Executive Assessment
Healthy 1
Jun 28, 2026, 11:55 AM
Process Health
Standard Incident Process with Good Efficiency but Actionable Rework and Delays
This incident management workflow demonstrates a good level of standardization and high efficiency, with nearly 75% of incidents following a single path and 65% of resolution time spent on active work. However, a notable 9.2% rework rate and a significant administrative delay of 4.75 hours after resolution are extending cycle times unnecessarily. Leadership should focus on reducing rework between active states and automating the final closure step. The strong data quality provides an excellent foundation for applying AI to improve routing and root cause analysis.
6.8 out of 10
Process Health Score
Good
✓ Confidence: High
The process is fundamentally sound, with high flow efficiency (65%) and a dominant standard path. The score is tempered by a material rework rate of 9.2% which introduces delays, and a significant, non-value-add delay of 4.75 hours between the 'Resolved' and 'Closed' states. Excellent data completeness provides high confidence in the findings.
Headline Signals
Flow Efficiency
Time IntelGood
65.01%
A high flow efficiency indicates that a majority of the incident lifecycle is spent in active, value-add work states, suggesting a healthy operational flow.
Rework Rate
ProcessWarning
9.2%
Nearly 1 in 10 incidents experience rework, bouncing between active states. This adds significant delay and manual effort, pointing to issues with initial assignment or information gathering.
Primary Path Dominance
VariantsGood
73.55% of items
The majority of work follows a single, standard process. This high level of standardization makes the workflow easier to manage, measure, and improve.
Post-Resolution Delay
TransitionsWarning
4.75 Hours
The average time between 'Resolved' and 'Closed' is the single longest delay in the process. This administrative lag inflates overall resolution metrics without adding value.
SLA Breach Rate
sla_signalsWarning
4%
While the breach rate is low, it indicates some incidents are failing to meet committed service levels, which can impact user satisfaction and points to isolated process bottlenecks.
Data Field Population
AttributesExcellent
100% for key fields
Key classification fields like priority, assignment group, and close code are consistently populated, providing a strong data foundation for analysis, reporting, and AI initiatives.
Time Profile
The average incident lifecycle is 17.2 hours. A healthy 65% of this time (11.2 hours) is spent in an active 'touch' state. The primary delays occur in the initial intake queue (2.4 hours) and during post-resolution administrative processing (3.6 hours), with the final closure step being particularly slow. This indicates the core resolution work is efficient, but the surrounding administrative steps offer clear opportunities for optimization.
Avg. Resolution Time
13.59 hours
Avg. Active Touch Time
11.22 hours
Avg. Queue Time
2.41 hours
Avg. Administrative Time
3.62 hours
Flow Efficiency
65.01%
Major DiscoveryRules
Find
1
High Process Standardization Creates a Stable Baseline
💡 standardisation
📊 Evidence
The top variant covers 73.6% of all incidents, and the top two variants together account for nearly 91% of the total volume.
🔎 Insight
There is a well-defined and consistently followed standard process for handling the majority of incidents.
💼 Business Impact
This high degree of standardization simplifies process management, provides reliable metrics, and creates a stable foundation for targeted automation and improvement initiatives.
Find
2
Rework Loops Between Active States Inflate Resolution Time
💡 rework
📊 Evidence
9.2% of incidents undergo rework. The most common rework loops are 'Assigned -> Active' and 'Work in Progress -> Assigned'. Rework variants take up to 40% longer to resolve than the standard path (23.9 vs 17.1 hours).
🔎 Insight
These rework loops suggest issues with incorrect initial assignments or agents needing to re-route tickets after beginning work, possibly due to insufficient information.
💼 Business Impact
Rework directly increases manual effort, delays resolution for affected users, and creates frustration for support staff. It is a key driver of inefficiency.
Find
3
Significant Administrative Delay Occurs After Resolution
💡 delay
📊 Evidence
The transition from 'Resolved' to 'Closed' is the longest single step, averaging 4.75 hours and affecting nearly 75% of all incidents.
🔎 Insight
A systemic delay exists in formally closing incidents after the work is complete. This is likely due to a configured waiting period for user confirmation or a manual batch closure process.
💼 Business Impact
This non-value-add time inflates overall lifecycle metrics, misrepresenting team performance and potentially keeping resources tied up in monitoring resolved incidents.
Find
4
Fragmented Close Codes Limit Root Cause Analysis
💡 data_quality
📊 Evidence
The 'close_code' field has 15 distinct values, but the distribution is flat, with the top value used in only 7.6% of cases. Several codes like 'None', 'Referred', and 'Request created' are ambiguous.
🔎 Insight
There is no clear, consistent method for categorizing incident outcomes. This prevents meaningful analysis of why incidents occur and how they are ultimately resolved.
💼 Business Impact
Without structured outcome data, it is difficult to perform effective root cause analysis, identify recurring problems, or spot opportunities for knowledge base articles and automation.
Find
5
Alternate Closure Path Creates Process Inconsistency
💡 standardisation
📊 Evidence
Over 20% of incidents move directly from 'Work in Progress' to 'Closed', bypassing the 'Resolved' state which is used by the main process path.
🔎 Insight
Two different methods are being used to close incidents. This may be by design for certain incident types or may reflect inconsistent agent behavior.
💼 Business Impact
Having multiple closure paths complicates process analysis and reporting, and can lead to confusion about the correct procedure to follow.
Path Insights
The process is highly standardized, with the top two paths covering over 90% of all incidents. The remaining 11 variants are low-volume and primarily represent different combinations of rework loops.
Standard Resolution Path
Dominant Path
This is the primary 'happy path', followed by 73.6% of incidents. It represents the standard, successful workflow from intake through resolution and closure.
Covers 1,471 of 2,000 itemsAverage duration is 17.1 hoursRepresents the core, intended processNo rework or loops occur on this path
Direct Closure Path
Dominant Path
The second most common path, used by 17.3% of incidents, bypasses the 'Resolved' state and moves directly from 'Work in Progress' to 'Closed'.
Covers 345 of 2,000 itemsDuration is similar to the main path (16.75 hours)Indicates a procedural variation in closing incidentsMay be appropriate for certain fast-tracked incidents
Assignment Rework Path
Problem Path
This path includes rework where an incident is sent back to 'Assigned' after work has already begun. This path takes nearly 24 hours, over 6 hours longer than the standard process.
Example: 'Work in Progress -> Assigned -> Closed'Indicates probable mis-assignment or escalationAverage duration is 23.9 hours (40% longer)Primary contributor to the overall rework rate
Re-Opened 'Resolved' Path
Problem Path
This path shows incidents that were moved to 'Resolved' but then re-opened and returned to 'Work in Progress', indicating the initial solution was not effective.
Example: 'Resolved -> Work in Progress -> Closed'Represents a failed first-time fixIntroduces delay and negatively impacts user experienceHighlights opportunities for better diagnostics or solutions
Leadership Priorities
🔐
Reduce Rework Through Smarter Assignment
Strategic
Rework is the largest source of inefficiency, impacting 9.2% of incidents and adding significant delays. Addressing it is key to improving speed and reducing wasted effort.
Expected Benefit
Faster resolution times, reduced manual effort, and improved first-contact resolution rates.
Likely Owner
Incident Management Process Owner
AI: Use AI to predict the correct assignment group based on incident descriptions, preventing manual mis-routing.Automation: Refine and automate routing rules in ServiceNow based on improved categorization and AI predictions.Risk if delayed: Continued operational drag, inflated resolution times, and persistent user and agent frustration.
📋
Automate Post-Resolution Closure
Quick Win
A 4.75-hour administrative delay after resolution is artificially inflating cycle time metrics. This is a simple process gap that can be easily closed.
Expected Benefit
More accurate lifecycle reporting, reduced manual monitoring of resolved tickets, and faster time-to-close.
Likely Owner
ServiceNow Platform Owner
AI: N/AAutomation: Implement or tighten an automated business rule in ServiceNow to close resolved incidents after a defined period of inactivity.Risk if delayed: Persistently skewed performance metrics and ongoing minor administrative burden.
✅
Standardize Incident Outcome Data
Foundational
Fragmented and ambiguous close codes are preventing effective root cause analysis, trapping the organization in a reactive support model.
Expected Benefit
Enable data-driven problem management, identify automation opportunities, and improve knowledge base content.
Likely Owner
Service Desk Leadership
AI: Use AI Topic Discovery or Clustering to analyze historical incident data and recommend a simplified, more effective set of close codes.Automation: Configure dependent choice lists or business rules to guide agents to the correct close code based on incident category.Risk if delayed: Inability to identify and eliminate sources of recurring incidents, leading to perpetually high ticket volumes.
Executive Decision Support
Key Risks if Delayed
Persistent Process Inefficiency
Failing to address the 9.2% rework rate will mean continued wasted effort, longer-than-necessary resolution times, and increased operational friction.
Urgency: High
Misleading Performance Metrics
The significant administrative delay between 'Resolved' and 'Closed' skews overall lifecycle metrics, which could lead to incorrect conclusions about team performance and mask the true resolution speed.
Urgency: Medium
Inability to Conduct Root Cause Analysis
Poor quality outcome data from inconsistent close codes prevents the identification of trends and recurring issues, hindering any transition to proactive problem management.
Urgency: Medium
Readiness & Constraints
AI Readiness
Medium
Automation Readiness
Medium
Data Readiness
High
Data readiness is high due to excellent field population, providing a strong foundation. AI readiness is medium, as outcome data (close codes) needs standardization to be effective. Automation readiness is medium; while the process is standardized, the organization's low appetite for automation presents a cultural adoption risk that must be managed.
Consultant Note
The analysis highlights clear opportunities in addressing rework and administrative delays. Subsequent deep-dive sessions should focus on the root cause of rework between 'Assigned' and 'Work in Progress', and validate the reason for the 4.75-hour delay from 'Resolved' to 'Closed' (e.g., system auto-closure settings). The fragmented 'close_code' field should be a primary focus for data quality improvement.
Evidence Base
metrics, transitions, variants, field usage, sample items, activity model, time intelligence, task_sla metrics
✓ Process Metrics✓ Transitions✓ Variants✓ Field Usage✓ Status Types✓ Time Intelligence✓ Sample Items