Nayef Bou Chaaya, Vice President, Middle East, Turkey and Central Asia at AVEVA, discusses the company’s priorities at ADIPEC 2026 and how industrial intelligence is helping energy organisations optimise asset performance, strengthen decision-making and achieve sustainability objectives.
What are AVEVA’s key priorities at ADIPEC 2026, and what technologies and solutions will you be showcasing to the energy industry?
This year’s ADIPEC theme, ‘Where global energy systems are strengthened’, aligns with how we see the sector. Resilience now depends on operating existing assets more effectively, so our priority is to demonstrate how connected data and AI can support every stage of an asset’s lifecycle, from design and construction to operations, maintenance and optimisation.
We will showcase CONNECT, our industrial intelligence platform, at our stand in Hall 4. Visitors will be able to explore data-centric engineering, digital twin foundations, AI-driven asset health monitoring and industrial knowledge graphs.
Our optimisation zone will bring together Unified Supply Chain, Process Optimisation and Predictive Asset Optimisation to demonstrate how organisations can anticipate issues and model ‘what-if’ scenarios.
At the AI zone in Hall 13, visitors will be able to query operational data using natural language and see how customers can connect their own large language models to industrial data through the CONNECT MCP Server. Together, these capabilities demonstrate how engineering, operations and business teams can work from a unified view of their assets.
As energy companies across the Middle East accelerate their digital transformation programmes, what are the biggest challenges they face in connecting operational technology, enterprise IT and industrial data?
Many companies across the region have invested in control systems and enterprise applications for years, often using solutions from different vendors with different data models. While each system may work effectively in isolation, operational, engineering and business data often remain fragmented across separate environments, making IT and OT integration difficult.
Security adds another layer of complexity. Connecting operational data to enterprise and cloud systems requires robust cybersecurity and clear governance from the outset, particularly in critical infrastructure environments.
The third challenge is organisational. Successful integration requires engineers, IT specialists, data scientists and business leaders to work together with shared ownership. Without this collaboration, initiatives often struggle to progress beyond the pilot stage.
We also see projects launched simply because competitors have adopted similar technologies, without first defining the business problem they are intended to solve. The most successful programmes begin with a clear business outcome and then establish the data foundation needed to achieve it.
AI is becoming increasingly important to industrial operations. How is AVEVA helping energy companies move beyond AI experimentation to deliver measurable improvements in productivity, asset performance and operational efficiency?
Moving beyond experimentation begins with identifying use cases where returns can be measured. In our experience, three areas stand out.
Predictive maintenance helps prevent machinery breakdowns by identifying potential problems before they occur. Advanced process control and first-principles models support process optimisation, increase throughput and reduce energy consumption. Across our deployments, energy consumption typically falls by 5–20%, while operational efficiency improves by 3–10%.
Another essential element is establishing a trusted data foundation that can be shared across multiple sites.
LANXESS provides a good example. By standardising its industrial data, the company scaled predictive maintenance to 225 live models and estimates that it avoided equipment damage and lost margins worth a low seven-figure sum in euros during the first year.
Industrial AI assistants can then help engineers and operators query this information using natural language, while appropriate governance ensures that people remain responsible for critical decisions.
Industrial data is often fragmented across legacy systems and operational environments. How can organisations build a trusted, unified data foundation that supports AI-driven decision-making and greater operational intelligence?
A trusted data foundation begins with capturing information once, close to its source, and giving it a consistent structure.
Solutions such as the AVEVA PI System capture, cleanse and store real-time operational data, ensuring that everyone working downstream has access to the same dataset.
The next step is adding context. Connecting operational data with engineering and business information enables AI algorithms and engineers to understand what the data means in relation to a specific asset.
Governance is equally important in ensuring data reliability. Quality checks, cybersecurity measures and appropriate access controls give users confidence in the information, including when it is accessed through AI-powered tools.
This foundation must also extend beyond individual sites. Once data is consistent and properly governed, organisations can share it securely with partners and AI platforms, bringing external expertise into their operations without creating additional silos.
Digital twins provide a way to connect this information throughout the asset lifecycle, from initial design through to ongoing operations.
Digital twins have been a major focus for AVEVA. How are these technologies evolving with AI and predictive analytics, and what tangible benefits are energy companies seeing in asset reliability, predictive maintenance and operational resilience?
Early digital twins were largely static models that were updated periodically. Industrial AI is transforming them into dynamic systems that continuously learn from operational data, detect anomalies, predict how conditions may develop and recommend appropriate actions.
Engineers can now interact with digital twins using natural language to investigate abnormal performance, identify potential causes and prepare maintenance activities.
AI-assisted development is also making it easier to deploy digital twins across entire enterprises rather than treating each implementation as a standalone project.
The benefits are increasingly measurable. Nigeria LNG developed a digital twin for its liquefaction trains, resulting in higher production rates and a 50% reduction in plant losses. The company has also confirmed that the system delivered a return on its investment.
In predictive maintenance, LANXESS used AVEVA Predictive Analytics to identify a potential failure in a thermal oxidiser two days before a scheduled shutdown.
This kind of early detection is critical to maintaining equipment availability, improving safety and controlling operational costs.
Energy companies face growing pressure to improve efficiency while meeting sustainability and emissions targets. How can industrial intelligence help organisations achieve both objectives without compromising operational performance?
The perceived tension between operational performance and emissions reduction becomes easier to manage when decisions are based on reliable information.
Industrial intelligence supports this in three important ways.
First, it makes emissions a measurable operational parameter. By tracking fuel consumption and emissions at asset level using the same trusted data that supports plant operations, organisations can identify inefficiencies as they occur.
As carbon pricing becomes more widespread, this visibility also helps companies understand the financial implications of inefficiency and report emissions intensity and absolute emissions with greater confidence.
Second, industrial intelligence turns data into actionable recommendations. When combined with AI, industrial data can identify specific measures to reduce CO2 emissions and energy consumption. It can also recommend more sustainable energy sources as organisations progress towards their energy transition objectives.
Third, it provides measurable evidence of the benefits.
At Dragon LNG, AtkinsRéalis combined AVEVA Process Simulation, the PI System and PI Vision to deploy an industrial digital twin that optimises the facility’s end-to-end production, compression and logistics network, delivering US$1 million in energy savings.
AVEVA has been promoting its CONNECT industrial intelligence platform. How is the platform helping organisations break down data silos, improve collaboration across engineering and operations, and accelerate decision-making?
CONNECT serves as a digital backbone across the entire asset lifecycle.
The platform integrates operational, engineering and IT data within a single managed environment, enabling engineers responsible for designing an asset and operators responsible for running it to access the same information and understand the original design intent.
Instead of reconciling multiple reports from disconnected systems, teams can work from a unified view, enabling faster decisions that remain consistent with the asset’s design and operational requirements.
CONNECT also makes industrial data accessible in a secure and controlled manner. Customers can share relevant plant and process information with specialists and partners, replacing traditional file transfers with secure, two-way data exchange.
For example, one power company uses this approach to share grid data with its customers, allowing them to verify the low-carbon origins of their electricity. The company has also transformed this information into an additional revenue stream.
The platform further prepares contextualised industrial data for AI applications, ensuring that AI-supported decisions are based on the same trusted information used across engineering, operations and business functions.
