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Day-one integrations include IBM watsonx.data, IBM MQ, IBM webMethods Hybrid Integration, and IBM Z
ARMONK, N.Y., March 17, 2026 /PRNewswire/ -- IBM (NYSE: IBM) today completed its acquisition of Confluent, Inc., the data streaming platform that more than 6,500 enterprises, including 40% of the Fortune 500, rely on to power real-time operations. Together, IBM and Confluent deliver a smart data platform that gives every AI model, agent, and automated workflow the real-time, trusted data needed to operate across on-premises and hybrid cloud environments at scale.
As enterprises move from AI experimentation to production, the critical barrier to success is the data — clean, governed, continuously refreshed —and delivered at the speed and scale AI demands. Yet in most enterprises today, data remains siloed across systems and environments, arriving hours or days after it is generated. Together IBM and Confluent provide the fabric through which AI agents can access the information they need, with the controls, governance, and real-time velocity to put information to work safely and at scale.
IDC estimates that more than one billion new logical applications will emerge by 2028 [1], driven by a new generation of AI that will only deliver value if the data behind it is live, trusted, and continuously flowing. That scale of demand requires a new kind of data foundation, and IBM and Confluent address that challenge directly, giving enterprises a single, governed platform where AI models and agents can operate with context, in real time, across every environment.
"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®, the standard for data streaming, Confluent is already embedded in the operational fabric of the world's largest enterprises, with a customer base that spans industries from financial services and healthcare to manufacturing and retail.
- Michelin relies on Confluent to manage real-time inventory across a supply chain spanning 170 countries — achieving 35% cost savings without sacrificing visibility or control [2].
- L'Oréal uses Confluent to stream real-time product and inventory updates across internal systems and third-party applications, helping the company respond faster to changing consumer demand [3].
- BMW Group streams IoT data from 30+ production sites and its global sales network in real time, connecting factory floor systems and cloud applications across the organization [4].
- Ticketmaster streams ticket inventory, sales, and customer activity in real time across hundreds of systems, reducing development friction and powering machine learning at scale [5].
"Since our founding, Confluent's mission has been to set the world's data in motion, making data streaming as foundational to the enterprise as the database. Joining IBM allows us to accelerate that mission at a much greater scale," said Jay Kreps, CEO and Co-founder of Confluent. "IBM's global reach and deep enterprise relationships will help us go further, faster. As enterprises move from experimenting with AI to running their business on it, helping data flow continuously across the business has never mattered more. I'm excited to see what we'll build together."
IBM and Confluent Product Synergies
Today's announcement brings immediate integrations across the IBM portfolio, including:
- AI-Ready, Real-Time Data. Enterprise AI technologies need current context, not yesterday's data. Confluent streams live operational events directly into watsonx.data – ensuring every model, agent, and workflow runs on continuously updated enterprise data, with lineage, policy enforcement, and quality controls included.
- Activate the modernized mainframe in the AI era. The most critical business transactions in the world have long run on IBM Z. With IBM Z and Confluent, organizations can identify and drive real-time events at the transaction source as well as stream transactional data directly for real-time analytics, automation, and AI workflows. This enables mission-critical transaction processing to integrate tightly with the rest of the business in real-time, at enterprise scale.
- Event-Driven Automation Across Hybrid Environments. IBM MQ and IBM webMethods Hybrid Integration form the foundation of enterprise event-driven automation, combining trusted transactional messaging with modern integration and orchestration across hybrid environments. Confluent extends this platform with high-scale event streaming, enabling applications, APIs, and AI agents to sense and act on business events in real time.
With Confluent, IBM Consulting and IBM partners, will help clients build the data foundation their AI needs — live, governed, and continuously flowing across every system and environment.
"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."
Under the terms of the agreement, IBM has acquired all of the issued and outstanding common shares of Confluent for $31 per share in cash, representing an enterprise value of approximately $11 billion.
For more information about today's news, please visit https://www.ibm.com/products/confluent
[1] *Source: IDC, 1 Billion New Logical Applications: More Background, doc #US51953724, April 2024
[2 -5] *Source: Confluent Case Studies & Testimonials
https://www.confluent.io/customers/michelin/
https://www.confluent.io/customers/loreal/
https://www.confluent.io/customers/bmw-group/
https://www.confluent.io/customers/ticketmaster/
About IBM
IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.
Press Contact
Sarah Benchaita
IBM Software Communications
sarah.benchaita@ibm.com
Forward-Looking Statements
Certain statements contained in this communication may be characterized as forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve risks, uncertainties and other factors that could cause actual results to differ materially from those expressed or implied. Forward‑looking statements in this press release may include statements regarding the expected benefits of the transaction, the impact of the transaction on IBM's and the acquired business's operations and financial results, and expectations following the completion of the transaction. There can be no assurance that the anticipated benefits of the transaction will be realized. All forward‑looking statements are based on information available to IBM as of the date of this press release. Additional risks and uncertainties are described in IBM's filings with the Securities and Exchange Commission, including its most recent Annual Report on Form 10‑K and Quarterly Report on Form 10‑Q. IBM undertakes no obligation to update forward‑looking statements, except as required by law.
Day-one integrations include IBM watsonx.data, IBM MQ, IBM webMethods Hybrid Integration, and IBM Z
ARMONK, N.Y., March 17, 2026 /PRNewswire/ -- IBM (NYSE: IBM) today completed its acquisition of Confluent, Inc., the data streaming platform that more than 6,500 enterprises, including 40% of the Fortune 500, rely on to power real-time operations. Together, IBM and Confluent deliver a smart data platform that gives every AI model, agent, and automated workflow the real-time, trusted data needed to operate across on-premises and hybrid cloud environments at scale.
As enterprises move from AI experimentation to production, the critical barrier to success is the data — clean, governed, continuously refreshed —and delivered at the speed and scale AI demands. Yet in most enterprises today, data remains siloed across systems and environments, arriving hours or days after it is generated. Together IBM and Confluent provide the fabric through which AI agents can access the information they need, with the controls, governance, and real-time velocity to put information to work safely and at scale.
IDC estimates that more than one billion new logical applications will emerge by 2028 [1], driven by a new generation of AI that will only deliver value if the data behind it is live, trusted, and continuously flowing. That scale of demand requires a new kind of data foundation, and IBM and Confluent address that challenge directly, giving enterprises a single, governed platform where AI models and agents can operate with context, in real time, across every environment.
"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®, the standard for data streaming, Confluent is already embedded in the operational fabric of the world's largest enterprises, with a customer base that spans industries from financial services and healthcare to manufacturing and retail.
- Michelin relies on Confluent to manage real-time inventory across a supply chain spanning 170 countries — achieving 35% cost savings without sacrificing visibility or control [2].
- L'Oréal uses Confluent to stream real-time product and inventory updates across internal systems and third-party applications, helping the company respond faster to changing consumer demand [3].
- BMW Group streams IoT data from 30+ production sites and its global sales network in real time, connecting factory floor systems and cloud applications across the organization [4].
- Ticketmaster streams ticket inventory, sales, and customer activity in real time across hundreds of systems, reducing development friction and powering machine learning at scale [5].
"Since our founding, Confluent's mission has been to set the world's data in motion, making data streaming as foundational to the enterprise as the database. Joining IBM allows us to accelerate that mission at a much greater scale," said Jay Kreps, CEO and Co-founder of Confluent. "IBM's global reach and deep enterprise relationships will help us go further, faster. As enterprises move from experimenting with AI to running their business on it, helping data flow continuously across the business has never mattered more. I'm excited to see what we'll build together."
IBM and Confluent Product Synergies
Today's announcement brings immediate integrations across the IBM portfolio, including:
- AI-Ready, Real-Time Data. Enterprise AI technologies need current context, not yesterday's data. Confluent streams live operational events directly into watsonx.data – ensuring every model, agent, and workflow runs on continuously updated enterprise data, with lineage, policy enforcement, and quality controls included.
- Activate the modernized mainframe in the AI era. The most critical business transactions in the world have long run on IBM Z. With IBM Z and Confluent, organizations can identify and drive real-time events at the transaction source as well as stream transactional data directly for real-time analytics, automation, and AI workflows. This enables mission-critical transaction processing to integrate tightly with the rest of the business in real-time, at enterprise scale.
- Event-Driven Automation Across Hybrid Environments. IBM MQ and IBM webMethods Hybrid Integration form the foundation of enterprise event-driven automation, combining trusted transactional messaging with modern integration and orchestration across hybrid environments. Confluent extends this platform with high-scale event streaming, enabling applications, APIs, and AI agents to sense and act on business events in real time.
With Confluent, IBM Consulting and IBM partners, will help clients build the data foundation their AI needs — live, governed, and continuously flowing across every system and environment.
"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."
Under the terms of the agreement, IBM has acquired all of the issued and outstanding common shares of Confluent for $31 per share in cash, representing an enterprise value of approximately $11 billion.
For more information about today's news, please visit https://www.ibm.com/products/confluent
[1] *Source: IDC, 1 Billion New Logical Applications: More Background, doc #US51953724, April 2024
[2 -5] *Source: Confluent Case Studies & Testimonials
https://www.confluent.io/customers/michelin/
https://www.confluent.io/customers/loreal/
https://www.confluent.io/customers/bmw-group/
https://www.confluent.io/customers/ticketmaster/
About IBM
IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.
Press Contact
Sarah Benchaita
IBM Software Communications
sarah.benchaita@ibm.com
Forward-Looking Statements
Certain statements contained in this communication may be characterized as forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve risks, uncertainties and other factors that could cause actual results to differ materially from those expressed or implied. Forward‑looking statements in this press release may include statements regarding the expected benefits of the transaction, the impact of the transaction on IBM's and the acquired business's operations and financial results, and expectations following the completion of the transaction. There can be no assurance that the anticipated benefits of the transaction will be realized. All forward‑looking statements are based on information available to IBM as of the date of this press release. Additional risks and uncertainties are described in IBM's filings with the Securities and Exchange Commission, including its most recent Annual Report on Form 10‑K and Quarterly Report on Form 10‑Q. IBM undertakes no obligation to update forward‑looking statements, except as required by law.
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
IBM Completes Acquisition of Confluent, Making Real Time Data the Engine of Enterprise AI and Agents
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SAN JOSE, Calif., March 17, 2026 /PRNewswire/ -- At NVIDIA GTC 2026, DeepRoute.ai presented a comprehensive introduction to its 40-billion-parameter Vision-Language-Action (VLA) Foundation Model architecture, representing a fundamental breakthrough in autonomous driving development. The model introduces a unified architecture that integrates perception, reasoning, and action, enabling systems not only to drive, but to understand and evaluate their own decision-making in real time.
DeepRoute.ai has already achieved significant commercial success, having delivered its advanced autonomous driving systems across more than 250,000 production vehicles. In October 2025, DeepRoute.ai captured nearly 40% market share among third-party suppliers in the high-level autonomous driving segment for a single month. Building on this momentum and fueled by the continuous evolution of its Foundation Model, the company is targeting deployment of one million vehicles equipped with its advanced driving solutions by the end of 2026.
Breaking the Bottleneck: From Days to Hours
Autonomous driving development has long been hampered by the inefficiencies of traditional "data closed-loop" workflows. In conventional systems, data must be manually collected, reviewed, annotated, and retrained—a process that typically requires more than five days per iteration. Meanwhile, companies accumulate vast volumes of raw driving data, most consisting of routine scenarios that offer limited training value and can even degrade model performance.
"At its core, autonomous driving is a scaling problem," said Tongyi Cao, CTO of DeepRoute.ai. "While the industry has made significant progress, true large-scale deployment remains elusive because traditional execution paths are flawed. The bottleneck is no longer about acquiring data; it is about how efficiently a system can filter out the noise and convert massive amounts of raw data into high-value training samples."
DeepRoute.ai's solution: compress the data processing cycle from over five days to approximately 12 hours through intelligent automation.
One Model, Three Roles: Driver, Analyst, and Critic
The 40B VLA Foundation Model performs three complementary functions simultaneously:
The Driver – Executes real-time driving actions based on visual inputs
The Analyst – Identifies critical driving events and explains decisions through causal reasoning
The Critic – Evaluates trajectories for safety, comfort, and human-like behavior
"Our solution to the industry's scaling bottleneck is a unified, 40-billion parameter Vision-Language-Action Foundation Model," Cao explained. "This model goes beyond basic vehicle control. It possesses the capability to analyze data and evaluate driving behavior. Simply put, this model serves not only as the 'driver,' but simultaneously as the 'analyst' and the 'critic.'"
By embedding these capabilities within a single foundation model, DeepRoute.ai has automated large portions of the data pipeline. The system autonomously identifies high-value events such as near-misses and rare scenarios, performs root-cause analysis, and generates reasoning annotations, all without manual intervention.
A Self-Evolving Data Flywheel
The architecture enables a self-reinforcing development cycle where improvements in driving performance directly enhance the system's ability to process and curate its own training data.
"Traditional data closed loops are highly dependent on manual human processes, which severely limits iteration speed," said Cao. "By leveraging our Foundation Model, we have entirely reconstructed this workflow. The model autonomously handles data mining, reason diagnosis, and behavior scoring. Every single iteration of this workflow compounds directly into a measurable enhancement of our AI capabilities."
This self-evolving flywheel accelerates capability growth while dramatically reducing reliance on manual labeling.
Scale and Momentum: 250K to 1M Vehicles
"By the end of 2025, we successfully delivered over 250,000 mass-produced vehicles equipped with DeepRoute.ai's autonomous driving systems," Cao said. "The Foundation Model serves as the core cornerstone for DeepRoute.ai's next-generation autonomous driving assistance and functions as a fundamental AI framework for the physical world. This unified architecture enables the system to go beyond mere execution; it understands complex traffic environments, explains the underlying logic of its decisions, and evaluates driving behaviors. This evolution provides its autonomous driving systems with more comprehensive cognitive and decision-making capabilities."
Through its presentation at GTC 2026, DeepRoute.ai demonstrated how its 40B Vision-Language-Action Foundation Model architecture is accelerating the path to scalable, safe autonomous driving through continuous, data-driven learning and rapid iteration cycles.
About DeepRoute.ai
DeepRoute.ai is a leading artificial intelligence company developing advanced autonomous driving systems. Driven by the vision of achieving Artificial General Intelligence (AGI) for the physical world, the company leverages state-of-the-art foundation models to deliver highly reliable, safety-first autonomous driving solutions. Backed by top-tier investors with over $700 million in funding, DeepRoute.ai has successfully deployed its systems across more than 200,000 mass-produced consumer vehicles. By prioritizing scalable and innovative smart mobility, the company is establishing a robust foundation to pioneer the future of commercial Robotaxi operations.
SAN JOSE, Calif., March 17, 2026 /PRNewswire/ -- At NVIDIA GTC 2026, DeepRoute.ai presented a comprehensive introduction to its 40-billion-parameter Vision-Language-Action (VLA) Foundation Model architecture, representing a fundamental breakthrough in autonomous driving development. The model introduces a unified architecture that integrates perception, reasoning, and action, enabling systems not only to drive, but to understand and evaluate their own decision-making in real time.
DeepRoute.ai has already achieved significant commercial success, having delivered its advanced autonomous driving systems across more than 250,000 production vehicles. In October 2025, DeepRoute.ai captured nearly 40% market share among third-party suppliers in the high-level autonomous driving segment for a single month. Building on this momentum and fueled by the continuous evolution of its Foundation Model, the company is targeting deployment of one million vehicles equipped with its advanced driving solutions by the end of 2026.
Breaking the Bottleneck: From Days to Hours
Autonomous driving development has long been hampered by the inefficiencies of traditional "data closed-loop" workflows. In conventional systems, data must be manually collected, reviewed, annotated, and retrained—a process that typically requires more than five days per iteration. Meanwhile, companies accumulate vast volumes of raw driving data, most consisting of routine scenarios that offer limited training value and can even degrade model performance.
"At its core, autonomous driving is a scaling problem," said Tongyi Cao, CTO of DeepRoute.ai. "While the industry has made significant progress, true large-scale deployment remains elusive because traditional execution paths are flawed. The bottleneck is no longer about acquiring data; it is about how efficiently a system can filter out the noise and convert massive amounts of raw data into high-value training samples."
DeepRoute.ai's solution: compress the data processing cycle from over five days to approximately 12 hours through intelligent automation.
One Model, Three Roles: Driver, Analyst, and Critic
The 40B VLA Foundation Model performs three complementary functions simultaneously:
The Driver – Executes real-time driving actions based on visual inputs
The Analyst – Identifies critical driving events and explains decisions through causal reasoning
The Critic – Evaluates trajectories for safety, comfort, and human-like behavior
"Our solution to the industry's scaling bottleneck is a unified, 40-billion parameter Vision-Language-Action Foundation Model," Cao explained. "This model goes beyond basic vehicle control. It possesses the capability to analyze data and evaluate driving behavior. Simply put, this model serves not only as the 'driver,' but simultaneously as the 'analyst' and the 'critic.'"
By embedding these capabilities within a single foundation model, DeepRoute.ai has automated large portions of the data pipeline. The system autonomously identifies high-value events such as near-misses and rare scenarios, performs root-cause analysis, and generates reasoning annotations, all without manual intervention.
A Self-Evolving Data Flywheel
The architecture enables a self-reinforcing development cycle where improvements in driving performance directly enhance the system's ability to process and curate its own training data.
"Traditional data closed loops are highly dependent on manual human processes, which severely limits iteration speed," said Cao. "By leveraging our Foundation Model, we have entirely reconstructed this workflow. The model autonomously handles data mining, reason diagnosis, and behavior scoring. Every single iteration of this workflow compounds directly into a measurable enhancement of our AI capabilities."
This self-evolving flywheel accelerates capability growth while dramatically reducing reliance on manual labeling.
Scale and Momentum: 250K to 1M Vehicles
"By the end of 2025, we successfully delivered over 250,000 mass-produced vehicles equipped with DeepRoute.ai's autonomous driving systems," Cao said. "The Foundation Model serves as the core cornerstone for DeepRoute.ai's next-generation autonomous driving assistance and functions as a fundamental AI framework for the physical world. This unified architecture enables the system to go beyond mere execution; it understands complex traffic environments, explains the underlying logic of its decisions, and evaluates driving behaviors. This evolution provides its autonomous driving systems with more comprehensive cognitive and decision-making capabilities."
Through its presentation at GTC 2026, DeepRoute.ai demonstrated how its 40B Vision-Language-Action Foundation Model architecture is accelerating the path to scalable, safe autonomous driving through continuous, data-driven learning and rapid iteration cycles.
About DeepRoute.ai
DeepRoute.ai is a leading artificial intelligence company developing advanced autonomous driving systems. Driven by the vision of achieving Artificial General Intelligence (AGI) for the physical world, the company leverages state-of-the-art foundation models to deliver highly reliable, safety-first autonomous driving solutions. Backed by top-tier investors with over $700 million in funding, DeepRoute.ai has successfully deployed its systems across more than 200,000 mass-produced consumer vehicles. By prioritizing scalable and innovative smart mobility, the company is establishing a robust foundation to pioneer the future of commercial Robotaxi operations.
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
DeepRoute.ai Presents 40B Vision-Language-Action Foundation Model at NVIDIA GTC 2026, Accelerating Autonomous Driving at Scale
DeepRoute.ai Presents 40B Vision-Language-Action Foundation Model at NVIDIA GTC 2026, Accelerating Autonomous Driving at Scale
DeepRoute.ai Presents 40B Vision-Language-Action Foundation Model at NVIDIA GTC 2026, Accelerating Autonomous Driving at Scale
DeepRoute.ai Presents 40B Vision-Language-Action Foundation Model at NVIDIA GTC 2026, Accelerating Autonomous Driving at Scale
