https://www.jeffwinterinsights.com/

Jeff Winter, Naperville, Chicago, IL (2026)

09/06/2026

When I was at Microsoft, we had roughly 400,000 partners.
Even a company that size couldn't pull off full digital transformation alone.
If Microsoft needs an ecosystem, so do you.
There is no version of Industry 4.0 where one company does it all in-house. You need partners. You need an ecosystem.
👥 Tag a partner (or a competitor 👀) who gets this.

09/06/2026

Everyone's obsessed with generative AI. The data says look elsewhere.
Sites reporting real gains saw:
📈 53% boost in labor productivity
📉 26% reduction in conversion costs
Where's that value actually coming from? Not GenAI replacing processes — it's GenAI as an assistant for frontline workers.
And the real workhorse? Tried-and-true machine learning. Still dominating the value — most people just aren't calling it "AI."
🤔 Are you chasing the flashy tech or the tech that actually pays off? Comment your take.

09/06/2026

Every company has a story it tells itself about why it wins. Sometimes that story is true — built through hard work, better products, trusted relationships, years of doing the job well.
But a story that once gave us confidence can become an excuse not to change. 📉
The world changes. Customers expect more. Competitors learn faster. Technology lowers old barriers. The process that once made us reliable starts making us slow.
That's how advantage fades — not because people stop caring, but because the standard keeps rising. Yesterday's excellence doesn't automatically meet tomorrow's test.
Manufacturing doesn't need less discipline. It needs a broader one — joined by faster learning, better decisions, and the willingness to let go of what made us proud when it no longer makes us better.
💬 Not whether you earned your advantage once — are you willing to keep earning it?

09/06/2026

Early in my career, I believed the right technology could straighten out broken ways of working. I was wrong. 🔧
The tool inherits everything that's already broken — every exception, every side agreement, every undocumented practice. Automation might improve speed and visibility, but it won't make the decisions leadership has been avoiding.
There's always one person — let's call him Jim — who just knows how it actually works, gaps and all.
Before you automate, ask: what standardization is actually needed? Who owns the decisions? Are we fixing the work, or just scaling the workaround?
You can't automate your way out of a process nobody's willing to fix first.
💬 Are your upgrades making the work better, or just making the mess easier to repeat at scale?

08/11/2026

Big goals are cheap. Readiness is expensive.
When pressure hits, you don't perform at your best, you default to your systems. In manufacturing, outcomes track to your lowest level of preparation: messy data, ad-hoc change control, unclear ownership, no drill, no result.
Building the foundation requires:
• Clear owners, roles, and decision rights for every critical path
• Clean, connected data with governance baked in
• Change control with instant rollback and auditable history
• Runbooks + simulations + drills (tabletop today, real world tomorrow)
• Leading indicators tied to actions, not 'feel-good' KPIs
• Post-mortems that change the process, not just the slide deck
Ambition sets direction. Readiness sets altitude. Set the goal. Then over-prepare for the moment you'll fall back on.
Readiness isn't a box you check; it's the muscles you build (Strategic, Cultural, Operational, and Technological).
So, the big question is... are you truly ready?

08/07/2026

Yes, I wasted a real bottle of wine to prove a point. 😂
Was it dramatic? Absolutely. Was it necessary? Also absolutely.

Because sometimes the best way to explain bad data architecture is to show a wine glass held together by Band-Aids and say:
“This is how some companies are trying to scale AI.”

A little spreadsheet here.

A manual export there.

A dashboard nobody fully trusts.

A “temporary” workaround that became permanent five years ago.

And then everyone acts surprised when AI does not magically work.

AI does not fix broken data foundations. It just makes the cracks more obvious.

So before we ask, “How do we scale AI?” maybe we should ask:
Are we building on solid architecture, or are we pouring good wine into a broken glass?

Want to learn more about dark data vs dismissed data, and how to take advantage of all the data you are already generating?

es, I wasted a real bottle of wine to prove a point. 😂
Was it dramatic? Absolutely. Was it necessary? Also absolutely.

Because sometimes the best way to explain bad data architecture is to show a wine glass held together by Band-Aids and say:
“This is how some companies are trying to scale AI.”

A little spreadsheet here.

A manual export there.

A dashboard nobody fully trusts.

A “temporary” workaround that became permanent five years ago.

And then everyone acts surprised when AI does not magically work.

AI does not fix broken data foundations. It just makes the cracks more obvious.

So before we ask, “How do we scale AI?” maybe we should ask:
Are we building on solid architecture, or are we pouring good wine into a broken glass?

Want to learn more about dark data vs dismissed data, and how to take advantage of all the data you are already generating?

08/07/2026

This represents months of work reflecting years of experience, and I hope to be one of my most provocative and impactful pieces of content. 😮

I call them 𝐓𝐡𝐞 𝟏𝟐 𝐋𝐚𝐰𝐬 𝐨𝐟 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝟒.𝟎.

These 12 laws represent the hidden rules and systemic phenomena that naturally occur when system connectivity, streaming data, and human behavior collide on a software-defined plant floor. Understanding them is incredibly valuable because they serve as the real-world guardrails determining whether a multi-million-dollar initiative will successfully scale or completely break. You need to know them now so they don't surprise you later on.

But this is only Rev 1.0. 𝐈 𝐧𝐞𝐞𝐝 𝐲𝐨𝐮𝐫 𝐢𝐧𝐩𝐮𝐭!

This collection of laws only gets sharper if it's stress-tested by the people living it every day. I want your brutal honesty to help shape the next iteration:

Which of these 12 totally resonates with you right now?

Which do you challenge? Where does the logic break when it hits your specific industry?

What did I miss? What is Law #13 that needs to be added to the next revision? (a.k.a the "Winter" law 🤣 ).

08/07/2026

AI is not asking leaders to become data scientists.
It is asking them to stop treating intelligence like a department.

For too long, companies have treated technology as something that happens “over there.” You know what I mean…IT owns the systems. Operations owns the process. Data teams own the dashboards. Executives own the strategy.

Sound familiar?

But then...BOOM! AI shows up and all of a sudden starts getting all up in everyone's business. and messing with everyone’s org charts.

Because AI does not fit neatly into one function.
It changes how decisions are made, which impacts how work gets redesigned, how trust is earned, how data is governed, and how quickly the business learns.

Basically it changes everything!
And... It is happening everywhere!

That is why the hardest part of AI is not selecting the right tool, platform, or model. 𝐈𝐭 𝐢𝐬 𝐦𝐢𝐧𝐝𝐬𝐞𝐭 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧.

Leaders need to think strategically enough to connect AI to real business priorities. They need enough data discipline to understand that bad inputs do not magically produce brilliant outcomes. They need enough humility to design AI around people, not around overhyped “autonomous” promises.
And they need enough agility to admit that the first answer probably will not be the final answer.

The companies that win with AI will not be the ones that simply “adopt AI.”
They will be the ones that change how they think, decide, learn, and operate because of it. Especially in industries where the stakes are physical, operational, and very real.

*𝐜𝐨𝐮𝐠𝐡 𝐜𝐨𝐮𝐠𝐡* Looking at you, manufacturing. 😉

AI may be artificial. But the leadership required to use it well is very human.
Don’t forget that. 😎

08/07/2026

Leading in Industry 4.0 isn't about walking ahead; it's about walking together. I want to go beyond just being a thought leader by fostering collaboration and building a community.

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐋𝐞𝐚𝐝𝐞𝐫?
A person who is recognized as an authority in a specific field of expertise and highly influential in shaping the thinking of others. They are often sought out as speakers, consultants, and advisors because of their expertise and ability to inspire and guide others. They are viewed as being at the forefront of their field, and their ideas and perspectives can be influential in shaping the direction of their industry or discipline. A thought leader can be thought of as the combination of a 𝐬𝐮𝐛𝐣𝐞𝐜𝐭 𝐦𝐚𝐭𝐭𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐭, an 𝐞𝐯𝐚𝐧𝐠𝐞𝐥𝐢𝐬𝐭, and an 𝐢𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞𝐫.

𝐌𝐲 𝐏𝐫𝐨𝐦𝐢𝐬𝐞:
My approach is about sharing knowledge, encouraging discussions, and bringing together diverse perspectives to drive innovation.

𝐌𝐲 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲:
𝟏. 𝐒𝐡𝐚𝐫𝐢𝐧𝐠 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞: Regularly providing updates on the latest stats, trends, best practices, and insights in Industry 4.0. I believe in the power of informed decision-making.
𝟐. 𝐄𝐧𝐠𝐚𝐠𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: Actively responding to your messages and comments. Your thoughts and questions are the heartbeat of our community and deserve attention.
𝟑. 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Working together on industry artifacts to not only share knowledge but to create it. Let's turn our collective expertise into tangible outcomes.
𝟒. 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠 𝐎𝐮𝐫 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐬: Serving as an industry representative at various industry associations, academic groups, and research teams. It's about taking our voice to the places where it can make a significant impact.
𝟓. 𝐋𝐢𝐬𝐭𝐞𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Committing to not just speaking but also listening. Your experiences, challenges, and successes are what will drive us forward.

Together, let's shape the future of Industry 4.0, making it more inclusive, innovative, and impactful!

08/07/2026

Nature is annoyingly good at strategy.

It doesn’t try to sound profound.

It just is…

We’re currently on Winter family vacation, and this video is from one of those places that even makes the kids say “wow” and makes your inbox feel very, very unimportant.

Anyone want to guess where we are? 😇

The water knows where it’s going. The canyon shows what persistence looks like. The view does all the explaining.

That’s the leadership lesson.

Clarity does not need to be loud.

The best strategies, like the best views, don’t require constant explanation.

People can see the direction. They can feel what matters. And nobody has to keep reminding them where to look.

Jeff Winter

09/06/2026

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