CDAO Malaysia schedule

During this 5-minute networking session, participants can build their network. Have fun!  

• Axiata’s AI journey as a case study – demonstrating how large, multi-market enterprises can embed AI native thinking to reshape operations, accelerate innovation, and create new competitive advantage
• Strategic and governance foundations essential for sustainable AI transformation, including responsible AI principles, organisational alignment, and capability building across all levels
• Real impact at scale illustrated through measurable outcomes across Axiata’s operating companies, showcasing how enterprise AI drives productivity, customer value, and long-term growth

Imagine for a moment that every single system, workflow, and AI agent in your company operated from the same shared brain. A single source of truth where every piece of data is understood in its full context. That's not a far-off dream—it's the new benchmark for an intelligent enterprise.

This vision is powered by what we call 'Trusted Context.' It’s the magic layer that transforms raw data into actionable intelligence. Building it requires a new way of thinking: one that deeply integrates world-class data management with the platforms where your business actually runs.
Today, we’re thrilled to show you how Informatica, as part of the Salesforce family, is making this vision a reality for companies like Takeda and Holiday Inn. We'll give you a blueprint for building your own foundation of trusted context and unleashing the true power of your data and AI.

• Moving beyond policy discussions to practical mechanisms that make governance work at scale (data products, lineage, ownership, quality controls)
• Designing operating models where governance is built into workflows, platforms, and AI pipelines — not layered on afterwards
• Lessons from organisations turning governance into a capability that improves speed, reliability, and decision confidence

Moderator:

Dr Behrang Hani Parhizkar CEO Chamrun Digital

Panellists:

Hana Salwani Mohd Zaini IT/Head of Clinical Application Management Division University Malaya Medical Centre

Keatky Lim SVP, Strategy Analytics and Digital Innovation, Group Insurance RHB Banking Group 

Dr. Faisha Shahriman Head, Risk & Resilience PLUS

• Why scale ≠ value: Exploring why simply integrating AI or analytics into workflows no longer guarantees competitive advantage — and what truly differentiates mature enterprises from those following hygiene practices
• Opportunities vs. Consequential Risks: A candid examination of the marginal value vs. the outsized risks (operational, ethical, security, compliance) when deploying agentic AI across enterprise functions
• Human Oversight, Control & Accountability: How to prevent drift toward humans-out-of-the-loop models, avoid hidden failure modes, and design governance that strengthens—not weakens—enterprise integrity

Moderator

Bobby Varanasi Chair - AI Ethics & Governance Group Malaysian Technical Standards Forum

Panellists:
Jacky Cheong Chief Data & AI Officer Confidencial

Kum Weng Siew Head of Data Transformation Bank Islam Malaysia Berhad

Dr Zan-Kai Chong Head of Artificial Intelligence Solutions DCAP Commercial 

Most AI initiatives don’t fail because of weak models — they fail because organisations can’t trust or activate the data those models rely on. As AI moves from experimentation to autonomy, fragmented data, static governance, and disconnected systems quietly become the real blockers to scale.

In this session, Boomi explores the shift required to move beyond pilots and unlock measurable AI value. Drawing on real-world examples and emerging best practices, he will show how data readiness, integration, and governance must evolve to support agentic AI operating safely inside core business processes. 

• If trust is a critical element for AI success, why are most organisations still struggling to scale AI adoption?
• What explains the gap between organisations claiming trust in AI and their actual efforts in governance, explainability, and ethical safeguards?
• Why do many AI transformations fail to scale beyond initial deployments, and what does this reveal about priorities in data, governance, and leadership?

While Generative AI revolutionized content creation, AI Agents are now transforming core operations: from orchestrating intricate supply chains to automating sophisticated risk assessments and real-time resource optimization. The "AI explosion" has reached a tipping point. To survive in 2026, organizations must shift from treating AI as a series of experimental silos to managing it as a governed enterprise utility.

• Why most AI initiatives fail to scale because organisations underestimate the importance of trusted, well‑governed data foundations
• What “AI‑ready data” actually looks like in practice, beyond tools and technology, including ownership, quality, and trust
• How organisations can take pragmatic, step‑by‑step actions to strengthen their data foundations and enable sustainable, scalable AI

Speakers

Tristan El Jed Head of Data & AI Enablement SEEK

Agentic AI is rewriting the rules of competitive advantage, but only for organizations whose data is ready to keep up. This presentation reveals the hidden “Agentic AI Data Integrity Gap” limiting enterprise AI today and shows how leading companies are closing it with a new standard of data: unified, enriched, real‑time, and governed. Key learnings:

• The Agentic AI Data Integrity Gap: why trapped, incomplete, outdated, inconsistent, and non‑compliant data undermines AI ROI
• The six principles of Agentic‑Ready Data: unify, enrich, operate in real time, shape for purpose, govern, and reduce cost through automation
• Real‑world applications and customer outcomes showing measurable impact in AI readiness, operational efficiency, risk reduction, and customer experience

This session links European regulatory rigor with Malaysia’s rapidly evolving AI and digital health agenda to address the “health data debt”: fragmented, inconsistent, and poorly governed data that hinders the expansion of clinical AI. Drawing on the European Health Data Space and Singapore as a regional benchmark, it offers CDOs a practical path to move from isolated pilot projects to reliable, scalable AI that delivers measurable clinical and business value.

Key Pillars:

Technical Readiness: Building interoperable, governed and AI-ready data foundations from fragmented clinical systems, aligned with Malaysia’s digital-health priorities and informed by EHDS principles of secure access, interoperability and responsible data reuse.

Strategic Governance: Operationalising Malaysia’s AIGE Guidelines and evolving data-protection framework, using European risk-based governance and Singapore’s experience as comparative benchmarks rather than imported templates.

Economic Impact: Moving beyond compliance and pilot counts to measure time-to-deployment, adoption, workflow efficiency, patient outcomes and risk-adjusted ROI.

• Embedding governance into fast-moving, multi-cloud data ecosystems
• Balancing agility with compliance across diverse regulatory landscapes
• Leveraging automation to scale data quality and trust

• Practical perspective on how organisations move from identifying the right business problem
• Shaping the analytics and data intervention to embed it into decision-making and execution
• Delivering measurable commercial and operational impact

• Unifying metrics, definitions, and governance across complex organisational structures
• How organisation is exploring agent-based orchestration for automated insights and workflows
• Delivering real business value through AI and data alignment

• Enabling experimentation and rapid insights while maintaining compliance
• Implementing frameworks that balance creativity, privacy, and regulatory requirements
• Translating responsible data practices into sustainable business advantage

• How organisations are moving away from centralised data lakes towards modular, purpose-built architectures that streamline data flow and reduce latency

• Leveraging automation, metadata management, and cost-aware storage tiers to optimise data movement without compromising accessibility or performance

• Designing architectures that balance scalability, governance, and real-time processing to support modern AI and analytics workloads

Speakers:

Mohammad Parsa Parhizkar Co-Founder Minedu AI

Mohammad Rohan Parhizkar Co-Founder Minedu AI 

• Re-architecting legacy systems to support cloud, hybrid, and next-gen workloads
• Balancing performance, cost, and security while modernising data foundations
• Enabling future-ready platforms that accelerate AI adoption and business transformation
  
  

Speakers:
Sachin Dwivedi Head of Data Strategy & Insights Coca-Cola Beverages

Dickson Tung Head of Data Platform Engineering PayNet

• Identifying how poor data quality drives hidden costs across operations, compliance, and customer experience
• Demonstrating the ROI of data readiness through reduced errors, faster decision-making, and stronger AI outcomes
• Building executive buy-in by linking data quality investments directly to business growth and risk reduction

Moderator:

Dr Behrang Hani Parhizkar CEO Chamrun Digital

Speakers:
Jeff Lim Head, Strategy & Analytics Sun Life Malaysia Assurance Berhad

Kum Weng Siew Head of Data Transformation Bank Islam Malaysia Berhad

Mohd Fakhri Mohd Noor Chief Digital Officer FarmByte 

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.