Every so often you get a glimpse of the future, but it may not be quite what the people writing the message are hoping that you glean.
I just got back from the International Manufacturing Technology Show in Chicago, and was incredibly impressed by new machine tools, robots everywhere and really smart people trying to figure out the ROI of the latest and greatest systems for manufacturing.
Of course, AI was a prominent topic. Some big infrastructure companies were promising how their solutions could help improve customer sales and experiences, drive connected and sustainable operations, and deliver faster and better R&D and product design. Production planning, supply chain and marketing are all part of these stories as well, and frankly, what is being discussed is well thought out, and seems plausible. There seem to be enough experiments going on at enough companies to learn what is working and not working. One number that stood out from a reputable source is that 60% of manufacturing companies are using Generative AI in production for something right now.
Not surprisingly, the software companies that are linked the closest to design and engineering are some of the software companies making the biggest claims about their new and upcoming products. After Marketing Automation software, CAD, Design, and engineering software might be the next disrupted marketing for generative AI. I don’t see Mechanical Engineers, GIS professionals or others being replaced, but their lives being made significantly easier by having a helper to manage small dimensional updates, material changes, etc. As an example, having an AI help in routine Product Lifecycle Management tasks would be godsend.
Now to the dog that didn’t bark—The backend for all of this.
For engineering and design software to be truly AI enabled, it needs training and historical data, and lots of it. This sort of data is specialized enough that it doesn’t always or necessarily translate easily to traditional data warehouses, RDBMS, etc. Additionally, the data might be on slow SANs or spinning drives collecting dust, or not online all of the time.
Additionally, for years, software companies have been eager to shift more CPU and GPU tasks to the cloud to harness the full power of large-scale rendering and design computing. However, this ambition has been hampered by bandwidth limitations and load issues—both internally and across the internet. I think a real computer scientist would throw up their hands in despair about the fact that these loads are not easy to cache, out of order, and hard to store in any sort of persistent manner. I spent a big part of today, trying to figure out how to talk about the load challenges of nested products in a manufacturing planning algorithm (MRP), and realizing even these loads are challenging.
Ironically, the companies that will solve this issue weren’t present at IMTS. The solution lies with cloud providers, who will need to re-engineer data centers and develop new interconnection models that allow seamless access to massive datasets and decentralized GPU and CPU processing. I believe this evolution will lead to what I call The Great Re-Cloud of 2026. The Old Cloud is going to need to be replaced by the New Cloud.
In this future, you, Mr or Ms Director of IT, are going to be presented some fantastic new options from your Enterprise Software Publisher. These new, game changing capabilities, however, are going to require a deeper sort of migration than you have been faced with previously, and advanced Cloud performance is going to be one of your evaluation criteria. The smart software publishers will partner with cool startups, and the not so smart publishers will try to build their own infrastructure (poorly). Some of these attempts are going to not work out, and it probably makes sense to have a Plan B in your back pocket.
You, however, will become an expert in high performance computing (Or recruit and develop trusted experts for your team). For the nerds, the best days are ahead of us.