CDAIO 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

Explore how organizations can drive better decision-making by leveraging trusted and governed data, emphasizing the importance of data quality, accuracy, and traceability. It highlights common challenges organizations face with ungoverned data, such as conflicting metrics, mistakes, bottlenecks, and dead ends — and why sound decisions, agents, and AI alike all depend on getting past them. See what it takes to make your data trustworthy enough to build every decision on solid ground.

• 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

Dr. Faisha Shahriman Head, Risk & Resilience PLUS

Edwin Chong Head, Digital Projects Gentari

• 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

Dr Behrang Hani Parhizkar CEO Chamrun Digital

Panellists:
Jacky Cheong Data & AI Governance Leader

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

Ahmad Shukri bin Abdul Hamid Principal Secretary, Transformation Unit Ministry of Home Affairs 

Skip the foundation, and scale just amplifies the cracks.

Most AI programs don't fail at the model — they fail at the data underneath it. Fragmented sources, ungoverned pipelines, and inconsistent master data don't disappear when you scale AI; they multiply. Every agent, every use case, every new deployment inherits the same structural weaknesses, only faster and at greater cost.

Data readiness isn't a prerequisite to check off before the real work begins — it's the operating discipline that determines whether AI value compounds or collapses under its own weight. Organizations that treat governance, data quality, and trusted data foundations as the starting point don't just deploy AI faster. They deploy it in a way that holds up as it scales.

• 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?

Dataiku’s Platform for AI Success helps enterprises scale AI and GenAI with the discipline leadership teams need: connecting trusted data to production-ready use cases, enabling broader participation across business and technical teams, and embedding governance, risk management, and oversight from the start. For CDAO leaders operating across regulated and diverse industries, it offers a practical way to democratize AI without losing control—improving visibility, consistency, and measurable business value while supporting responsible adoption at enterprise scale.

• 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

AI has become a boardroom priority, yet many organisations struggle to move beyond pilots and proofs of concept. The reason often isn't the technology, it's that their data isn't ready to support it.

And now, with AI agents that make decisions and act on their own, the stakes for getting the data foundation right have never been higher. Ungrounded AI doesn't fail loudly; it answers confidently and incorrectly, making trusted data more critical than ever.

This session will explore why visible, high-quality, governed, and enriched data is the critical foundation for successful enterprise AI initiatives. Drawing on real-world examples, Stu will show how organisations can improve data quality and accessibility, enrich records with location intelligence, and strengthen data governance to accelerate AI adoption, reduce risk, and deliver more reliable, measurable outcomes.

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.

• 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

• 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

Most enterprises have spent the last several years modernizing their data estates, moving workloads into platforms built for scale and speed. That investment was never designed to answer the question enterprises are asking now: Is this data trustworthy enough for an autonomous agent to act on without a person checking its work?

In this session, Ataccama and Emisha will walk through the Modern AI Stack Blue—the complete five-layer architecture diagram—and explain why most enterprises are building Layers 1, 2, 4, and 5 while skipping the one that determines whether agents can operate reliably. This session will also cover how to structure a Data Trust Layer so AI agents can proceed, pause, or escalate based on certifiable quality signals, not assumptions.

Speaker

Della Zhuang VP of Growth Ataccama

Vikram Tandon Director Emisha  

Speakers:

Mohammad Parsa Parhizkar Co-Founder Minedu AI

Mohammad Rohan Parhizkar Co-Founder Minedu AI 

• 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

Mohd Fakhri Mohd Noor Data Analytics Leader

Benjamin Shepherdson Head of Privacy CelcomDigi 

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.