Maximising AI's value through a strong data foundation

Durgesh Jha, Reliability Solutions director at Emerson, explains how a strong data backbone is critical to unlock AI’s value in GCC manufacturing

In the GCC’s highly competitive landscape, AI is becoming a defining factor in operational performance across all sectors, and process manufacturing is no exception. Companies throughout the region are increasingly integrating AI into their operations, with adoption rates rising to approximately 84% of organisations.

Yet this rise also highlights a major strategic challenge: AI’s value hinges entirely on the quality and consistency of data feeding it.

AI software consumes tremendous amounts of data to operate effectively. In traditional route-based models, technicians may collect data at set intervals only to find that equipment conditions changed hours or days after the last inspection, creating visibility gaps that limit AI’s effectiveness and delay corrective action. Therefore, if a plant continues to rely on intermittent manual rounds to collect data, even the most powerful AI tools will struggle to deliver the insights necessary to drive real improvement.

A data foundation for AI

Before adopting the latest AI technology, it is important to establish a strong data foundation. Recent regional data shows that 62% of manufacturers are investing in industrial data platforms to support AI and GenAI adoption, with many plants moving away from manual, route-based data collection in favour of automated online condition monitoring. By integrating a combination of both wireless and wired condition monitoring sensors, each with a varying array of capabilities, reliability, sustainability, and integrity teams can tailor data collection frequencies to their unique needs. This solution also prepares these teams to implement the modern technologies that will drive operational excellence and competitive advantage in the years to come.

Gulf industry players can achieve continuous asset monitoring, spanning equipment condition, performance, and integrity through a range of available solutions. These include small, easy-to-install wireless vibration, acoustic noise, and ultrasonic thickness sensors such as Emerson’s AMS Wireless Vibration Monitor, which collect spectrum and waveform data from balance of plant assets and deliver an intuitive health and performance score to technicians. At the same time, that same data provides the industrial AI systems with a more consistent and reliable flow of information, as automated sensors continuously capture asset data, reducing reliance on manual rounds and allowing technicians to focus on interpreting insights and responding to emerging issues.

Other advanced monitoring solutions, like Emerson’s AMS Asset Monitor, not only deliver continuous data to both plant reliability personnel and AI tools but also use built-in (asset models) to perform AI-driven edge analysis directly at the asset level.

Through embedded analytics, these systems can automatically predict common issues, such as imbalance, gear fault, looseness, and under-lubrication, in the most common assets including pumps, blowers, motors, gearboxes, and other rotating machinery. Additional features, such as ensured continuous power supply and automated protective responses when faults are predicted, make edge analytics devices a critical element of any continuous condition monitoring strategy.

So, not only can teams have a constant flow of critical data to their enterprise AI software, but they also gain AI insights right at the asset or delivered to their mobile devices.

Data drives success

With GCC economic growth projected at 4.5% in 2026, alongside an intensified regional focus on strengthening local industrial and AI capabilities, plants are facing growing manpower challenges to manage regular manual maintenance rounds. Traditional route-based data collection introduces critical visibility gaps — technicians may spend weeks gathering data only to discover that a fault developed hours after the last collection. Continuous monitoring closes these gaps by delivering near real-time insight into asset health.

At the same time, AI-driven operations require higher volumes of continuous, high-quality data.

Together, these pressures have inspired many organisations to shift their strategy, implementing continuous condition monitoring technologies to free technicians up for more valuable tasks around the plant.

Moreover, AI-based condition monitoring is accelerating the shift toward predictive and prescriptive maintenance models. Instead of responding to failures after they occur, or replacing components on fixed schedules regardless of condition, plants can intervene precisely when data indicates emerging risk. This transition not only reduces unplanned downtime but also optimises maintenance budgets and extends asset life.

But it doesn’t stop there. Those same tools also drive high-level analytics that promote increased optimisation, decision support, and reliability, ultimately driving competitive advantage.

In the end, AI in manufacturing is not a standalone technology initiative; it is a data strategy. For GCC manufacturers seeking to scale AI adoption, the priority must be building a resilient, continuous, and intelligent data backbone capable of supporting both today’s operational needs and tomorrow’s industrial ambitions.