From expertise to execution: Low-code, no-code, and the future of industrial apps

Posted: July 03, 2026

Here’s a hypothetical: Say a reliability engineer spots a pattern on the line. For instance, maybe a particular pump configuration consistently precedes bearing failure. She submits a request for a monitoring dashboard. When the dashboard finally arrives, it’s very close to what she envisioned, but not quite. The developers built it faithfully to her description—temperatures rising, pressure differential widening—but what our engineer didn't think to mention is that the readings only matter during sustained high-load operation after startup stabilization. It was so obvious to her that she hadn’t thought to stress the point in her request. During normal operations, the same numbers are totally unremarkable. Unfortunately, the dashboard doesn't know the difference. It fires constantly, and within two weeks, operators have learned to ignore it. When it fires for real, nobody bothers to look up.

This dashboard might be 90% of what our engineer needed, but in industrial operations, 90% can easily turn out 100% useless. And in this hypothetical case, it doesn’t matter anyway. By the time the dashboard finally works its way through development queues and competing priorities, let’s say eight months have passed. By now, the plant has already upgraded the pump line.

This hypothetical scenario may seem like a wild run of bad luck, but for professionals in process industries, it probably rings a bell. The problem isn't a lack of data, technical talent, or work ethic. It's the distance between the people who understand plant processes and the people who can build the digital tools to improve them.

Why industrial app development gets stuck in the queue

The traditional software development model doesn't fit neatly into today's industrial realities. When someone on the shop floor identifies an opportunity—a warning signal buried in sensor data, a quality control step that could be automated—the path to a useful, deployable solution often runs through a queue. Most likely, a long one. Across process industries, IT resources are stretched thin, and in many organizations the divide runs deeper than bandwidth. Cisco's 2024 State of Industrial Networking Report found that 41% of industrial organizations still operate with OT and IT teams working independently.^[1] When operational expertise and application development remain siloed, even relatively straightforward improvements can take months to move from concept to deployment.

And these days, a lot can change in the space of months. Supply chains reconfigure; energy prices fluctuate; sustainability mandates evolve and tighten. All the while, as the speed of operational change ramps up, the speed of traditional software delivery remains more or less unchanged.

Even when solutions make it through the queue in time, there’s the second challenge: in the process of turning shop floor experience into operational apps, it’s easy for crucial elements to get lost in the translation. Industrial process knowledge is complex and in large part, tacit, rooted in experience, and difficult to fully capture in development tickets.

To accelerate application development, improve functionality, and shorten that distance between operational and IT expertise, many leading industrial organizations are coming to the same answer: Low-code and no-code solutions.

What are LCNC solutions and what do they do?

Low-code and no-code (LCNC) solutions are visual software development platforms. Instead of traditional, line-by-line coding, LCNC solutions equip users with drag-and-drop functionality and pre-built components, widgets, and wizards to build various operational applications without—or with very limited—technical expertise. These applications might include monitoring dashboards and workflow automation, alerts and notifications, reporting tools, predictive models, and more.

Both low-code and no-code solutions empower non-technical users, reduce costs, and accelerate development, but each caters to different skill levels and project complexities. In the industrial context, that looks like:

No-code: Targeted at plant operators, domain experts, and non-technical business users, these systems rely primarily on visual creation tools and pre-built functionality and require little to no traditional coding.

Low-code: Built for tech-savvy automation engineers and IT professionals, they combine visual tools with the ability to inject minimal custom scripts to manage complex integrations, edge configurations, and heavy data modeling.

What aren’t LCNC solutions and what don’t they do?

We’ve talked about what LCNC solutions are. Now let’s be clear about what they’re not. First of all, they’re not a shortcut around IT, nor are they a license for ungoverned app sprawl.

These platforms don't replace IT teams. They redirect them. Rather than spending their limited capacity building every dashboard, workflow, and predictive model from scratch, IT can focus on what only they can do: governing the platform, securing the architecture, and ensuring that everything built on top of it is scalable, compliant, and maintainable. In other words, IT defines the guardrails; domain experts build within them. Oversight doesn’t go away. App development just moves closer to the expertise.

Second, low-code and no-code solutions don’t replace the engineering-grade systems that run industrial operations at the control level. The logic governing a reactor, a valve, or a safety system, is not the domain of a drag-and-drop interface. Where LCNC solutions operate is the layer above all that: the dashboards, workflows, predictive models, and operator guidance tools that help people understand what's happening now, what’s likely to happen next, and what to do about it.

LCNC in action: TotalEnergies, Western Power, and Cameron LNG

No two LCNC initiatives look exactly alike. The operational challenge might be predictive maintenance, real-time visibility, or broader access to industrial data. The common thread is that they allow organizations to translate operational knowhow into usable digital tools faster and with less reliance on traditional development cycles. Here are three examples of organizations putting that principle into action:

TotalEnergies

Over a period of about four years, TotalEnergies recorded 105 breakdowns due to failures in process-critical assets, particularly in its power generation, water injection, and gas compression equipment. The final straw came in 2009, when an accident took an offshore platform offline for six months, resulting in a loss of 120,000 barrels every day, plus tens of millions of dollars in maintenance costs. This was the genesis of a solution the team calls RAID (Remote Assistance Intervention and Diagnosis), which they built on AVEVA™ PI System™ and AVEVA™ Predictive Analytics.

Using the no-code capabilities of AVEVA Predictive Analytics, the RAID team worked closely with rotating equipment experts to build and deploy AI-driven predictive models across more than 320 shaft lines. The new system generates early warnings that have helped teams identify hundreds of developing equipment issues before they escalated into failures. In upstream operations, the system helped avert a production shortfall equivalent to nearly 500,000 barrels of output. Beyond reducing downtime, and avoiding catastrophic failures, the project also helped TotalEnergies extend its use of predictive monitoring to additional assets and use cases across the business.

Western Power

When Western Power opened a new network operations center in Perth, the utility needed a way to give operators real-time visibility into a vast network spanning more than 255,000 square kilometers.

Using AVEVA PI System and AVEVA™ PI Vision™, the team created more than 50 dashboards and operational displays for the new control center. With the LCNC visualization capabilities of AVEVA PI Vision, engineers were able to rapidly build and deploy displays tailored to different operational needs, helping operators make faster, more informed decisions while improving visibility across the network.

Cameron LNG

At Cameron LNG, critical operations data was largely confined to the control room, leaving engineers, maintenance teams, and other stakeholders with only limited visibility. To make real-time information more accessible, the company deployed AVEVA PI System and AVEVA PI Vision to create role-based dashboards and plant visualizations that could be shared across the organization.

Using the LCNC dashboard and visualization capabilities of AVEVA PI Vision, the team extended access to operational insights beyond control-system specialists, giving more employees a common view of plant performance for smarter, more collaborative decision-making.

Closing the gap between expertise and execution

Even with the most cutting-edge LCNC solutions, everyone is not suddenly a software developer. Instead, LCNC solutions empower the people closest to operational problems to participate more directly in solving them. When the experts can build, test, and refine their own tools—within governed, IT-defined boundaries—those tools arrive faster, fit better, and reflect the kind of tacit operational knowledge that requirements documents rarely capture fully.

Now, AI is poised to push this trend even further, lowering the technical expertise barrier further still. We’re approaching a future in which domain experts can describe what they need in plain language and have working tools emerge from that description (in fact, that future is just about here; read about AVEVA’s Industrial AI Assistant). The gap between expertise and execution is shrinking. For process industries, the organizations that close it fastest will have the advantage in efficiency, and in their ability to adapt to whatever comes next.

Ready to close the gap between expertise and execution?