Change Control Transformation at Enterprise Scale

A large U.S. credit card issuer operating across retail, commercial, and co-brand portfolios faced a change control process that had become simultaneously overburdened and undercontrolled. With hundreds of production systems, strict regulatory obligations under SOX, PCI-DSS, and federal banking oversight, and thousands of change requests per year, the process was not a matter of convenience — it was a direct compliance requirement and risk control.

The process had accumulated organically across multiple acquisitions, technology cycles, and organizational restructurings. Requests originated from four separate intake systems — a legacy ticketing platform, a shared email inbox, a SharePoint-based form, and a homegrown web portal — with no integration among them. Approvals required sign-off from two to five stakeholders depending on change risk category, but routing logic was undocumented and applied inconsistently by individual coordinators.

Change coordinators spent an estimated 60–70% of their working hours manually copying request details from intake systems into a change management database, then again into a regulatory reporting tool. Each handoff introduced transcription errors. Internal audits found that approximately 22% of submitted change records contained at least one data discrepancy relative to the originating request.

Requests and approvals routinely took two to four weeks. In some cases, the process stretched to two months. High-risk or emergency changes requiring CAB review had no structured pre-population of review materials. The team estimated that 3–5% of non-critical requests were never formally closed.

The root cause was not a lack of policy or discipline. The root cause was that the process had no governing backbone. Four disconnected intake systems, manual routing logic that existed only in coordinator knowledge, and multiple copy-paste handoffs between systems of record meant that every change request was a manual assembly project.

• No single source of truth: The same change request existed in different states across four systems, with no mechanism to detect or reconcile discrepancies
• Routing by tribal knowledge: Risk classification was performed manually against a printed rubric, producing inconsistent categorization
• No escalation or SLA enforcement: Stalled requests were invisible until someone manually checked
• Audit liability at every handoff: Each manual re-entry step was a point where data integrity could degrade

The coordinators were the process. They were spending their time moving data between systems rather than managing risk, analyzing trends, or improving controls. The process consumed them without producing the visibility or auditability that a regulated institution requires.

BSC Analytics engaged the client's IT Governance, Technology Risk, and Enterprise Architecture teams over a discovery and design phase. The resulting platform was deployed on Ariad in a phased rollout over 16 weeks.

Unified Intake Across All Channels BSC consolidated all four intake channels into a single governed intake layer on Ariad's process orchestration plane. Submitters continued using their existing interfaces, but all submissions were normalized into a canonical change request schema before entering the workflow.

AI-Assisted Risk Classification and Routing BSC configured Ariad's AI decision layer to automate risk classification as a first-class workflow step:

• Standard/Low-Risk: Pre-approved against standing authorization policies
• Moderate-Risk: Routed directly to designated system owner with SLA timers
• High-Risk/Emergency: Automatically triggers CAB review preparation

Automated Approval Workflows with Escalation Ariad's control plane manages the full approval lifecycle, enforcing SLAs at each stage and triggering escalations when deadlines are missed.

Downstream System Updates Without Re-Entry Upon approval, Ariad automatically updates all downstream systems of record. The end-to-end record is consistent by construction — there is no re-keying step at which a discrepancy can be introduced.

Within six months of full deployment, the client reported outcomes across every dimension that IT Governance was tracking.

Cycle Time and Efficiency

• Standard Changes: From 14–21 days to under 24 hours
• High-Risk / CAB: From 4–8 weeks to 5–7 business days
• Coordinator Time: From 3.5–5 hours per request to under 20 minutes
• Error Rate: From ~22% to under 1%
• Unclosed Requests: From 3–5% to under 0.1%

The team now spends its time on exception handling and process improvement, not on moving data between systems.