{"id":137506,"date":"2026-04-01T08:43:58","date_gmt":"2026-04-01T07:43:58","guid":{"rendered":"https:\/\/www.intelligentcio.com\/me\/?p=137506"},"modified":"2026-04-16T11:46:40","modified_gmt":"2026-04-16T10:46:40","slug":"elsewedy-electric-and-ibm-advance-enterprise-scale-agentic-ai-adoption-using-watsonx-portfolio","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/me\/2026\/04\/01\/elsewedy-electric-and-ibm-advance-enterprise-scale-agentic-ai-adoption-using-watsonx-portfolio\/","title":{"rendered":"Elsewedy Electric and IBM advance enterprise-scale Agentic AI adoption using watsonx portfolio"},"content":{"rendered":"\n

As organisations accelerate their Artificial Intelligence ambitions, the focus is shifting from experimentation to scalable, governed deployment that delivers measurable business value. This collaboration between IBM and Elsewedy Electric highlights how Agentic AI can be embedded into core enterprise workflows to drive efficiency, strengthen decision-making and support long-term Digital Transformation. We hear from Ahmed Elsewedy, President and Chief Executive Officer at Elsewedy Electric; Dr. Hazem Shatila, Chief Artificial Intelligence Officer, Elsewedy Electric; and Marwa Abbas, General Manager, North East Africa, IBM.<\/em><\/p>\n\n\n\n

IBM announced a strategic collaboration with Elsewedy Electric, an integrated energy solutions provider based in Egypt, to advance enterprise-grade Agentic AI adoption across the organisation. The initiative reflects Elsewedy Electric\u2019s move beyond isolated AI experimentation towards governed, action-oriented AI embedded within core enterprise workflows, leveraging IBM watsonx.ai and IBM watsonx Orchestrate.<\/p>\n\n\n\n

The collaboration establishes a structured foundation for scaling AI across Elsewedy Electric operations, enabling faster decision-making, improved operational efficiency and trusted, long-term innovation across mission-critical business domains.<\/p>\n\n\n\n

Elsewedy Electric\u2019s AI journey is anchored in its ambition to embed AI as a core enterprise capability spanning operations, supply chain, human resources, finance, legal and market intelligence. To support this vision, IBM\u2019s Client Engineering team partnered with Elsewedy Electric at an early stage to deliver a successful proof of concept focused on Expanding Markets Investment Research. The initiative demonstrated how AI can accelerate market intelligence and investment analysis under real-world conditions, while building confidence in AI\u2019s enterprise value and establishing momentum for scaled deployment.<\/p>\n\n\n\n

Building on this foundation, IBM worked closely with Elsewedy Electric to assess strategic business priorities and define an enterprise AI roadmap comprising more than 30 prioritised, high-impact use cases across core enterprise domains, providing a clear pathway from experimentation to enterprise-scale execution.<\/p>\n\n\n\n

At the core of the initiative is watsonx.ai, which enables Elsewedy Electric to build, govern and deploy enterprise-grade Generative AI models using trusted operational and enterprise data. Watsonx Orchestrate extends these capabilities by operationalising Agentic AI \u2013 coordinating digital assistants and agents, automating AI-driven workflows and embedding intelligence directly into day-to-day HR, planning and enterprise processes to enable consistent execution at scale under governance controls.<\/p>\n\n\n\n

Ahmed Elsewedy, President and Chief Executive Officer at Elsewedy Electric, said: \u201cScaling AI requires more than advanced technology; it requires the right foundations to turn innovation into sustainable value. Working with IBM will help us to establish a structured, enterprise-wide approach to Agentic AI \u2013 one that aligns strategy, governance and execution and enables us to deploy AI responsibly and effectively across the organisation.\u201d<\/p>\n\n\n\n

With IBM\u2019s support, more than 10 AI use cases have already progressed into advanced delivery stages, delivering measurable productivity gains.<\/p>\n\n\n\n

\u201cAs organisations move from experimenting with AI to embedding it into how they operate, governance and scalability become just as important as innovation,\u201d said Marwa Abbas, General Manager, IBM North East Africa. \u201cThrough the watsonx portfolio, Elsewedy Electric is advancing towards enterprise-grade Agentic AI \u2013 moving beyond isolated use cases to AI capabilities that deliver measurable outcomes across the organisation.\u201d<\/p>\n\n\n\n

Dr. Hazem Shatila, Chief Artificial Intelligence Officer, Elsewedy Electric, said: \u201cAI is becoming a core capability that powers Elsewedy Electric\u2019s future, not just a technology layer. By adopting IBM\u2019s framework for change and AI agent-based approach, we were able to establish clear governance and standardised processes that enable us to consistently prioritise the right use cases, measure their impact and scale AI across the organisation with confidence.\u201d<\/p>\n\n\n\n

A key enabler of the transformation has been Xyris Technologies, IBM\u2019s on-ground implementation partner. Along with Elsewedy Electric\u2019s AI team, a portfolio of use cases were identified as a part of the company\u2019s long-term AI integration ambitions.<\/p>\n\n\n\n

This collaboration reinforces IBM\u2019s commitment to enabling secure, scalable and responsible AI adoption for industrial organisations, supporting Digital Transformation, operational resilience and long-term competitiveness across the manufacturing and energy sectors.<\/p>\n\n\n\n

We asked key executives further questions to find out more about the project.<\/em><\/p>\n\n\n

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Ahmed Elsewedy, President and Chief Executive Officer at Elsewedy Electric<\/em><\/strong><\/p>\n\n\n\n

How is Agentic AI transforming core enterprise workflows across Elsewedy Electric\u2019s operations?<\/strong><\/p>\n\n\n\n

Agentic AI is transforming the way we operate across both our corporate functions and industrial environment. At Elsewedy Electric, we work across a wide spectrum of industries, from cables and construction to infrastructure and digital solutions, with operations spanning multiple global markets. Managing that level of complexity requires faster, more informed decision-making across the entire value chain.<\/p>\n\n\n\n

Through our collaboration with IBM, we are embedding AI into functions such as finance, HR, legal, planning and market intelligence, while also enabling smarter co-ordination and better visibility across manufacturing and factory operations. AI is helping streamline planning, improve forecasting accuracy, and enhance execution across subsidiaries and production facilities.<\/p>\n\n\n\n

Just as importantly, it allows our teams to shift their focus toward higher-value decisions, while AI handles more repetitive and data-intensive processes. In industrial environments, it also plays a role in supporting safer operations through earlier risk detection and more proactive monitoring. The result is a more agile and responsive organisation, better equipped to meet the demands of global markets.<\/p>\n\n\n\n

What challenges have you faced in moving from isolated AI use cases to a fully governed, enterprise-wide AI strategy?<\/strong><\/p>\n\n\n\n

The main challenge was not the technology itself, but ensuring alignment across a large and diverse organisation. With over 50 subsidiaries and 34 production facilities, moving from isolated pilots to a fully scaled AI model required consistency in how we approach data, processes and decision-making.<\/p>\n\n\n\n

It was essential for leadership to take clear ownership of this transformation and position AI as a business enabler rather than a standalone technology initiative. At the same time, we worked to build a shared mindset across the organisation, one that recognises AI as a practical tool for improving performance.<\/p>\n\n\n\n

This meant putting in place a unified framework with clear governance, common standards, and integrated systems that allow insights to flow seamlessly across both corporate functions and factory operations. That consistency is what enables us to scale AI in a responsible and effective way.<\/p>\n\n\n\n

How do you ensure that AI adoption delivers measurable business value while maintaining strong governance and scalability?<\/strong><\/p>\n\n\n\n

We approach AI as an enabler of business performance, where every initiative is linked to a clear objective and measurable outcome. This may include improvements in productivity, faster decision-making, stronger planning, or more efficient resource allocation across our operations.<\/p>\n\n\n\n

Given the scale of our business, even incremental gains can have a meaningful impact. We track this through key operational indicators such as delivery timelines, cost efficiency and productivity across business units.<\/p>\n\n\n\n

At the same time, governance remains central to how we scale. AI systems are embedded within our existing operational frameworks, ensuring 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.<\/p>\n\n\n

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Dr. Hazem Shatila, Chief Artificial Intelligence Officer, Elsewedy Electric<\/em><\/strong><\/p>\n\n\n\n

How are you ensuring strong governance and standardisation as AI is scaled across multiple business functions?<\/strong><\/p>\n\n\n\n

We are scaling AI through a unified, enterprise-wide governance model that covers both corporate functions and industrial operations. IBM\u2019s watsonx.ai is enabling us to build, govern and deploy enterprise-grade Generative AI models using trusted data.<\/p>\n\n\n\n

watsonx orchestrate brings AI into Enterprise operations by automating workflows, co-ordinating digital agents and scaling govern execution. This approach ensures that AI is deployed in a consistent and co-ordinated manner across the organisation.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

This level of standardisation is particularly important in factory environments, where AI must support not only efficiency and decision-making, 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, long-term impact.<\/p>\n\n\n\n

What criteria do you use to prioritise high-impact AI use cases across the organisation?<\/strong><\/p>\n\n\n\n

Prioritisation is driven by a combination of strategic relevance and measurable business impact. We focus on use cases where AI can deliver clear improvements, particularly in areas that involve large volumes of data, repetitive processes or complex forecasting.<\/p>\n\n\n\n

At the same time, we assess data readiness and implementation feasibility to ensure that use cases can be deployed effectively. Scalability is also a key consideration, as successful applications should be adaptable across multiple business units.<\/p>\n\n\n\n

We place particular emphasis on use cases that enhance co-ordination between corporate teams, supply chain and factory operations, as well as those that contribute to productivity, operational resilience and safer working environments. Alongside this, continuous experimentation allows us to refine our approach and identify where AI can deliver the greatest value.<\/p>\n\n\n\n

How do you measure the success and business value of AI deployments as they move from pilot to enterprise scale?<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n

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Marwa Abbas, General Manager, North East Africa, IBM<\/em><\/strong><\/p>\n\n\n\n

How is IBM supporting organisations in moving from AI experimentation to enterprise-scale deployment with strong governance?<\/strong><\/p>\n\n\n\n

At IBM, we see a consistent pattern. AI adoption is high, but many organisations are still stuck in the pilot phase. In fact, nearly 95% of AI pilots never reach production. The challenge is not lack of ambition. It is fragmentation.<\/p>\n\n\n\n

The shift that matters is moving from experimentation to embedding AI into core workflows. AI is shifting from tool to teammate, but it still requires purpose, data discipline and governance.<\/p>\n\n\n\n

Only about 1% of enterprise data sits inside models today. The organisations that connect the other 99% and fix fragmentation will see real ROI. That requires better orchestration of how humans, data and systems work together across the enterprise.<\/p>\n\n\n\n

This is where IBM comes in. We help organisations move beyond experimentation by combining enterprise-grade AI development with governed orchestration of AI agents and workflows – enabling scalable, secure and accountable AI embedded into real business operations.<\/p>\n\n\n\n

Importantly, our open, hybrid approach ensures organisations can scale AI without vendor lock-in – maintaining flexibility while embedding governance, security and accountability from the outset.<\/p>\n\n\n\n

Governance is foundational to that effort. It needs to be built into the lifecycle so outcomes are explainable, trusted and compliant. That is how organisations move from isolated pilots to AI that is repeatable, accountable and operating at scale.<\/p>\n\n\n\n

What role do platforms such as watsonx.ai<\/a> and watsonx Orchestrate play in enabling scalable and responsible Agentic AI adoption across complex industrial environments?<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

We are already seeing this in practice. IBM\u2019s 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.<\/p>\n\n\n\n

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.<\/p>\n","protected":false},"excerpt":{"rendered":"

As organisations accelerate their Artificial Intelligence ambitions, the focus is shifting from experimentation to scalable, governed deployment that delivers measurable business value. This collaboration between IBM and Elsewedy Electric highlights how Agentic AI can be embedded into core enterprise workflows to drive efficiency, strengthen decision-making and support long-term Digital Transformation. We hear from Ahmed Elsewedy, […]<\/p>\n","protected":false},"author":4189,"featured_media":137511,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[687,332,13],"tags":[19142,19962,20863,3355,637,1050,908,22295,2159,16000,15837,464,20462,581,22296],"class_list":["post-137506","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-energy","category-software","category-top-stories","tag-agentic-ai","tag-ai-governance","tag-ai-orchestration","tag-artificial-intelligence","tag-automation","tag-business-intelligence","tag-digital-transformation","tag-elsewedy-electric","tag-energy-sector","tag-enterprise-ai","tag-generative-ai","tag-ibm","tag-industrial-ai","tag-manufacturing","tag-watsonx"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/posts\/137506","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/users\/4189"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/comments?post=137506"}],"version-history":[{"count":4,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/posts\/137506\/revisions"}],"predecessor-version":[{"id":137838,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/posts\/137506\/revisions\/137838"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/media\/137511"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/media?parent=137506"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/categories?post=137506"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/me\/wp-json\/wp\/v2\/tags?post=137506"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}