In a world where speed and convenience are paramount, AI is fundamentally re-engineering how work is done, who does it, and why. AI is seen as a growth opportunity for the labor-challenged industrial sector, opening up new revenue opportunities while also being a productivity tool.

Last year, Google Gemini, Alphabet’s flagship generative AI, was tapped by Honeywell to provide insights across the industrial giant’s massive data set that can lead to reduced maintenance costs, increased productivity, and opportunities to upskill employees.

Siemens and Microsoft also announced a gen AI deal for the industrial sector, which included an AI copilot for use across industries.

Not surprisingly, there’s a high appetite to adopt the tech. Honeywell’s A Pivotal Moment for Industrial AI research shows decision makers in the industrial AI space are enthusiastic and are discovering new use cases during AI implementation. A PwC study of over 400 operations and manufacturing executives from over 30 countries in Europe, the Middle East, and Africa found that 40% of respondents expect AI to increase operating profits by at least 5%. 

“Use cases in industrial sectors cover a wide spectrum that includes supply chain risk prediction, demand forecasting and supply chain planning, production process optimization, and quality control,” says Dr. Bashar Al-Jawhari, Localisation Practice Leader, Partner, PwC Middle East.

“Given the Middle East governments’ drive to diversify their economies, AI can catalyze this journey.”

Experts say awareness is high, there will be an inflection point, and 2025-2026 will be a big year for the adoption of industrial AI.  

POWER THE NEXT PHASE OF GROWTH

There’s strong support that industrial AI is essential for the next growth phase in the Middle East industrial sectors. As these sectors transform and diversify while dealing with an evolving workforce, industrial AI will be a key enabler of this process.

“AI is paving the way for more efficient, sustainable, and technologically advanced industries – thus it is critical for competitiveness in the global marketplace,” says Nakul Duggal, group general manager, automotive, industrial and embedded IoT, and cloud, Qualcomm Technologies.

Qualcomm is expanding its footprint in the region, given the increasing demand for our AI-enabled solutions, Duggal adds, “We are set to help with training, tuning, and deploying AI models tailored to the unique needs of organizations in the Middle East.” 

To establish a strong foundation for AI, companies must develop a cohesive enterprise strategy. One certainty is that industrial AI will enhance the intricate and interconnected industrial sector by facilitating more autonomous operations.“This will create a new workforce equipped with valuable insights as the older generation retires,” says Michele Cacciari, Global Industry Lead—Oil & Gas at Aveva.

Moreover, the convergence of agentic AI and digital twins will enable Industry 5.0 to transition industries from optimized, human-controlled systems to autonomous, self-learning ecosystems. 

“By embedding intelligence, agentic AI facilitates real-time decisions, predictive insights, and goal-driven operations with minimal human intervention,” says Alessio Bagnaresi, Vice President of AI – Technology, MEA at Oracle. “This shift promises to tackle critical challenges like climate change, resource scarcity, and energy demand optimization.”

TAILORED AI SOLUTIONS

Besides enabling industrial automation, AI can accelerate digital transformation, create smarter, more adaptive manufacturing and logistics systems, and drive efficiency and resilience in an increasingly complex industrial landscape.

By integrating generative AI and AI agents with robotics, IoT, and advanced analytics, companies can enhance their industrial applications and optimize industrial processes, improving product quality, profitability, and sustainability.

In the region, asset-heavy industries like manufacturing, oil and gas, nuclear, chemicals, energy, and pharmaceuticals, especially, necessitate tailored AI solutions.

“Asset-heavy industries operate critical assets and complex processes that require high-level standards in terms of safety and quality,” says Cacciari. “They require tailored AI solutions that can augment/infuse the ‘generic’ foundations of the traditional AI/analytics platform with specific industry knowledge.”

AI must adapt to local operational conditions, support edge computing, especially on an offshore oil rig, and integrate with many legacy systems. Additionally, Vijay Jaswal, Chief Technology Officer, APJ, ME&A at IFS, says multilingual interfaces and industry-specific models are essential to ensure safety, efficiency, and compliance. “Tailored AI helps maximize asset performance and align with national strategic goals.”

In downstream oil and gas, for instance,  Bagnaresi says, AI agents can transform refineries through knowledge orchestration, which enables information sharing and coordinated decisions across units. Real-time integration provides trusted, real-time data for immediate and transparent decision-making, while autonomous computing also supports extensive scenario evaluations and continuous learning for AI agents.

“Beyond refineries, we see compelling opportunities to extend AI transformation in the O&G retail network. Our agentic capabilities can transform gas station operations through dynamic inventory management, predictive maintenance, and personalized customer engagement,” says Bagnaresi.

“These systems can also optimize pricing in real-time based on market conditions, creating a cohesive AI ecosystem that delivers excellence from production to point-of-sale, strengthening the competitive advantage of our O&G customers,” he adds. 

For example, Industrial Scientific, a global leader in gas detection and safety solutions, partnered with Oracle to implement an AI-powered solution called SensAI to automate support operations. This resulted in a time saving of over 185 hours, improved customer experience by reducing response time from days to minutes, and increased operational efficiency.

While large enterprises and industrial conglomerates have opportunities to streamline and automate workflows and extract business insights using generative AI to meet the requirements of complex industrial operations, Duggal says AI solutions work best “when they are optimized using the organization’s own data and knowledge base.”

For example, Qualcomm is collaborating with Aramco to develop mobile worker assistants. A virtual assistant is tailored to the company’s operational procedures and manuals to support workers in equipment maintenance tasks. 

“Given that Aramco’s installations for this use case are ‘air-gapped’—meaning they are isolated from the internet and commercial cloud services for security purposes—we worked closely with them to deploy AI on-premises appliances. These appliances function as a local server capable of performing the necessary AI processing on-site,” adds Duggal. 

BOTTOM-LINE IMPACT

However, as businesses continue to spend sizeable sums on AI, there seems to be an uncertainty about where to apply AI solutions to capture bottom-line impact.

“I have witnessed this uncertainty in regional industrial companies in terms of where to apply AI to capture bottom-line impact. Many struggle to move beyond PoCs, unsure which use cases deliver real ROI in complex, asset-heavy environments,” says Jaswal. 

He adds that at IFS, this adoption has been simplified as industrial AI is purpose-built for these challenges, embedding intelligence into core EAM, ERP, and FSM processes. “With a library of ready-made quick-start AI use cases, such as predictive maintenance, spare parts optimization, and workforce productivity insights, IFS.ai enables organizations to rapidly start, unlock value, and drive measurable outcomes with AI.”

Clearly, for AI to deliver real value, businesses must overcome many hurdles while building a solid foundation for sustainable AI integration.

Historically, asset-heavy industries have lagged behind other sectors in the adoption of digital technologies, mainly AI, because they are unable to fully take advantage of OT data, lack proven and successful implementation beyond simple, ad-hoc, and isolated use cases, and the technical workforce’s resistance to adopting new technologies in “critical” processes, says Cacciari.  

“We have seen multiple attempts, but a full-scale adoption has been very limited,” adds Cacciari. “Luckily, new advancements in technology and the increased readiness of technology solutions in the market help avoid these challenges, and we are seeing a rise in the adoption of AI solutions in our industries.”

While industrial companies must apply AI to use cases where the business case is clear, and the KPIs requiring optimization are quantifiable, Bagnaresi says it is equally important for IT departments and lines of business to collaborate and agree on the business use cases to develop, deploy, and govern. 

“AI is a team sport, a shared responsibility between business and IT. Through proper planning and by treating AI as a structured program within the organization, uncertainties can be eliminated, ultimately driving effective change and successful project management.”

FRAUGHT WITH CHALLENGES

Undoubtedly, AI has become the engine driving innovation across industries —from boosting operational efficiency to delivering insights and discovering new opportunities. Yet, this potential remains out of reach for many because the road to AI adoption is strewn with challenges that often derail success. A lack of infrastructure is holding back AI adoption at their companies.

While industrial companies are experimenting with AI use cases, Al-Jawhari says at least 40% of them expect a bottom-line impact in four areas: reduced operational costs, improved decision-making, reduced personnel costs, and efficiency gains.

“However, the current challenge is to move from the piloting phase to the full adoption of AI across the business.”

Another challenge, Duggal says, is tailoring AI solutions to the companies’ unique needs and deploying AI projects that deliver demonstrable results that impact the bottom-line.

It is about striking the right balance between optimizing production, quality, and energy efficiency. AI adoption would be faster if they had a stronger data infrastructure in place. “The ability to collect data is critical to understand the time-value of data to glean the insights needed in supporting real-time decision making,” says Bagnaresi.

According to Duggal, we have only scratched the surface with the deployment of AI in industrial settings. AI can address even the most difficult industry challenges with other technologies, such as edge cloud processing and 5G connectivity. 

“We have been very busy collaborating with companies in the Middle East and bringing together the platforms to enable organizations with efficient data acquisition, training, and tuning of AI models at the edge, optimizing workflows and predicting bottlenecks.”

AI may be cutting-edge, but it still needs people to build, manage, and guide it responsibly. IT leaders face the challenges of finding the AI-related skills they need. What’s crucial is strengthening indigenous AI capabilities and prioritizing workforce development to build a resilient industrial ecosystem.

In the Middle East, Al-Jawhari says, companies are hiring AI experts, developing specialist AI expertise, and educating the broader workforce on AI use. Additionally, they are partnering with external professional service providers and hyper-scalers to take advantage of new and emerging AI innovations for enterprise-ready cloud environments. 

“By developing indigenous AI proficiencies, local companies can give themselves a clear competitive advantage, grow their use of AI even further, and attract fresh AI talent to their businesses,” adds Al-Jawhari.