Fabrizio Landini, Global Data Centre Segment Leader at Hitachi Group, explains why the AI boom will stall unless data centre operators finally close the gap between IT and OT.
Over a quarter of organisations (37%) still report little or no collaboration between IT and OT teams (Cyolo/Ponemon Institute). This divide made sense in an earlier era. IT focused on storage, networking and compute, whilst OT managed physical infrastructure like power distribution, environmental controls and cooling systems. The two departments rarely needed to align beyond simple capacity planning.
Now, AI has altered that dynamic. Modern AI platforms are compute-heavy, generating huge thermal loads that require flexible power allocation and real-time optimisation of cooling systems. When an LLM (large language model) is trained, every element of the data centre – from cooling output to network bandwidth to power draw – must respond in a coordinated way. That level of coordination is difficult to achieve if IT and OT systems remain siloed and unable to communicate.
Let’s take a look at why IT/OT convergence remains such a challenge for data centre operators, and why AI growth depends on more than technical integration between the two teams.
The data centre of the future
Data centres are no longer just rows of servers in climate-controlled rooms. They’re complex, dynamic ecosystems where digital workloads and physical infrastructure need to operate in close alignment. Yet historically, information technology (IT) and operational technology (OT) have been managed in isolation, with separate teams, tools and priorities.
IT/OT convergence addresses this challenge by creating a centralised data and control plane that spans both domains. It allows operators to view the facility as a single, integrated system, rather than as a set of disconnected components. The result can include faster response times, better resource utilisation, and a stronger foundation for AI-assisted operations.
The real challenge is cultural, not technical
The benefits of IT/OT convergence are well understood. Despite this, true convergence remains elusive for many operators. While there are technical hurdles to overcome, the more persistent challenge is often cultural. IT and OT teams can have different priorities and risk tolerances. IT teams may move quickly, be receptive to change, and prioritise flexibility. OT teams, on the other hand, typically prioritise stability and reliability, and may resist change that introduces operational risk. Both approaches have clear strengths, but finding common ground can be difficult.
Successful convergence requires building bridges between these cultures. This can include creating cross-functional teams, establishing shared metrics, and developing a common language that both IT and OT professionals can use. It also means investing in training so that IT professionals understand physical systems, and OT professionals understand digital networks.
On the technical side, IT and OT systems often speak different languages. IT networks run on standard protocols like Ethernet and TCP/IP. OT systems may use proprietary industrial protocols designed for specific equipment. Unifying the two requires middleware, protocol translation and careful integration work. Legacy OT systems can also be difficult to integrate with modern tooling. Building management systems (BMS), for example, can be a stumbling block for data centre operators, particularly where older deployments have limited connectivity or rely on proprietary protocols.
Practical steps to achieve IT/OT convergence
It’s understandable if data centre operators feel overwhelmed by these challenges, and uncertain about where to begin. A phased approach is typically more realistic than attempting full convergence at once.
1. Ensure real-time data flow
Before you can converge operations, you need unified visibility. This means connecting OT systems to a common data platform, standardising data formats, and enabling real-time data flow. Begin with non-critical systems to build confidence and support a test-and-learn approach, before moving onto mission-critical infrastructure.
2. Create centralised dashboards
Once data is flowing, develop visualisation tools that give both IT and OT teams a shared view of the data centre. This helps reduce information silos and makes interdependencies more visible.
3. Automate responses
With unified visibility in place, operators can begin automating responses that span IT and OT domains. For example, when a high-power AI workload starts, cooling output can be adjusted automatically, with relevant notifications sent to power management systems.
4. Enable predictive monitoring and maintenance
One of AI’s most useful capabilities is anticipating faults before they result in service-impacting failures, helping teams prioritise corrective actions earlier. The quality of these predictions depends on high-quality historical data, robust analytics and appropriate machine learning models – but where the foundations are in place, the operational benefits can be meaningful.
The road ahead
As AI-driven workloads increase over the next decade, IT/OT convergence may shift from a competitive differentiator to a prerequisite for resilience and continuity. Operators best positioned to succeed are likely to be those that close the gaps between digital workloads and physical infrastructure. Without effective system integration, data remains fragmented – limiting visibility, reducing the accuracy of analytics, and constraining the operational value that AI tools can deliver.
Importantly, this doesn’t need to be tackled all at once. Many organisations make progress incrementally, focusing first on visibility and data quality, then on automation and optimisation as confidence and capability grow.
