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There’s a number that’s been floating around the business press for two decades now. Different versions, different sources, different transformations — but the headline never really changes.

Roughly 70% of major transformations fail.

Cloud adoption. Digital transformation. ERP. Platform-based architectures. Agile. Now AI. The technology changes. The failure rate doesn’t.

That should be the most uncomfortable statistic in modern business, because it’s telling us something the industry has been studiously refusing to hear. The failures aren’t about the technology. They’ve never been about the technology. They’re about everything that has to happen around the technology for it to actually work.

And the consulting firms whose entire business is studying this — McKinsey, BCG, Deloitte, the Stanford HAI Index — have been remarkably consistent on the math. Let me walk through what the most recent research actually says, because the picture in 2026 is sharper than the old “70% fail” cliché.

What the 2025-2026 data actually shows

McKinsey’s 2025 State of AI survey tested 25 attributes across nearly 2,000 organizations and found that workflow redesign had the single strongest correlation with EBIT impact from AI. Not model selection. Not data infrastructure. Not vendor choice. Workflow redesign.

High-performing organizations — the roughly 6% that reported meaningful financial returns — were nearly three times more likely to have fundamentally redesigned their workflows around AI rather than layering AI onto existing processes.

BCG’s 2025 global study of 1,250 companies tells the same story in different words. Only about 5% create substantial AI value at scale, while 60% generate no material value from their AI investments despite meaningful spending.

Sixty percent. Spending real money. Getting nothing back. Not because the technology doesn’t work — the technology works fine — but because the differentiator is not the technology. It’s whether the organization treats AI as a tool to add or a reason to redesign how work gets done.

This is the punchline buried under thousands of pages of consulting research: the technology is the easy part.

The 10-20-70 principle

BCG has been promoting a framework that captures this better than any other framing I’ve seen. They call it the 10-20-70 principle.

Companies should devote 10% of their efforts to algorithms and 20% to technology and data; the remaining 70% of their efforts should focus on people and processes to make sure that the changes stick.

Read that again, because it’s almost the inverse of how most enterprise AI budgets are actually allocated.

Most leaders I work with are spending roughly the opposite. Seventy percent of the budget goes to platforms, tools, models, vendors, and infrastructure. Twenty percent goes to data and integration. Ten percent — if that — goes to the people and processes that determine whether any of it actually changes the way work gets done.

Then they’re surprised when nothing changes.

The 10-20-70 numbers are directional, not absolute. MIT Sloan found that 70% of AI’s value depends on complementary investments in people and process, not on the sophistication of the technology. Different study, different methodology, same conclusion.

The technology is increasingly commoditized. Any mid-market company can access the same frontier models from OpenAI, Anthropic, or Google. What’s not commoditized is your organization’s capacity to absorb, adopt, and operationalize that technology. That’s the moat now.

This pattern is not new

Here’s the part that should give every leader pause.

This isn’t a new finding. We’ve seen this same pattern in every major technology wave of the last quarter-century.

Cloud adoption (2010-2020). Most enterprises that moved workloads to AWS or Azure without changing their operating model got cloud bills instead of cloud value. The 70% that struggled weren’t fighting the technology — they were fighting their own org charts, procurement processes, and engineering cultures.

Digital transformation (2015-present). McKinsey’s research on digital transformation has reported failure rates of 70-80% for over a decade. Same root cause. Companies bought new tools and tried to run them on old operating models.

Platform-based architectures (2018-2024). Microservices, APIs, event-driven systems. The tech worked. The pattern of failure was the same — teams structured around the old monolith couldn’t suddenly behave like teams owning loosely coupled services, no matter what the architecture diagram said.

Agile adoption (2010-present). This one is almost a parody at this point. Every large enterprise has “adopted agile.” Very few have actually changed how decisions get made, how funding flows, or how work gets prioritized. The framework is on the wall. The behaviors haven’t moved.

In every case, the pattern is identical:

• The technology works.
• The leaders adopt it.
• The org doesn’t absorb it.
• Two years later, someone declares the transformation “stalled.”
• A new technology arrives.
• The cycle restarts.

AI is going to follow this script unless leaders deliberately choose otherwise.

What “deliberately choosing otherwise” actually looks like

The good news is that the small minority of organizations getting this right are extremely well-studied. We know what they do differently.

High performers are 3.6x more likely to pursue transformational change and 55% fundamentally rework workflows when deploying AI. They’re not adding AI on top. They’re rebuilding the work around AI.

They invest the 10-20-70 the way BCG describes it — most of the money goes to change capacity, not to compute.

They have executive sponsors who are publicly committed and operationally engaged, not just funding the program from a distance.

They build shared language across the organization, so the CEO, the CIO, the frontline manager, and the analyst all mean the same thing when they say “AI.” Without shared language, every conversation restarts from zero.

They redesign workflows end-to-end rather than automating existing ones. If you take a broken, manual, approval-heavy workflow and add AI on top of it, you get a slightly faster broken workflow. That’s not transformation. That’s expensive friction reduction.

They measure value rigorously. Not “did we deploy the tool” but “did the work change, did the cycle time drop, did the cost-to-serve improve, did the customer outcome improve.”

And — this is the one most leaders skip — they invest in workforce capability ahead of deployment. Companies that are realizing the most value from AI also have the most ambitious upskilling programs, and put the resources in place to support them.

The hard truth for Canadian enterprise leaders in 2026

If you’re running an AI program right now, you are statistically much more likely to end up in the 60% generating no material value than in the 5% capturing real returns.

That’s not pessimism. That’s what the data says.

The path to being in the 5% is not picking a better LLM. It’s not waiting for the next model release. It’s not hiring more AI engineers.

It’s investing the 70% of effort that almost everyone underspends — change capacity, workflow redesign, executive fluency, shared language, workforce capability, governance that doesn’t choke delivery.

This is exactly why the AI-Native curriculum is structured the way it is. Foundations builds shared language. Change Agent develops the people who can actually land the change. Leading the AI-Native Organization equips executives to make the release-rate, prioritization, and workforce decisions that determine whether the 70% gets the attention it deserves.

The data has been telling us this story for twenty years. Cloud. Digital. Platforms. Agile. Now AI.

The pattern doesn’t change because the technology changes.

The pattern changes because leaders choose to invest where the value actually lives.

The closing thought

There’s a comforting story leaders sometimes tell themselves: this time it will be different, because this technology is more powerful.

It’s not going to be different. It’s never been different.

The technology was powerful in 2005. It was powerful in 2015. It’s powerful in 2026. And in every era, the same 60-70% of organizations have failed to extract value from it for the same reason — because they treated transformation as a technology problem when it has always been a people-and-process problem.

The 5% who get this right aren’t smarter. They’re not better resourced. They’re not luckier.

They’re just willing to spend their effort where the value lives — not where the marketing budget is loudest.

Build the 70%. The other 30% will follow.

The post Success in the Age of AI Isn’t a Technology Story. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

There’s a short quote often shared in leadership circles:

“It’s impossible,” said pride.
“It’s risky,” said experience.
“It’s pointless,” said reason.
“Give it a try,” said the heart.

While its exact origin is unclear, the message reflects a well-studied tension in psychology and decision-making:

the conflict between logic, past experience, ego — and intuition.

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The Four Voices Behind Every Decision

Every leader recognizes these voices — even if they don’t name them.

Pride says:
Don’t fail. Don’t look weak. Protect your reputation.

Experience says:
You’ve seen this before. It didn’t work. Don’t repeat mistakes.

Reason says:
The data doesn’t support this. The odds are low. The ROI is unclear.

And then there’s the heart — or what research often calls intuition.

It says:
There’s something here. Try anyway.

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What Research Actually Says

Modern research doesn’t dismiss intuition.

In fact, studies on decision-making by Daniel Kahneman show that humans operate with two systems:

• System 1 — fast, intuitive, pattern-based thinking
• System 2 — slow, analytical, logical reasoning

Both are necessary.

Similarly, work by Gary Klein shows that experienced professionals often make high-quality decisions using recognition-primed intuition — especially in uncertain environments.

So the “heart” is not irrational.

It is often compressed experience + pattern recognition + instinct.

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The Leadership Trap

In organizations, three voices tend to dominate:

• pride (protecting image)
• experience (protecting from past mistakes)
• reason (protecting through analysis)

All three are valuable.

But together, they can create paralysis.

Leaders become:

• overly cautious
• slow to act
• resistant to new ideas
• dependent on perfect data

And in fast-changing environments — especially with AI — waiting for certainty is often the biggest risk.

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The Role of the “Heart” in Leadership

The “heart” is not about ignoring logic.

It’s about acting when logic is incomplete.

In innovation, transformation, and leadership:

• data is often delayed
• patterns are still forming
• outcomes are uncertain

This is where leaders must:

• take calculated risks
• test small before scaling
• move before full certainty exists

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Coaching Insight: Balancing the Voices

In executive coaching, one of the most powerful shifts is helping leaders balance these internal voices.

Not eliminate them — but integrate them.

The goal is not:

• pure intuition
• pure analysis

It is informed courage.

Leaders who succeed:

• respect experience — but don’t become trapped by it
• use data — but don’t wait for perfection
• manage ego — but don’t let it block action
• listen to intuition — but validate through action

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Final Thought

Most opportunities don’t come with certainty.

They come with tension.

Pride will resist.
Experience will warn.
Reason will question.

And sometimes, progress only happens when something inside says:

“Try anyway.”

The post “It’s impossible,” said pride.“It’s risky,” said experience. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

In the last article we said Eric Ries was right about MVPs, and Steve Jobs was right about not shipping the embarrassing version, and the discipline was knowing which mode you were in.

Then a reader asked the obvious follow-up.

What about Elon Musk?

Because Musk doesn’t ship the embarrassing version in private the way Jobs did. He also doesn’t quite ship the Ries MVP either. What he does is something else entirely — and it’s worth naming, because most enterprise leaders don’t realize there’s a whole taxonomy of MVPs out there, each one doing a fundamentally different job.

Lump them all together under the same three-letter label and you’ll make bad decisions about which one to use, when.

Let me lay out the full landscape.

The six kinds of MVP

1. The Smoke Test (or Landing Page MVP). Put up a single web page describing the product, measure how many people sign up, click “buy,” or hand over an email. What you’re testing is interest. Buffer famously launched this way — a landing page that described the product, a button that didn’t lead anywhere yet, and a measurement of how many people clicked it. If nobody clicks, you’ve learned something important before writing a single line of code.

2. The Pre-order / Sell-Before-You-Build. Take real money — or a refundable deposit — before the product exists. What you’re testing is purchase intent, which is a much stronger signal than interest. This is the Musk move. Cybertruck launched in 2019 with a $100 refundable deposit and racked up reservations by the millions. The Tesla Semi was pre-sold to fleet customers like PepsiCo years before any truck shipped. The new Roadster has been on pre-order since 2017. The product wasn’t there. The market signal was.

3. The Concierge MVP. You manually deliver the service yourself to a tiny group of users, and they know it’s manual. Food on the Table famously started this way — the founder personally researched recipes and built grocery lists for individual families before any software was built. What you’re testing is whether anyone actually wants the outcome, regardless of how it gets delivered.

4. The Wizard of Oz MVP. Same as concierge, except users don’t know it’s manual. The product looks fully automated. Behind the curtain, humans are doing all the work. Zappos’s original model was this — Nick Swinmurn photographed shoes at a local store, posted them online for sale, and when someone bought a pair, he went to the store, bought them, and shipped them. The customer thought they were dealing with a shoe warehouse. They were dealing with a guy and a camera. What you’re testing is the full workflow and product-market fit without paying to build the technology.

5. The Piecemeal MVP (sometimes called Frankenstein). You stitch together existing tools — Stripe, Typeform, Zapier, Airtable, off-the-shelf SaaS — and deliver the actual service end-to-end without writing custom code. What you’re testing is whether the end-to-end value proposition holds together, before you commit to building any of it from scratch.

6. The Single-Feature Product. You build one feature really well rather than the full vision in watered-down form. Dropbox launched as “files sync across your computers” — not file sharing, not collaboration, not enterprise admin, not version control. Just sync. Instagram launched as “square photos with filters” — not stories, not reels, not shopping, not DMs. Just filtered photos. What you’re testing is whether the single core thing is valuable enough to build an audience around.

Six different artifacts. Six different jobs. One label that gets used for all of them.

What Musk is actually doing

Look at #2 again, because this is where the most interesting and least-discussed pattern in modern product strategy lives.

When Musk unveiled the Cybertruck in 2019, the product didn’t exist. There were no factories tooled for it. There were no production prototypes. There was a stage prop, a famously broken window demo, and a $100 refundable deposit button on Tesla’s website.

By Monday morning he had over 200,000 reservations. Within weeks, more than 250,000. Within a few years, reportedly over 1.9 million.

The conventional way to read this is “great marketing.” That undersells what was actually happening.

What Musk was doing was running a Sell-Before-You-Build MVP at enormous scale. The product was the question. The reservation was the answer. He wasn’t gathering opinions about whether people wanted a futuristic electric pickup. He was gathering signals — measurable, monetizable, public — that real people would put real money down to be in line for one.

That’s a very different test from what Eric Ries described, and a very different test from what Steve Jobs did with the iPhone reveal. Jobs revealed a finished product. Ries said ship the embarrassing version to real users. Musk did neither. He revealed a concept and asked people to vote with their wallets.

The same pattern shows up across most of Musk’s product launches:

• The original Tesla Roadster (2006) — pre-sold to early adopters with deposits before production lines were ready.
• The Tesla Semi (2017) — unveiled with pre-orders from PepsiCo and others while the truck was still years from delivery.
• The new Roadster (2017) — taking deposits years before production.
• The Cybertruck (2019) — millions of reservations before any consumer had driven one.

The pattern isn’t a stunt. It’s a methodology. You build a vision, attach a deposit, and let the deposits tell you whether the market is there before you tool a factory.

Why this method is so different from “the MVP” most leaders think they know

Most leaders, when they hear “MVP,” reach for one of two mental models.

The first is the Ries model — ship something rough, get user feedback, iterate. Build to learn.

The second, more common in enterprise settings, is a scope-reduction model — ship the smallest version of the product we’re going to build anyway, just to get it out the door faster. This is often called an MVP but isn’t actually testing anything. It’s a project plan.

The Musk model is neither of those. It’s something more like a market sensor than a product. The point isn’t to gather user feedback (the product doesn’t exist yet, so there’s nothing to give feedback on). The point isn’t to ship a smaller version of what you’re going to build (you haven’t committed to building anything yet). The point is to find out if the market is real before you commit capital.

That’s a different test, run against a different audience, with a different decision at the end of it.

If the deposits roll in, you build it. If they don’t, you don’t. The MVP isn’t the product. The MVP is the purchase intent measurement.

When this kind of MVP fits — and when it doesn’t

This is the part that matters most for enterprise leaders, because the Sell-Before-You-Build MVP looks tempting from a distance but is much harder to deploy than it appears.

It fits when:

• You have brand credibility strong enough that people will believe the product will eventually ship. Tesla had that. Most companies don’t.
• The product is concrete enough to be visualized — a vehicle, a device, a tangible offering with a price tag. Abstract enterprise software doesn’t pre-order well.
• The buyer is willing to put something at stake — money, time, a signed letter of intent, a slot on a roadmap. Sentiment doesn’t count.
• The cost of being wrong is high enough to justify a market test before you commit. Tooling a Cybertruck factory costs a lot more than running a smoke-test landing page.

It doesn’t fit when:

• The “deposit” is too soft to mean anything. A $100 refundable reservation measures curiosity, not commitment. So does an internal survey that says “I’d definitely use this AI tool.” Both are real signals, but neither is real demand.
• The product is something the buyer can’t visualize concretely yet. You can pre-sell a pickup truck. You can’t pre-sell “an AI strategy.”
• The buyer doesn’t trust you to deliver. New entrants and unknown brands struggle to convert pre-orders even when the underlying product would be a hit.

The honest version of this method requires distinguishing between the signal of people saying they want something and the signal of people committing something real to get it.

Translating this to AI-Native transformation

Here’s why this matters in 2026 for anyone running AI initiatives inside a Canadian enterprise.

Right now, every leader I work with is drowning in soft signals.

The CEO came back from a conference and said “we should be using AI for X.” Three department heads have said “I’d love a tool that does Y.” A consultant’s report says “the market for Z is going to be huge.” Internal surveys come back showing “73% of employees are interested in AI-assisted workflows.”

None of this is real demand. It’s the enterprise equivalent of a refundable $100 deposit — directionally interesting, but not strong enough to bet a factory on.

What would real demand look like? It would look like a department head signing a service-level agreement that says “if you ship this AI tool, I will commit 30% of my team’s time to using it for six months.” It would look like a budget line moved from one cost center to a new one. It would look like an executive sponsor who’s willing to publicly attach their name to the outcome.

Those are commitments. The rest is curiosity.

The reason the Musk-style Sell-Before-You-Build MVP is so interesting as a lens isn’t because his specific technique transfers directly to your AI program. It’s because it forces a question most enterprise AI programs don’t ask:

Before I build this, what’s the actual commitment from the people who claim to want it?

If the answer is “nothing — they just said it would be cool,” you’re not running an MVP. You’re funding a hypothesis with no skin in the game on the other side. That’s the most common way enterprise AI programs waste money in 2026.

The discipline of knowing which MVP you’re running

Once you can see the six different kinds of MVP, the discipline of running one becomes clearer.

You’re not asking “should we build an MVP.” You’re asking:

1. What am I trying to measure? Interest, purchase intent, workflow viability, outcome desire, full end-to-end value, or single-feature pull?
2. Which MVP type measures that? A landing page measures interest. A pre-order measures purchase intent. A Wizard of Oz measures workflow viability. A concierge measures outcome desire. A piecemeal measures end-to-end value. A single-feature product measures pull on the core feature.
3. What’s the threshold for a green light? Set it before you start. “If we get fewer than X conversions in Y weeks, we don’t build this.” Most enterprises never set this threshold, which is why their MVPs never produce a decision — they just produce more meetings.
4. What’s the cost of being wrong in each direction? Building something nobody wants is expensive. Killing something the market actually wanted is also expensive. The MVP type you choose should match which mistake costs more.

That’s not a product framework. That’s a decision framework.

The closing thought

Eric Ries was right. Steve Jobs was right. Elon Musk is doing something different from both of them, and it’s also right, in its context.

The MVP isn’t one thing. It’s six things. Each one is doing a different job. Pick the one that matches the question you’re actually trying to answer.

Most leaders ship “an MVP” without ever naming the question. That’s not a methodology — that’s a label slapped onto whatever they were going to build anyway.

Name the question. Pick the MVP type that answers it. Set the threshold that decides the next step.

That’s the discipline. That’s what every great product story — Bezos, Jobs, Ries, Musk — has in common when you strip the names off.

They knew what they were measuring before they shipped anything.

The post There Are Six Kinds of MVP first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

There’s a great story David Pogue tells about writing the original iPhone manual in 2007.

The iPhone had just launched. Pogue was writing iPhone: The Missing Manual. The book needed roughly 400 full-color screenshots. There was just one problem.

The iPhone had no way to take a screenshot.

No keystroke. No gesture. Nothing.

Pogue knew Apple had a way — their marketing materials were full of them. So he called PR. They said yes, there’s an internal tool, but it’s an ugly command-line thing and we don’t let it out.

He pushed. Could he just borrow it for the book?

Apple’s answer: no, but fly to Cupertino and we’ll put you in a conference room under observation, you can generate the screenshots on our equipment, and go home with the JPEGs.

He booked the flight.

Right before he flew out, Apple PR called back. Steve Jobs had heard about the arrangement and killed it. No journalist was going to sit in an Apple conference room using Apple’s ugly internal tools.

The new plan: send us a spreadsheet of every screenshot you need — what’s on screen, where the windows are, what the data shows — and we’ll assign an engineer to spend the summer building them for you.

That actually happened. One engineer. Entire summer. Four hundred screenshots.

A year later, Apple was launching a new iPhone, Pogue needed to update the book, and he came back asking for screenshots again. This time Apple said no. Instead they said: we’ll just build the feature. Press two buttons at the same time and the phone will screenshot itself.

That is the gesture you have used every single time you’ve taken a screenshot on an iPhone, for almost two decades.

It exists because one journalist made it more annoying for Apple to not ship the feature than to ship it.

What this story is actually about

It’s tempting to read this as a cute origin story. It isn’t. It’s a clean lesson in how good product decisions actually get made.

For two years, Apple had a workaround. A clunky internal tool. A summer-long engineering project. A flight to Cupertino. Each one was just barely good enough to avoid building the real thing.

It took a second request — the same problem returning — to force the answer that should have existed from day one.

The screenshot feature didn’t ship because someone had a vision. It shipped because a workaround stopped being cheaper than a solution.

Why this matters for your AI strategy

Every enterprise I walk into has its own version of the Cupertino conference room.

A senior leader needs a report, so an analyst spends two days pulling it together every month. A customer service team can’t search its own knowledge base, so they maintain a private Slack channel of “things we figured out.” Finance reconciles three systems by hand because integrating them was deprioritized in 2022. Legal reviews the same five contract clauses on every deal because nobody’s templated them.

These are screenshot moments. Workarounds that became permanent because the workaround was just barely cheaper than the fix.

AI is doing something interesting to these workarounds. It’s changing the math.

The cost of building the real solution — the templated contract review, the self-serve report, the searchable knowledge base, the reconciled system of record — used to be high enough that the workaround won. With AI in the toolkit, the cost has collapsed. The summer-long engineering project is now a two-week sprint. The flight to Cupertino is a well-designed prompt.

But — and this is the part most leaders miss — the workarounds don’t disappear on their own.

Somebody has to point at them.

The diagnostic question

Walk through your organization this week and ask one question in every function:

“What’s the thing we’ve been working around for so long that we stopped noticing it?”

That’s your screenshot list.

It’s not the strategic AI initiative your steering committee is debating. It’s not the big platform decision. It’s the boring, embarrassing, decade-old workaround that everyone has quietly accommodated because the cost of fixing it always seemed higher than the cost of living with it.

AI changes that math. And the organizations that win the next 18 months aren’t the ones with the boldest AI vision. They’re the ones who systematically hunt down their workarounds and replace them.

The closing thought

The screenshot feature on your phone exists because one persistent person made the workaround more expensive than the fix.

Inside your organization, nobody is calling Apple PR. Nobody is booking a flight to Cupertino. The workarounds just sit there, year after year, costing real money and real time, hidden behind the phrase “that’s just how we do it.”

AI-Native organizations aren’t defined by how many bold initiatives they launch.

They’re defined by how aggressively they hunt down the things that should have been built years ago.

Find your screenshots.

The post Why the Best AI Features Will Come From the Most Annoying Constraints first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

You’ve likely seen this quote often attributed to Albert Einstein:

“Everyone is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing it is unsuccessful.”

There’s one problem.

There is no strong evidence Einstein actually said it.

But the idea itself is powerful — and backed by research.

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The Real Insight Behind the Quote

The message reflects a core principle in psychology and education:

Performance is contextual.

People don’t fail simply because they lack ability.
They often fail because they are measured against the wrong criteria.

Research in multiple intelligences, introduced by Howard Gardner, shows that intelligence is not one-dimensional.

People excel differently:

• analytical thinking
• creativity
• interpersonal skills
• spatial reasoning
• practical execution

Yet most systems evaluate using a narrow definition of success.

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The Organizational Version of the “Fish Problem”

In companies, this happens every day.

We see:

• great engineers forced into management roles
• creative thinkers measured only on process compliance
• strong communicators evaluated purely on technical output
• innovators constrained by rigid KPIs

And then we ask:

“Why are they underperforming?”

They’re not.

They’re just being asked to climb trees.

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Why This Matters More in the AI Era

With AI automating routine and standardized work, human value is shifting toward:

• creativity
• adaptability
• problem framing
• emotional intelligence
• innovation

But many organizations still measure performance using industrial-era metrics.

The gap is growing.

And talent is being misjudged because the system hasn’t evolved.

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Coaching Insight: Redefining the Measure

In executive coaching, one of the most impactful shifts is helping leaders ask:

Not:
“Why isn’t this person performing?”

But:
“Are we measuring the right thing?”

Great leaders:

• align roles with strengths
• redefine success criteria
• create environments where different talents can thrive
• stop forcing uniformity

Because performance improves dramatically when people are placed in the right context.

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The Cost of Getting It Wrong

When organizations misjudge talent:

• confidence drops
• engagement declines
• potential is lost
• innovation slows

And individuals begin to internalize the wrong story:

“I’m not good enough.”

When the truth is:

“I’m in the wrong system.”

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Final Thought

The fish was never unsuccessful.

The system was misaligned.

In leadership, the real challenge is not identifying talent.

It’s recognizing it correctly.

The post Everyone Is a Genius first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.