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From Workload Automation to Agentic Orchestration: What the 2025 EMA™ Radar Reveals About Enterprise Control

Workload automation has moved far beyond job scheduling. Today, enterprises are using orchestration platforms to coordinate applications, data pipelines, infrastructure, and increasingly, AI-driven workflows across hybrid and multi-cloud environments. But not all automation platforms are evolving at the same pace.

Speakers

Dan Twing President and COO , EMA

Guy Eden VP Product Management , BMC Software

event summary

Why AI-driven orchestration is redefining enterprise control beyond traditional workload automation

Rising operational complexity and AI adoption are reshaping how enterprises manage workflows. Industry leaders explore the shift toward intelligent, outcome-driven orchestration and the governance required to sustain control.

core insights

From Automation to autonomy: The future of enterprise orchestration

As enterprises scale across hybrid systems and AI adoption accelerates, orchestration is evolving from deterministic automation to adaptive, intelligent control.

1

Workload automation is shifting toward outcome-based orchestration

Organizations are moving beyond time-based scheduling toward end-to-end business outcomes. This shift matters because success is now measured by results, not processes—requiring orchestration systems to understand intent, not just execution.

2

AI introduces adaptive workflows—but increases governance complexity

AI enables dynamic decision-making and flexible execution paths instead of predefined workflows. However, this creates new risks, making governance, auditability, and human oversight essential to maintain control in probabilistic environments.

3

Enterprise complexity demands unified, observable control layers

Modern environments span mainframe, cloud, SaaS, and data pipelines, creating fragmented workflows. A centralized control plane with deep observability is required to coordinate systems and provide the context AI needs to make reliable decisions.

Buyers must rethink orchestration as a governance and intelligence problem—not just automation. Decision-making shifts toward balancing AI flexibility with operational reliability and compliance.

Key takeaways:

  • Prioritize outcomes over processes when evaluating orchestration strategies
  • Embed governance early to control AI-driven decision-making and ensure compliance
  • Adopt a unified control layer to manage hybrid and multi-domain environments
  • Invest in observability and data context to enable trustworthy automation
  • Start with controlled use cases and scale AI adoption with human oversight in place

“It's not AI for AI's sake… everything is about better operations.”

Bring order to complex workflows

Control-M orchestrates workflows across applications, data platforms, and business systems; not just individual processes.
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