Tina Baumgartner

Technology Challenges of AI in Manufacturing: 4 Issues You Need to Address

TL;DR:The hardest part of implementing AI in manufacturing is often not the model itself, but the surrounding technology landscape. Four key technology challenges of AI in manufacturing come up repeatedly: integrating AI with existing systems, ensuring the network infrastructure can handle high-volume data streams, particularly image data from fast production lines, deciding between cloud and […]

Technology Challenges of AI in Manufacturing: 4 Issues You Need to Address Read More »

AI Change Management in Manufacturing: 4 Challenges to Be Aware of

TL;DR:When manufacturers implement AI, the hardest work often is not developing models or collecting data but AI change management. Four recurring challenges show up: fear of job cuts on the shop floor, misalignment among executives on what AI is for, lack of education and coordination across affected departments such as quality, operations, engineering, and maintenance;

AI Change Management in Manufacturing: 4 Challenges to Be Aware of Read More »

AI Implementation in Manufacturing: Don’t Let These 4 Data Challenges Slow You Down

TL;DR:Four data challenges that often slow down AI implementation in manufacturing: knowing which data to collect in the first place, not having (or not being able to access) the right data, lacking a clear strategy for storing and curating the growing data volume, and not having a cloud strategy that scales economically. For quality control, missing

AI Implementation in Manufacturing: Don’t Let These 4 Data Challenges Slow You Down Read More »

Video: KI in der Fertigung (AI in Manufacturing)

We recently gave a presentation about AI in Manufacturing to an audience at Cyberlago, a German organization in the Lake Constance region that promotes digitization, digital transformation, and IT. Here is the link to our presentation titled KI in der Fertigung (in German) and an annotated pdf version. KI in der Fertigung CyberLago final annotated

Video: KI in der Fertigung (AI in Manufacturing) Read More »

The Role of AI Models in Visual Inspection

TL;DR: AI models in visual inspection move the work from hand-coded rules to learning directly from real defects and good parts. When properly trained and validated, AI models in visual inspection deliver more consistent, scalable decisions across lines and plants—while engineers and quality teams still define the right problems, data, and guardrails. If you are

The Role of AI Models in Visual Inspection Read More »

4 Questions to Ask Before Swapping out Human Labor For AI

TL;DR:Before swapping out human labor for AI, manufacturers benefit from slowing down and asking four basic questions: is the task actually suitable for AI, what outcomes are you optimizing for, how will this change work for the people doing the job today, and how will you measure and govern the system over time. Done well,

4 Questions to Ask Before Swapping out Human Labor For AI Read More »

How to trust AI in Manufacturing?

TL;DR:Trust AI in manufacturing on the shop floor is less a technical problem and more a human one. Operators and engineers want to know whether an AI model is actually right, not just fast. Trust builds in two main ways. First, consistent performance over time: when a model repeatedly catches real defects, flags issues early,

How to trust AI in Manufacturing? Read More »

Transitioning Manufacturing to Industry 4.0 – Perspective from the Shopfloor

Recently Facebook served up a sponsored post in my feed that caught my immediate attention: an article about the work of John Carrier, a senior lecturer at MIT Sloan, about the challenges of manufacturing companies transitioning to Industry 4.0. This drew my attention, not just because I am a Sloanie myself, but because helping companies

Transitioning Manufacturing to Industry 4.0 – Perspective from the Shopfloor Read More »

Transfer Learning – Your Shortcut to Custom AI-Models

AI models learn by seeing examples. That’s AI’s defining superpower; instead of painstakingly defining an object at the pixel level you can just show it some pictures and it will learn to identify that object. However, even if it is a superpower, it doesn’t come without challenges. The challenge lies in the word “some”. “Some”

Transfer Learning – Your Shortcut to Custom AI-Models Read More »

Better, Faster, Cheaper – AI for CPG Manufacturers

Consumer Packaged Goods (CPG) manufacturers deal with a challenging set of demands and constraints related to quality inspection: they operate very high-speed lines with cycle times in the tens or hundreds of milliseconds and they are under constant pressure to reduce cost. Can AI help CPG manufacturers address these issues? CPG Quality inspection now Currently,

Better, Faster, Cheaper – AI for CPG Manufacturers Read More »

Expert Systems are the ASCIs of AI – Machine Learning the CPUs

One of my colleagues led a workshop on AI in manufacturing recently and a comment of one of the participants deserves a deep dive. “We don’t need expensive ML servers,” she said, “we can just code this up in SQL.” At the heart of this comment is a fundamental misconception that artificial intelligence equals expert

Expert Systems are the ASCIs of AI – Machine Learning the CPUs Read More »

Data – The Secret to Successful AI Deployment in Manufacturing

There is a secret to a successful AI deployment in manufacturing and it is not cool algorithms – it’s data. In fact, 80% of the effort of an average AI implementation is getting the data needed in the right format so you can start training the algorithms. Generating, collecting and cleansing data is a decidedly

Data – The Secret to Successful AI Deployment in Manufacturing Read More »