There is a reason I keep talking about AI, technology and the changing way buyers find and evaluate manufacturers. The technology itself is only part of the story because the bigger issue is how quickly manufacturers are willing to adapt when technology starts changing the way business gets done.
If you have spent any time around manufacturing, you already know that most manufacturers are not standing in line to be the first company to try something new. There are exceptions, of course, but the culture of manufacturing tends to favor proven results, fiscal responsibility and a healthy skepticism about the latest “game-changing” technology. I understand that mindset because when you are responsible for payroll, equipment, inventory, customers and a production schedule, experimenting with something unproven can feel like an unnecessary risk.
That is where the Technology Adoption Lifecycle becomes interesting. The model generally divides adopters into five groups: innovators, early adopters, early majority, late majority and laggards. Innovators are willing to experiment before everyone else, while early adopters see strategic opportunity and are willing to move before the evidence is overwhelming. The early majority wants proof, the late majority wants even more proof and often waits until adoption becomes economically necessary, while laggards may not change until the old way is no longer an option.
Manufacturers, particularly small and midsized manufacturers, often sit somewhere between the early majority and late majority. They want to see that something works, they want to know what the ROI is, and they want to hear from another manufacturer who has already done it. They also want the technology to be stable, affordable and worth the disruption of changing a process that may have been in place for years.
There is nothing inherently wrong with that approach because some of the caution is exactly what makes good manufacturers good at what they do. The problem is that technology adoption does not happen in a vacuum, and by the time a manufacturer feels completely comfortable adopting a technology, its customers, competitors and employees may already expect it.
AI is a perfect example. I have written and spoken about practical AI for manufacturers because I do not believe every company needs to jump on every new AI tool that appears. I am much more interested in where AI can remove waste, improve a process, capture tribal knowledge, help review RFQs, identify quoting mistakes, improve customer communication or give a small team capabilities it did not have before.
That is a very different conversation from telling every manufacturer that it needs AI simply because everyone else is talking about it. The same principle applies to CRM systems, automation, SEO, AI search visibility, digital marketing, analytics and even something as basic as a manufacturer’s website because technology should solve a business problem or create an opportunity rather than become another expense simply because it is new.
There is another side to being fiscally conservative that manufacturers need to consider, and that is the cost of waiting. I have seen manufacturers spend years relying on outdated websites, inconsistent sales processes, manual systems and tribal knowledge because the current approach technically still works, even though the company is slowly becoming less efficient and less competitive.
The website is still online, the salespeople are still calling people, the owner still knows who to contact and someone in the office still remembers how the process works. The company is still getting orders, so there is little urgency to change, but eventually the customer changes, the buying process changes, the competitive landscape changes, or the employees who knew how everything worked retire or move on.
At that point, the manufacturer may discover that the systems supporting yesterday’s growth are not built for tomorrow’s buyer. Prospects are using Google, LinkedIn, AI search and other digital resources to research suppliers before they ever talk to sales, while competitors that have invested in better systems and better customer experiences are becoming easier to find and easier to do business with.
This is where the adoption curve intersects with the growth plateau I have been talking about. A company can get to $3 million, $10 million or $25 million using relationships, individual heroics and processes that live largely inside people’s heads, but eventually the business needs a different operating model. Technology reaches a similar point because there comes a time when continuing to do things manually or relying on outdated systems creates more risk than adopting a new approach.
The question I ask manufacturers is not simply what technology they should buy. I want to know what has changed in their business, their customers’ expectations and their market that their current systems are no longer equipped to handle, because that answer tells us much more about what needs to change than a list of the latest technology ever could.
That is also why my work is not about handing a manufacturer a list of the latest marketing tools and telling them to start spending money. My role is to look at the business holistically by using sales assessments, SWOT analysis, competitive research, Voice of the Customer work and Lean Six Sigma principles to understand where the real gaps are before we decide whether the answer is a process change, a people issue, a technology investment, a marketing strategy, a sales improvement or some combination of them.
Sometimes the answer is not to buy another piece of software because the company has not fully implemented what it already owns. Sometimes the answer is to automate a process employees are wasting hours performing manually, fix the website before spending another dollar driving people to it, or rethink how prospects are nurtured when the sales cycle can stretch for months or even years. In other cases, the answer may be recognizing that AI is not a threat to the business but a tool that can make a small manufacturing team dramatically more capable.
The manufacturers that will thrive are not necessarily going to be the ones that adopt every new technology first. They will be the ones that learn how to evaluate change intelligently, understand the business case and move when the evidence shows that staying where they are has become more expensive than changing.
Being cautious is not the same as being resistant to change, and fiscal responsibility is not the same as refusing to invest. Waiting for everyone else to prove something works can be a reasonable strategy in some situations, but it becomes a dangerous one when the market is changing faster than the company is willing to respond.
Manufacturing has always been about making things better, faster, safer and more efficiently, and technology is simply giving manufacturers new ways to do that. The manufacturers that combine their natural caution with a willingness to evaluate change before it becomes urgent will be in a much stronger position than those that wait until customers, competitors or economic pressure force the decision for them.