Applicable Use Cases
22
Data Ingestion
Enter account details, volume metrics, FTE distribution, workforce pyramid and technology stack before the workshop
Section A — Account & Scope
Section B — Monthly Volume Metrics
Section C — FTE Distribution
FTE Distribution Rule (AMS)
- Total FTEs entered in “AMS Overall” is evenly distributed across all AMS phases.
- Default allocation is 100% ATCI FTEs.
- Users may modify values as needed.
Validation (Mandatory):
Total FTEs = ATCI FTEs + Onshore FTEs for every row.
| Process Area | Total FTEs | ATCI FTEs | Onshore FTEs | Offshore % | Process Area Weightage |
|---|---|---|---|---|---|
| AMS — Overall | 0 | 0 | |||
| AMS — Incident Management | 0 | 0 | |||
| AMS — Service Request | 0% | 0 | |||
| AMS — Non-Ticketing | 0% | 0.0 | |||
| AMS — Change/Defects Fixes | 0% | 0.0 | |||
| AMS — Problem & Defect L3 | 0% | 0.0 | |||
| AD — Overall | 0% | 0.0 | |||
| IMS — Core Overall | 0% | 0.0 | |||
| Service Management | 0% | 0.0 | |||
| Grand Total | 0% | 0.0 |
Section D — Workforce Pyramid & LCR ($/hr)
| Career Level | Code | ATCI FTE Count | ATCI Blended Rate ($/hr) | Onshore FTE Count | Onshore Blended Rate ($/hr) |
|---|---|---|---|---|---|
| Managing Director | CL4 | ||||
| Assoc. Director / Principal | CL5 | ||||
| Senior Manager | CL6 | ||||
| Manager | CL7 | ||||
| Associate Manager (Sr. Consultant) | CL8 | ||||
| Team Lead (Consultant) | CL9 | ||||
| Sr. Analyst | CL10 | ||||
| Analyst | CL11 | ||||
| Associate (ASE) | CL12 | ||||
| TOTAL / Blended | — |
Section E — Technology Stack
Core infrastructure, platform and AI/ML technologies in use or planned. Capture adoption level across the team.
| Technology | Category | Status | % In Use | Version / Notes | |
|---|---|---|---|---|---|
| MS Copilot / Azure OpenAI | AI/ML |
%
|
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| Python / LangChain / LLM | AI/ML |
%
|
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| Dynatrace / Splunk / Datadog | Observability |
%
|
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| Azure / AWS / GCP Cloud | Cloud |
%
|
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| SAP / ERP | Cloud |
%
|
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| Salesforce CRM | CRM |
%
|
Section F — Tools & Adoption
Delivery and productivity tools used by the team. Capture current availability and team-level adoption percentage.
| Tool | Category | Status | % Adoption | Notes | |
|---|---|---|---|---|---|
| ServiceNow (ITSM) | ITSM |
%
|
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| GitHub / Azure DevOps | DevOps |
%
|
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| GenWizard / Quasar++ | Automation |
%
|
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| Jira / Confluence | Collab |
%
|
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| MS Teams / Slack | Comms |
%
|
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| Power BI / Tableau | Analytics |
%
|
Assessment Questionnaire
Rate AI readiness across AMS, AD & IMS service lines. All three sections are optional — complete only the service lines in scope for this engagement.
AIRA Combined Score
AMS + AD + IMS · Up to 30 Questions
—
Combined
—
AMS
—
AD
—
IMS
—
Maturity Band
Application Maintenance Services (AMS) — 10 Questions
Incident · SR · Problem · Change · Monitoring · Knowledge · Security · Demand · Performance · Governance
Answered: 0/10 | Avg: —
Additional Inputs
Bottom-Up Approach — Upload or manually enter operational ticket data to identify top automation candidates via Pareto analysis.
Please note: Delivery/ Project to obtain requisite approval before uploading delivery data
Please note: Delivery/ Project to obtain requisite approval before uploading delivery data
Upload Operational Data Files
Upload INC Data
.xlsx · Category, Sub-Category, Volume
Upload SR Data
.xlsx · Category, Sub-Category, Volume
Upload CR Data
.xlsx · Change Type, Category, Volume
Upload Problem Ticket Data
.xlsx · Problem Category, Root Cause, Volume
Upload NT Data
.xlsx · Activity, Type, Monthly Hours
Pareto Analysis
Top 80% automation candidates from INC, SR, CR, Problem and Non-Ticketing data. Review volumes, then proceed to Use Case -> Solution Mapping.
Total Incidents
0
Top 80% Rows
0
Potential Addressable Volume
0
| # | Category | Sub-Category | Volume | % Share | Cumulative % | Pareto Flag |
|---|
Total SRs
0
Top 80% Rows
0
Potential Addressable Volume
0
| # | Category | Sub-Category | Volume | % Share | Cumulative % | Pareto Flag |
|---|
Total CRs
0
Top 80% Rows
0
Potential Addressable Volume
0
| # | Change Type | Category | Volume | % Share | Cumulative % | Pareto Flag |
|---|
Total Problem Tickets
0
Top 80% Rows
0
Potential Addressable Volume
0
| # | Problem Category | Root Cause Area | Volume | % Share | Cumulative % | Pareto Flag |
|---|
Total NT Activities
0
Monthly Effort (hrs)
0
| # | Type | Activity | Description | Monthly Hrs | Frequency | Pareto Flag |
|---|
Use Case -> Solution Mapping
AI-driven automation opportunities from AIRA assessment (Top-Down) and Pareto analysis (Bottom-Up) across all service lines in scope.
Total Use Cases
—
AMS + AD + IMS + Other
Top-Down
—
From maturity gap analysis
Bottom-Up (Pareto)
—
From INC / SR / CR / PT / NT data
Other
—
Unclassified / cross-service
Productivity & Profitability
| Process Area | PA Wt. Std | PA Wt. Client | Current FTE | Tasks | Task Wt. Std | Task Wt. Client | Use Cases | Source | Pattern | Agent / Solution | Coverage (UC Wt.) | Volume / Frequency per Month | Effort per Ticket / Frequency (hrs) | Applicability % | Efficiency Gain % | Potential Effort Savings (hrs/month) | Agreed Efficiency Gain % (Workshop) | SME Comments | Agreed Effort Savings (hrs/month) | Potential Productivity % |
|---|
Executive Dashboard
Consolidated view of automation opportunities, productivity impact, and strategic enablers for