As companies rush to deploy artificial intelligence across their operations, many are hitting a familiar roadblock: data.
In most enterprises, critical information sits scattered across systems, arrives too late, or lacks proper governance. Without real-time, reliable data streams, AI models struggle to deliver meaningful results.
To tackle this challenge, IBM has completed its acquisition of Confluent, a data-streaming platform widely used by large enterprises.
The deal, announced on March 17, values Confluent at roughly $11 billion, with IBM paying $31 per share in cash for all outstanding shares.
Bringing real-time data into the AI era
The acquisition aims to combine IBM’s enterprise software ecosystem with Confluent’s streaming technology to create a platform capable of delivering live, trusted data to AI systems across hybrid and on-premises environments.
The move comes at a time when businesses are transitioning from experimenting with AI to running production workloads powered by it.
“Transactions happen in milliseconds, and AI decisions need to happen just as fast. With Confluent, we are giving clients the ability to move trusted data continuously across their entire operation so their AI models and agents can act on what is happening right now, not on data that is hours old,” said Rob Thomas, Senior Vice President, IBM Software and Chief Commercial Officer. “Together, IBM and Confluent give enterprises the foundation for a new operating model – one where AI runs on live data, drives decisions in real time, and delivers value at scale.”
Built on Apache Kafka
Confluent’s platform is built on Apache Kafka, the widely used open-source system for processing real-time data streams.
Today, more than 6,500 enterprises, including around 40% of Fortune 500 companies, rely on the technology to power mission-critical systems.
Across industries, the platform already supports real-time operations at scale. For example:
- Michelin uses Confluent to manage inventory across supply chains spanning 170 countries, helping reduce costs while maintaining operational visibility.
- L’Oréal streams product and inventory updates across internal and partner systems to respond faster to consumer demand.
- BMW Group processes IoT data from more than 30 manufacturing sites and global sales networks.
- Ticketmaster streams ticket sales and customer activity across hundreds of systems to support analytics and machine learning.
By integrating this streaming layer with IBM’s software stack, the companies aim to enable AI models and automated workflows to operate using continuously updated enterprise data.
Integration across IBM’s AI and hybrid cloud ecosystem
IBM says Confluent’s technology will connect directly with its AI and hybrid cloud platforms, enabling new capabilities for enterprise customers.
One key integration will feed real-time event streams into watsonx.data, IBM’s AI data platform.
This will allow AI models, digital agents, and automated workflows to operate using live operational data with built-in governance, lineage tracking, and quality controls.
The acquisition also expands IBM’s event-driven architecture. Confluent’s streaming capabilities will complement tools such as IBM MQ and IBM webMethods Hybrid Integration, enabling applications and AI agents to respond instantly to events across hybrid cloud environments.
Another focus is the modernisation of IBM’s mainframe ecosystem. By connecting Confluent with IBM Z, enterprises will be able to stream transaction data directly from core systems for analytics, automation, and AI processing in real time.
For IBM, the acquisition strengthens its position in the race to build the infrastructure powering enterprise AI systems.
“The shift from AI experimentation to production deployment has exposed a critical gap in enterprise data architecture: the inability to deliver trusted, real-time data to the systems that need it most. AI agents and automated workflows don’t operate on historical data; they require live operational signals, continuously flowing across the enterprise as events occur,” said Sanjeev Mohan, Principal Analyst, SanjMo. “IBM has made significant progress assembling a portfolio that addresses both sides of this equation: governance and infrastructure for data at rest, and a platform for data in motion. For enterprises whose architecture and priorities align with this approach, it is a compelling stack worth evaluating.”
