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Agentic automation is widely positioned as the next productivity breakthrough for enterprise IT. Building on the efficiency gains already delivered by traditional automation, AI agents capable of evaluating conditions, determining next steps, and executing actions autonomously promise faster operations, reduced manual effort, and leaner, more responsive teams.
For many organizations, this hasn’t quite panned out yet. Instead of reducing workload, teams have found themselves spending more time troubleshooting unpredictable behavior, validating outcomes, and tweaking AI-driven workflows than accelerating their operations or working on new and exciting projects. In other words, it has made teams feel busier, not more productive.
Why is this happening?
In most cases, the issue isn’t with agentic automation itself. Instead, it stems from the poorly designed automation foundations that organizations have built over time, into which it is introduced.
Fragmented Automation: The Hidden Drain on Productivity
Many organizations have been struggling with inconsistent and overly complex automation long before introducing AI agents. This is due to a common problem: scripts and workflows being created by individuals or teams in isolation to address immediate needs, without standardized policies or a robust framework for overseeing and governing automation across the broader infrastructure.
Within this fragmented landscape, execution logic varies significantly depending on the tools, services, environments, and systems involved. Moreover, access controls and credential management are often applied inconsistently. The result is automations that are tightly coupled to specific individuals with administrative privileges and domain knowledge, rather than being designed for safe reuse across teams.
Each individual automation may save time locally, but, collectively, they introduce severe complexity when things go wrong. When an automation breaks something in an operational workflow or is exploited in a cyber-attack, engineers have to spend time figuring out where automation lives, who is responsible for it, which version is safe to use, and how changes to it might affect processes elsewhere in the system.
This is already a bottleneck on the productivity of conventional automation strategies. When agentic automation is layered onto this fragmented foundation, AI agents only end up amplifying the inconsistencies and structural weaknesses that they have inherited.
Why Agentic Automation Amplifies Fragmentation Instead of Fixing It
Agentic automation does not replace automation logic; it accelerates it. When AI agents are introduced into an operational IT environment, they rely on the same data sources, tools, and execution mechanisms that human operators use to perform real work.
In a fragmented automation landscape, agents are forced to navigate a maze of disconnected systems, inconsistent controls, and brittle processes. The inevitably produces a mess of inconsistent results, unintended actions, and elevated security risks.
Rather than reducing effort, agents accelerate the impact of issues that already exist. Operating at machine speed, they scale small inconsistencies rapidly: errors are repeated more quickly, misconfigurations propagate further, and operational noise increases across the environment.
For IT teams, this means more oversight requirements, more troubleshooting, and more remediation work, all of which erode the productivity gains organizations were expecting.
How to Fix It: Building a Centralized Automation Foundation
For teams grappling with overcomplex and fragmented automation environments, attempting to layer agentic automation on top of existing dysfunctionality can feel like a losing battle. As AI hype accelerates expectations, it can reinforce the perception that agentic automation’s promised productivity gains are more marketing than substance.
Yet real-world AI experiments show that, in the right conditions, meaningful productivity improvements are certainly achievable. The solution is not to abandon agentic automation initiatives, but to focus first on building the foundation for safe, governable automation that gives agents the clean environment they need to work effectively. In this, a centralized approach is key.
Centralization does not mean eliminating autonomy or slowing teams down with red tape. Rather, it means standardizing the core execution, governance, and observability controls that apply whenever automation runs. When automation operates through a shared platform with consistent governance, organizations gain:
- A single execution model across environments, enabling centralized oversight and coordination
- Reusable, versioned automation assets that can be shared and refined across teams
- Predictable security and access controls enforced by default
- Unified logging and traceability to support audit and regulatory requirements
This centralized model reduces coordination overhead dramatically, as teams have a single platform from which to access, monitor, and deploy automation that aligns to their specific responsibilities.
Engineers no longer need to reinvent workflows or reverse-engineer scripts built by other teams, nor worry about unintentionally breaking processes they cannot see.
Quality improves as automation logic is reused and iteratively refined rather than duplicated, resulting in more consistent and scalable outcomes.
Security teams breathe a sigh of relief knowing that access controls are enforced consistently, regardless of where or how automation runs.
With this as its foundation and inheritance, agentic automation has the potential to deliver the real productivity gains that it promised.
A centralized automation platform provides the standardized execution and orchestration layer that makes scalable autonomy possible. By enforcing consistent policies, identity controls, and logging across all automation, organizations create an environment where both human- and AI-driven actions are safe, predictable, and observable by design.
This is where ScriptRunner plays a critical role. ScriptRunner centralizes automation across Microsoft and hybrid environments by providing a governed execution platform that standardizes how automation is created, triggered, and controlled. In practice, this includes:
- Standardized script and workflow creation models, including structured input forms and reusable building blocks, ensuring automations are consistent, predictable, and easier to maintain.
- Enforced governance and access controls that apply least-privilege principles by design, rather than relying on embedded credentials or ad-hoc permissions.
- Governed self-service portals that allow approved users, teams, or AI agents to trigger automation safely without direct system access or elevated privileges.
- Unified monitoring and logging, giving IT teams a single, authoritative view of what ran, who or what triggered it, and what changes were made across environments.
- Centralized policy enforcement across scripts, workflows, and agent-driven actions, ensuring security, compliance, and operational standards are applied consistently at scale.
For agentic automation, this means that agents can run decision-making and execution tasks at scale while remaining governed and observable by design. Productivity improves because agents operate within a stable, unified framework, not a fragmented and opaque toolchain.
By consolidating automation execution with ScriptRunner, enterprises can therefore turn agentic automation into a force multiplier rather than a burden. Instead of adding supervision overhead, agentic automation fulfils its promise of reducing manual effort, accelerating response times, and improving operational quality, delivering the productivity gains organizations expect.
If you’re ready to move from fragmented automation to a unified, enterprise-grade automation strategy, book a meeting with ScriptRunner to see how it works in practice.