Leaders Roundtable: The AI Execution Gap: How FSI Leaders are Fixing the Data Foundations [Singapore]

Financial services organisations in Singapore are moving quickly to operationalise AI. But leaders face a consistent set of constraints: legacy data estates that are hard to modernise, siloed architectures, governance models not designed for AI, and regulatory scrutiny that adds friction at every layer. The result: deployments remain concentrated in internal productivity use cases, with limited adoption in customer-facing applications beyond narrow service automation.

Ecosystm research confirms the pattern:

Only 21% of FSI leaders express confidence in their data strategy
70% cite governance and compliance as a key barrier
60% are still working through how to make data reliably accessible for AI use

Addressing this requires a shift in how data is organised and governed, enabling it to be accessed consistently, used safely across structured and unstructured sources, and connected into AI workflows without creating additional fragmentation. It requires a foundation where data is AI-ready by design, not retrofitted through layers of integration and exception handling.

Join us for a candid, off-the-record Ecosystm Leaders Roundtable, where senior peers and regional technology experts work through the real constraints shaping AI adoption today and the decisions that matter next.

The discussion will cover:

Best practice lens. Global and regional perspectives on how financial institutions are evolving data strategy, governance, and AI operating models
Operating model gaps. Where alignment between business, technology, and risk breaks down, and what is improving execution at leadership level
Regulatory trade-offs. How institutions are managing regulatory scrutiny while maintaining delivery velocity in AI adoption
Ecosystem decisions. The factors shaping high-stakes build vs. buy vs. partner choices in a rapidly evolving AI ecosystem
Value realisation shift. How data investment is being reframed from infrastructure spend to a driver of revenue and margin growth

Plus: Attendees receive a standing invitation to a complimentary, tailored data strategy session with the Snowflake x Accenture FSI leadership team.

Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. The world’s largest companies use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. Learn more at snowflake.com (NYSE: SNOW)

Accenture is a leading global professional services company known for providing strategy, consulting, digital, technology, and operations services. It is particularly recognized for large-scale enterprise digital transformation, cloud computing, artificial intelligence (AI), and IT implementation for Fortune 500 companies and governments worldwide.

Sash Mukherjee

VP Industry Insights,

Ecosystm

Jonathan Beaulier

Chief Revenue Officer, Snowflake

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Please see below some images and key takeaways.

Here are some insights that emerged:

AI readiness is a full-stack challenge. Data remains the foundation, but successful AI depends on more than data alone. Organisations need alignment across data, knowledge, models, orchestration, and lifecycle management. The strongest AI initiatives are those where every layer of the stack can be operationalised.
AI is forcing operating model redesign. As AI moves into production, weaknesses in ownership, decision-making, accountability, and cross-functional coordination become more visible. Scaling AI depends on redesigning how business, technology, data, and risk teams work together, not just introducing additional technology.
Governance & optionality are strategic assets. Leading organisations are placing greater emphasis on governance, lifecycle management, and platform flexibility to manage cost, reduce lock-in, and preserve freedom of choice as models, platforms, and market conditions continue to evolve.
AI economics is a board agenda. This includes model efficiency, platform choices, and measurable business outcomes. At the same time, AI is reshaping work, roles, and workforce structures. Long-term value will depend on linking AI investments directly to business impact while preparing the organisation to operate differently.
AI execution depends on ecosystem capability. AI projects require orchestrating a combination of internal teams, technology platforms, and external partners to accelerate outcomes. The decision factors are speed, capability, scalability, regulatory requirements, and competitive differentiation.