Predictions for 2025: The Convergence of AI, IT, and OT

At the end of the year, I prefer looking ahead rather than looking back—and sometimes, I even take a shot at predicting the future! I believe the future lies in the convergence of AI, IT, and OT—a distributed model where data processing and decision-making happen closer to the source. Predicting the future is always a challenge, but it’s also a great way to spark fresh ideas and inspire meaningful conversations, so please share your feedback!

Where is this proposed shift coming from? Today’s industries are producing massive real-time data streams from IoT devices, sensors, and machines. IDC predicts that 20% of the world’s 157 zettabytes of global data will be generated at the edge in 2024. Yet many organizations still rely on outdated approaches to data management, pushing everything to the cloud in hopes of extracting value.

For years, companies have aimed to bridge the gap between operational technology (OT) and information technology (IT), working to integrate the systems that run industrial operations with those that manage enterprise data and analytics. For most companies, this has meant implementing piecemeal integrations or relying on middleware solutions to connect OT and IT systems. While these approaches have provided incremental improvements, they often fall short of delivering real-time insights, and as AI use cases mature, the time for change is now.

Ok, enough already! Let’s dive into my three predictions that will shape the future of IoT in 2025.

1. AI, OT, and IT Converge

Historically, IT has managed centralized data systems and analytics, while OT has operated industrial machines and infrastructure. These two domains ran in silos, and the pace of IT/OT convergence has been slower than hoped. Many organizations have struggled to break down silos and fully integrate these systems, limiting their ability to unlock true value. But what was a “nice to have” is now becoming a necessity. 

Why? AI is accelerating this convergence by demanding faster, more cohesive data flows to fuel insights. The true value lies not just in integrating IT and OT systems but also in leveraging the new insights and intelligence that AI can deliver when both systems are seamlessly integrated. Unified architectures like UNS are emerging as the backbone of this convergence, enabling seamless integration of operational and IT data for faster, smarter decisions. 

In the future, the convergence of IT and OT, with AI as the driving force, will be game-changing. Organizations will achieve complete visibility across the enterprise, breaking down silos to create a unified and actionable view of operations and data. By seamlessly integrating data flows and harnessing AI-driven insights, companies will find operational efficiencies, minimize downtime, optimize resource utilization, and accelerate innovation. The result will be more than just smarter systems—it will be agile, data-driven enterprises that excel in competitive and rapidly-evolving markets. Organizations that fully embrace this convergence will set the benchmark for speed, agility, and efficiency in the modern era of operations.

2. The Edge is the AI Battleground

Edge computing began as a way to process local data and reduce bandwidth costs. Many organizations adopted edge solutions to minimize the expense and latency of transferring data to centralized cloud systems. However, these implementations often stopped short of leveraging the full potential of edge computing, focusing only on basic data aggregation and filtering while relying on cloud infrastructure for decision-making.

Centralizing data in the cloud initially seemed like the natural progression as businesses embraced AI and analytics. Yet, this approach has revealed its limitations. Latency, bandwidth costs, and connectivity issues make cloud-only AI impractical for real-time decision-making, particularly in industries like manufacturing, logistics, and healthcare, where milliseconds matter. Rising cloud costs are compounding the problem. Gartner predicts that by 2026, 40% of organizations will slow their cloud adoption due to the increasing difficulty of managing public cloud expenditures. What was once seen as a pathway to unlimited scalability is now fraught with escalating costs and diminishing returns.

This is where edge computing emerges as the answer. By deploying AI models directly at the edge—closer to where data is generated—businesses can overcome the limitations of cloud-only strategies. Edge computing eliminates latency, reduces bandwidth costs, and ensures continuous operations, even during cloud outages. For example, in manufacturing, edge AI can detect defects and trigger immediate adjustments to production lines, preventing downtime and saving money.

The future of AI is not “cloud-only.” It is a hybrid, distributed model where the edge plays a critical role in real-time decision-making while the cloud remains essential for training large-scale AI models and storing long-term data. This combination leverages the strengths of both systems: the speed and immediacy of edge computing, coupled with the scalability and analytical power of the cloud.

Edge computing will evolve from its current role into the central enabler of AI-driven innovation. In the future, edge devices will not merely collect data but will act as intelligent hubs, seamlessly running advanced AI models and driving decision-making closer to the source. Organizations that embrace this hybrid model will redefine modern, AI-powered operations, setting new standards for performance and adaptability. The edge will become the critical battleground where the next wave of AI innovation is shaped and realized.

3. Finally, the IoT Historian is Dead

For decades, industrial historians were at the center of operational data management. In their time, they were sufficient for a slower, batch-processing world where static environments and limited data requirements defined the pace of industry. But in today’s era of rapid innovation, massive data streams, and real-time decision-making, historians are no longer fit for purpose. They are relics of a bygone era, unable to meet the demands for scale, speed, and agility that modern industries require.

Legacy historians were designed for isolated, static environments and simply cannot handle the sheer volume and velocity of data generated today. They lack the real-time contextualization required to power AI models or drive operational decisions at the speed modern businesses demand. Even worse, they create silos that fragment data across systems, stifling innovation and collaboration. In a world where milliseconds matter, clinging to historians isn’t just inefficient—it’s a liability.

So, what comes next? The replacement for the historian isn’t a one-for-one tech stack swap; it’s an entirely new paradigm. The Unified Namespace (UNS) architecture offers a modern solution to this challenge. By serving as a structured, real-time directory that integrates OT and IT systems, UNS doesn’t just modernize industrial data strategies—it redefines them. It enables businesses to standardize data streams, eliminate silos, and unlock real-time insights for AI and automation.

With UNS, the promise goes beyond data capture. This architecture transforms raw data into actionable intelligence instantly, empowering organizations to act with speed and precision. Companies that adopt UNS will gain the agility and resilience to outpace competitors, seizing opportunities in real time. Those that remain tied to historians risk being left behind, stuck in a world where slow, siloed data management can no longer keep up with the pace of modern industry. The future belongs to those who embrace UNS and the power of real-time, integrated intelligence. 

The convergence of AI, IT, and OT is reshaping how industries operate. By moving away from outdated systems like IoT historians and over-reliance on cloud-only strategies, organizations can unlock the power of real-time decision-making at the edge.