We’re excited to announce that Rick Heilshorn has been selected to speak at the AIAG Quality Summit this September. This opportunity reflects years of hands-on experience in engineering, manufacturing, quality, and AI-driven innovation and we’re proud to see MAD-Ai contributing to the future of smarter manufacturing and quality systems. If you’re attending the summit, make sure to put Rick’s session on your to-do list.

Agentic AI executes. At the Tactical Edge, knowing isn't enough. While chatbots give insights, MAD-Ai’s Agentic Workforce takes action. Our agents don’t just flag bottlenecks—they navigate manuals, validate parts, and draft recovery plans to close the loop. Stop admiring the problem. We automate the 60% of "busy work" so engineers can actually engineer. In 2026, don't hire a digital librarian—hire a laborer. #AgenticAI #ActionableAI #MADAi #SpeedToImpact #IntegriLLC

The Problem Supplier responses often stall progress because expectations are not consistently defined or communicated: Defect descriptions lack precision Requirements for root cause and corrective action are interpreted differently Tone varies between overly aggressive and overly passive Multiple revision cycles delay closure This creates friction not because suppliers are unwilling—but because alignment is weak. Before (Traditional State) Back-and-forth clarification on basi

Corrective actions are often treated as a single step: define the fix and implement it. In reality, corrective action is the output of a much larger process. Before a corrective action is defined, quality engineers must: Validate the problem statement Confirm containment effectiveness Analyze potential root causes Evaluate contributing factors across process, material, and logistics Each of these steps involves gathering, interpreting, and structuring information from multipl

8D is a standardized methodology. The steps are well-defined. Templates exist. Expectations are clear. And yet, it consistently takes longer than expected. The reason is not the framework—it is the work required to complete it. An 8D does not start with clean, structured data. It starts with: Incomplete issue descriptions Inconsistent defect information Multiple stakeholders providing input at different times Pressure from customers for rapid response Before an engineer can e

There is a gap between how AI is marketed and how it is actually used on the plant floor. In practice, quality engineers are not using AI for abstract analytics or high-level insights. They are using it for very specific, repeatable tasks tied to daily responsibilities. Common use cases include: Building structured 8D reports from issue descriptions Developing 3-Legged 5-Why analyses and corrective actions Drafting supplier responses and customer dispute documentation Reviewi

Treating AI as a traditional project with a fixed scope and "go-live" date is a recipe for stagnation. Unlike static software, AI is a compounding capability that evolves through constant iteration and model advancements. To succeed, leaders must shift from project-based funding to treating AI as permanent operating infrastructure. If your model assumes stability in a period of hyper-acceleration, you aren’t building for growth—you’re designing for legacy.

The next phase of Industry 4.0 has arrived: moving from passive data collection to autonomous execution. While sensors provide data, MAD-Ai provides the Quality Intelligence layer needed to turn that noise into action. Instead of merely analyzing what went wrong, MAD-Ai enables a 24/7 execution layer that prevents failures before they occur. Stop focusing on "what happened" and start building a system that autonomously decides "what is being done about it."