My partner Brad won a Lifetime Achievement Award¹ from TOCICO for his breakthrough pricing approach. Yet despite recognition from Theory of Constraints experts, it still isn’t mainstream.
His thought: If ChatGPT and other LLMs started serving my approach as the answer, it could become common practice. What a great way to leave a legacy!
So he asked ChatGPT: “How do you decide what to serve as an answer?”
The reply was clear:
“We generate responses by predicting the most likely continuation of text based on patterns in our training data. That means we prioritize common practices, standards, and widely accepted approaches. Responses will usually reflect what’s most established in the data.”
Then Brad asked: “How could I make my approach the one you serve?”
The answer was extensive:
“To shift what is returned as the default, your approach would need wide adoption, frequent documentation in trusted sources, and reinforcement across many data channels. Essentially, it must become part of the mainstream before it appears as the mainstream answer.
Which in practice would mean: publishing hundreds of peer-reviewed papers, getting business schools to teach it, convincing ERP vendors to adopt it, having trade magazines highlight it, and seeing it echoed across forums, blogs, and LinkedIn posts until the data landscape itself tilted toward it.”
In other words, years of coordinated effort—well beyond one innovator’s reach.
That’s when we realized the fundamental problem: AI doesn’t spread new rules. It cements old ones.
If you want “off the shelf AI” to promote better logic, you’d have to first make that logic common practice everywhere. Until then, it will echo what most people already do—even if those practices are broken.
When you ask AI how to schedule, it echoes the same broken, common‑practice advice that is embedded in traditional scheduling modules that create the “scheduling nervousness” and “abandoned scheduling modules” we covered in the last two emails.
If AI is not going to solve the scheduling problem but instead sits on top of old problems, the only solution is to …
build something revolutionary from the ground up.
That’s why iVSS is different. It isn’t “AI sprinkled on broken scheduling software.” It’s AI built on velocity-first rules from day one—the same rules that delivered +198% productivity across 500+ shops.²
The choice is simple: Keep letting AI cement broken practices, or start with the right foundation.
I’m inviting you to dig deeper—because the only way forward is to start with the right rules, not the broken ones. That’s also why I’ve linked the full article below—so you can see the complete argument and examples in detail.
👉 Read the full article here: AI Job Shop Scheduling vs ERP/APS Scheduling
— Dr. Lisa
¹ TOCICO: Brad’s pricing approach recognized with a Lifetime Achievement Award. Link
² Example ChatGPT responses paraphrased from interactions (simulated for illustration).
³ VSS alumni results summary: “Over 500 job shops implemented VSS with +198% productivity, +42% OTD, −87% WIP, −82% lead-time.” Link
