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.

We are facing a complete upheaval in Marketing technology, and it is affecting our colleagues who are looking to us to help them use technology to make the Business different and better.

What worked before isn’t working anymore.  Marketing automation systems that rely on cold emails and cleverly worded messages are seeing all time low open rates.  AI based email messaging is exploding, with more relevant messages, careful timing, and a more and more apathetic audience that is tired of their email exploding every day.   Multi-channel approaches may create awareness, but there is only so much awareness to go around.

At the same time, more and more startups and associated capital are creating more and more marketing platforms, with more and more promises to help you and your company connect effectively with customers – and prospects, a very different challenge that’s often conflated under the same name.  How many new platforms?  How about around 28% year over year for the last 13 years, or up over 9000% total?

To rephrase the first line in this column—It seems that Marketing Tech companies need Marketing Tech to sell their Marketing Tech.

With 14,000+ platforms to choose from, Marketing will need help sorting out the difference between the platforms themselves and the business capabilities these platforms can enable. Without a clearly delineated technology-to-capability map, your business might end up with a bunch of properly installed and functioning, but ultimately unused shiny balls sitting on a virtual shelf.

It is possible that you and your marketing team are already in conversations about this situation.   The Marketing department is probably also questioning its budget, staffing, agency partnerships and more. Given that this is the first place that AI is disrupting a major part of your business, you are probably working out a roadmap on how to respond.

Here are a few guesses as to where to start—

1. AI thrives and feeds on consistent, complete data sets, and won’t work in silos. Do you remember that Data Warehouse project that you needed to start?  Now is the time to get cracking.  That Data Warehouse won’t build itself, and the team really needs your wisdom to get this key project started by the end of the year.  (This may turn out to be the most important project of your career). A strong data foundation will support AI initiatives and set the stage for future success. Whether you use some sort of Large Language Model AI or another type, your data warehouse is going to the key ( or at least, a key) resource to train and educate the AI.
2. You will need the strongest BS detector you can muster to sort out the good ideas from the bad ones in Marketing Technology—Remember, these people are all Marketers! They are really good at ideation and getting these ideas in front of you. Ask for references, trial periods, and contracts that are easy to move on from.
3. Your Marketing colleagues may want to ask you about how to measure success and get attribution. Here, you need to break some news to them—Google and anybody else’s ROI and attribution tools are more hunches and superstitions than fully baked solutions.  Just because Google tells you that you are getting 200% ROI doesn’t mean that Sales is getting Leads that convert.  They may or may not.  Be nice, but firm when you tell them that these vendor tools are ultimately self serving.
4. The good news is that creativity and small experiments have never been more appropriate tactics. With your newly developed Data Warehouse, with super complete, clean data, you can help others look for key insights, market trends, and different ways of connecting with the customer base and community.  Measure everything, and double down on anything that is promising for a phase II clinical experiment.  Stay flexible and be ready to pivot based on what the data reveals.

Remember, this is the first challenge AI will be handing your company, but not the last.  By prioritizing data integrity, approaching new tools with a critical eye, setting realistic expectations for success metrics, and fostering a culture of experimentation, you can help break the trail in the forest for the team.  This isn’t just about keeping up with trends; it’s about staying ahead and staying relevant for the long term.