The convergence of operational technology (OT) with information technology (IT) is reshaping industries. At its core, OT/IT convergence is “the integration of IT and OT technologies, processes and organizational structures to optimize industrial operations,” according to IoT Analytics.
In practice, this means connecting factory-floor systems (OT) — machines, sensors and controllers — with enterprise data systems and cloud analytics (IT). The goal is a unified ecosystem where real-time operational data fuels better decisions, efficiency gains and new services. This trend is no longer optional; companies are rapidly realizing that to thrive in the era of AI and Industry 4.0, their historically siloed OT and IT domains must work in tandem.
At the very edge of this evolution sits Physical AI (also called “Generative Physical AI”). This is the fusion of AI’s generative models with the physical world of robots, drones and autonomous machines. Physical AI systems perceive, simulate and act in real environments by leveraging OT-generated sensor and actuator data to train embodied intelligence in virtual 3D spaces before deployment.
1. AI’s 360-degree role in driving convergence
A key catalyst in OT/IT convergence is artificial intelligence (AI). Sukant Acharya, Global Head of AIoT Practice at HCLTech, stresses, “I see AI playing a 360-degree role in enabling and influencing how OT/IT convergence will happen.”
According to Acharya, “AI acts as a driver, an enabler and also a benefactor in this transformation.” As a driver, AI unlocks new possibilities for businesses to reinvent how products are made, processes are run and services delivered — changes that require IT and OT systems to come together. Recent advances in AI are ‘accelerating the convergence of IT and OT, unlocking troves of industrial data to improve productivity, quality and availability'. In practical terms, AI-driven analytics and machine learning are pushing more OT data into IT cloud platforms and turning insights into immediate actions on the shop floor.
In sectors deploying Physical AI, such as robotics fleets, autonomous drones and smart machinery, AI models trained in OT-powered digital twins, like NVIDIA Omniverse with Cosmos, generate synthetic data to teach robots and drones spatial reasoning, collision avoidance and fine motor tasks long before they ever touch the physical world.
AI is also an enabler of convergence, particularly when it comes to integrating systems securely and efficiently. For instance, AI-powered tools can help automate data integration between OT sensors and IT applications or detect and respond to security anomalies at the interface of OT and IT.
On the security front (a classic OT/IT pain point), AI-based threat intelligence and predictive algorithms help bridge the gap by safeguarding converged environments.
Finally, AI is a beneficiary of OT/IT convergence in that it stands to gain from the rich new data and use cases emerging as previously isolated OT systems come online. From AI optimizers embedded in industrial machines to generative AI assisting frontline operators, as OT and IT merge, organizations can experiment with AI in contexts never before possible.
In short, AI both fuels and feeds on OT/IT convergence: it propels integration forward and in turn, a unified OT/IT environment provides fertile ground for AI’s continued evolution.
2. Addressing security: A continuous journey, not a roadblock
Whenever OT and IT systems start connecting, security concerns inevitably arise. After all, critical industrial equipment is now exposed to IT networks and cyber threats. Acharya’s perspective is that security, while vital, should not be viewed as a show-stopping obstacle.
“We have to ask ourselves — when has security not been a concern?” he remarks, pointing out that every major technology shift has come with security challenges. “Avoiding convergence due to security fears is the wrong approach.”
In other words, refusing to modernize OT/IT just because of security worries is a mistake. Instead, security must be tackled in parallel with convergence efforts. Security isn’t a destination — it’s a journey, as Acharya puts it, meaning it requires ongoing improvement and will never be “finished.”
The convergence of OT and IT actually offers an opportunity to strengthen security holistically. “Converging OT and IT provides the perfect chance to re-evaluate your entire security profile — physical, logical and across all dimensions,” he says. Organizations can use convergence projects to implement modern security tools, protocols and zero-trust architectures that cover both realms end-to-end.
Industry data reinforces Acharya’s stance. In one recent survey, 33% of organizations not pursuing OT/IT convergence cited security risk as a top reason for holding back. Acharya would likely call this cautious camp short-sighted.
The same survey found that 59% of companies actively pursuing convergence did so to reduce security risk. Rather than seeing security as a roadblock, companies should see converging OT and IT as a chance to close longstanding security gaps in operational systems.
There will always be new threats. For example, manufacturing was the target in 71% of ransomware incidents in 2023, but merging OT with IT allows better visibility and unified threat response across previously isolated networks.
With the right investments in tools and training, businesses can modernize their production systems securely, treating security as a continuous improvement journey. As Acharya advises, it’s about not fearing change but preparing for it. Organizations must revalidate, reassess and reimplement security measures to fortify the converged environment.
3. Business implications: Reimagining operations and unlocking value
The business implications of OT/IT convergence are transformational. By bridging these domains, organizations can completely reimagine their operations and business models.
“OT/IT convergence creates a powerful opportunity to reimagine the business,” notes Acharya. “It enables organizations to rethink how they manage customer operations, asset operations and even employee operations — touching nearly every function across the value stream.”
In practical terms, breaking down the OT/IT divide lets companies manage end-to-end processes, such as the factory floor and customer experience, in a more integrated way.
Acharya emphasizes that “this convergence allows businesses to unlock new models, drive greater efficiency and reduce costs in ways that were not possible before.” Indeed, new digital services, like predictive maintenance or usage-based product offerings, become possible only when real-time OT data flows into IT systems. Efficiency gains come from optimizing workflows holistically rather than in silos and cost reductions emerge by eliminating duplicate systems and manual interventions.
The rapidly evolving technology ecosystem makes acting now imperative. Acharya warns that if you delay convergence, you risk falling behind more agile competitors. “If you want to lead the AI wave, OT and IT must come together. Otherwise, you’ll end up with fragmented, local optimizations instead of realizing the full, integrated potential across the organization. Working in silos will limit your outcomes and you won’t be able to truly maximize the power and impact of AI,” he says.
This statement underlines that AI’s full benefits, from advanced analytics to automation, only materialize when data and workflows are integrated across the enterprise. Running OT and IT separately results in suboptimal, piecemeal improvements at best.
Acharya’s sense of urgency is echoed by industry projections. Analysts estimate that adoption of converged IT/OT technologies is accelerating. For example, new industrial projects featuring OT/IT convergence could jump from ~10% today to ~50% within five years. Likewise, by 2025, a large majority of industrial enterprises will have some form of OT/IT integration in place. This means the window to gain a competitive advantage from convergence is now. Companies that move early can define new standards in their operations and customer offerings, whereas late movers risk playing catch-up in a landscape where digital-physical integration and AI-driven processes are the norm.
4. Why put OT first in OT/IT convergence?
In discussions about merging IT and OT, the focus starts with OT and for good reason.
As Acharya explains, “We put OT first for three key reasons. First, when we talk about convergence, we need to ask: where is the focus, where is the opportunity and where will value be created? The answer lies in OT — this is where a major transformation is underway.”
He emphasizes that operational technology on the factory floor holds untapped potential that must be harnessed. Indeed, from smart manufacturing and industrial IoT to real-time automation, major disruptions are happening in OT; far more than in back-office IT systems.
Acharya notes that “the entire gamut of change is concentrated in the OT domain.” This is why the convergence narrative is OT/IT (not IT/OT): to drive the right focus on where value will be created, manage the greater risks on the OT side and appropriately align transformation efforts.
External market data backs this view. The technologies at the OT/IT intersection are booming. In 2024, the addressable OT/IT integration market was about $720 billion and it’s projected to grow roughly 8.5% annually to surpass $1 trillion by 2027. Clearly, organizations see immense value in modernizing OT and bringing it together with IT, validating Acharya’s OT-first emphasis.
5. The road ahead: From value chains to converged value engines
Looking ahead, Acharya predicts a shift from traditional value chains to converged value engines with flexible ecosystems built on modular micro-capabilities.
“Micro capabilities will actually be the building blocks for competitive advantage,” he explains, enabling businesses to quickly reconfigure processes and innovate continuously.
He outlines four core principles shaping this evolution:
- OT/IT and broader tech convergence as the integration layer
- Micro capabilities that are adaptable and composable
- Data intelligence prioritizes actionable insight over raw analytics
- Compute smart, where processing happens wherever it’s most efficient: cloud, in-memory, near-edge or far-edge
This hybrid model is rapidly becoming a reality. According to Gartner, 75% of enterprise data will be created and processed at the edge by 2025, up from just 10% in 2018. This reflects the rise of distributed, smart computing.
Businesses that adopt this agile architecture will be best positioned to thrive in an AI-enabled, converged future. As Acharya notes, “We are approaching that trend very, very fast.”
As OT and IT come together, they form the backbone of next-generation Physical AI ecosystems, powering robots, machines and drones that learn in virtual environments and perform safely and autonomously in the real world.
Platforms like NVIDIA Omniverse, Cosmos and MEGA illustrate how OT-driven digital twins and world foundation models create an operating system for Physical AI, underscoring that true industrial innovation only emerges when OT is integral to every layer of AI deployment.
