managers
7. Fix KPIs later Old metrics punish new behavior now Align a few measures with the change
8. Nobody has to lose Real change reallocates power Name tradeoffs in the open

1. “If We Communicate Well Enough, People Will Change”

The myth: Town halls, videos, cascade decks, and a stirring CEO note are change management.

Why it still feels true in 2026: Communication is visible, relatively cheap, and easy to put on a leader’s calendar. You can screenshot the town hall. You cannot screenshot someone’s third Tuesday trying to do the new job with the old incentives.

The truth: People were informed. They were not enabled. Capacity is practice, time, coaching, tools that work, and permission to stop the workaround. A perfect cascade into an impossible workflow is still an impossible workflow — now with better branding.

What to do instead: Treat communication as necessary and insufficient. Fund change muscle: champions, spaced practice, manager enablement, and protected learning time. If the only thing you can point to is “we told them,” you have a broadcast, not a transformation.

2. “Resistance Means the People Are the Problem”

The myth: Pushback is mindset, culture, or “they’re not ready.”

Why it still feels true in 2026: Blaming people is easier than redesigning handoffs, decision rights, staffing, or a tool that punishes the median user. “Change fatigue” becomes a character flaw instead of a design diagnosis.

The truth: Much of what we call resistance is rational. The new path takes longer. The workaround still works. Nobody can undo a bad system decision. Frontline staff are accountable for outcomes they lack authority to deliver. That is not a TED Talk problem. That is a work-design problem.

What to do instead: Diagnose design before you diagnose character. Ask what the current system rewards, what still gets blamed, and whether a competent person could succeed without heroics. Then fix that. Culture change that ignores power is a pep talk.

3. “Go-Live Means We Transformed”

The myth: Cutover day is the finish line. The program was green. Therefore the organization changed.

Why it still feels true in 2026: Transformations are still funded to a date. Vendors get paid at go-live. Steering committees want to declare victory. Adoption is someone else’s Tuesday.

The truth: Go-live is a technical event. Transformation is a new way of working that survives contact with the median user and the median manager — after the PMO leaves, after the hypercare slack channel goes quiet, after the old spreadsheet still sits on the shared drive.

What to do instead: Name the behavior that must change after day one. Keep a durable workflow owner. Measure usage quality, effort, and retired workarounds — not only whether the system was available. A green cutover with a red experience is not a win. It is a stall with cake.

4. “AI Means We Need Less Change Management”

The myth: Intuitive tools adopt themselves. Agents replace the messy human work of change. Buy the copilot; skip the operating-model conversation.

Why it still feels true in 2026: Vendor narratives and executive impatience. “It’s just ChatGPT for work” sounds cheaper than redesigning jobs, incentives, and decision rights.

The truth: AI changes more work, not less — roles, accountability, what counts as a good day, who is allowed to decide. Unmanaged AI is denser busywork or humans as rubber stamps. A soft landing automates glue and protects judgment. A hard landing is speed without a human contract.

What to do instead: Put job redesign, deep-work policy, escalation paths, and “what stays human-accountable” in the same business case as the model. If your AI investment has no change plan, you do not have an AI strategy. You have a procurement.

5. “We Can Run the New Way and the Old Way Until People Are Ready”

The myth: Dual systems are kindness. Keep the spreadsheet, the side channel, the legacy path “just in case.”

Why it still feels true in 2026: Turning something off has political cost. Coexistence looks like empathy. It is often avoidance with a humanitarian caption.

The truth: Shadow process makes adoption optional. The easy path stays the legacy path. You do not have one transformation. You have two operating systems and a workforce paying the switching tax every day.

What to do instead: Time-box coexistence. Publish an explicit kill date or trigger for the old way. Make the new way the only easy way — with support, not with a surprise lockout. If you cannot name when the workaround dies, you have not finished the design of change. You have postponed it.

6. “Training Equals Capability”

The myth: A course, a certification, a prompt workshop, or a learning-path completion rate creates the new operating system.

Why it still feels true in 2026: Training is procurable, schedulable, and reportable. Dashboards love completions. Boards can be shown a number.

The truth: Capability is spaced practice on real work, manager coaching, and a job designed so the new way is easier than the old one. Counting who finished the module is not the same as counting who can do the Tuesday without a hero.

What to do instead: Enable managers first. Practice in the workflow. Stop treating attendance as competence. If people pass the course and still run the shadow path, the course was not the intervention you needed.

7. “We’ll Fix Incentives After Adoption Stabilizes”

The myth: Don’t touch KPIs, bonuses, or scorecards until the dust settles. Metric fights feel like a second transformation.

Why it still feels true in 2026: Changing measures is politically expensive. Leaders hope behavior will magically precede the scorecard.

The truth: People do what still gets measured. Old metrics punish the new behavior in real time — speed-to-close over quality, tickets over resolution, utilization over judgment, containment over trust. Waiting to “stabilize” is how you train the organization to fake adoption while hitting last year’s numbers.

What to do instead: Align a few critical measures with the change, not after it. You do not need a perfect metrics overhaul. You need the handful of incentives that currently make the new way a career risk.

8. “We Can Transform Without Anyone Losing”

The myth: If we message it well enough, change can be all-upside. Harmony is available if we choose the right narrative.

Why it still feels true in 2026: Leaders want a clean story. Vendors want no villains. Culture decks want “win-win.” Nobody wants to say out loud that a pet process, a turf boundary, or a status role will shrink.

The truth: Real change reallocates power, attention, vendors, and status. Pretending otherwise produces symbolic change — principles without tradeoffs, maps without owners, AI without decision rights. Transformation that cannot name a loser cannot name a choice.

What to do instead: Surface winners and losers early. Govern tradeoffs in the open. Dignity for people is non-negotiable; immunity for obsolete operating models is not. Human-centered change is honest about cost so it can be fair about support.

How Do You Stop Believing Change Myths in 2026?

Before the next transformation review, put these five questions on the table. If you cannot answer them, you are still managing a myth:

  1. What did we enable — not only announce?
  2. What “resistance” is actually design — work, power, tools, time?
  3. What behavior after go-live counts as success for the median person?
  4. What old path and old metric still run the place?
  5. Who loses if this works — and have we said so, with a plan for the humans caught in the tradeoff?

Myths make change look cheaper. Humans still pay the invoice. In 2026, the leaders who land change will not be the ones with the best launch video. They will be the ones who stop confusing a story about change with the human system that has to live it on a normal Tuesday.

Frequently Asked Questions

What are common change management myths in 2026?

Eight myths still circulating: communication equals change; resistance means people are the problem; go-live equals transformation; AI reduces the need for change management; dual systems can run until people are ready; training equals capability; incentives can wait; and you can transform without anyone losing.

Does AI reduce the need for change management?

No. AI typically increases the need for change work because it alters jobs, decision rights, and what counts as a good day’s work. Tools do not adopt themselves. Unmanaged AI often creates denser busywork or humans rubber-stamping the model. A soft landing redesigns accountability and protects human judgment while automating glue work.

Why do transformations fail even with good communication?

Because being informed is not the same as being able to succeed. If the workflow, incentives, staffing, and permission structures still reward the old way, a perfect cascade will not produce adoption. Communication is necessary; enablement, work redesign, and killing shadow processes are what make change real.

Is employee resistance the main reason change fails?

Often no. Much so-called resistance is a rational response to bad design: tools that don’t fit the job, metrics that punish the new behavior, no authority to recover, and legacy paths that remain easier. Diagnose the system before you diagnose the people.

Does go-live mean a digital transformation succeeded?

No. Go-live is a technical cutover. Success is a new way of working that the median employee can run after the program team leaves — measured in behavior, retired workarounds, and aligned incentives, not only system availability.

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.

Image credits: Pixabay

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We Must Stop Fooling Ourselves and Get Our Facts Straight

We Must Stop Fooling Ourselves and Get Our Facts Straight

GUEST POST from Greg Satell

Mehdi Hasan’s brutal takedown of Matt Taibbi was almost painful to watch. Taibbi, a longtime muckraking journalist of some renown, was invited by Elon Musk to review internal communications that came to be known as the Twitter Files and made big headlines with accusations regarding government censorship of social media.

Yet as Hasan quickly revealed, Taibbi got basic facts wrong, either not understanding what he was looking at, doing sloppy work or just plainly being disingenuous. What Taibbi was reporting as censorship was, in fact, a normal, deliberative process for flagging problematic content, most of which was not taken down.

He looked foolish, but I could feel his pain. In both of my books, I had similarly foolish errors. The difference was that I sent out sections to be fact-checked by experts and people with first-hand knowledge of events before I published. The truth is that it’s not easy to get facts straight. It takes hard work and humility to get things right. We need to be careful.

A Stupid Mistake

Some of the most famous business stories we hear are simply not accurate. Gurus and pundits love to tell you that after inventing digital photography Kodak ignored the market. Nothing could be further from the truth. In fact, its EasyShare line of cameras were top sellers. It also made big investments in quality printing for digital photos. The problem was that it made most of its money on developing film, a business that completely disappeared.

Another popular fable is that Xerox failed to commercialize the technology developed at its Palo Alto Research Center (PARC), when in fact the laser printer developed there saved the company. What also conveniently gets left out is that Steve Jobs was able to get access to the company’s technology to build the Macintosh because Xerox had invested in Apple and then profited handsomely from that investment.

But my favorite mistold myth is that of Blockbuster, which supposedly ignored Netflix until it was too late. As Gina Keating, who covered the story for years at Reuters, explains in her book Netflixed, the video giant moved relatively quickly and came up with a successful strategy, but the CEO, John Antioco, left after a fight with investor Carl Icahn and the strategy was reversed.

Yet that’s not exactly how I told the story. For years I reported that Antioco was fired. I even wrote it up that way in my book Cascades until I contacted the former CEO to fact-check it. He was incredibly generous with his time, corrected me and then gave me additional insights that improved the book.

To this day, I don’t know exactly why I made the mistake. In fact, as soon as he pointed it out I knew I was wrong. Somehow the notion that he was fired got stuck in my head and, with no one to correct me, it just stayed there. We like to think that we remember things as they happened, but unfortunately our brains don’t work that way.

Why We Get Fooled

We tend to imagine that our minds are some sort of machines, recording what we see and hear, then storing those experiences away to be retrieved at a later time, but that’s not how our brains work at all. Humans have a need to build narratives. We like things to fit into neat patterns and fill in the gaps in our knowledge so that everything makes sense.

Psychologists often point to a halo effect, the tendency for an impression created in one area to influence opinion in another. For example, when someone is physically attractive, we tend to infer other good qualities and when a company is successful, we tend to think other good things about it.

The truth is that our thinking is riddled with subtle yet predictable biases. We are apt to be influenced not by the most rigorous information, but what we can most readily access. We make confounding errors that confuse correlation with causality and then look for information that confirms our judgments while discounting evidence to the contrary.

I’m sure that both Matt Taibbi and I fell into a number of these pitfalls. We observed a set of facts, perceived a pattern, built a narrative and then began filling in gaps with things that we thought we knew. As we looked for more evidence, we seized on what bolstered the stories we were telling ourselves, while ignoring contrary facts.

The difference, of course, is that I went and checked with a primary source, who immediately pointed out my error and, as soon as he did, it broke the spell. I immediately remembered reading in Keating’s book that he resigned and agreed to stay on for six months while a new CEO was being hired. Our brains do weird things.

How Our Errors Perpetuate

In addition to our own cognitive biases, there are a number of external factors that conspire to perpetuate our beliefs. The first is that we tend to embed ourselves in networks that have similar experiences and perspectives that we do. Scientific evidence shows that we conform to the views around us and that effect extends out to three degrees of relationships.

Once we find our tribe, we tend to view outsiders suspiciously and are less likely to scrutinize allies. In a study of adults that were randomly assigned to “leopards” and “tigers,” fMRI studies noted hostility to out-group members. Research from MIT suggests that when we are around people we expect to agree with us, we don’t check facts closely and are more likely to share false information.

In David McRraney’s new book, How to Change a Mind, he points out that people who are able to leave cults or reject long-held conspiracy theories first build alternative social networks. Our associations form an important part of our identity, so we are loath to change our opinions that signal inclusion into our tribe. There are deep evolutionary forces that drive us to be stalwart citizens of the communities we join.

Taibbi was, for years, a respected investigative journalist at Rolling Stone magazine. There, he had editors and fact checkers to answer to. Now, as an independent journalist, he has only the networks that he chooses to give him feedback and, being human like all of us, he subtly conforms to a set of dispositions and perspectives.

I probably fell prey to similar influences. As someone who researches innovation, I spend a lot of time with people who regard Netflix as a hero and Blockbuster as something of a bumbler. That probably affected how I perceived Antioco’s departure from the company. We all have blind spots and fall prey to the operational glitches in our brains. No one is immune.

Learning How To Not Fool Ourselves

In one of my favorite essays the physicist Richard Feynman wrote, “The first principle is that you must not fool yourself — and you are the easiest person to fool. So you have to be very careful about that,” He goes on further to say that simply being honest isn’t enough, you also need to “bend over backwards” to provide information so that others may prove you wrong.

So the first step is to be hyper-vigilant and aware that your brain has a tendency to fool you. It will quickly grasp on the most readily available data and detect patterns that may or may not be there. Then it will seek out other evidence that confirms those initial hunches while disregarding contrary evidence.

This is especially true of smart, accomplished people. Those who have been right in the past, who have proved the doubters wrong, are going to be less likely to see the warning signs. In many cases, they will even see opposition to their views as evidence they are on the right track. There’s a sucker born every minute and they’re usually the ones who think that they’re playing it smart.

Checking ourselves isn’t nearly enough, we need to actively seek out other views and perspectives. Some of this can be done with formal processes such as pre-mortems and red teams, but a lot of it is just acknowledging that we have blind spots, building the habit of reaching out to others and improving our listening skills.

Perhaps most of all, we need to have a sense of humility. It’s far too easy to be impressed with ourselves and far too difficult to see how we’re being led astray. There is often a negative correlation between our level of certainty and the likelihood of us being wrong. We all need to make an effort to believe less of what we think.

— Article courtesy of the Digital Tonto blog
— Image credit: 1 of 1,050+ FREE quotes for your meetings & presentations at http://misterinnovation.com

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