HiveMQ’s decision to name Barry Libert as Chairman and CEO signals the beginning of a more ambitious phase for the company. Libert has built his career around helping boards and executive teams adopt platform thinking, modern data strategies, and AI-native business models. His experience at Anaconda, where the company achieved unicorn status and strong recurring revenue, gives HiveMQ leadership that is well-positioned to scale both product adoption and ecosystem development. The addition of senior executives, including Chief Revenue Officer Mike Weinert and Chief Product Officer Tim Hal,l further supports the company’s renewed emphasis on innovation and measurable customer value.
HiveMQ is moving with confidence because the timing appears favorable. The industrial AI sector is expanding rapidly as industries seek to modernize equipment, reduce downtime, improve quality, and lower operational costs. Research from IoT Analytics shows that the industrial AI market is growing at an impressive rate of 23 percent annually and will likely exceed 154 billion dollars by 2030. Manufacturers, pharmaceutical companies, energy providers, and large data center operators already recognize that operational data is a key foundation for AI and automation. Global AI spending is accelerating even faster, surpassing hundreds of billions of dollars per year, which further suggests that the shift to intelligent industrial systems is underway.
Even with a strong leadership announcement and a rapidly expanding market, HiveMQ’s strongest story is its product portfolio and the companies that rely on it every day. HiveMQ powers the connected operations of BMW, Eli Lilly, major energy operators, global telecom networks, and several of the largest industrial brands in the world. These organizations trust HiveMQ because its platform appears capable of supporting mission-critical operations, providing stability, security, and the ability to manage millions of real-time data interactions. Many customers depend on HiveMQ to run multi-plant facilities, complex robotics, autonomous material handling systems, and high-precision production lines.
A significant part of HiveMQ’s technology expansion is HiveMQ Pulse, the company’s recently released data intelligence layer. Pulse takes raw industrial data, which is often fragmented, inconsistent, or incomplete, and turns it into structured, contextualized, and AI-ready information. This allows enterprises to compute key performance metrics in stream, apply business rules at the edge, and prepare all their operational data for predictive maintenance, process optimization, and faster decision-making. The platform is designed to support both plant-floor operators and data science teams without requiring heavy engineering resources.
In a recent webinar about Pulse’s impact on smart manufacturing, respected manufacturing advisor Walker Reynolds said that Pulse “solves one of the biggest problems in the industry” and that “leaders who understand Pulse will likely be ahead of others in digital transformation.” Comments like these highlight why organizations with high regulatory requirements or mission-critical processes tend to choose HiveMQ.
Beyond Pulse, HiveMQ’s full suite of products includes the Broker, Edge, and Data Hub. The Broker is known for its low latency, high-throughput data streaming. Edge provides a secure connection and transformation at the plant level. Data Hub handles schema management, validation, and governance to help ensure that every system receives clean and trusted information. Together, these components allow companies to unify OT and IT, reduce data engineering burden, and run AI workloads across cloud and edge systems.
The result is a platform that supports a wide range of use cases. Automotive manufacturers leverage HiveMQ for real-time robotic control and production sequencing. Pharmaceutical companies use it to maintain quality and compliance while increasing output. Energy providers use it to balance loads, reduce consumption, and introduce autonomous corrective actions in their infrastructure. Large data center operators rely on HiveMQ to detect anomalies early, reduce cooling costs, and optimize power use.
HiveMQ is also investing in new tools that make AI more accessible. The company is building interactive workspaces and visualization environments that allow users to test data flows, see metrics update in real time, and try simple AI models without complex configuration. These tools are designed to help teams understand how AI fits into their operations and begin experimenting at low risk.
Barry Libert describes this approach clearly. “People should be able to explore AI the same way they explore any new technology, by trying it, seeing the results, and learning quickly.” His focus on accessibility, combined with the company’s strong product direction, aligns well with what customers need as they move from pilot projects to full-scale industrial AI.
HiveMQ has built a reputation for reliability, security, and openness. With Libert now guiding a product-led strategy, the company is positioned to define how the next decade of industrial intelligence is built.
