And I kept hearing the same frustration over and over:

"We need someone who can tie our back-end production systems into our front-end accounting and planning systems."

"These vendors sell us pieces, but nobody helps us make it all work together."

"We're spending a fortune on technology that doesn't talk to each other."

At the time, businesses were rushing to adopt computers in manufacturing, attempting to marry front-end accounting and sales systems with back-end production operations. The enthusiasm was electric. The failure rate was catastrophic.

Companies were spending millions on technology that never delivered. Not because the individual technologies were flawed, but because three critical gaps existed:

Poor system design. Vendors were selling solutions. Companies were buying technology. Nobody was architecting integrated systems that actually worked together.

Unqualified decision-makers. Executives who'd never seen a PLC were making purchasing decisions for factory automation. The people calling the shots didn't understand what they were buying.

No operational framework. Even when the hardware worked, there were no Standard Operating Procedures, no training protocols, no methodology for this marriage of technologies that had never worked together before.

I saw the void clearly. My clients were telling me exactly what they needed - complete systems support similar to what EDS was providing to Fortune 500 companies, but for mid-market manufacturers.

I Tried to Do It the Right Way First

I approached my employer with an idea: create a new business venture within the company to provide complete systems integration and support. We had engineering capability. We had client relationships. We understood industrial systems. We were perfectly positioned to fill this gap.

They weren't interested.

So in 1990, based on direct client feedback and the tremendous void I saw in the market, I founded Industrial Systems Design Group LLC.

I recognized this wasn't a technology problem, it was an integration and implementation problem. Companies desperately needed someone who could design the system, implement it, and support it. Not sell them pieces. Not run impressive demos. Actually make disparate technologies work together in production.

The Technological Landscape of the Early 1990s

To understand why ISDG was needed, you have to understand what the technology landscape looked like at the time.

Manufacturers were just beginning to adopt computers in production. Mainframes handled accounting and payroll. PCs were appearing in engineering departments. PLCs (Programmable Logic Controllers) were controlling individual machines on the factory floor. But these systems didn't talk to each other.

Connecting the factory floor to front-end systems like accounting, planning, and sales was uncharted territory.

You could buy accounting software. You could buy manufacturing control systems. You could buy inventory management tools. You could buy scales and batching systems like the ones I'd been selling. But no one offered complete, end-to-end design, implementation, and support that made these disparate systems work together as an integrated whole.

The vendors sold their pieces. The systems integrators of the day, companies like Ross Perot's EDS, which General Motors acquired for $2.5 billion in 1984, worked with Fortune 500 companies on massive, multi-year implementations.

ISDG changed that for mid-market manufacturers.

As founder, CEO, COO, and lead systems designer, I wasn't just advising from the sidelines; I was in every seat. I made the business decisions, ran the operations, and designed the technical systems. I lived the full reality of what it takes to turn innovative technology into working business solutions.

I focused on companies that had the same integration challenges as GM but without billion-dollar budgets or three-year timelines. They needed someone who could design the system, implement it, and support it - all under one roof.

They needed someone who understood their business problems because I'd been listening to them on factory floors for years.

Building the Future Before It Had a Name

In 1991, we built one of the first computer-controlled, camera-guided, AI-driven autonomous robots - a system that integrated computers, cameras, and early learning algorithms long before "AI" became a buzzword.

This wasn't just about robotics. It was a proof of concept that intelligent automation could exist outside university labs, in real-world production environments.

What made it groundbreaking wasn't any single component - it was the integration:

· Computer control (not ladder logic PLCs that required specialists to reprogram)

· Machine vision (cameras that could "see" and make decisions based on what they saw)

· Early AI learning algorithms (the system could adapt to variations without being reprogrammed)

· Real-time processing (decisions made in milliseconds, not batch processed overnight)

· Physical automation (all of this controlling actual manufacturing equipment)

These technologies had never been connected before. Each came from a different vendor, with different protocols, different expectations, different failure modes. Making them work together as a cohesive system, not just in a lab demo but in production where downtime costs money and mistakes cost credibility, required understanding not just each technology, but the integration architecture that would make them function as one.

I designed it. I built it. I made it work in production.

And here's what I learned: The hard part wasn't the individual technologies. The hard part was the integration, the operational framework, and the change management required to make humans trust and work alongside this new capability.

Sound familiar? It should. Because that's exactly what's happening with AI today.

Then I Learned a Hard Lesson About IP and Power

In 1999, Industrial Systems Design Group closed. Not because the business failed - because it succeeded too well.

A client, significantly larger than my company, decided they wanted the integration methodology and technology I'd developed for their system. Not to license it. Not to partner on it. They wanted to own it and market it as their own.

When I wouldn’t play along, they took me to court.

We won the legal battle. The IP was mine. The methodology was mine. The innovations were mine.

But they had deeper pockets, and they knew it. By the time the legal fees were paid, Industrial Systems Design Group had no financial resources left. (Windows of opportunity closed due to resources being tied up in litigation.) I'd protected my intellectual property but lost my company in the process.

That experience taught me something that shapes how I work today: When you're the smaller player with the real innovation, you're vulnerable to larger entities that see acquisition or appropriation, as cheaper than innovation.

It's a lesson that's directly relevant to companies deploying AI today. You're integrating technology from vendors who are bigger than you, have deeper pockets than you, and whose interests may not align with yours three years from now.

From Wearing Every Hat to Focusing on Operations

From 2000 onward, I continued as an independent consultant, helping organizations navigate complex technology integrations. Then, from 2012 to 2023, I served as Chief Operating Officer for a high-tech conveyance company in the Healthcare Sector.

After years of wearing every hat at Industrial Systems Design Group - CEO, COO, and system designer, I focused entirely on the operational executive role.

For over a decade, I sat in the COO chair, making the technology investment decisions I'd spent years implementing. I dealt with vendors promising transformation. I evaluated pilots that looked promising in demos but failed in production. I watched as consultants applied yesterday's frameworks to tomorrow's problems.

But unlike most COOs, I could read the technical documentation. I understood what the system designers were actually building. I knew the difference between "this is technically possible" and "this will work in our production environment."

I learned what it's like to be the executive being sold solutions by people who've never run operations at scale. But I also experienced what it's like when the technical team says something can't be done, and I had the background to know if they were right or just hadn't thought it through.

And I never forgot what it's like to be on the other side - building something innovative, running the company, and navigating the power dynamics of larger players. Because I'd been the CEO, the COO, and the engineer. All at once.

I'm Watching the Same Movie Again And I Can't Stay Silent

Now, in 2025, I'm watching the AI wave build.

The technology has changed. The failure patterns haven't.

And I'm hearing the same frustrations from executives that I heard from manufacturing clients in the 1980s:

"We need someone who can integrate AI with our existing systems."

"These vendors sell us AI tools, but nobody helps us make it all work together."

"We're spending a fortune on AI pilots that don't scale to production."

Companies are deploying AI initiatives at record rates. 42% of them are abandoning most of these projects before they reach production - up from just 17% failure rate the year before. Only 5% of AI pilots achieve meaningful business impact. The enthusiasm is identical. The wreckage is mounting.

And the three failure points? Still the same:

· Design failures: Companies pursuing "AI for AI's sake" without alignment to actual business processes

· Unqualified decision-makers: Executives dazzled by demos, making million-dollar commitments to technology they don't understand

· Lack of operational framework: No SOPs, no integration methodology, no systematic approach to marrying AI with existing systems

But there's a fourth problem I see clearly now, one I learned the hard way in 1999:

· Dependency without protection: Companies building their business processes on vendor platforms without understanding the power dynamics, exit strategies, or long-term implications.

I've lived through this transition from every angle:

· As the sales engineer who listened to clients' integration problems on factory floors

· As the system designer who built the technology and made it work in production

· As the CEO who made strategic decisions and managed vendor relationships

· As the COO who ran operations and knew what "works in production" really means

· As the smaller innovator who learned what happens when you face a larger adversary determined to take what you built

And now I'm watching companies make the exact same mistakes with AI that they made with ERP systems in the 1990s, plus some new ones.

The difference is: this time I bring every perspective to the table.

I can identify the real market need because I spent years listening to clients on factory floors.
I can evaluate technical architecture because I designed systems.
I can assess business strategy because I was a CEO making those decisions.
I can judge operational viability because I spent over a decade as a COO.
I can spot vendor lock-in and power imbalances because I lived through the legal battle.

Most consultants have one of these perspectives. Some have two. I have all five.

Why I'm Doing This Now

I could stay on the sidelines. I could watch from a distance as another generation learns these lessons the expensive way.

But I can't.

Because I'm hearing the same client frustrations I heard in the 1980s. And I know how this story ends if nobody steps in to help.

When you've seen this pattern twice, when you've literally sat in every chair from sales engineer to system designer to CEO to COO, when you know exactly where the failures will occur and exactly how to prevent them, when you've personally experienced what happens when innovation meets power imbalance, staying silent feels irresponsible.

The companies deploying AI right now don't know they're repeating history. Their vendors don't know because they weren't there the first time. Their consultants don't know because they're reading the same articles everyone else is reading. Their technical teams don't know because this is their first paradigm shift. Their executives don't know because they've never designed a system or run operations at this level.

But I know.

I know because clients told me in the 1980s what they needed and I built it.
I know what questions they should be asking but aren't, from the boardroom to the server room.
I know what problems will surface in month six that nobody's planning for.
I know the difference between a system designed for success and a pilot destined for failure.
I know how to protect your interests when you're the smaller player working with bigger vendors.
And I know what it looks like from every seat at the table, because I've sat in all of them.

What I'm Offering

I'm not rebuilding Industrial Systems Design Group. I'm not creating another consulting firm. I'm offering something more focused:

Strategic guidance for companies navigating the AI transition from someone who identified this exact market gap 40 years ago, built the solution, survived the legal battles over IP, and spent decades in operational leadership.

I help executives ask the right questions before they commit millions to the wrong approach.
I help technical teams design AI systems that will actually work in production, not just in demos.
I help operations leaders build the frameworks AI requires to deliver real value.
And I help protect your organization's interests when dealing with AI vendors who are bigger, richer, and may not have your long-term interests at heart.

This isn't consulting from someone who studied systems integration in business school. It's not advice from someone who's only been on one side of the table. It's the hard-won knowledge from:

· Listening to clients on factory floors in the 1980s describe exactly what they needed

· Designing and building groundbreaking integration systems in the 1990s before the market even had a name for what we were doing

· Running a company as founder, CEO, and COO simultaneously

· Fighting and winning a legal battle to protect IP against a larger adversary

· 20+ years of independent consulting across industries

· 12 years as COO of a high-tech company, making these decisions from the executive chair

· Actually building the technology, running the operations, and making the strategic decisions, not just advising others to do it

The Stakes Are Higher This Time

In the 1990s, companies that failed at ERP integration lost money and time. They recovered.

With AI, the stakes are different. The companies that figure this out gain years of competitive advantage. The ones that don't, that spend millions on failed initiatives while becoming dependent on technology they don't control, may not get a second chance.

I've already watched one generation of companies make these mistakes. I've been the sales engineer who heard their pain. I've been the system designer who knew it wouldn't work. I've been the CEO who had to make the call anyway. I've been the COO who had to make it work despite the flawed foundation. And I learned my own hard lessons about power, IP, and survival when you're the smaller innovator.

I have no interest in watching other companies fall into the same traps when I know how to help them avoid it - from every angle.

If you're evaluating AI initiatives and want to avoid becoming part of the 85% failure rate, or worse, becoming dependent on technology you don't control, let's talk.

Not because I need another consulting project, but because you need someone who's already fought these battles from every position: sales engineer, designer, CEO, COO, and defendant in an IP lawsuit.

History doesn't repeat itself, but it certainly rhymes. And I know all the verses - from every instrument in the orchestra.

How I Discovered the Gap

In the mid-1980s, I was working as a sales engineer for an industrial force testing equipment company. We sold everything from laboratory balances to in-motion truck scales, batching and blending systems, and process control equipment. I worked closely with our engineering department and spent my days on client sites, understanding their operations.

My Story...

I've been here before. Not with AI specifically, but with the exact same pattern of failure, frustration, and opportunity.

See if there's a fit - book a short introductory call: