Industrial AI Starts with Access: Unlocking Predictive Maintenance Through Secure OT Data

Learn how secure OT data access and industrial data streaming connect machines to AI for predictive maintenance, reduced downtime, and stronger resilience.

Industrial AI is transforming how manufacturers and critical infrastructure operators prevent downtime. Yet most predictive maintenance programs run into challenges, because the data that fuels them remains locked inside OT systems. According to ABB’s Value of Reliability report, 69% of plants experience unplanned outages at least once a month, with each hour of downtime costing an average of $125,000, or nearly $1 million per eight-hour shift. For production leaders, every unexpected failure means more than lost output; it means lost trust, lost time, and a scramble to understand what went wrong. 

Predictive Maintenance (PdM) promised to change that, and it has... for some. Siemens’ True Cost of Downtime study found that industrial organizations implementing PdM have achieved a 50% reduction in unplanned downtime and an 85% improvement in forecasting accuracy. The message is clear: data-driven maintenance works. 

But here’s the catch: most manufacturers can’t actually get the data they need in the way they need it.

AI’s Growing Role in Manufacturing

AI is not just an experimental exercise. According to IDC’s Future Enterprise Resiliency & Spending Survey (2025), 95% of manufacturers are investing in AI, with over 40% of budgets now directed to Generative and Predictive AI initiatives. From predictive maintenance to automated quality control, AI has become the engine of digital transformation and Industry 4.0—driving faster decisions, stronger resilience, and more efficient production. 

Use cases are rapidly maturing: 

• Predictive AI anticipates failures and schedules maintenance before a line goes down. 

• Interpretive AI powers real-time defect detection and visual inspection on the floor. 

• Generative AI optimizes product design and maintenance workflows through pattern learning. 

As Rockwell’s State of Smart Manufacturing 2025 report notes, half of all manufacturers plan to use AI/ML to support quality control in the next 12 months. The industrial AI revolution isn’t coming, it’s here. 

So why do so many organizations still struggle to realize their full potential? 

See what’s next for OT security. [Read the Gartner® Cool Vendors™ 2025 Report →]  

The OT Data Dilemma: AI Starts with Access

AI is only as good as the data it receives. Most collect massive volumes of data, but less than half use it effectively (Rockwell, 2025). And the first step to AI that is often overlooked is secure, consistent access to the data that makes AI possible. 

In IT, this is straightforward. You can install agents, locate databases, and centralize data so large language models (LLMs) can analyze it. In OT, it is a different story. Often the locations where data historians hold the data to feed PdM analysis sit in aging environments that can’t host agents (due to limited memory, compute, or old OS) or shouldn’t. 

That is the OT data dilemma. Manufacturers have more data than ever, but less insight. They’re running powerful AI platforms with limited visibility into what’s actually happening on the plant floor, creating blind spots in quality, maintenance, and operational efficiency. 

Until you have the access problem solved, AI will operate with partial visibility.

Unlocking the Full Value of OT Data with Secure, Real-Time Industrial Data Streaming

At Dispel, we believe the next wave of industrial AI won’t just be defined by algorithms; it’ll be defined by OT data access. 
 
Our Industrial Data Streaming solution allows organizations to securely reach data historians every few minutes or hours, detect changes, and transfer information to cloud or on-prem AI systems without installing agents or disrupting production. 
 
This creates a positive feedback loop. The faster and safer an organization can access its data, the better it can maintain that data and the more value it delivers through predictive maintenance and optimization. 
 
Together with Dispel OTFusion, which unifies applications, data, and systems under a Zero Trust framework across many facilities, Industrial Data Streaming turns isolated environments into secure, intelligent networks that AI can learn from in real time. 
 
When access becomes secure, AI becomes actionable, and industrial performance becomes predictable. That is the foundation of digital transformation, and it starts with secure, real-time data streaming.