AI, automation, edge computing, and more: these technologies are advancing rapidly and will continue to shape manufacturing operations in 2026. As these innovations enter the marketplace and reach factory floors, business leaders will need to make critical decisions as to which tools are best suited for their operations and how to implement them strategically.
Here are our top five predictions on what the industry will encounter next year regarding the latest technology, and how manufacturers can respond accordingly:
Table of Contents
#1 AI Will Expand to Process Numerical Data
By now, many are familiar with using generative AI for natural language Q&A. Today’s large language models (LLMs) successfully execute tasks such as summarizing articles, drafting messages, and answering questions you’d otherwise feed to a search engine. However. LLMs still struggle to handle complex datasets—such as the large swaths of operational data generated on plant floors.
For complex industries like manufacturing, we’re still waiting for generative AI that can accurately interpret large-scale numerical datasets and understand the operational intricacies of production environments.
The next generation of AI technology must be able to properly understand the data science, statistical analysis, and system controls necessary to generate accurate, numerical insights about production. In this industry, accuracy matters. AI tools will need to consistently provide correct information to best prevent quality deviations, unnecessary waste, or delays.
#2 Companies Will Adopt AI Incrementally
Throughout 2025, we’ve seen a lot of hype around AI, which can overinflate expectations. AI won’t revolutionize your operations overnight—or even over the course of days or weeks. In 2026, manufacturers will implement AI through targeted, incremental use cases rather than broad, sweeping change.
For example, the rise of niche AI agents may be one of the first steps towards implementing agentic technology in manufacturing. These agents are designed to apply AI to just one specific step of a process. For example, an AI agent specifically designed to work with an MES may be implemented to monitor and report on a particular KPI; a task that, historically, may have been done manually. This isn’t an all-encompassing AI solution; it’s a tool that takes action to streamline a specific task.
This step-by-step approach will help manufacturers avoid the pitfalls of inflated expectations and ensure that AI complements, rather than disrupts, existing processes.
#3 Growing Emphasis on Data Quality
Once again, AI is the star of this prediction. As AI tools have gained traction, so has the awareness that LLMs are only as accurate and robust as the data behind them. To maximize the benefits of AI tools, organizations must optimize their data management strategies so they can train their LLMs with high-quality inputs.
High-quality data depends on two key factors:
- Meaningful Data Collection: Capturing meaningful data is about quality over quantity. Focus on gathering clean, accurate, consistent, and timely data via IIoT devices or software system integrations.
- Contextualization: Raw data can be meaningless without contextualization. Ensuring your data comes with metadata and operational context helps your LLM—and other systems—stay organized, establish benchmarks, and minimize errors.
#4 Continued Pursuit of Standardization
Utilizing technology to standardize production is nothing new, and standardization will continue to be a priority in 2026.
Manufacturers are looking to strike a balance between standardization and flexible extensibility. Instead of one-size-fits-most solutions, manufacturers are gravitating towards offerings with adaptable capabilities. Modern software must facilitate the rapid deployment of standardized, proven functionality and workflows, while also allowing users to adjust and expand deployments as needed.
For example, the TrakSYS SIMS deployment model has become increasingly popular with the growing prevalence of reliable remote internet connectivity. Single-Instance-Multi-Site, or SIMS, allows manufacturers to implement a single, standardized version of TrakSYS and deploy it across multiple plants. This allows common workflows, naming conventions, tags, pick lists, and more to be defined just once and then rolled out uniformly across locations—all while allowing plant-level flexibility when necessary.
#5 Employees Will Need Upskilling in Response to New Tech
New technology demands fresh skill sets. In 2026, we expect a growing emphasis on upskilling and reskilling workers to help them navigate next-gen tools with confidence. After all, the true value of new software investments is only realized when they’re properly implemented and utilized correctly by properly trained users.
And upskilling doesn’t have to start when a new solution is implemented; new skills don’t have to be solution-dependent. Continuously educating your teams helps to build confidence, improve adaptability, and reduce the pressure of reactive training when new solutions are introduced to operations. Areas for practical skill development include data analytics, digital communication, and robotics and automation technology.
Conclusion
The upcoming year holds immense potential for manufacturing technology and the organizations that utilize it. Technology surrounding AI, data, and automation will play a significant role in 2026, though success will hinge on strategic, purposeful adoption.
Interested to learn how our MES, TrakSYS, aligns with these predictions? Contact us today.