Transforming Industries with AI Inside the Enterprise - Insights from CDO Vision 2026 Dubai
Why governance, ROI discipline, and data foundations now define success
At CDO Vision 2026 in Dubai, the panel session titled Transforming Industries with AI: Enterprise Success Stories focused on moving beyond AI hype toward practical execution. The discussion examined how large organizations are deploying artificial intelligence in production environments, how they prioritize use cases, and why many projects fail before delivering measurable value.
The session was moderated by Hazem Ahmed, Data Science and AI Leader at Emirates. The discussion featured Orlando Beakbane, Principal Strategic Lead GCC Customer Success at Braze; Kevin Neogy, Group Head of Digital Transformation, AI & Robotics at The Kanoo Group; and Deepak Kumar, Head of Data Science and AI at Al-Futtaim.
Framing the opening of the discussion: Most organizations are experimenting with AI. Far fewer are scaling it.
Strategy Begins with Business Ownership
AI programs accelerate when business teams are directly involved in product development. Instead of isolating AI within IT departments, organizations are embedding domain experts into solution design. Institutional knowledge becomes the starting point. Bottlenecks are identified by those closest to operations.
This approach reduces development cycles. When business stakeholders act as product owners, solutions are shaped around real constraints rather than theoretical models. Governance and security controls remain centralized, but ideation and prioritization sit with the business.
Speed matters. However, speed without alignment leads to stalled pilots. The initiatives that progress are tightly linked to measurable business objectives and operational impact.
Why Most AI Projects Fail Before Production
Industry estimates suggest that a large percentage of AI proofs of concept never reach production. The technical model may work in isolation, yet scaling introduces new challenges.
Data quality is the primary constraint. Synthetic or curated data can make early prototypes appear successful. Production environments expose inconsistencies, missing fields, drift, and integration gaps. Without strong data governance, model performance degrades quickly.
Operationalization introduces further complexity. Observability, monitoring, model accuracy checks, cost controls, and traceability become mandatory once hundreds of users interact with a system. Token consumption rises. Infrastructure costs increase. Questions shift from “Does it work?” to “Is it reliable, secure, and economically viable?”
Production AI requires foundations: clean data, infrastructure readiness, monitoring frameworks, and clear accountability.
Prioritization, ROI, and the Build vs. Buy Decision
Organizations are filtering AI initiatives through structured prioritization frameworks. Alignment to business goals, expected return on investment, implementation cost, and readiness of data and infrastructure determine sequencing.
Some use cases demand internal development. These are typically cross-functional workflows deeply embedded in proprietary processes—such as inventory forecasting combined with automated ordering, demand prediction, and rule-based decision triggers. These systems depend on nuanced business logic and domain-specific data.
Other components are increasingly sourced externally. Proven AI engines, especially those that have demonstrated performance in similar contexts, reduce time to value and implementation risk. The shift toward buying or partnering reflects a desire for faster deployment and validated outcomes.
The decision is less ideological than practical. The question is not whether to build or buy, but which layer of the stack demands ownership.
Scaling Requires Trust and Observability
Technology accounts for only part of AI adoption. Workforce trust determines long-term usage.
Employees resist tools they do not understand or help design. Adoption improves when teams co-create solutions and see clear augmentation of their roles rather than displacement. Internal advocacy spreads faster than top-down mandates.
At scale, observability becomes central. Accuracy monitoring, drift detection, usability tracking, cost transparency, and access traceability ensure systems remain reliable and accountable. Scaling without visibility introduces operational risk.
The discussion concluded with three recurring themes: speed is necessary but insufficient; partnerships accelerate outcomes when technology is proven; and foundational data quality determines long-term success.