Structuring change approvals in platform operations
A technology company with 3,400 employees runs more than 260 platform services, with around 900 changes awaiting approval every month.
- Technology
- IT-Service-Management
Key metric
Time per change approval
160120min
Average duration of a change approval in platform operations
At a glance
Starting point
The company runs a central platform landscape with more than 260 services for internal and external applications. IT operations plans and implements around 900 changes a month, from configuration adjustments and releases to infrastructure updates. A team of about 18 full-time staff coordinates these changes across infrastructure, applications and security. Each change takes an average of 160 minutes of coordination, including risk analysis, approvals and scheduling. Coordination happens via tickets, emails, chat messages and meetings. Information on dependencies, maintenance windows and security requirements is scattered across change calendars, runbooks and past changes, leading to follow-up questions, duplicate checks and delayed approvals.
- 260
- platform services
- 900
- changes per month
The challenge
Changes often affect several platform services and teams at once. For every change, the impact on availability, security and downstream systems has to be checked. Today, change managers gather information manually from tickets, monitoring dashboards, calendars and wikis. Approvals are obtained by email or chat, often without a consistent checklist. This leads to varying quality in risk analyses: some changes are treated overly cautiously, while others are approved on unclear assumptions. Conflicts between maintenance windows go unnoticed for a long time because dependencies are not captured systematically. Managers in IT operations have little visibility into which changes are delayed and why, and how much time goes into coordination instead of implementation.
Typical symptoms in the organization
Why this remained unsolved
Until now, the problem was accepted as the necessary complexity of running a large platform. Attempts to introduce rigid checklists or central approval forms failed because of the variety of changes and the teams' limited time. Classic automation could only support individual steps, such as calendar bookings, but not the substantive assessment of risks and dependencies. It was also unclear how approvals could be structured without creating extra bureaucracy or weakening the responsibility of the specialist teams. The topic was too big for small process tweaks, yet too cross-cutting to be prioritized as a standalone IT project with clear system boundaries.
Our solution
An agentic workflow was introduced that brings together change tickets, maintenance calendars, monitoring alerts and existing knowledge from runbooks and wikis. The workflow analyzes new changes, identifies affected components, dependencies and security relevance and proposes a structured approval path. For each change, it creates a review plan with clear tasks, the teams involved and the evidence required. During planning, the workflow monitors schedule changes, adds context from similar changes and points out conflicts with other maintenance windows. Approvals are bundled in the ticketing system, including documented assumptions and risks. This reduces duplicate checks, makes critical conflicts visible earlier and documents approvals traceably. Teams keep their familiar tools but receive structured suggestions and checklists in the ticketing system that adapt to the specific content of each change.
Where generative AI comes in
Generative AI understands free text in change tickets, risk assessments and runbooks, maps it to affected components and dependencies and creates structured review plans. It summarizes long coordination threads, highlights critical assumptions and suggests approval steps, without teams having to maintain rigid rules for every change type.
- Integration of the central ticketing system, the maintenance calendar and relevant monitoring alerts, including access to history and planned changes.
- Context enrichment from existing runbooks, wikis and known risk patterns that serve as the knowledge base for review plans and approval paths.
- Agentic workflow that analyzes changes, identifies the teams involved, structures review steps and guides approvals in the ticketing system.
- Conflict detection for maintenance windows and dependent services that makes scheduling overlaps and critical combinations visible early.
- Feedback loops in which teams rate and correct suggestions improve the quality of review plans and approval paths.
From change request to documented approval
Capture the change
New changes are created in the ticketing system; the workflow reads the description, affected services and planned time windows and identifies initial dependencies.
Create a review plan
The workflow suggests the teams involved, the necessary risk and security checks and the evidence required, and documents this as a structured checklist in the ticket.
Detect conflicts
Scheduling and dependency conflicts with other changes or maintenance windows are identified automatically, and the workflow suggests alternatives or bundling.
Bundle approvals
Teams work through their review steps while the workflow collects approvals and open items in the ticket and summarizes the decision status for change managers.
Learn from each run
Once a change is complete, its duration, conflicts and rework are analyzed, and the workflow adjusts its suggestions based on this experience.
Measured: after nine months in operation
Period: after nine months in operation.
| Metric | Value | Basis |
|---|---|---|
| Time per change approvalKey metric | from 160 to 120 minutes per approval | Time tracking before/aftermeasured |
| Complete risk reviewsQuality metric | Up from 65 to 88 percent | Sampleestimated |
| Postponed changes | Down from 90 to 55 per month | System measurementcustomer-reported |
Cost-effectiveness
The solution only reaches its full effect if changes are consistently recorded in the ticketing system and maintenance windows are kept up to date. In areas with many ad hoc changes made without a ticket, conflicts and risks remain hard to see.
Integration and operation
Transferability
The agentic workflow for change approvals can be transferred to other complex operating environments where many changes involve dependencies and approvals. What matters is a central case management system and documented knowledge about risks and maintenance windows. Generative AI understands free text, attachments and histories and creates structured review plans, so organizations with many interfaces can ease the burden of approvals and spot conflicts earlier.
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