The data-reactive business: how real-time data supports reliable AI in production

For CIOs and CDOs, there is ever-growing pressure to turn promising pilots into dependable systems. Decision-makers want to see measurable value without introducing unacceptable risk.

The challenge is often framed as a question of which models to use, which skills the team lacks, or where investment is coming from to fund meaningful change. Yet as AI moves into customer-facing and operational environments, the success of such pilots increasingly depends on something more fundamental: whether it has access to trusted information about what is happening now.

AI pilots are usually built in isolation. They tackle a narrow use case, interrogating a small, controlled data set to support a limited group of users.

A live enterprise environment is very different. AI must contend with legacy systems, fragmented ownership, inconsistent data definitions and business conditions that are constantly changing. A system that performs well in isolation may prove far less reliable when it encounters the complexity of day-to-day operations.

That gap was illustrated starkly in July, when OpenAI disclosed that models it was evaluating had escaped an isolated testing environment and compromised parts of Hugging Face’s production infrastructure while pursuing a narrow testing goal. While this is an exceptional example, the underlying principles are the same: behaviour under controlled conditions can change dramatically when a system encounters live infrastructure, permissions, data and workflows.

Industry research also underlines the scale of this challenge. Here at Confluent, our 2026 Data Streaming Report found that only 32% of organisations are currently running agentic AI in production; data infrastructure, quality and governance were cited as the leading barriers. Looking further afield, research from SAP Engagement Cloud has found that more than half (54%) of enterprise decision-makers say their organisations cannot access and use customer or stakeholder data in real time.

This suggests that the obstacle to successful AI pilots is not necessarily a lack of ambition or viable use cases. Instead, the difficulty is providing AI systems with data that is current, consistent and trustworthy to support real-world decisions.

When real-time data matters

Different business decisions operate on different clocks. Traditional warehouses, dashboards and reports remain valuable for understanding performance, identifying long-term trends and informing strategic planning. A quarterly financial report, for example, does not need to take place by the millisecond.

That same retrospective model becomes less effective when an organisation needs to recognise and respond to an event as it unfolds. Fraud detection, personalised customer experiences, predictive maintenance and agentic workflows all depend on a to-the-second understanding of events.

If an AI system is expected to recommend an action or trigger a workflow, it cannot rely on a reality that is already out of date. Its response may appear technically reasonable while being operationally wrong.

This is the distinction between being data-driven and becoming data-reactive.

A data-driven organisation uses information to understand what has happened and make better decisions about what should happen next. A data-reactive organisation goes further, using live, trusted data to respond to events as they occur, while there is still time to influence the outcome.

Building a data-reactive foundation

Becoming data-reactive starts with capturing business events as they happen. From payments and orders to customer actions and sensor readings, signals should be enriched and validated as close to their source as possible. Establishing their meaning and quality before they are distributed across the organisation is hugely important.

Following this logic, governance must move upstream too. Quality checks, access controls and policies should be applied as data begins its journey, rather than after it has spread across multiple systems. This allows teams to use current information without sacrificing the consistency, security or trust that production AI requires.

That trusted data should then be made reusable. Rather than building a separate pipeline for every use case, teams can draw on shared streams of consistent information. This reduces duplication and gives different systems a common view of the events shaping the business.

The final requirement is action. Data becomes reactive when it can trigger a recommendation or workflow while intervention can still change the outcome. Speed alone is not the objective; the response must also reflect the right context and controls. Otherwise, an organisation simply risks making the wrong decision faster.

This shift towards earlier validation and governance is known in engineering circles as “shifting left”. If poor-quality data moves unchecked through the business, the cost of correcting it grows with every pipeline, system and team it reaches. Addressing problems near their source prevents this integration tax from accumulating downstream.

Where leaders should start

The benefits of shift left are becoming increasingly clear. Confluent’s Data Streaming Report found that 77% of IT leaders had benefited from shifting left, up from 66% in 2025. Nine in ten identified at least four potential advantages, including better data quality, lower processing costs, faster troubleshooting and less work for downstream teams.

For CIOs and CDOs, the first step is to identify where information delays pose material business risk. They can then determine which AI use cases influence live decisions and therefore depend most heavily on current context. Revenue, fraud, compliance, resilience or customer experience are all likely candidates.

Leaders should also examine where teams repeatedly extract, clean and transform the same information, and where controls are being applied too late. Mapping this duplication alongside gaps in quality, access, lineage and security will expose the data flows that create the greatest cost and risk.

The same high-value data flows will often appear across several of these questions. Those recurring streams provide a practical starting point: organisations can turn them into reusable assets that support multiple AI, analytical and operational applications, rather than attempting to transform the entire estate at once.

The next stage of data maturity

None of this requires organisations to dismantle the warehouses, dashboards or reporting processes that already serve them well. Retrospective insight remains essential.

Becoming data-reactive means adding the capability to support live operational decisions alongside established systems, tailored to each use case. The technologies we’re talking about here are often better together.

Becoming data-driven helped organisations better understand their businesses. As AI assumes a greater role in customer-facing and operational decisions, the next stage of maturity is enabling those businesses to respond. That requires businesses to be data-reactive: ensuring data arrives with the freshness, context, and trust needed to act with impact before the moment has passed.