As AI adoption accelerates across the Asia Pacific, many organisations are discovering that experimentation alone does not translate into real business impact. Confluent, a data streaming platform provider, is positioning itself at the centre of this shift by helping enterprises build the real-time data foundations needed to scale AI sustainably.

In 2026, Confluent’s strategic focus centres on enabling companies to transition from early AI pilots to production deployments that deliver measurable financial returns. According to Greg Taylor, Vice President and General Manager for APAC at Confluent, this transition has become a pressing priority as organisations across Southeast Asia adopt AI faster than the global average.

“Across Southeast Asia, nearly half of companies are already beyond the pilot stage of AI adoption,” Taylor says in an email interview with e27. “However, many leaders still struggle to translate adoption into meaningful business outcomes.”

One of the biggest obstacles lies in data fragmentation. Confluent’s research shows that 70 per cent of APAC leaders cite fragmented data as a major barrier, while 68 per cent report insufficient skills and expertise to manage AI projects and workflows effectively.

Real-time data as the foundation for AI

For Confluent, the solution lies in addressing a fundamental infrastructure challenge: ensuring that AI systems have access to reliable, real-time data.

Modern AI applications—from fraud detection to personalised customer experiences—require continuously updated information rather than static datasets. Yet many organisations still rely on legacy architectures built around batch-based pipelines that deliver stale or incomplete data.

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Taylor argues that this architectural mismatch limits the value organisations can extract from AI.

“Trustworthy AI output requires quality, real-time data as its foundation,” he explains. “Without that, even the most advanced models will underperform.”

This belief underpins Confluent’s core mission: enabling data to move freely across an organisation so businesses can act on information as it happens. By streaming data continuously across systems, Confluent aims to create what Taylor describes as a “central nervous system” for enterprise data.

Such infrastructure allows organisations to support real-time decision-making, intelligent automation, and AI-driven applications across business functions.

To strengthen this vision, Confluent has introduced new capabilities designed specifically for AI-powered environments.

In late 2025, the company launched Confluent Intelligence, a fully managed service that streams and processes both historical and real-time data to support AI applications at scale.

The platform includes several key components, including a Real-Time Context Engine that delivers structured context to applications and AI agents using the Model Context Protocol (MCP). It also offers Streaming Agents that simplify the development of event-driven AI agents, alongside built-in machine learning functions that help teams derive insights from streaming data more quickly.

More recently, Confluent expanded these capabilities by introducing support for the Agent2Agent (A2A) protocol, allowing AI systems across an enterprise to communicate and collaborate more effectively.

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Together, these capabilities are designed to help organisations operationalise AI rather than treat it as a standalone experiment.

Navigating APAC’s diverse markets

While technology innovation remains central, Confluent’s long-term growth strategy also reflects the unique complexity of the Asia Pacific.

Unlike more uniform markets, the APAC region comprises economies with vastly different levels of digital maturity, regulatory environments, and infrastructure readiness. Confluent’s approach focuses on flexibility—both technologically and commercially.

In Southeast Asia, for example, many enterprises still rely heavily on on-premises systems while gradually transitioning to cloud-based environments. Confluent supports this transition by offering deployment models that span cloud, hybrid, and self-managed environments.

This flexibility allows organisations to modernise at their own pace while addressing regulatory requirements such as data residency and sovereign cloud policies.

In parallel, Confluent is expanding its partner ecosystem across the region. Through initiatives such as the newly launched Sell With Confluent reseller programme, partners gain automated quoting tools, faster approvals, and co-marketing resources to accelerate adoption of data streaming solutions.

The programme reflects the company’s ambition to capture a share of the growing US$100 billion data streaming platform market.

As AI adoption deepens, governance is emerging as another critical pillar of sustainable technology deployment.

Across the Asia Pacific, governments are moving beyond high-level AI strategies toward enforceable regulatory frameworks. Countries such as India and Vietnam are introducing AI governance guidelines and laws, while markets including Singapore, Australia, Japan, and South Korea are strengthening national AI policies.

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These developments will require companies to demonstrate transparency, data lineage, and robust model oversight.

For Confluent, governance must be embedded directly into data infrastructure rather than added later as a compliance layer. The company has introduced tools such as Unified Stream Manager (USM) to help organisations synchronise metadata across hybrid systems and maintain stronger oversight of data pipelines.

Ultimately, Taylor believes that companies building sustainable AI businesses will treat data not as a byproduct but as a core product.

“Data needs to be continuously streamed, enriched, governed and reusable across the organisation,” he says.

By focusing on real-time data architecture, governance, and regional partnerships, Confluent is positioning itself as a critical infrastructure provider for the next phase of AI adoption in the Asia Pacific—one where experimentation gives way to operational scale and lasting business value.

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Image Credit: Confluent

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