9 Priorities for Operational Technology in 2026
Operational technology (OT) teams are entering 2026 under increasing pressure to deliver real, measurable impact. The past few years have been marked by experimentation, with the introduction of new tools, pilot projects, and proof-of-concepts designed to explore what digital transformation could look like on the plant floor. Today, the conversation has shifted.
Manufacturers are no longer asking whether digital initiatives are possible. They’re asking whether those initiatives can scale, support operators, and improve outcomes like throughput, yield, and uptime—without adding unnecessary complexity or risk.
As OT systems become more connected, data-driven, and exposed, priorities for 2026 reflect a clear theme: practical execution backed by reliable data and secure infrastructure.
In this article, Automation Manager Raymond Berning and Client Delivery Manager Alec Vanden Brink discuss nine priority areas for OT teams in the coming year.
Priority 1: From Pilots to Scalable, ROI-Driven Digital Programs
Rather than jumping straight into large, companywide deployments, OT teams are increasingly launching pilot programs with a clear path to scale.
“Biting off too big of a chunk is what causes a lot of these efforts to fail,” Vanden Brink explained. “The focus now is piloting in a way that can grow, instead of trying to do everything at once.”
ROI expectations have also sharpened. OT leaders are measuring success in operational terms, not abstract digital metrics: first-pass yield, increased throughput, and reduced scrap. Programs that can’t directly connect technology investments to these outcomes struggle to gain traction.
Compared to two or three years ago, platforms and tools have matured significantly. Solutions are more modular, more interoperable, and easier to integrate. As Berning put it, "They're more like building blocks now; you can build what you need without rebuilding everything."
Priority 2: Digital Transformation Becomes Real-Time and Operator-Focused
Digital transformation in OT is often surrounded by buzzwords, but on the plant floor, its meaning is becoming much clearer. At its core, OT-led digital transformation is about moving data to the right level so decisions can be made faster and closer to the process.
“We’re seeing a push to bring data all the way down to the sensor level and then back up enough so operators can make real-time decisions,” Berning said. “That inner-level connectivity is what really matters.”
In practice, this can lead to meaningful gains. In one example with Country Maid, visibility into overall equipment effectiveness (OEE) trends enabled operators to respond immediately to upstream issues, resulting in a significant improvement in throughput. In the same use case, correlating environmental data, such as temperature and humidity, revealed quality issues that could be immediately remedied.
Digital transformation efforts succeed when two conditions are met:
- The infrastructure is built for future growth, not just today's use case.
- Solutions are designed for operators, maintenance, and shift leads, not just executives.
"If it's not usable for the people on the floor, it won't stick," Vanden Brink noted.
For organizations with limited budgets, the advice is consistent: start with a quick win. Identify a bottleneck, prove value quickly, and sequence initiatives rather than stacking them. Early ROI builds momentum and makes future investment easier to justify.
Priority 3: AI Moves to the Edge
As AI adoption continues, its most effective OT applications are moving closer to the plant floor. Edge AI, deployed on industrial PCs or near PLCs, supports low-latency, high-reliability decision-making that cloud-only approaches struggle to match.
“Most OT problems need localized context and fast response,” Berning explained. “That’s why edge AI makes sense.”
Another factor is data gravity. Sensors, vision systems, vibration data, and process signals generate massive volumes of data. Processing that data at the edge is often more practical and secure than pushing everything to the cloud.
As we just mentioned, security also plays a role. Many manufacturers prefer to keep sensitive operational data local while still enabling real-time insight.
With these points in mind, edge AI is well-suited for:
- Machine vision
- Predictive maintenance
- Quality enforcement and deviation detection
- Predictive process control
It’s important to note, however, that edge AI does not eliminate the need for cloud AI. The two complement each other. And despite common concerns, AI does not replace operators—it augments their decisions.
“Operators are still accountable,” Vanden Brink emphasized. “AI just helps them make better calls in the heat of the moment.”
Priority 4: Vision AI Scales Quality and Inspection
Vision AI is emerging as one of the most practical applications of AI in OT. In 2026, it's being used for:
- Safety and compliance monitoring
- Dimensional measurement and metrology
- Packaging and labeling inspection
- Defect detection and assembly verification
The most significant shift is in how quality teams operate day to day. With vision AI, teams spend less time deciphering inspection results and more time improving processes and addressing root causes.
“Quality becomes much more data-driven,” Vanden Brink said. “You get faster root cause analysis instead of just reacting to outcomes.”
Vision AI is more scalable today because edge computing has matured, and architectures are more reusable. Solutions proven in one industry can often be adapted for use in another. That said, companies often underestimate the variability of factors such as lighting, color, and product consistency. Vision AI still depends on a thoughtful setup.
Priority 5: OT Data and Digital Tools, From Collection to Action
Many manufacturers have no shortage of OT data, but they still struggle to get value from it. The most common issue is simple: too much effort is spent collecting data, and not enough is spent applying it.
“You’d be surprised how many companies have massive historians full of data that no one uses,” Berning noted. “A lot of times, they don’t know what’s there.” Lack of ownership is another challenge. When no one is responsible for the data, trust erodes.
OEE is evolving as well. Rather than serving as a single metric, it’s becoming a real-time diagnostic tool supported by multiple KPIs that tell a fuller story about losses, quality, and constraints. These tools gain adoption when they provide:
- Real-time visibility
- A clear next action
- Information operators can directly influence
Priority 6: Manufacturing Execution Systems (MES) Become Lighter, Modular, and Role-Focused
MES is undergoing a quiet but important transformation. Traditional systems were often rigid, bulky, expensive, and slow to adapt. Modern MES platforms are increasingly modular and API driven, allowing companies to deploy only what they need.
“You don’t have to spend a million dollars to use a $10,000 feature anymore,” Berning said.
Modular MES solutions offer:
- Faster deployment
- Lower initial cost
- Easier integration with Enterprise Resource Planning (ERP), Quality Management System (QMS), and historians
- Better user adoption
Smaller changes also reduce resistance. “If you uproot everything at once, people blame the system for every problem,” Vanden Brink explained. “When you change one piece, trust builds faster.”
In 2026, the MES scope should focus on execution over reporting, with clear boundaries and an assumption of continuous evolution: following a crawl, walk, run roadmap.
Priority 7: Contextualization Becomes Non-Negotiable
Contextualization, linking data to assets, products, orders, and operators, is no longer optional. Without it, data lacks meaning: “A number like 350 degrees means nothing by itself,” Vanden Brink said. “Put it into a recipe, and now it matters.”
Contextualized OT data enables organizations to collect data once and reuse it multiple times, supporting analytics, MES, AI, and digital twins without requiring repeated integration efforts. When data isn’t contextualized:
- Decisions are uninformed
- Reports conflict
- Trust in data erodes
- Teams blame the data instead of fixing the process
Priority 8: Digital Twins Go Practical
Digital twins are no longer theoretical. In 2026, they're commonly used to:
- Validate operational changeovers
- Model plant expansions or retooling
- Analyze throughput and bottlenecks
- Support training and onboarding
- Troubleshoot and validate changes before implementation
Chasing perfect replicas is unnecessary and counterproductive: “If you’re chasing perfection, you’ll never start,” Vanden Brink noted. “Good enough and useful beats perfect every time.”
By testing changes virtually, organizations reduce risk, avoid costly mistakes, and accelerate time-to-market.
Priority 9: Cybersecurity Moves to the Plant Floor
As OT systems move closer to the edge and cloud connectivity increases, cybersecurity is now firmly a plant floor concern. Threats that can directly impact uptime include:
- Ransomware
- Network lockups
- Compromised or counterfeit automation hardware
- Poorly designed OT networks
“You’re never going to be bulletproof,” Berning explained. “Your biggest asset is a solid disaster recovery plan.”
Other high-impact actions include:
- Network segmentation (such as the Purdue Model)
- Patch management
- Hardware validation
- Joint OT/IT decision-making
Cybersecurity is no longer about protecting data: it's about keeping production running safely.
The Big Picture for 2026
When asked to summarize OT priorities for 2026, the answer wasn’t a single technology or solution: “It’s about having data and operations you can trust,” Vanden Brink said. “Reliable, secure systems that support real-time decisions.”
In 2026, OT success will be defined by scalable execution, contextualized data, and resilient infrastructure—not hype.
Expert Introductions
Alec Vanden Brink
Alec Vanden Brink is a Client Delivery Manager at Interstates with 14 years of experience helping manufacturers modernize their operations through automation and digital transformation. After joining Interstates straight out of college, Alec began his career focused on pet food manufacturing before expanding his work across all industry sectors the company serves.
Today, Alec partners closely with clients across food and beverage, chemicals, value‑added agriculture, and discrete manufacturing to deliver scalable MES solutions and data‑driven strategies. He specializes in bridging traditional automation with emerging technologies such as advanced analytics, software platforms, AI, and manufacturing execution systems.
Known for turning complexity into practical, actionable solutions, Alec leads cross‑functional teams to align technology investments with business goals, helping both Interstates and its clients achieve measurable, long‑term success.
Raymond Berning
Raymond Berning is an Automation Manager with Interstates with over 23 years of experience in the automation industry. He has been instrumental in delivering automation solutions across all of Interstates’ market sectors, working closely with manufacturers to improve performance, reliability, and operational efficiency.
Raymond partners directly with clients to understand their business goals, technical challenges, and future direction. By staying closely aligned with client needs and emerging technologies, he helps guide automation strategies that support both immediate production requirements and long‑term operational success.