DevOps teams must build AI operating models to manage hybrid complexity
DevOps decision makers at IBM's Think conference in Boston this week confronted a fundamental shift: AI is no longer a component of their discipline. It has become the discipline itself.
The challenge is stark. Organizations now run applications, infrastructure, and networks across cloud, on-premises, and Z systems. AI workloads intensify the scale, cost, and operational risk across all of it. One billion new applications will arrive in enterprises over the next five years because of generative AI, IBM's Dinesh Nirmal said Tuesday. Each containerized application will have hundreds or thousands of microservices, potentially governed by billions of agents.
Without coordinated control systems, operations teams cannot manage this scale. That's where an AI operating model becomes essential.
The core problem: scale outpaces human response
DevOps has evolved over the past decade to collect vast amounts of observability data. Those tools now generate so much signal that humans cannot act on it fast enough. Automated solutions must both interpret the data and execute responses across increasingly complex environments.
IBM introduced the Concert platform this week as a unified operational layer spanning applications, infrastructure, networks, and security. Concert integrates existing IBM tools including Instana, Turbonomic, and CloudPak for AIOps to create a single source of truth about application and infrastructure relationships.
A new tool called HCP Terraform, powered by Infragraph, adds real-time visualization. Infragraph maintains a continuously updated graph database of infrastructure, applications, data, and security relationships in hybrid cloud environments. When combined with Concert, operations teams can understand their environment, make decisions, and act-all grounded in current state data.
What operations teams need to implement now
Four priorities emerged from the conference:
- Unified standards and policy-driven automation. Consistent policies across hybrid environments reduce risk and keep systems reliable as complexity grows.
- AI as connective tissue, not a silo. Rather than creating isolated AI operations, AI-driven systems should bind hybrid environments together.
- Observability that explains impact. Real-time insights must surface not just what broke, but why it matters and where to act.
- Coordinated automation at scale. AI-driven operations reduce manual toil, optimize costs, and enable reliable scaling as complexity grows.
Security and access control in agentic systems
Greater complexity demands tighter security. IBM announced Concert Protect and Secure Coder to identify exposure continuously across agentic IT environments. The system detects exposure, scores risk, remediates threats through automated workflows, and feeds learnings back into the platform.
The access control challenge is severe. For every human accessing an enterprise system, 120 non-humans-agents, services, and automated processes-will soon access it, Nirmal said. Traditional identity and access management strategies cannot handle this scale. Operations teams must adopt AI-driven policy management to control who and what can access which systems.
Building a unified automation fabric
At a Wednesday panel, leaders from Marriott and Honda discussed how pairing Concert with provisioning tools like Ansible creates a unified automation fabric. By coordinating deterministic, event-driven, and agentic workflows through a single policy-driven system, IT teams ensure AI systems operate with appropriate access at the right time.
The message from IBM executives was direct: without an AI operating model, operations teams cannot keep pace with the volume of AI systems entering the enterprise. The tools exist. The question is whether teams implement them before complexity becomes unmanageable.
For operations professionals tasked with managing this shift, consider exploring AI learning resources focused on operations management and AI agents and automation to build the skills needed for AI-driven operations.
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