How software is closing the SME productivity gap

Posted: June 30, 2026

Small and medium-sized businesses have long missed out on the productivity gains of industrial software—not for lack of interest, but lack of capital.

But that is now changing thanks to AI and out-of-the-box solutions that finally put productivity-enhancing technology within reach of more than just the largest companies.  

This newfound access has potentially enormous implications. The productivity gap between the top of the pack and everybody else is vast: Micro-, small and medium-sized enterprises (MSMEs) account for two-thirds of business employment in advanced economies and half of all value added, according to McKinsey.    

Yet their productivity is far below that of large companies. In manufacturing, where the gap is bigger than in most industries, the mean productivity of MSMEs in advanced economies is only 53% that of large companies.

Why? A big reason is access to technology. While the largest industrial companies use AI and digital twins to optimize production, smaller players have been shut out of the same opportunities, lacking the needed infrastructure, subject matter experts, and support staff. 

Case in point: in a recent Goldman Sachs survey of 10,000 small businesses, 42% reported they don’t have the resources and expertise to successfully deploy AI in their operations.  

Why industrial software is getting more accessible for SMEs

While the barrier to entry used to be prohibitive, these tools are now becoming more accessible—both when it comes to deploying and operating them.  

One of the biggest barriers to implementing industrial twins, for instance, is misaligned data. The time and expertise needed to normalize naming across many different data sets can be prohibitive.  

Now, AI is helping to address this by automating the harmonization of data sources. As a result, curating and owning an industrial digital twin requires far less labor and expertise, making it a much more feasible prospect for smaller companies.

The same is true of AI and advanced analytics: out-of-the-box models now let companies build sophisticated applications without a large data science team, dramatically lowering the barrier to entry.

Small businesses hoping to take advantage of this newfound accessibility should look for opportunities to use native connectivity between different layers of their existing technology stacks. Doing so allows them to leverage SaaS capabilities that can be scaled as needed without large upfront investment in hardware.  

But beware: How companies realize value from their data is critical to success. The solution is not a database somewhere in a cloud, or a “smart” spreadsheet. Instead, companies should put smarter tools directly into the hands of frontline workers, allowing them to use those insights to optimize operations on the ground.

For example, embedding AI into the way of working within a plant means it becomes part of the system and not something that gets added to it (and can disappear again just as easily). This simplifies training, enablement, and change management—resulting not only in lower start-up costs, but also in greater value while running it.  

Three steps to guide SMEs’ software transformation

Of course, historically, half of digital transformation initiatives either fail or stall out. To maximize the chances of success, companies should stick to three key steps to reach digital maturity:

Putting in place a solid data foundation: The first step is making sure data isn’t locked into silos. Successful businesses need a data architecture that can accommodate everything from existing operational technology systems to additional streams from industrial internet of things sensors and smart devices.  

Integrating the tools into ways of working: Running a spreadsheet off to the side is not good enough—it won’t last. Instead, companies need to make sure every initiative becomes part of daily meetings and processes.  

Making it scalable to amplify value: If done correctly, these tools should then be ready to be implemented on other lines, or at other sites, and for additional data to open up new opportunities for optimization and productivity.

To achieve this transformation, companies should look for partners whose offerings span both the IT and OT domains (and have already done the integration work to bring them together), and who can provide out-of-box models and capabilities that can be brought online quickly.  

Even the largest firms fall prey to over- or misdirected investment and the build-versus-buy analysis—thinking they are better off building their own system from scratch. As a smaller company, you can’t afford to start an elaborate process to build a proprietary platform. Thanks to advances in AI and out-of-the-box solutions, you no longer have to.