How legacy tech is getting smarter with enterprise AI

Legacy workload automation tools now incorporate agentic AI into critical enterprise systems

Enterprise automation is entering a new era. Systems once built to run command sequences across servers now handle AI-driven processes that can think, adapt, and learn. These workload automation tools, used for decades to keep complex IT operations reliable, are being modernized to connect deeply with agentic AI. The purpose is simple but powerful: to make AI workflows as predictable, secure, and controllable as existing corporate infrastructure.

Workload automation has always been critical for enterprise stability. It ensures that data moves between systems precisely when and how it should, enabling consistent performance across ERP, mainframe, and hybrid cloud environments. Now, these same tools are being extended to manage agentic AI models, which means companies can deploy intelligent automation without introducing chaos. These updates offer a deterministic layer, a structured framework where AI can operate with traceability and governance.

This is a structural shift in how enterprises merge innovation with reliability. For business leaders, this matters because agentic AI integration doesn’t disrupt what already works. Instead, it complements it, giving enterprises AI-powered functionality within trusted operational routines. Dan Twing, Analyst at Enterprise Management Associates (EMA), put it succinctly when he described workload automation as “the glue” that binds new and old systems together. It’s this “glue” that allows enterprises to add intelligence without compromising consistency.

Executives should view these developments as an opportunity to modernize strategically, not for speed alone but for stability, allowing their organizations to evolve into AI-driven enterprises while maintaining the precision that critical systems demand.

Broadcom’s automic software integrates native AI orchestration into enterprise workload automation

Broadcom is at the center of this transformation. With the release of Automic Version 26, the company takes a major step in embedding AI directly into enterprise automation workflows. The new “Agentic AI Job” type allows AI agents to interact with established systems in a way that feels native and secure. Workflows that once required several technical steps can now be created through a simple text prompt. The system automatically converts this prompt into executable tasks governed by built-in role-based access, audit controls, and logging.

This development extends automation beyond IT teams. Rajeev Kumar, Head of Products for Workload Automation at Broadcom, described how business analysts can now generate full automation workflows, such as collecting data, running analysis, producing reports, and sending AI-generated summaries, through basic natural language commands. This shift reduces dependence on technical specialists and opens automation access to operational leaders, analysts, and managers who better understand the business objectives driving these workflows.

Broadcom’s broader positioning also matters. As Stephen Elliot, Analyst at IDC, noted, the company operates at the core of global digital infrastructure, combining the power of Broadcom hardware, VMware, and enterprise software. That scale gives it a unique ability to connect physical network performance with software-level intelligence, creating a cohesive AI integration ecosystem most competitors can’t match.

For executives, two key implications stand out. First, Broadcom’s Automic expansion reduces the cost and time of implementing AI automation, allowing organizations to experiment and deploy new intelligent workflows faster. Second, it reflects how AI will become not just an addition to enterprise systems but a built-in capability, governed, observable, and accessible across departments. This isn’t about replacing legacy frameworks but evolving them into high-performance, intelligent automation platforms that align with how enterprises actually operate today.

BMC adopts a cautious, strategic approach toward integrating AI

BMC is taking a measured, strategy-first approach to AI automation. The company’s recent updates to its Control-M platform bring in AI assistants and multi-agent orchestration, but instead of moving fast just to experiment, BMC emphasizes building AI that complements enterprise-scale reliability. The company’s partnerships with CrewAI, LangGraph, and Snowflake Cortex enable advanced AI use cases, but the focus remains on alignment with business outcomes rather than rapid feature expansion.

Ram Chakravarti, CTO at BMC, has made it clear that AI must serve practical business goals. He notes that when AI projects are not tied to a company’s overall digital strategy, they tend to remain as “science experiments,” offering little tangible return. BMC has already seen measurable improvements with this philosophy, one pilot customer reduced the time required for federated data exchange from 30 days to less than 12 hours by embedding AI agent orchestration into its workflows.

The underlying principle is that AI integration should not disrupt the systems that already deliver predictable results. Control-M’s enhancements still prioritize deterministic execution and oversight, key components for enterprise IT where accuracy is non-negotiable. By incorporating AI in a controlled, modular manner, BMC ensures that companies retain full visibility into dependencies, service-level commitments, and performance consistency.

For decision-makers, BMC’s approach offers a repeatable model for deploying AI thoughtfully: set clear use cases, ensure governance from day one, and scale in alignment with existing business operations. This creates a path toward AI adoption that reduces operational risk while increasing long-term reliability and performance predictability.

Broadcom and BMC are pursuing different approaches to mainframe modernization using AI

Both Broadcom and BMC are expanding AI capabilities across mainframes, but their strategies differ in depth and scope. Broadcom has taken a foundational step by embedding its Model Context Protocol (MCP) into key tools such as Rally and Endevor, enabling AI connectivity to mainframes and distributed systems. This integration allows AI models to communicate directly with core infrastructure, extending automation possibilities into older but still essential enterprise systems.

BMC’s approach goes further. Its Automated Mainframe Intelligence (AMI) platform is evolving from simple analytics toward full autonomy in operational tasks. The next iteration of AMI aims to move beyond recommendations to actual execution, diagnosing system issues, performing security validation, and recovering from disruptions without human intervention. The goal is to create mainframes that manage themselves, informed by historical data, support documentation, and incident logs.

Steven Dickens, CEO at HyperFrame Research, highlighted this contrast clearly. He described Broadcom’s integration as fundamental connectivity, important and necessary, but saw BMC’s direction as broader and more progressive, pulling together technical knowledge bases, operational data, and AI models for intelligent automation that can scale across multiple mainframe operations.

For C-suite leaders, the difference matters. Broadcom’s method strengthens existing systems, ensuring stability across hybrid architectures. BMC’s strategy, meanwhile, positions AI as part of a longer-term modernization process aimed at creating adaptive, self-managing infrastructure. Both paths support modernization, but BMC’s deeper AI inclusion indicates a stronger bet on autonomous operations as a defining feature of the next generation of enterprise IT.

IBM’s collaboration with arm signifies a renewed hybrid modernization effort for mainframe ecosystems

IBM’s recent collaboration with semiconductor manufacturer Arm reflects a clear plan to extend mainframe capability into broader compute environments. Through this partnership, IBM Z and LinuxOne systems will support applications built on Arm’s low-power processors via virtualization. The move is designed to increase flexibility and improve compatibility with modern cloud and mobile workloads, widening IBM’s relevance in hybrid enterprise IT.

Historically, IBM has integrated diverse processor technologies into its mainframes, including x86 chips through the zBX systems introduced more than a decade ago. This earlier work allowed non-mainframe workloads to operate within mainframe environments, though some technical challenges persisted in storage and third-party application support. The Arm collaboration is structured to overcome those limits, offering better integration, improved energy efficiency, and broader software compatibility across enterprise applications.

Steven Dickens, CEO at HyperFrame Research, commented that Arm gains access to IBM’s highly performant instruction set and deep experience in system resiliency, while IBM brings increased flexibility and partnership reach. He also noted that measurable results from this initiative are expected around the next major IBM Z release, projected for 2028 based on IBM’s standard hardware cadence.

For executives, the significance is that IBM is not just maintaining mainframe relevance, it is expanding its ecosystem in a way that aligns with evolving computing standards. By including Arm technology, IBM positions mainframes as open, energy-efficient, and integrated within hybrid IT architectures. The long-term strategic payoff will come from the balance of performance, cost efficiency, and openness, which remains an increasing priority for enterprise modernization.

Market analysis positions broadcom and BMC favorably against IBM in workload automation value rankings

Recent market analysis confirms Broadcom and BMC’s strong positioning in the enterprise automation race. The October 2025 EMA Radar Report for Workload Automation and Orchestration rated IBM’s automation toolset in the “strong value” category but listed Broadcom’s Automic and BMC’s Control-M as “Value Leaders.” These rankings reflect each vendor’s readiness for large-scale enterprise AI integration, spanning capability, maturity, and user impact.

Broadcom’s advancements in integrating AI orchestration directly within Automic’s operations give it a clear market advantage in time-to-value and accessibility. Similarly, BMC’s structured approach to aligning AI deployments with governance and predictability resonates with enterprises that manage sensitive, regulated environments. Both vendors are meeting current enterprise priorities, security, visible outcomes, and scale, without overhauling core infrastructure unnecessarily.

IBM remains a significant player due to its hybrid cloud and mainframe integrations with Red Hat’s OpenShift platform and its AIOps-supported tools. However, analysts view IBM’s focus as broader, blending automation within an ecosystem conversation rather than pushing forward narrowly on workload execution and orchestration. This positioning strengthens IBM’s hybrid appeal but limits its comparative agility in automation innovation.

For C-suite executives, these findings underscore the immediate market reality: Broadcom and BMC are offering mature, enterprise-ready automation platforms with built-in AI capabilities that deliver measurable efficiency. IBM is investing strategically in long-term hybrid integration, but Broadcom and BMC currently hold the execution advantage for firms wanting productivity and control from AI-driven automation today.

The integration of legacy automation reliability with adaptive AI

The enterprise automation landscape is converging toward a unified model where stability and intelligence operate together. Vendors such as Broadcom, BMC, and IBM are transforming traditional workload orchestration into controlled AI-enabled ecosystems. This means legacy systems are no longer separate from modernization efforts, they form the operational backbone that gives AI the structure and governance it needs to run safely at scale.

AI-driven automation now focuses on adaptability, continuous learning, and informed decision-making. By tying these capabilities into systems that already handle critical enterprise workloads, organizations gain the benefit of intelligence without losing operational certainty. For enterprises, this balance matters. It ensures that high-value systems, finance, supply chain, and customer operations, retain their reliability while becoming faster, more predictive, and more responsive to data-driven demands.

This convergence also introduces a new type of business readiness. Instead of maintaining parallel systems for legacy operations and AI experimentation, companies can unify their IT operations under a single governance model. The result is lower overhead, stronger compliance, and faster innovation cycles, all while maintaining the rigor required for mission-critical environments.

For C-suite leaders, the opportunity lies in adopting AI-integrated automation not as a technology upgrade but as an operational evolution. The aim is continuity with capability, preserving what works while enhancing it with intelligence. This approach sets the foundation for resilient enterprises capable of adapting quickly to new demands without compromising on control, consistency, or security.

In conclusion

Enterprises are entering a practical phase of AI transformation. What’s happening now isn’t about replacing infrastructure, it’s about upgrading it intelligently. Broadcom, BMC, and IBM are showing that mature systems can evolve, balancing innovation with the reliability enterprises depend on.

For leaders, this shift carries clear strategic meaning. It’s no longer enough to run stable systems. Success now requires making them adaptive, secure, and responsive to real-time business needs. Agentic AI embedded into trusted automation frameworks gives enterprises that control, governed, explainable, and measurable.

The advantage goes to organizations that modernize with intent. AI-enabled automation brings agility where it matters most, operations, decision-making, and performance. The companies treating AI as an infrastructure evolution rather than a side project will move faster, operate smarter, and navigate disruption with confidence.