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AI is no longer a future-state initiative. Enterprises are actively deploying AI agents, embedding AI-driven steps into business-critical workflows, and scaling automation across hybrid environments at a pace that is outrunning their ability to govern and manage execution outcomes.
But there is a problem hiding in plain sight. Most organizations have focused their AI investments on models, platforms, and data — and far less on what happens after an AI agent takes an action. Who governs that action? What happens when it fails? How do you ensure it executes within the boundaries the business requires?
This webinar is a dialogue between BMC and AWS that sits at the center of how enterprises are navigating this challenge, drawing on real patterns they are seeing across shared customers today.
Attendees will leave knowing the answer to the following question: “As AI agents enter production, how do enterprises maintain the execution reliability, governance, and control that mission-critical operations demand — without slowing down innovation?”
Key Takeaways:
- A clear distinction between automation, orchestration, and agentic orchestration — reinforcing that enterprise AI doesn’t stall at the model, it stalls at scale, and why that matters operationally as AI scales.
- Risk you can name: A clear way to articulate execution risk — not as a technical problem, but as a business continuity and governance challenge.
- Actionable principles for approaching agentic orchestration design — directly applicable to your own environments.
Speakers:
Tom Geva, Lead Solution Marketing Manager, BMC
Ben Schreiner, Head of Business Innovation Advisors, AWS
In collaboration with
Tom Geva, Lead Solution Marketing Manager, BMC
Ben Schreiner, Head of Business Innovation Advisors, AWS
event summary
Why AI success now depends on orchestration, governance, and operational trust
As enterprises move AI from experimentation into production, new challenges around governance, visibility, and operational control are emerging. Industry leaders from BMC and AWS explore why scaling AI requires more than capable models, highlighting the importance of trust, orchestration, and business accountability.
core insights
Scaling AI requires operational discipline, not just innovation
As AI adoption expands across business functions, organizations are discovering that successful pilots rarely translate into enterprise-wide outcomes. Production success depends on governance, ownership, approvals, and accountability becoming part of every AI-driven process.
1
AI initiatives are outpacing governance models
Organizations are deploying AI rapidly, often before establishing clear governance frameworks. As AI begins taking actions instead of simply providing recommendations, businesses need stronger controls, approvals, and oversight mechanisms.
2
Fragmented AI ecosystems create visibility gaps
AI workflows increasingly span cloud services, data platforms, enterprise applications, and legacy systems. Without end-to-end visibility, organizations struggle to understand dependencies, measure outcomes, and maintain operational accountability.
3
Trust becomes the key factor in adoption
The challenge is no longer gaining access to AI capabilities. Organizations must ensure data quality, operational transparency, and traceability so users can confidently rely on AI-driven decisions and actions.
what this means
Turning orchestration into measurable business impact
Enterprises are entering a new phase of AI maturity where success depends on managing workflows rather than individual models. Decision-makers must embed governance, data readiness, and accountability into operational processes before autonomous AI can scale safely and consistently.
Key takeaways:
- Establish clear ownership and approval paths for AI-driven workflows before moving them into production.
- Map dependencies across data pipelines, applications, and AI services to create end-to-end visibility.
- Validate data quality and readiness before triggering AI-powered actions or decisions.
- Build governance, auditability, and compliance controls into workflows from the beginning rather than retrofitting them later.
- Design architectures for flexibility so models, tools, and processes can evolve without disrupting business operations.
AI agent lose value if they are deployed in isolation. They have to be anchored in explicit business processes, coordinating decisions, executions, and human oversight across applications.”
— Gartner (quoted during the webinar)
Bring order to complex workflows
Control-M orchestrates workflows across applications, data platforms, and business systems; not just individual processes.