How CXOs Are Structuring AI for the Boardroom - Insights from CDO Vision 2026 Dubai
Governance, data maturity, and accountability in scaling AI across large organizations
At CDO Vision 2026 Dubai, the session titled “The Boardroom Conversation - How CXOs Shape AI Strategy” focused on how executives are moving AI from experimentation into operational reality. The discussion centered on board reporting, ownership, governance, data maturity, and long-term positioning.
The session was moderated by Ram Jalan, Digital Transformation and AI Innovation Leader. Participants included Ashish Pandey, CIO at Aster Pharmacy; Gopi Maren, Chief Specialist, Data & AI Governance Office at Dubai Roads and Transport Authority; and Awad El-Sidiq, Head of Artificial Intelligence at ADNOC Distribution. The perspectives came from healthcare retail, public infrastructure, and energy distribution, each operating at scale and under pressure to demonstrate measurable outcomes.
Reporting AI to the Board
Boards are asking what has been delivered in the past 12 to 18 months.
AI programs are being evaluated against business priorities: cost reduction, operational efficiency, revenue contribution, regulatory risk mitigation, and customer experience improvement. The number of pilots is less relevant than measurable impact.
Many initiatives begin in sandbox environments. The challenge is moving from proof-of-concept to production. That transition requires a defined business case, executive sponsorship, and clarity on expected outcomes. Projects without this structure remain technical exercises.
Several organizations now route proposed AI initiatives through cross-functional review structures. Use cases are assessed against strategic objectives before approval. Once deployed, performance is monitored. Launch is not treated as completion; realized impact determines success.
Some investments are made for strategic positioning rather than immediate return. In competitive sectors, delaying adoption until ROI is fully proven can create disadvantage. Even in those cases, long-term value remains the benchmark.
Governance and Accountability
AI governance is structured around shared accountability.
Standards are being formalized around explainability, fairness, data quality, and regulatory exposure. Model documentation and impact assessments are introduced before production release to clarify expected behavior and potential risk.
Ownership does not sit solely with IT or a governance office. Business units that define the problem remain responsible through implementation and value realization. AI teams enable architecture, tooling, and execution support. Business leaders report outcomes.
Cross-functional oversight (often including legal, compliance, risk, data, and operational leadership) reduces ambiguity. Clear ownership from the outset prevents projects from stalling.
AI initiatives without a committed business sponsor struggle to scale. Defined accountability accelerates execution.
Data and Structural Design
AI capability is constrained by data maturity.
If source systems contain inconsistencies or incomplete records, models replicate those weaknesses. Improving data quality is treated as a continuous discipline rather than a one-time preparation phase.
Many enterprises already possess significant internal data assets that remain underused because business teams lack visibility. Data awareness and literacy programs are expanding to increase discoverability and reuse. As maturity improves, prioritization becomes clearer.
Structural design reinforces this foundation. Core infrastructure, data platforms, governance frameworks, security controls, is centralized. Execution occurs closer to the business.
Excessive central control slows delivery. Unmanaged decentralization creates duplication and operational fragility. Independent spreadsheet-based analytics and manual data extracts introduce risk and inefficiency. The preferred model centralizes shared systems while enabling controlled self-service within defined guardrails.
Positioning for 2026 and Beyond
Public AI platforms have raised expectations inside enterprises.
Internal capability, however, depends on data readiness, process clarity, and stakeholder alignment. Enterprise AI deployment progresses incrementally.
Some organizations are investing ahead of immediate profitability to build internal capability. Others are prioritizing initiatives directly tied to defined KPIs. In both approaches, sustained measurable contribution determines continuation.
Three priorities define the next phase: governance that scales with deployment, continued improvement in data quality and literacy, and business-led AI initiatives anchored in defined operational problems.
AI is now embedded in planning cycles and budget discussions. Boards expect reporting discipline, financial accountability, and ongoing performance.