Ecosystm Connect: Getting the AI Stack Right: Building for Real-World AI Use

It has become amply apparent that AI cannot sit as a layer on top of enterprise systems. To create real value, it needs to be embedded into how organisations sense, decide, act, and defend in real time.

In Singapore, organisations have progressed to measuring AI against business outcomes. Key priorities include customer experience, operations, and decision support, with a focus on improving productivity, responsiveness, and accuracy in live environments.

But Ecosystm research also exposes a structural gap in Singapore organisations.

• Only 39% are confident their infrastructure can support AI growth
• Just 19% have real-time monitoring and decisioning capabilities
• 53% are strengthening security specifically for AI workloads

AI is already running in production, but the supporting systems remain uneven, particularly across data, infrastructure, and security.

Closing this gap requires tighter integration across the stack – from systems that automate work and workflows, to infrastructure that can handle real-time data, to decision systems embedded in daily operations, and security that can detect and respond to risk as it emerges, supported by foundation models that can be reused across use cases.

This is also where ecosystem-level support becomes important. IMDA’s Accreditation and Spark Programmes, supported by a structured evaluation framework, identify and validate startups and scaleups working across these layers and delivering measurable enterprise impact.

Join us at this session where we focus on how AI can be applied in live environments – across systems that run work, inform decisions, manage infrastructure, and secure operations – and what is already working in production, and how it is being operationalised.

Theme 1: AI Systems Embedded in Work

Embedding AI in day-to-day workflows is where the next stage of progress lies. This includes systems that turn operational signals into actions, reduce manual coordination across processes, enable automated interactions across customer and service environments, and embed intelligence directly into the applications used to run operations.

Theme 2: Foundations for Operational AI

What determines whether AI works in production is operational readiness. That depends on data that can be accessed and processed in real time, infrastructure that can handle scale and variability, security that adapts as exposure expands, and systems that provide visibility so issues can be identified early.

Don’t miss this opportunity to connect with innovators shaping how organisations run AI in production across data, infrastructure, execution, and security.