Enterprise automation has officially entered a new period. It is no longer about automating isolated, repetitive tasks; it is about coordinating reasoning digital actors alongside traditional execution systems across a highly distributed landscape.
As generative AI and agentic architectures rapidly embed themselves within business workflows, operational leadership faces a fundamental shift. According to Enterprise Management Associates (EMA)’s recent research, From Outcomes to Authority: Defining the Enterprise Control Plane, the true challenge isn’t making systems smarter - it’s making federated autonomy governable.
Here are our top three takeaways from EMA's findings, and how Automation by Broadcom’s product vision and execution are already delivering solutions for the challenges identified.
1. Enterprise automation is permanently federated
A common architectural myth is that enterprise operations will eventually converge around a single, centralized automation engine.
The data say otherwise.
EMA’s research reveals that cross-domain complexity is structural and permanent. About half of organizations report that between 26% and 50% of their automation workflows span multiple operational domains, while more than one-third indicate that a majority of their processes cross these boundaries.
Orchestration is a distributed capability. ITSM, ERPs, public and private cloud infrastructure, data infrastructure and pipelines, CI/CD pipelines, and AI Agents all have their own localized execution context. The strategic headache this creates is not a technology architecture problem; it is a governance problem of maintaining accountability across disparate systems.
The Automation by Broadcom approach
Instead of attempting a forced consolidation of execution systems, Automation by Broadcom embraces architectural federation. Through a vast breadth of orchestration integration ecosystem and an AI-agnostic design featuring native Model Context Protocol (MCP) client and server capabilities, the platform acts as the cross-domain coordinator.
It allows business applications, hybrid infrastructure, and multi-agent AI ecosystems to query operational states and securely trigger tasks and workflows across legacy and modern tech boundaries. Furthermore, to ensure application and operations teams can seamlessly adopt this coordination layer, the platform features an intuitive prompt-to-workflow capability.
This innovation eliminates the traditional friction and inertia of building cross-domain coordination, accelerating deployment and time-to-value like never before.
2. AI authority must be "earned" through runtime assurance
Historically, IT organizations validated automation before deployment and monitored execution after the fact. AI changes this paradigm completely by introducing real-time reasoning into production. Because interpretation now happens during runtime, traditional execution metrics (like a successful completion code) no longer guarantee a successful business outcome.
The risk is real. EMA found that approximately 30% of respondents encounter incorrect or problematic AI outcomes frequently or very frequently, and more than 75% of organizations have required human correction or a rollback of AI-driven actions. Consequently, enterprises are treating autonomy as an operational authority that must be earned incrementally based on demonstrated stability and strict policy compliance.
The Automation by Broadcom approach
To bridge this AI "trust gap”, Automation by Broadcom embeds non-deterministic AI reasoning safely within deterministic constructs. The platform achieves this with the introduction of dedicated, auditable AI Jobs that encapsulate AI prompt logic and agent interactions into standard, rigorous object definitions. This allows operations teams to roll out complex, cross-system workflows while enforcing strict Role-Based Access Control (RBAC), end-to-end logging, and immediate reversibility natively within the core orchestration engine.
Furthermore, time-tested automation connects directly to business-critical systems such as mainframes, ERPs, and ITSM platforms ensuring that embedded validation gates maintain a confidence-inspiring level of operational quality across the diverse ecosystem.
3. Context degradation is the ultimate AI bottleneck
Intelligence without state awareness cannot be trusted to make consequential business decisions. As business workflows cross organizational and technical silos, critical context is routinely abstracted, degraded, or lost. EMA notes that among organizations that have rejected automated AI recommendations, insufficient context or explanation ranks as a leading cause, cited by nearly 40% of practitioners.
The primary limiting factor for enterprise AI isn't the underlying large language model (LLM). Instead, the issues rests with the system's visibility into current dependencies, business rules, and real-time operational conditions.
The Automation by Broadcom approach
Automation by Broadcom addresses context degradation by pairing execution orchestration with the cross-vendor observability of Automation Analytics & Intelligence (AAI). AAI provides a unified view of operational state across fragmented scheduling and orchestration platforms (including third-party tools like Control-M, IWS, or Airflow estates).
By mapping the entire automation landscape, AAI delivers real-time, SLA-aware process visibility and dynamic critical path analysis. It leverages historical operational intelligence and predictive forecasting that can feed AI models the exact context and dependency state required to make reliable, trustworthy operational decisions.
The Automation by Broadcom vision: the Intelligent Control Plane for the autonomous enterprise
True operational balance requires a unified orchestration and observability layer capable of connecting business stakeholders, process builders, operators, and distributed digital systems.
Our vision for the Intelligent Control Plane fulfills this requirement by establishing a centralized orchestration, observability, governance and optimization layer positioned above a highly diverse environment. This control plane harmonizes operations of business workflows across private and public clouds, mainframes, diverse data ecosystems, and enterprise applications like ERPs and ITSM. Crucially, it brings various domain-specific automation and orchestration platforms including AI agents and agentic applications into the same cohesive fold.
At its core, the control plane synthesizes three critical capabilities: SLA-aware process visibility with guided remediation, comprehensive cost analytics for smarter automation investments, and secure AI-powered orchestration that unifies traditional automation with probabilistic AI prompts and agents. Backed by invariant platform services including robust RBAC, federated identity, full auditability, and clear reversibility, this architecture guarantees that as enterprises scale their intelligent operations, business outcomes remain fully visible, compliant, and under absolute human control.
We are bringing this vision to reality by embracing foundational architectural building blocks that elevate our entire portfolio of solutions rather than just a single offering.
This vision is already in action, coming to life through major product releases such as Automic Automation v26 and Automation Analytics & Intelligence v26, with further innovations arriving in upcoming releases AutoSys Workload Automation.
Read the EMA Report: From Outcomes to Authority: Defining the Enterprise Control Plane
Frequently Asked Questions
Why is enterprise automation considered permanently federated in modern architectures?
Research indicates that up to half of all automation workflows span multiple operational domains, meaning orchestration must remain a distributed capability across localized execution contexts rather than consolidating into a single engine.
What operational risks necessitate "earning" AI authority through runtime assurance?
Approximately 30% of organizations frequently encounter incorrect or problematic AI outcomes, and more than 75% have required human intervention or rollbacks due to errors introduced during real-time runtime reasoning.
How does context degradation impact autonomous AI decision-making?
When workflows cross organizational silos, critical operational data is abstracted or lost. Without clear visibility into real-time dependencies and business rules, LLMs cannot make trustworthy decisions, leading practitioners to reject automated recommendations.
What specific capabilities does Broadcom's Intelligent Control Plane provide to resolve these challenges?
The platform encapsulates AI interactions into auditable "AI Jobs" to enforce strict role-based access control. It also pairs execution orchestration with cross-vendor visibility via Automation Analytics & Intelligence (AAI) to feed AI models the exact historical and predictive context they need.
