Data Streaming: The Key to Tackling Data Challenges for AI Success

As artificial intelligence (AI) matures from experimentation into production use cases, the symbiotic relationship between data and AI becomes increasingly clear. To deliver real business impact-smarter automation, better customer experiences, and massive cost takeout-AI use cases are only as powerful as the data they're running on. Similarly, if experiments with AI fail to make it into production, the most common reason isn't a lack of application or a shortcoming of the powerful models. It's often a fundamental data problem.

Without real-time, trustworthy, and easily accessible data, AI is stuck in the lab.

This is where data streaming comes in.

Today's Data Challenges

First, what are the challenges of the ways we tend to work with data today?

Most companies still treat data as something to be stored and analyzed ex post facto. Let's take a simplified look at how we've evolved to work with data in databases. We mostly extract data from operational systems through a number of steps or complex hops, moving it into the analytical estate. The historical assumption has been that data is actionable only via reports from analytics. The diagram below shows some of these steps and overall traditional processes.

To support this flow, we've augmented databases, building costly software and infrastructure (including compute, networking, and storage). To make matters worse, in many scenarios, the above processes are run multiple times in a medallion architecture to improve the quality and structure of the data we're working with:

• Bronze Layer: Raw or partially cleansed, mostly unstructured data

• Silver Layer: Cleansed data, with some structure

• Gold Layer: Business-specific data

It's now clear that this traditional approach is clunky and suboptimal and impedes AI use cases, as it results in the following:

• Complexity: Multiple systems, pipelines, and data stores, including data warehouses, lakes, and lakehouses, often result in data duplication and unnecessary processing.

• Longer latencies: Insights arrive too late for real-time decisions and actions.

• Data silos: Key information is disjointed, stored between operational and analytical systems.

• Increasing costs: We see escalating software, infrastructure, and personnel costs as well as risk and governance challenges, with data sprawl and loss of control.

According to the 2025 Data Streaming Report, 68% of organizations cite inconsistent data sources and 63% cite data silos as the top barriers to turning data into business impact.

Data streaming helps address these challenges, enabling us to get the full value from AI.

Why Data Streaming Changes the Game

Data streaming flips the established ways of working with data described above: Instead of moving data through batch processes, it makes live data available everywhere it's needed-across operational and analytical systems, in real time.

That means AI agents acting on the stream of data have the following advantages:

• Agents can act instantly, not hours or days later.

• Data quality issues are caught early, before they cascade downstream.

• Architectural simplification cuts expensive compute, networking, and storage costs as well as people and risk costs.

This shifts the emphasis of the data strategy upstream-from the analytical estate to the operational estate. We refer to this as shifting left-getting closer to events at source.

Data streaming also supports two important trends. First, companies are increasingly becoming software, as humans and human-readable reports are being removed from core business processes. These are being replaced by core processes that are specified, monitored, and executed by software and AI.

Second, with the introduction of Tableflow, high-quality, reliable, and reusable data products available in the data stream can be converted easily to tables and vice versa. This means the operational and analytical data estates are starting to blur. Automated operations can require static insights from data at rest, and this integration allows queries to combine real-time data with stored data in tables. We can massively simplify data management, avoiding anti-patterns such as reverse ETL, which pulls data from the static analytical estate back into the operational and transactional systems.

This is not theoretical. In our report, 44% of IT leaders say streaming has delivered 5x return on investment.

Streaming + AI in the Real World

We're already seeing AI powered by streaming data across industries:

• Fraud detection: Financial institutions are stopping fraud in real time as AI models act on streaming transactions. See how EVO Banco does it in this case study.

• Retail and ecommerce: Dynamic pricing and personalized offers are triggered the moment a customer engages. See how L'Oréal does it in this case study.

• Manufacturing: Predictive maintenance is achieved, as streaming Internet of Things (IoT) data feeds AI models to prevent costly downtime. See how BMW Group does it in this case study.

In each case, data streaming transforms AI from an experiment into an operational advantage.

Considerations When Implementing Data Streaming

Embedding new innovations, such as data streaming and AI, isn't always just about a technology or product adoption. We also see changes to ways of working for individuals, teams, and organizations as a whole.

Let's look at history for an example. Back in the 15th century, books were painstakingly copied by hand, making them rare and expensive. This changed when Johannes Gutenberg invented the printing press, resulting in the mass production of books. Arguably, this mechanical device led to the wider dissemination of knowledge, the Renaissance, and even society as we know it today.

Like many innovations, the printing press was initially met with apprehension. Hand scribes feared their loss of status, livelihood, and spiritual significance. Similarly, we see challenges in data streaming adoption. To address this, Confluent has created the Data Streaming Organization (DSO) framework to help make AI accessible, reliable, and impactful at enterprise scale.

Is comparing data streaming with the printing press justified? For centuries, books existed as the best educational technology in human history. Reid Hoffman recently noted, "I now believe that AI-which builds upon books-is the best educational technology created in human history thus far." Given the potential impact of AI and its dependence on real-time, trustworthy, and accessible data, I think this comparison is fair.

Why Confluent

Like hand-scribing books in the past, the process of data management is ripe for disruption. With the emergence of AI and agentic AI, this disruption couldn't be more timely. Our data streaming platform is the enterprise-grade backbone that connects data across clouds and systems, with the governance, security, and scalability leaders expect-providing the modern data and AI platform businesses need today. Our DSO framework provides the people, process, and platform guidance to implement data streaming. (Stay tuned for a full breakdown in an upcoming blog post.)

With general innovations, the saying goes: "At first, it's underestimated. Then it's dismissed as a niche tool. And suddenly, it's the standard." I firmly believe data streaming will become the standard for growing AI into production-ready enterprise systems. Only companies with the right data foundation will capture AI's value.

Download the 2025 Data Streaming Report to see how global enterprises are using streaming to unlock AI, or connect with our team to explore what this could mean for your organization.