Partners connect platform adoption with business outcomes as Databricks sees more AI opportunities in the region

Databricks says enterprises across ASEAN and Greater China are moving from isolated AI pilots to broader data and AI strategies tied to business KPIs.

Databricks is seeing enterprise conversations across ASEAN and Greater China move from isolated AI projects to broader data and AI strategies, Cecily Ng, Vice President and General Manager of ASEAN and Greater China at Databricks, said in an interview.

Ng said customers are now asking how to standardize platforms, govern data centrally, and support multiple AI workloads at scale. The change reflects a move away from standalone proofs of concept toward production environments that require stronger data management and governance.

"Conversations are moving from isolated AI use cases to enterprise-wide data and AI strategies," Ng said. "Customers are asking how to standardize platforms, govern data centrally, and support multiple AI workloads at scale."

Moving beyond AI pilots

She said enterprises are also placing more emphasis on measurable outcomes, including revenue growth, operational efficiency, and risk reduction. AI initiatives are increasingly being linked to business KPIs rather than treated as experimental projects.

"A few years ago, customers were still experimenting with AI and POCs, but now AI initiatives are being tied directly to business KPIs," Ng said.

Databricks is also seeing more production-grade deployments across its customer base. These include real-time data pipelines, customer-facing AI applications, and workloads used in core business operations.

Ng cited Suntory Beverage & Food International as one example. The beverage company, which operates across more than 80 markets and produces Japanese whiskeys including Hibiki and Yamazaki, deployed agentic AI capabilities to automate repeatable analysis and support planning across teams.

The company integrated transactional data with external sources such as market data, weather data, and macroeconomic indicators on a single governed platform. According to Ng, this helped internal teams better understand factors affecting product sales and adjust sales strategies in real time.

Partners support enterprise AI adoption

As AI projects become more complex, Ng said partners play a larger role in helping customers connect platform adoption with business outcomes. She said this requires domain expertise and industry context, especially when companies move from deployment to broader adoption.

"As AI projects scale, partners play a critical role in turning platform capability into business impact, bringing the domain expertise and industry context needed to translate our technology platform into specific, high-value use cases," Ng said.

Databricks works with more than 1,000 partners across Asia Pacific and Japan. Ng pointed to the company's partnership with Accenture, which has supported projects including Singtel's unified data platform for real-time analytics.

The Databricks-Accenture partnership also involved the deployment of the Telco Fraud Analytics Accelerator and AI/BI Genie. These tools are aimed at supporting analytics and AI use cases in telecommunications.

Ng also highlighted Tiger Analytics' work with Pelabuhan Tanjung Pelepas, a transshipment port in Malaysia. The project brought together data from more than 10 operational sources into a lakehouse environment.

The platform gives PTP a more complete and timely view across operations, productivity, maintenance, and finance. It has also reduced data silos, enabled real-time operational reporting, and improved the speed and accuracy of decision-making, according to Ng.

The data foundation is intended to support future machine learning and business intelligence use cases. These include resource deployment optimization, delinquency prediction, and IoT-based anomaly detection.

Ng said partner ecosystems are becoming more important as enterprises move from adopting platforms to embedding AI and analytics into business processes. "At this stage, success is less about deploying a platform and more about driving adoption and outcomes," she said.