It is ironic that as people are struggling with attention [1,2] one of the most powerful features of AI is its ability to be attentive. Given a specific task, these systems will focus on it with the intensity of a grandmaster in the middle of a chess match — no coffee breaks, no doomscrolling. In fact, the now-famous research paper that kicked off much of this AI boom was even titled Attention Is All You Need [3].
This ability to “attend” offers incredible potential for the manufacturing industry, but it doesn’t come without some risks.
In manufacturing, the stakes are high. A chatbot giving a slightly off answer on your movie preferences is an annoyance. A production line doing something slightly off can mean scrapped material, missed deadlines, or worse. AI is great at getting you most of the way there, but it can struggle with those last few polishing steps. This is what we call the final 5% trap.
Let’s break it down:
- What is AI is actually good at?
- What is it terrible at?
- What lessons can we learn to ensure your manufacturing solutions provide you with a competitive advantage?
1) What AI Is Good At – Strengths Manufacturing Can Leverage
What AI strengths can you leverage in your manufacturing business? AI excels at a lot of the same things traditional computing has always been good at, but with an infinitely more flexible, context-aware twist:
- Never needing a break – AI can monitor a process 24/7 without fatigue, or distraction. Perfect for QC monitoring or sensor data review.
- Exploring many possibilities quickly – Like early chess engines, AI can simulate scenarios rapidly and pick the most promising one. In manufacturing, that might mean exploring thousands of scheduling or routing options quickly and efficiently.
- Bringing a lot of context to a challenge – With techniques like retrieval-augmented generation (RAG), and growing “context-windows”, integrations with your internal data are becoming easier and more powerful. AI can make connections that would take a human hours or days to discover.
- Natural language interfaces – Ask a machine in plain English for “the last 10 production runs with yield below 90%” and get an instant, clear answer.
2) What to Avoid with AI – Pain Points For Manufacturing
While this is an exciting time for manufacturing companies to set themselves apart from their competitors, here are some challenges you should be aware of:
- Polish – AI can get you 95% of the way to a solution fast. But deceptively, that last 5% — making it bulletproof and production-safe — can be expensive, time-consuming, or even impossible. This final 5% trap is what causes a lot of promising projects to fail. A vision AI demo detecting defects in 95% of cases during a POC is exciting. But on the production line, that 5% error rate can still mean hundreds of faulty parts escaping detection each day.
- Predictability & QA – Manufacturing thrives on consistency. AI, by nature, is probabilistic. Even small variations can clash with zero-defect standards.
- Poor world modeling – AI sometimes “thinks” in ways we don’t expect. Remember the Google AI that suggested using glue to keep pizza toppings from sliding off? Funny in a kitchen, dangerous in a plant.[4]
3) Key Lessons for Manufacturing Leaders
How can you leverage the exciting benefits of AI while avoiding the pitfalls and the final 5% trap?
- Love the proof of concept — but don’t confuse it with production readiness. The polishing takes time, and sometimes it simply isn’t possible to get systems 100% predictable.
- Aim for 95% zones. Deploy AI where near-perfect is good enough, and keep humans in control where precision is non-negotiable.
- Think “AI as a consultant.” Let it watch, suggest, and flag, but make sure your people make the calls.
The biggest opportunity areas for AI include:
- Continuous monitoring – An AI assistant that reviews process metrics across the entire plant and flags anomalies instantly.
- Material suggestions – AI can do things like proposing alternative suppliers or materials for engineers to evaluate.
- Predictive maintenance – AI excels at flagging unusual metrics like vibration patterns or energy spikes that could signal equipment issues.
- Bottleneck identification – Companies seeking a competitive advantage leverage AI to suggest ways to reduce waste or reschedule to avoid bottlenecks.
Moving Forward Wisely
AI has already proven it can add value to manufacturing. The companies winning right now are the ones that truly understand the new technology, they are selective, strategic, and skeptical. They’re choosing projects wisely, running pilots, validating outputs, and staying aware of the final 5% trap before trusting AI in production.
The opportunities are endless, but for manufacturing leaders, the challenge is to stay focused and practical to ensure a profitable deployment that makes sense on the production floor.
At Ascent DI we believe in the immense potential AI offers manufacturers because it directly impacts product quality, improves efficiency, and ultimately, customer satisfaction and will drive your future business success.
Citations:
- “…the presence of the smartphone has a negative influence on the working speed and thus on cognitive performance and attention.” nature.com
- “…frequent exposure to rapid, fragmented content on platforms like TikTok and Snapchat overstimulates cognitive processes, leading to decreased working memory capacity, impaired cognitive control, and challenges in maintaining focus.” scirp.org
- “We propose a new simple network architecture, the Transformer, based solely on attention mechanisms…” arxiv.org
- “Google’s AI Recommended Adding Glue To Pizza And Other Misinformation…” forbes.com
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