CASE STUDY
The result is a more agile and responsive organisation, better equipped to meet the demands of global markets. consistency in accountability, quality and oversight. For us, value creation and governance go hand in hand, allowing AI to deliver sustainable impact across the organisation.
Dr. Hazem Shatila, Chief AI Officer, Elsewedy Electric
How are you ensuring strong governance and standardisation as AI is scaled across multiple business functions?
We are scaling AI through a unified, enterprisewide governance model that covers both corporate functions and industrial operations. IBM’ s watsonx. ai is enabling us to build, govern and deploy enterprise-grade Generative AI models using trusted data.
watsonx orchestrate brings AI into Enterprise operations by automating workflows, coordinating digital agents and scaling govern execution. This approach ensures that AI is deployed in a consistent and co-ordinated manner across the organisation.
By working with IBM, we have focused on standardising the full AI lifecycle, from how use cases are identified and prioritised to how they are approved, deployed and monitored. This includes clear controls around data, validation, accountability and oversight, ensuring transparency and consistency at every stage.
This level of standardisation is particularly important in factory environments, where AI must support not only efficiency and decisionmaking, but also safe and reliable operations. By embedding governance into the way AI systems are developed and deployed, we are creating a strong foundation for scalable, longterm impact.
How do you measure the success and business value of AI deployments as they move from pilot to enterprise scale?
The way we measure success evolves as AI initiatives mature. At the pilot stage, the focus is on model performance, accuracy and reliability. As deployments move to enterprise scale, the emphasis shifts toward tangible business outcomes.
We look at indicators such as operational efficiency, productivity, planning effectiveness, and the quality of decision-making across the organisation. In industrial environments, we also consider improvements in operational visibility and workplace safety.
Equally important are adoption and consistency, ensuring that AI solutions deliver reliable, repeatable results across both corporate functions and factory operations. Ultimately, success is defined by the ability to create meaningful, scalable impact that improves how the business operates as a whole.
Marwa Abbas, General Manager, North East Africa, IBM
What role do platforms such as watsonx. ai and watsonx Orchestrate play in enabling scalable and responsible Agentic AI adoption across complex industrial environments?
AI is evolving from answering questions to taking initiative. We are entering an era of agentic workflows, where systems reason, plan and act alongside people. Value comes when enterprises move from one-off tools to workflows that are outcome driven and embedded across functions.
watsonx. ai provides a governed foundation to build and deploy models on enterprise data. watsonx Orchestrate connects those models into workflows, enabling AI agents to operate across HR, IT, finance and operations. Together, they bring AI into the core of how work gets done.
The key is orchestration. It is how humans, data, and systems interact to create outcomes that are explainable and trusted. This becomes critical as organisations scale from a few use cases to enterprise-wide adoption.
We are already seeing this in practice. IBM’ s collaboration with Elsewedy Electric shows how organisations are moving beyond isolated experimentation to enterprise-scale, governed AI. Using watsonx. ai and watsonx Orchestrate, Elsewedy Electric is embedding agentic AI across core functions including operations, supply chain, HR and finance, with a clear focus on execution under governance.
The project includes more than 30 prioritised use cases and is designed to scale AI across the organisation in a structured, repeatable way. Crucially, it combines model development with orchestration of workflows, so AI agents can operate within real business processes while maintaining accuracy, traceability and compliance. •
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