Leaders Roundtable
AI at Enterprise Scale: Executive Blueprint for Unified Data & Cloud
Malaysian organisations are increasingly viewing AI strategically, moving beyond experimentation toward real business value – no longer focused solely on productivity gains.
Yet, despite ongoing discussions, many still lack clarity on how to operationalise AI effectively and face the same barriers they did two years ago.
Ecosystm research highlights the challenges in Malaysian organisations:
- 75% struggle to demonstrate ROI from AI initiatives, revealing gaps in strategy.
- 75% cite poor data quality or limited access, slowing model training, integration, and adoption.
AI success rests on two connected pillars: strategic leadership and data readiness. Leaders convert AI from concept to measurable impact by aligning initiatives with business goals, embedding accountability, and keeping outcomes visible. Equally vital is seamless access to structured and unstructured data, backed by scalable infrastructure that lets AI deliver real results. A simpler, structured approach to AI and data readiness ensures leaders can continuously assess outcomes and drive enterprise-wide value.
Join us for an invitation-only session with peers to turn ongoing conversations into actionable strategies for leveraging data to scale AI, accelerate adoption, and drive measurable business outcomes.
Discussion topics include:
- Data Discovery and Sharing
Enabling teams to find, understand, and use all data types – structured and unstructured to accelerate innovation and measure the impact of insights. - AI Performance Monitoring
Tracking AI accuracy, detecting issues, managing bias, and assessing outcomes in real time. - AI Workflow Integration
Embedding AI insights into daily processes while maintaining security and compliance.
Gain practical frameworks to turn data into competitive advantages, scale AI with confidence, and achieve measurable business results.
Snowflake
Snowflake is a cloud-based data platform and data warehouse that enables organizations to store, process, and analyze large volumes of data.
AWS
Amazon Web Services (AWS) represents Amazon’s cloud computing platform, enabling individuals and organizations to access essential IT resources including storage solutions, computing power, and database management via internet connectivity.
Sash Mukherjee
VP Industry Insights,
Ecosystm
Satchit Joglekar
Senior Regional Director ASEAN, Snowflake
Alan Chiew
Country Manager, Malaysia, Snowflake
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Please see below some images and key takeaways.
➤ Data Definition and Localisation. Consistent definitions, governance, and management are essential for enterprise AI. Aligning global frameworks with local regulations ensures accurate insights, richer models, and a balance between global coherence and local adaptability.
➤ Shared Data Ownership. Ownership extends beyond IT to those generating business value. Transparent metrics, joint decision-making, and shared responsibility turn data into a strategic asset, strengthen governance, and foster a data-literate organisation.
➤ FinOps and Cost Accountability. Managing AI infrastructure costs requires collaboration across technology, business, and finance teams. Continuous transparency aligns spend with outcomes, builds trust, and positions efficiency as a strategic competency.
➤ AI Centres of Excellence. CoEs coordinate initiatives, enforce standards, and maintain visibility across deployments. They transform AI from experimental projects into auditable, governed, enterprise-grade capabilities that evolve through feedback and learning.
➤ Data Activation. Value emerges when data drives action. Unified platforms break down silos, enable seamless analytics and AI, and provide traceability from source to outcome. Active use of data fuels innovation, reduces bias, and strengthens enterprise-wide decision-making.