FEATURE
Many organisations have successfully deployed tools that improve individual productivity, but translating those gains into measurable enterprise value requires a more disciplined approach.
Al-Anqari believes organisations should establish success criteria before deployment begins.
" The key is establishing clear success criteria before deployment begins, not after," he said.
Relevant measures include operational efficiency, workforce productivity, faster decision-making and risk reduction.
For government organisations, value may be reflected in improved public services and operational performance. Private enterprises may prioritise cost savings, service quality or business growth.
Buzzalina similarly believes AI initiatives should begin with clearly defined business problems rather than a search for potential applications of the technology.
He advocates measuring incremental improvements in productivity, inventory management, customer experience and decision-making.
Smaller initiatives that demonstrate tangible results can create the confidence needed for broader adoption.
Ayman recommends evaluating individual workflows before introducing AI, establishing baselines for processing time, operating cost and error rates.
The same measures can then be assessed after deployment, including the ongoing costs of operating and governing the solution.
Amjed believes enterprises must also consider the full economics of AI adoption.
Infrastructure, integration, data preparation, governance, employee adoption and ongoing operations all contribute to the total cost of deployment.
He distinguishes between immediate productivity improvements and more substantial transformation opportunities created by redesigning end-to-end processes.
Pienovi argues that the commercial models surrounding AI must also evolve.
Globant ' s Glob. AI model links pricing to consumption and certified deliverables rather than traditional time-based services.
According to Pienovi, this approach creates a closer connection between technology investment and business outcomes.
He cited Globant delivery examples in which FIFA achieved a 20 % improvement in throughput efficiency while maintaining quality, while a commercial bank completed a COBOL migration originally scoped for 14 months in two months.
These examples illustrate the potential for AI to improve delivery performance when technology is integrated into clearly defined processes.
However, sustainable returns will depend on organisations maintaining consistent measurement as applications expand across business functions.
Building an AI-ready enterprise
The transition from AI experimentation to enterprise-wide deployment is ultimately an organisational transformation as much as a technological one.
Success requires more than advanced models or additional computing capacity. Enterprises need integrated platforms, reliable information, clear governance, skilled employees and leadership capable of connecting AI investments to strategic priorities.
Data readiness will determine whether intelligent systems can operate effectively. Integration will shape how seamlessly AI interacts with existing applications.
Governance will establish the boundaries within which autonomous systems can function, while measurable business outcomes will determine whether investments deliver lasting value.
As agentic AI matures, these foundations will become even more important. Organisations will need to rethink workflows, redistribute responsibilities between people and intelligent systems and develop operating models that balance automation with human judgement.
For Middle East enterprises, the opportunity is substantial. Regional investment in digital infrastructure, sovereign AI capabilities and technology modernisation provides a foundation for more ambitious deployments. •
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INTELLIGENT CIO MIDDLE EAST www. intelligentcio. com