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Support operations leaders increasingly need precise, timely insights to align resources and exceed SLAs—especially as digital ticket volumes rise. In this case, a shared services organization leveraged Scoop’s AI-driven analytics to mine its accounts payable helpdesk history, revealing actionable patterns in issue timing, resolution speed, and demand distribution. The resulting insights enabled targeted workforce planning and further streamlined support processes, moving beyond static dashboards toward agentic optimization. For leaders seeking lasting service quality improvements and automation-augmented decision-making, this story demonstrates the value of a modern, ML-powered analytics approach.
The Scoop-powered analysis translated extensive historical ticket data into clear, actionable guidance for the support team. Patterns uncovered by the automated pipeline enabled a shift from anecdotal planning to data-backed operational decisions:
Staffing plans were immediately optimized for periods of demonstrated peak demand, particularly nights and evenings—a significant departure from traditional 9-to-5 models. Ticket aging analysis validated that over three-quarters of issues were already being resolved within a week, confirming efficiency while highlighting where process improvements could further compress aging for lingering cases. Distribution insights across priority and issue type highlighted where additional skill development or technical resources would be most relevant.
Through its agentic, automated workflow, Scoop accelerated both understanding and action, empowering leaders to make workforce and policy decisions rooted in operational reality.
Scoop’s pipeline ingested and analyzed all resolved tickets in the S2P Accounts Payable system, covering a comprehensive operational timespan.
Over three-quarters of all tickets were closed within 2–7 days, reflecting streamlined, effective issue resolution practices.
Over three-quarters of all tickets were closed within 2–7 days, reflecting streamlined, effective issue resolution practices.
The overwhelming majority of support requests related to hardware, highlighting a potential focus area for knowledge base expansion or automation.
Very few tickets originated on weekends, indicating reliably low out-of-hours support demand during these periods.
Shared services organizations supporting enterprise operations face mounting pressure to provide responsive, round-the-clock issue resolution while containing overhead. Traditional business intelligence tools offer limited depth, often requiring manual effort to surface patterns across large ticket datasets. Resource allocation is a particular challenge: without granular understanding of when and what types of support issues arise, leaders risk over- or under-staffing, failing to meet performance commitments.
In this case, the Accounts Payable service desk sought clarity on historical ticket creation patterns, issue types, and resolution timelines—insights critical for optimizing staffing models, ensuring consistent support, and preventing service gaps. Legacy BI solutions could not automatically reveal nuanced temporal patterns or the aging dynamics of issue resolution, resulting in reactive rather than proactive planning.
The organization provided Scoop with a comprehensive service desk dataset encompassing 3,550 resolved tickets from its S2P Accounts Payable system. Each record tracked ticket creation time, issue category, priority, and time to resolution, representing a complete case history ideal for quantitative analysis of resource needs.
Scoop’s end-to-end, agentic AI solution automated the entire analytics lifecycle:
The organization provided Scoop with a comprehensive service desk dataset encompassing 3,550 resolved tickets from its S2P Accounts Payable system. Each record tracked ticket creation time, issue category, priority, and time to resolution, representing a complete case history ideal for quantitative analysis of resource needs.
Scoop’s end-to-end, agentic AI solution automated the entire analytics lifecycle:
By transforming raw operational data into highly relevant, prescriptive recommendations, Scoop augmented decision-making and enabled proactive support management.
Scoop’s automated ML analysis surfaced insight sets rarely visible via manual dashboard reviews. The concentration of nearly 67% of tickets during night and evening hours ran counter to standard staffing assumptions, challenging the legacy belief that support demand would align with standard business hours. Moreover, the near-zero occurrence of tickets created at midnight, confirmed across a multi-thousand ticket sample, revealed a natural “quiet window”—a finding difficult to confidently extract from raw exports or basic BI visualization alone.
The dominance of moderate and low-priority tickets (with zero high-priority incidents recorded) reduced the need for costly, around-the-clock escalation staffing. Equally, the uniform weekday distribution and consistently minimal weekend volumes simplified forward staffing models, allowing leaders to defend resource shifts with hard data.
Without agentic, context-aware ML, such nuanced temporal splits and their implications for workforce planning—especially the interplay between ticket type, aging, and timing—would remain buried. Scoop surfaced these intersecting dimensions automatically, with no need for specialized data science intervention, ensuring support leaders could act confidently on comprehensive patterns, rather than isolated metrics.
With Scoop’s findings, the service desk revisited its shift scheduling and on-call procedures, reallocating labor to align with actual demand—especially during validated peak evening and night periods. The almost complete absence of midnight and weekend tickets informed right-sizing of after-hours coverage and focused investment in self-service tools for non-urgent issues. Training efforts are being directed toward hardware triage and moderate-priority ticket workflows, matching the composition of support requests. Future planned analyses include periodic re-evaluation of ticket age metrics and ongoing monitoring for changes in issue mix or seasonality, leveraging Scoop’s automated refresh and alerting capabilities to maintain optimal resourcing as patterns evolve.