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The 5 Questions Every Executive Should Ask Before Deploying AI
Based on 2025 Research: Why 42-95% of AI Initiatives Fail
LET ME GUESS YOUR SITUATION
Your company has committed substantial resources to AI. You've got pilots running. Consultants have delivered impressive demos. The board is excited about "transformation." Everything looks promising.
Then, quietly, projects start stalling:
→ The AI that worked in the demo can't handle your actual data → Integration with existing systems becomes a nightmare
→ Your team doesn't trust the outputs → Costs spiral beyond projections → Timelines slip from months to quarters to "under review"
Six months from now, you'll be one of the 42% of companies that abandoned most of their AI initiatives this year.
I know this because I've already watched this movie. Different technology. Identical failure patterns.
THIS MAY BE YOUR FIRST RODEO. IT'S NOT MINE.
In 1991, I was building computer-controlled, camera-guided, AI-driven autonomous robots when most factories were still using ladder logic PLCs. I wasn't just bolting new technology onto old systems, I was creating the integration methodology that made them work together.
While Ross Perot's EDS was charging General Motors $2.5 billion to integrate their computer systems, I was solving the same class of problems for mid-market manufacturers:
How do you marry technologies that have never worked together? How do you design systems that actually deliver on their promise? How do you make disparate vendor solutions function as one integrated whole?
The specific technologies were different. The integration challenges were identical to what's happening with AI today.
WHY THIS FEELS LIKE DÉJÀ VU
In the early 1990s, the technology landscape was fragmented: → Mainframes handled accounting → PCs lived in engineering departments
→ PLCs controlled factory equipment → These systems didn't talk to each other
Sound familiar?
Today's landscape looks remarkably similar: → Legacy ERP systems handle business operations → Cloud applications manage various functions → AI models promise intelligent automation → These systems don't integrate naturally
The challenge then: Making computers, cameras, vision systems, and learning algorithms work together as one system.
The challenge now: Making AI, existing business systems, data pipelines, and human workflows work together as one system.
Same movie. Different technology. Identical failure patterns.
THE FOUR REASONS YOUR AI PROJECT WILL FAIL
After 30+ years of watching technology paradigm shifts and building intelligent automation systems before AI was trendy, I can tell you exactly where your AI initiative will break:
1. Design Failure
Your AI wasn't designed as a system. It was purchased as a product.
You're trying to bolt AI onto processes that were never designed to accommodate it. Nobody mapped the actual workflow. Nobody designed the data architecture. Nobody asked, "How does this integrate with the 47 other systems we're running?"
I learned this in 1991: You can't just connect a camera to a robot and call it "vision-guided." You have to architect how the camera feeds data, how the computer processes it, how the control system responds, and how the human operators interact with it. Every interface matters. Every handoff is a potential failure point.
The failure isn't in the AI model. It's in the integration architecture that should have been designed before you bought anything.
2. Leadership Failure
The people approving your AI budget typically don't understand AI.
They watched a compelling demo. They heard the ROI projections. In some cases, they're making million-dollar decisions about technology they couldn't explain to a technical team if their life depended on it.
This isn't an insult, it's a pattern. In the 1990s, executives who'd never programmed a computer were buying ERP systems. Today, executives who don't understand machine learning are deploying AI agents. Same movie, different decade.
The dangerous part: The demo always works. It's production that fails. Because the demo doesn't have your messy data, your legacy systems, your organizational complexity, or your actual business constraints.
3. Operational Failure
You have no methodology for making AI work in production.
Where are your SOPs for AI governance? Your training protocols? Your framework for human-AI workflow integration? Your change management process? Your procedures for when the AI is wrong?
You're trying to run AI the same way you run traditional software. It won't work. AI requires a fundamentally different operational approach and nobody's building that for you.
4. Dependency Without Protection
You're building your business on vendor platforms without understanding the power dynamics.
This is the lesson I learned the expensive way. In 1999, a client tried to appropriate the technology and methodology I'd developed. We won the legal battle but lost the company to legal fees.
Today's version: You're integrating AI from vendors who are bigger than you, richer than you, and whose interests may not align with yours three years from now. You're building critical business processes on their platforms. But do you have an exit strategy? Can you switch vendors? Do you own your data and your business logic?
Most companies don't think about this until it's too late.
WHY COMPANIES KEEP MAKING THE SAME MISTAKES
Here's what nobody tells you about technology transformations:
Many of the vendors selling you AI solutions have never implemented AI at scale in a business like yours. They've sold software. They've run demos. They've closed deals. But they haven't lived through the messy reality of integrating new technology into complex, legacy-laden organizations.
Your consultants are reading the same AI articles you are. They're smart people applying frameworks designed for traditional IT projects to a fundamentally different technology paradigm.
Your internal team is learning on your dime. They're brilliant technologists. But they've never integrated a genuinely new technology class into production. They've deployed software, but they've never built the integration architecture for technologies that have never worked together before.
You're all pioneers. Which means you're all going to make the expensive mistakes pioneers make.
Unless you work with someone who's already been through this transition.
I'VE ALREADY LIVED THROUGH YOUR NEXT 5 YEARS
I founded Industrial Systems Design Group in the early 1990 because I saw companies desperately needed someone who understood both the technology and the business integration challenge.
Not someone who could sell them hardware.
Not someone who could run a pretty demo.
Someone who could design integrated systems that actually worked in production.
As founder, CEO, COO, and lead system designer, I didn't just advise, I did. I made the strategic decisions, ran the operations, and designed the technical architecture. I integrated technologies that had never been connected before and made them work reliably in production environments where downtime costs money.
Here's what makes my experience directly relevant to today's AI challenges:
In the early 1990s I integrated: → Computers (processing power) → Cameras (data input) → Vision systems (data interpretation) → Learning algorithms (adaptive intelligence) → Control systems (physical action) → Human operators (workflow integration)
In 2025, you're integrating: → AI models (processing power) → Data pipelines (data input) → Analytics systems (data interpretation) → Machine learning (adaptive intelligence) → Business systems (organizational action) → Human workers (workflow integration)
Same integration challenge. Different components.
WHAT WORKING TOGETHER LOOKS LIKE
I don't sell AI tools. I don't run flashy demos. I don't promise transformation in 90 days.
What I do is translate 30+ years of integration experience into your specific situation:
I help you design AI systems that work.
Not in a lab. Not in a pilot. In production, integrated with your actual business processes, delivering measurable value. Because I've done this before, built intelligent automation that had to work every single shift.
I help your leadership make qualified decisions.
I translate the technical reality into business terms that let you evaluate AI initiatives the same way you'd evaluate any major capital investment. Because I've sat in the CEO chair making these decisions and the COO chair living with the consequences.
I help you build the operational framework AI requires.
The SOPs, governance structures, training protocols, and change management processes that turn exciting technology into reliable business capability. Because I learned that the operational framework determines success more than the technology itself.
I help you protect your interests when dealing with larger vendors.
After fighting and winning a legal battle to protect my IP against a bigger adversary, I know the power dynamics at play and how to ensure you maintain control of your business processes, your data, and your strategic flexibility.
THE ENGAGEMENT
Strategic Assessment (Weeks 1-2)
→ Comprehensive evaluation of your AI readiness across five critical failure dimensions → Integration architecture review: Do your systems actually support AI, or are you bolting it onto infrastructure that can't handle it? → Identification of your highest-risk gaps before they become expensive problems → Clear roadmap of what needs to happen before any technology decisions
System Design Consultation (Weeks 3-8)
→ Integration architecture design, not just AI selection, but how AI connects to your actual business systems → Vendor evaluation from technical, operational, and strategic perspectives → Protection strategy to prevent vendor lock-in and maintain organizational control over your processes and data → Technical translation for executive decision-making
Operational Framework Development (Weeks 9-16)
→ Standard Operating Procedures for AI governance and quality control → Training and change management protocols that build trust in human-AI workflows → Error handling and continuous improvement systems (because AI will be wrong, and you need procedures for when it is) → Adoption frameworks that drive usage, not resistance
Implementation Oversight (Ongoing)
→ Independent review of vendor deliverables and integration quality → Course correction before small problems become expensive failures → Executive translation: technical reality in business terms → Protection of your interests throughout the vendor relationship
MY GUARANTEE
I won't tell you what you want to hear. I'll tell you what you need to know.
If your AI initiative is headed for failure, I'll tell you exactly why and exactly what needs to change. If your integration architecture won't support AI in production, you'll know before you waste resources learning it the hard way. If your vendor is overselling and under-delivering, I'll call it out. If your internal team doesn't have the right expertise, we'll address it.
This isn't about making you feel good about your AI strategy. It's about making your AI strategy actually work in production.
Because I've already built intelligent automation that worked every single shift. And I know the difference between "works in a demo" and "works in your business."
THE DECISION YOU'RE ACTUALLY MAKING
You're going to deploy AI. That's not the question.
The question is: Will you be in the 42% that fails, or the small percentage that succeeds?
Right now, you're probably on track to fail. Not because your team isn't talented, not because you haven't invested enough, but because you're making the same structural mistakes that doom most technology transformations.
You're treating integration as an afterthought.
Integration is the entire challenge. The individual technologies work fine. It's making them work together that determines success or failure.
You're letting vendors drive your architecture.
I learned what happens when you become dependent on larger players who don't share your interests. Today's AI vendors are creating the same dependencies, unless you architect for independence from day one.
You're deploying technology without operational frameworks.
Humans working alongside intelligent automation need clear procedures, training, and trust-building. You can't just "turn on the AI" and expect adoption.
I can't give you back the money you've already spent. But I can prevent you from wasting resources proving what I already know won't work.
CLOSING
Because somewhere out there is a version of your company that successfully deployed AI. That version started by asking better questions and working with someone who'd already built intelligent automation that worked in production.
The only question is: Is that version you?
SCHEDULE A 20-30-MINUTE CALL TO SEE IF THERE'S A FIT
No sales pitch. No obligation. Just an honest conversation from someone who's built intelligent automation in production environments and knows exactly where the integration challenges hide.
Schedule a 20-30-minute introductory call
Your AI Initiative Is About to Fail
(And You Don't Even Know Why)
42% of companies abandoned most of their AI initiatives in 2025, up from just 17% in 2024.
85% fail to achieve their intended outcomes. Only 5% see rapid revenue acceleration.
I know this because I've already watched this movie. In early 1990s, I built one of the first AI-driven autonomous robots. Today's AI failures follow the exact same patterns as the ERP integration crisis of the 1990s.
After 30+ years as system designer, CEO, and COO, I help companies avoid becoming part of the 85% failure rate.