Why real-time data unlocks the true power of enterprise AI

Real-time data is essential for effective enterprise AI deployment

AI is only as good as the data feeding it. Without reliable, real-time streams of information, even the most advanced models fail to deliver consistent results. Across industries, leaders are moving from theoretical discussions about AI to actual deployment, but they’re hitting roadblocks around data quality and access. Stephen Deasy, Chief Technology Officer at Confluent, emphasized that real-time data is the backbone of successful enterprise AI, particularly in applications like fraud detection where lag or inconsistency can have immediate financial consequences.

Today, companies are aligning their data architectures with AI’s operational demands. Real-time data allows systems to adapt instantly to new inputs, something static datasets simply can’t achieve. This isn’t just about speed; it’s about relevance. The faster your data moves through your systems, the closer your AI decisions get to reflecting the real state of the business in that moment.

For executives, real-time data activation should be viewed as a core transformation rather than a routine upgrade. The benefits go well beyond efficiency gains. Real-time AI systems deliver dynamic customer experiences, detect risk instantly, and continuously fine-tune operations. Those who fail to adopt this capability will find their decision-making lagging behind faster, more agile competitors. The reality is that AI without high-quality, real-time data is little more than potential, it only becomes power when it’s fed live, accurate information.

Transitioning from systems of record to systems of action requires fresh data capture and real-time processing

Traditional business systems rely on stored data, records of what has already happened. The next generation of systems, what Greg Taylor, Senior Vice-President and Asia-Pacific General Manager at Confluent, calls “systems of action,” are built to respond to events as they happen. They don’t just store data; they use it to act immediately.

This shift demands infrastructure capable of capturing data the instant it’s created and making it usable without delay. Organizations adopting this approach are already seeing material impact. Some Confluent customers, for example, have reached automation levels between 60% and 70%. These gains aren’t theoretical, they reflect a measurable shift toward operational intelligence. When AI has access to new data in real time, it can trigger immediate actions such as approving transactions, adjusting pricing, or preventing fraud, all without waiting for manual review.

For C-suite leaders, the takeaway is clear: real-time processing is not an IT initiative, it’s a strategy for speed and precision. Businesses that incorporate real-time responsiveness into their operations position themselves for competitive advantage in markets where customer expectations and risks evolve daily. Real-time data systems allow leaders to see developments as they unfold and act before challenges become problems.

The transition from records to action is the foundation for true business automation. It empowers enterprises to make decisions rooted in the most recent insights, not outdated information. In essence, adopting real-time systems transforms data from a reporting tool into a living operational asset, something every business leader aiming for long-term resilience should prioritize.

Effective real-time systems require robust governance and business oversight.

Real-time systems are powerful, but without governance, they can introduce new risks. Greg Taylor, Senior Vice-President and Asia-Pacific General Manager at Confluent, pointed out that business experts must be directly involved in system oversight to ensure responsible use of AI. Their role is to monitor outputs, identify errors, such as AI “hallucinations,” or false results, and make sure automated decisions align with the organization’s objectives and compliance standards.

Governance in this context means more than checking for technical accuracy. It’s a structured, ongoing process that combines domain knowledge with operational control. As AI decisions increasingly affect customers, employees, and finances, business oversight ensures that actions taken by automated systems remain transparent and traceable. This approach builds trust internally and externally.

For executives, governance shouldn’t be viewed as an obstacle to innovation. It’s an enabler that allows AI systems to function confidently within defined limits. Strong oversight helps refine data quality, reinforce accountability, and enable faster improvements over time. Businesses that integrate governance into their real-time AI frameworks don’t slow down innovation, they make it more sustainable, consistent, and trustworthy.

Confluent’s platform flexibility supports hybrid and customizable data infrastructures for AI

Real-time data needs flexibility. Enterprises operate across different technology environments, some on-premise, some in the cloud, and many in hybrid setups. Confluent’s platform provides exactly that flexibility, allowing organizations to run in the configuration that fits their operational, compliance, and performance needs. Stephen Deasy, Chief Technology Officer at Confluent, emphasized that this adaptability allows AI models to draw directly from real-time data across any infrastructure.

The ability to integrate seamlessly across multiple systems and adhere to open standards matters to large organizations with complex ecosystems. It lowers the cost and complexity of scaling data-driven initiatives and reduces vendor lock-in. With Confluent’s architecture, enterprises can stream data directly into AI models, aligning data strategy with business goals.

For decision-makers, flexible infrastructure is more than convenience, it’s strategic insurance. Technology evolves quickly, and organizations need systems that can evolve with it. A platform that supports hybrid deployments gives businesses the control, resilience, and scalability required to deliver real-time intelligence now and adapt to future demands. Ensuring that your data architecture can operate across multiple environments isn’t just a technical preference; it’s a long-term safeguard against obsolescence.

Generative AI is reshaping enterprise software dynamics and vendor relationships

Generative AI is changing how enterprises build and negotiate technology solutions. Greg Taylor, Senior Vice-President and Asia-Pacific General Manager at Confluent, noted that companies are no longer waiting for vendors to deliver every new feature. They’re using generative AI to develop and extend capabilities internally, giving them greater autonomy and bargaining power. Stephen Deasy, Chief Technology Officer at Confluent, added that this shift creates constant pressure on software suppliers to innovate faster and provide more value.

This evolution in the enterprise software landscape is reducing the traditional dependency on vendors. It gives customers the freedom to experiment, iterate, and develop in real time, closing the gap between need and delivery. While this trend increases expectations for speed and customization, it also raises the performance bar for every software provider. Companies that fail to adapt will struggle to maintain relevance in a market now defined by flexibility and self-sufficiency.

For executives, the message is direct: generative AI is not only a technology upgrade, it’s a market shift. Investing in internal capability to design, test, and integrate AI-driven tools strengthens strategic positioning. By balancing vendor collaboration with in-house development, leaders can accelerate innovation while maintaining control of pace, cost, and quality. In this new dynamic, agility becomes a measurable advantage.

Bendigo bank’s deployment of confluent illustrates tangible operational improvements

Bendigo Bank’s experience demonstrates how well-structured data streaming can deliver measurable efficiency gains. Sam Fursdon, Principal AI Engineer at Bendigo Bank, explained that the bank adopted Confluent Flink to reduce system strain associated with open banking compliance and its mobile-only subsidiary, Up. The integration allowed the bank to cut mainframe API calls by 50%, clearing overnight batch processing by early morning and improving overall workload management.

The results are significant. The bank’s latency, the time between a transaction and when updated information is available, dropped to just 2.3 seconds during business hours. This means customers can receive transaction notifications in near real time, enhancing transparency and customer satisfaction. Confluent’s integration with the bank’s continuous integration and continuous deployment (CI/CD) pipeline also streamlined operations, creating a more reliable deployment cycle and reinforcing data quality.

For banking executives, this case highlights how modern data infrastructure upgrades can unlock direct and measurable results without full-scale system replacements. Moving core processes to real-time streaming frees existing mainframe capacity, lowers operational friction, and strengthens customer engagement. By making accurate data instantly available across the organization, leaders gain a foundation that supports both regulatory efficiency and innovation in digital banking services.

Telstra employs real-time data streaming to enhance network reliability and customer experience

Telstra’s approach to real-time data streaming is focused on delivering faster insights and stronger service reliability. Javed Bolim, Technology Product Owner for Observability at Telstra, described how the company captures and streams events continuously across its mobile network for instant analysis. This process enables the early detection of service issues, often before customers notice a problem, while improving decision accuracy through real-time data correlation across multiple network signals.

This streaming architecture also enhances service assurance during large-scale events, including high-demand occasions such as Boxing Day test matches at the Melbourne Cricket Ground (MCG). The system’s scalability allows teams to launch new use cases quickly without reengineering dataflows, giving Telstra flexibility to adapt to ongoing business needs. Data is filtered, enriched, and stored for multiple applications, supporting use cases from infrastructure monitoring to customer value validation.

For executives, Telstra’s deployment underscores how real-time visibility can shape both customer satisfaction and operational optimization. The company’s investment in continuous streaming and analytics has turned network observability into a proactive capability rather than a reactive one. Bolim also emphasized the importance of building the right internal expertise and securing management buy-in. These factors ensure that technology investments deliver meaningful business outcomes, not just technical improvements.

Coles transformed its fragmented event systems into a unified, efficient data platform

Coles’ decision to consolidate dozens of disparate event systems into one enterprise-grade platform marked a critical shift in its data strategy. Simon Bedford, Principal Engineer at Coles, explained that before the transformation, the company faced duplication, unnecessary costs, security challenges, and operational friction. Adopting Confluent allowed Coles to streamline these systems into a centralized, discoverable data platform that integrated tooling, monitoring, observability, and automated governance.

This initiative created structural clarity and efficiency across the organization, allowing teams to work with a single, standardized source of truth. Governance and provisioning were automated, and the platform was treated as an internal software-as-a-service (SaaS) offering, giving developers self-service access to data resources through GitOps and continuous integration/continuous deployment (CI/CD) pipelines. With observability and cost attribution managed through telemetry, the company achieved transparency in both performance and expenditure.

For business leaders, Coles’ experience shows how consolidation leads to operational simplicity, cost savings, and rapid innovation. The streamlined architecture improved data reusability and gave developers a frictionless experience, critical for fostering adoption and collaboration. Bedford noted that this new reliability and transparency have earned the platform widespread trust across the business. The outcome: faster delivery to market, reduced integration costs, and a stronger foundation for scaling AI-driven initiatives in the future.

Real-time data infrastructure underpins overall enterprise agility and AI readiness

Across every industry, real-time data has become the foundation of operational agility and digital scalability. Insights from leaders at Confluent, alongside case studies from Bendigo Bank, Telstra, and Coles, reveal how modern enterprises are using continuous data flows to optimize performance, reduce inefficiencies, and deliver stronger customer outcomes. By allowing systems to ingest, process, and act on live data, organizations are shifting from descriptive analysis to continuous intelligence, where insights drive immediate business actions.

The benefits are clear. Enterprises that build robust real-time data infrastructures cut latency, improve service reliability, and accelerate time-to-market. In banking, it means faster transactions and improved compliance visibility. In telecommunications, it enhances network stability and customer experience. In retail, it supports stronger governance and data reuse across teams. Each outcome points to a shared reality: data timeliness determines execution speed, and execution speed determines competitiveness.

For executives, focusing on real-time data readiness is now a strategic priority, not a supporting initiative. A unified streaming architecture fosters consistency, transparency, and scalability, core attributes that make AI adoption easier and more impactful. The ability to feed high-quality, current data directly into AI models ensures that decision-making aligns with real-world conditions.

This convergence of AI and real-time infrastructure closes the gap between information and action. As Stephen Deasy, Chief Technology Officer at Confluent, and Greg Taylor, Senior Vice-President and Asia-Pacific General Manager at Confluent, consistently emphasize, organizations that master real-time data integration position themselves ahead of the competition. When supported by strong governance, agile development, and system flexibility, a robust data streaming framework becomes more than a technology foundation, it becomes a continuous driver of business performance and innovation.

Concluding thoughts

Real-time data is no longer an optional upgrade. It’s the structural foundation for how modern enterprises operate, compete, and grow. The businesses investing in live, high-quality data streams aren’t just improving processes, they’re reshaping how decisions are made and how value is created.

For executives, the path forward is about aligning data strategy with business speed. The ability to act on fresh information in milliseconds defines how effectively organizations respond to customers, risks, and market shifts. Every gain in responsiveness compounds over time, positioning those who move now ahead of those waiting to adapt.

Leaders who prioritize real-time infrastructure, governance, and system flexibility will see faster innovation cycles, reduced operational drag, and scalable AI performance. The outcome isn’t only technical, it’s cultural. It sets the stage for organizations that think, decide, and act with clarity at the pace of change.