Executive Assessment
Incidents - Status
Jul 9, 2026, 01:06 PM
Process Health
Incident Management Workflow is Highly Inefficient and Unpredictable, Dominated by Rework and Delays
The Incident Management process is in a critical state, characterized by a lack of standardization, excessive rework, and significant delays. With 40% of incidents requiring rework and the process fragmented into 164 different paths, there is no consistent 'happy path'. A severe bottleneck exists in closing completed incidents, adding an average of 7 days of non-productive time. Flow efficiency is extremely low at 12.8%, meaning most of an incident's lifecycle is spent waiting. Leadership must prioritize standardizing the workflow, eliminating rework loops, and automating administrative tasks to restore operational health.
3.5 out of 10
Process Health Score
Weak
✓ Confidence: High
The score is driven by multiple severe negative signals: an extremely high rework rate (40%), massive process fragmentation (164 variants), and very low flow efficiency (12.8%). A major administrative delay of nearly 169 hours between 'Completed' and 'Closed' further inflates cycle times. While data fields are well-populated, the underlying operational process is chaotic and inefficient.
Headline Signals
Rework Rate
ProcessCritical
40.04%
An extremely high percentage of incidents require additional work after being considered in progress or resolved, indicating significant wasted effort, unclear processes, or poor initial triage.
Process Variants
ProcessCritical
164
An excessive number of unique paths to resolution shows a lack of a standard, repeatable process. This makes the workflow unpredictable, difficult to manage, and nearly impossible to automate effectively.
Flow Efficiency
Time IntelCritical
12.84%
Only 12.8% of the total incident lifecycle is spent in active 'touch' time. The vast majority is consumed by waiting in queues or for administrative closure, highlighting major process bottlenecks.
Administrative Closure Delay
TransitionsCritical
168.9 hours
Incidents wait an average of 7 days to be formally closed after work is completed. This is a significant source of artificial delay that inflates resolution metrics and indicates a broken or missing automation.
Top Path Dominance
VariantsWarning
27.9%
The single most common process path is followed by only 28% of incidents. This confirms that there is no true 'standard' process, leading to inconsistent outcomes and high variability.
Time Profile
The time profile reveals a highly inefficient process. The average incident lifecycle is dominated by 159.7 hours of administrative time, primarily the delay between completion and closure. A further 44.7 hours are spent in wait states. In contrast, only 32.9 hours are spent on active work ('touch time'). This imbalance is the direct cause of the extremely low 12.8% flow efficiency, where work is idle for the vast majority of its lifecycle.
Avg. Touch Time
32.9 hours
Avg. Wait Time
44.7 hours
Avg. Admin Time
159.7 hours
Avg. Cycle Time
81.5 hours
Flow Efficiency
12.8%
Major DiscoveryRules
Find
1
Extreme Process Fragmentation Prevents Standardization
💡 standardisation
📊 Evidence
The workflow has 164 unique variants for 1,044 incidents. The most common path, 'Work in Progress -> Completed -> Closed', only accounts for 28% of the total volume.
🔎 Insight
There is no standard operating procedure for handling incidents. Teams follow dozens of different paths, leading to unpredictability, inconsistent data capture, and an inability to identify systemic improvement opportunities.
💼 Business Impact
Inconsistent service delivery, difficulty in training staff, and a near-total inability to apply meaningful automation or AI. Performance management is unreliable due to high process variability.
Find
2
Excessive Rework Creates a Cycle of Inefficiency
💡 rework
📊 Evidence
40% of all incidents experience rework. Frequent rework loops are visible, such as 'Work in Progress -> Pending -> Work in Progress' (affecting 38% of items) and 'Work in Progress -> Open' (17% of items).
🔎 Insight
A significant portion of effort is spent re-addressing the same issues. This churn indicates problems with initial diagnosis, information gathering, or dependencies not being met before work begins.
💼 Business Impact
Dramatically increases the effort and time required to resolve incidents, frustrates support staff and end-users, and wastes significant operational capacity.
Find
3
A Severe Bottleneck in Administrative Closure Inflates Resolution Time
💡 delay
📊 Evidence
The transition from 'Completed' to 'Closed' takes an average of 168.9 hours (approx. 7 days) and affects 93% of all incidents.
🔎 Insight
A manual or broken process is preventing the timely closure of resolved incidents. This is pure administrative waste and does not represent active work or value-add time.
💼 Business Impact
Artificially inflates key performance metrics like Mean Time to Resolution (MTTR), masks the true performance of the resolution teams, and creates misleading reports.
Find
4
Process is Dominated by Idle Time
💡 time_efficiency
📊 Evidence
Flow efficiency is only 12.8%. The average incident spends 159.7 hours in administrative states and 44.7 hours waiting, compared to only 32.9 hours of active work.
🔎 Insight
The workflow is fundamentally inefficient, with incidents sitting idle for nearly 90% of their lifecycle. This points to systemic bottlenecks, delays in handoffs, and long periods waiting for external input or system actions.
💼 Business Impact
Poor resource utilization, extended resolution times, and a negative experience for end-users waiting for a resolution. It limits the capacity of the support organization.
Find
5
Work Classification Lacks Specificity, Hindering Analysis and Automation
💡 classification
📊 Evidence
The 'Subcategory' field has 39 distinct values, but the top 10 values only cover 51% of incidents. Generic values like 'I have a Question' (8.8%) and 'N/A' (3.8%) are common.
🔎 Insight
While the field is consistently populated, the classification scheme is too broad or poorly enforced. This makes it difficult to route incidents accurately, analyze trends for specific issue types, or identify targeted automation opportunities.
💼 Business Impact
Increased manual triage effort, potential for mis-routing, and poor data quality for strategic decision-making. It is a key blocker for developing effective AI and automation solutions.
Path Insights
The process is extremely fragmented, with 164 unique paths. The top 12 variants shown cover only 73% of the work, meaning over a quarter of all incidents follow dozens of other, less common paths. This 'long tail' of variation makes the process inherently unstable and difficult to manage.
Work in Progress -> Completed -> Closed
Dominant Path
The most common path, covering 28% of incidents. While it appears straightforward, it is still subject to the massive 7-day administrative delay at the end of the process.
Most frequent path (291 items)Represents the intended 'happy path'Average duration is 182.6 hoursAffected by the 169-hour 'Completed' to 'Closed' delay
Completed -> Closed
Dominant Path
The second most common path (15% of incidents) where items are resolved without ever entering a 'Work in Progress' state. This may indicate first-contact resolution or mis-categorized requests.
Covers 156 items (15%)Skips 'Work in Progress' statusAlso impacted by the 169-hour closure delay
Work in Progress -> Pending -> Work in Progress -> ...
Problem Path
This path, affecting 5.6% of incidents, exemplifies a common rework loop where an incident is put on hold and then re-activated. This adds significant delay and effort.
Covers 58 itemsRepresents a common wait/rework cycleAverage duration increases to 272 hoursIndicates dependency on external input
Work in Progress -> Open -> Work in Progress -> ...
Problem Path
This rework pattern, affecting 5% of incidents, shows items being sent back to the initial 'Open' queue from an active state. This suggests incorrect assignment or a need for re-triage.
Covers 52 itemsRepresents a re-triage or re-assignment loopAverage duration balloons to 350 hoursHighlights issues in initial routing or diagnosis
Leadership Priorities
🔐
Automate the 'Completed' to 'Closed' Transition
Quick Win
This is the single largest source of delay in the process, affecting over 93% of incidents and adding 7 days of non-value-add time. Fixing this will immediately improve MTTR metrics and free up administrative capacity.
Expected Benefit
Drastically reduce overall incident resolution time, improve the accuracy of performance metrics, and eliminate manual administrative effort.
Likely Owner
IT Service Management / Platform Owner
AI: None required; this is a straightforward automation.Automation: Implement a business rule or scheduled job to automatically move incidents from 'Completed' to 'Closed' after a short, defined period (e.g., 24-48 hours).Risk if delayed: Performance metrics will remain artificially inflated and misleading, masking true operational efficiency.
📋
Standardize the Incident Workflow and Eliminate Rework
Foundational
The process is too chaotic (164 variants) and wasteful (40% rework) to be effective. A lack of standardization is the root cause of unpredictability and inefficiency.
Expected Benefit
Increased predictability, reduced resolution times, improved service quality, and a stable foundation for future automation and AI initiatives.
Likely Owner
Head of IT Operations / Service Delivery
AI: Use process mining to identify the most efficient resolution paths for different incident types. Use AI to predict incidents at high risk of rework for proactive intervention.Automation: Configure playbooks or workflows that guide agents through the standardized process for high-volume incident types.Risk if delayed: Continued operational inefficiency, poor user experience, and an inability to scale or meaningfully improve the service.
✅
Improve Incident Classification at the Point of Intake
Strategic
Poor classification in the 'Subcategory' field leads to mis-routing, rework, and prevents effective analysis. Better data is required to enable intelligent automation and targeted improvements.
Expected Benefit
Faster and more accurate routing, reduced manual triage, better visibility into incident trends, and higher quality data for AI models.
Likely Owner
Service Desk Manager / ITSM Process Owner
AI: Deploy AI to analyze incident summaries (short descriptions) and automatically suggest the correct category and subcategory, reducing manual error.Automation: Use the improved classification data to build rules that automatically assign incidents to the correct specialist team.Risk if delayed: Automation and improvement efforts will be based on poor data, leading to low ROI and ineffective solutions.
Executive Decision Support
Key Risks if Delayed
Persistently High Operational Inefficiency
Without intervention, the 40% rework rate and excessive delays will continue to consume significant resources, limiting the team's capacity to handle incoming volume or focus on proactive work.
Urgency: High
Poor and Unpredictable User Experience
Long and highly variable resolution times frustrate end-users and damage the credibility of the IT support function. The lack of a standard process means users receive an inconsistent quality of service.
Urgency: High
Inability to Leverage AI and Automation
The current process chaos makes it impossible to implement effective, scalable automation or AI. The organization will miss opportunities to improve efficiency and will fall behind on modern service management practices.
Urgency: Medium
Readiness & Constraints
AI Readiness
Low
Automation Readiness
Medium
Data Readiness
Medium
The organization is not ready for advanced AI due to the chaotic, non-standard process and weak classification data. However, readiness for targeted automation is Medium, with a clear 'quick win' opportunity in automating incident closure. Data readiness is Medium because while fields are populated, the quality of the classification schema needs significant improvement before it can reliably fuel AI models.
Consultant Note
For the next phase of analysis, focus on the root causes of the 40% rework rate by examining the transitions between 'Work in Progress', 'Pending', and 'Open'. Additionally, quantify the impact of the 164 process variants on resolution time and user satisfaction. The 169-hour 'Completed' to 'Closed' delay should be presented as a primary, actionable finding for immediate remediation.
Evidence Base
metrics, transitions, variants, field usage, sample items, activity model, time intelligence
✓ Process Metrics✓ Transitions✓ Variants✓ Field Usage✓ Status Types✓ Time Intelligence✓ Sample Items