FEATURE: TRANSFORMING THE ENTERPRISE WITH AI and work alongside deterministic robots and people to automate lengthy or error-prone tasks,” says Ashraf El Zarka, Vice President and Regional Managing Director, MEA and Pakistan, UiPath.
CIOs can unlock significant business value today by utilising agentic AI to enhance customer interactions and optimise workflows. Unlike traditional automation, agentic AI can transform processes, scale operations, and improve decision-making with data-specific insights.
“ A practical approach involves integrating AI with human oversight, allowing employees to work alongside AI. CIOs should prioritise change management and promote collaboration between AI and staff to drive productivity and innovation,” says Saran B Paramasivam, Regional Director Middle East and Africa, Zoho.
Accelerating AI adoption
To become AI-ready, enterprises should begin by aligning AI initiatives with business goals and clearly defining the objectives of AI adoption. A robust data strategy is foundational, ensuring that data is clean, well-governed, and accessible across the organisation. Investing in scalable infrastructure, whether onpremises or in the cloud, is crucial to handle the demands of AI workloads.
Encouraging collaboration between IT, business units, and data science teams is key to integrating AI successfully into business processes. Best practices also include establishing AI governance frameworks to ensure ethical, transparent, and secure AI use, while fostering a culture of continuous learning to upskill employees.
“ Ultimately, having clear communication, strong leadership, and a commitment to innovation will enable the enterprise to adopt AI seamlessly,” says Sid Bhatia, Area VP and General Manager META, Dataiku.
A successful AI strategy begins with a clear understanding of business objectives. To generate meaningful ROI, AI initiatives should align with core business goals, prioritising use cases that offer the greatest impact.
Once AI-driven opportunities are identified, fostering a culture of cross-functional collaboration is critical. AI implementation requires engagement from IT, operations and data teams to ensure seamless integration and maximise value.
“ Data management plays a pivotal role in realising AI’ s value. While some AI applications can operate without proprietary data, most rely heavily on a well-maintained data infrastructure,” says Endava’ s Jackson.
“ Organisations should have a comprehensive understanding of their structured and unstructured data assets. If mature data governance processes are not yet in place, establishing them should be a priority to ensure accuracy, security, and compliance,” adds Jackson.
“ Two areas that we have seen highly impactful in driving business value are digital twins, where AIpowered virtual replicas of physical assets can solve business challenges like optimising maintenance, predicting failures, and simulating scenarios before costly real-world implementation,” says Samuel Huber, CEO MENA and Global President of Enterprise, Infinite Reality.
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