Agentic AI & Intelligent Automation (AAI) Service Line Skills

Agentic AI & Intelligent Automation (AAI) Service Line Skills

Service Line Code: AAI Description: Process automation, workflow automation, custom AI solutions, multi-agent systems Version: 1.0 Last Updated: 2026-02-02

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Service Line Overview

Agentic AI & Intelligent Automation helps organizations leverage automation technologies:

• Robotic Process Automation (RPA)
• Intelligent Process Automation (IPA)
• AI-Powered Agents & Assistants
• Hyperautomation Strategies
• Process Mining & Optimization

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Available Skills

#	Skill	Command	Purpose
1	Automation Assessment	/aai-assess	Identify automation opportunities
2	Process Mining Analysis	/aai-process-mine	Analyze processes for automation
3	RPA Feasibility Study	/aai-rpa-feasibility	Assess RPA feasibility and ROI
4	Agent Design Blueprint	/aai-agent-design	Design multi-agent AI systems
5	Automation ROI Calculator	/aai-roi	Calculate automation ROI
6	Bot Inventory Manager	/aai-bot-inventory	Manage RPA bot inventory
7	Hyperautomation Roadmap	/aai-hyperauto	Plan hyperautomation journey
8	Agent Guardrails Designer	/aai-guardrails	Design safety guardrails for AI agents

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Skill 1: Automation Assessment (/aai-assess)

Purpose

Identify and prioritize automation opportunities across the organization.

Automation Opportunity Assessment (AOA) Framework

┌─────────────────────────────────────────────────────────────────────────────┐
│                    AUTOMATION OPPORTUNITY ASSESSMENT                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  1. DISCOVER         2. ASSESS          3. PRIORITIZE      4. ROADMAP      │
│  ──────────          ───────            ────────────       ─────────       │
│  Process inventory   Automation fit     Value vs effort    Implementation  │
│  Pain points         Complexity         Quick wins         Phased approach │
│  Volume/frequency    Technology match   Strategic impact   Resource plan   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Automation Suitability Criteria

Criterion	Weight	Score (1-5)	Description
Rule-Based	20%		Process follows clear, documented rules
Repetitive	15%		High volume, frequent execution
Stable	15%		Process unlikely to change frequently
Digital Inputs	15%		Structured, digital data inputs
Low Exception Rate	10%		Few exceptions requiring human judgment
High Volume	10%		Significant transaction volume
Time-Critical	10%		Speed improvement adds value
Error-Prone	5%		Manual errors are costly

Automation Technology Selector

Process Characteristics	Recommended Technology
Simple, rule-based, structured data	RPA (UiPath, Automation Anywhere, Blue Prism)
Document processing, unstructured data	IDP (Intelligent Document Processing)
Decision-making, complex rules	BPM + Decision Engine
Natural language, conversations	Conversational AI (Chatbots, Voice)
Predictive, pattern recognition	ML/AI Models
End-to-end process orchestration	Hyperautomation Platform
Autonomous task execution	AI Agents (Agentic AI)

Automation Complexity Matrix

                    HIGH COMPLEXITY
                          │
         Intelligent      │    AI/ML
         Automation       │    Solutions
         (IPA)            │    (Custom)
                          │
    ──────────────────────┼──────────────────────
                          │
         Basic RPA        │    Process
         (Attended)       │    Optimization
                          │    (No automation)
                          │
                    LOW COMPLEXITY
         LOW VOLUME ──────┴────── HIGH VOLUME

Output Format

═══════════════════════════════════════════════════════════════════════════════
AUTOMATION OPPORTUNITY ASSESSMENT
Organization: [Name]
Assessment Date: [Date]
Processes Assessed: [N]
═══════════════════════════════════════════════════════════════════════════════

EXECUTIVE SUMMARY:
─────────────────────────────────────────────────────────────────────────────
Total Processes Assessed:     [N]
High Automation Potential:    [N] ([X]%)
Medium Automation Potential:  [N] ([X]%)
Low Automation Potential:     [N] ([X]%)
Not Suitable:                 [N] ([X]%)

Estimated Annual Savings:     $[X]M
Estimated FTE Savings:        [X] FTEs
Estimated Implementation:     $[X]K - $[X]K

AUTOMATION OPPORTUNITIES (Prioritized):
─────────────────────────────────────────────────────────────────────────────
│ Rank │ Process               │ Score │ Technology │ Savings  │ Effort │ Priority │
├──────┼───────────────────────┼───────┼────────────┼──────────┼────────┼──────────┤
│ 1    │ Invoice Processing    │ 92%   │ RPA + IDP  │ $450K/yr │ Medium │ Quick Win│
│ 2    │ Employee Onboarding   │ 88%   │ RPA        │ $280K/yr │ Low    │ Quick Win│
│ 3    │ Report Generation     │ 85%   │ RPA        │ $180K/yr │ Low    │ Quick Win│
│ 4    │ Customer Inquiries    │ 78%   │ Chatbot    │ $320K/yr │ High   │ Strategic│
│ 5    │ Fraud Detection       │ 75%   │ ML Model   │ $600K/yr │ High   │ Strategic│
─────────────────────────────────────────────────────────────────────────────

QUICK WINS (High Value, Low Effort):
─────────────────────────────────────────────────────────────────────────────
1. [Process 1] - Est. 4 weeks, $50K investment, $280K annual savings
2. [Process 2] - Est. 6 weeks, $75K investment, $180K annual savings
3. [Process 3] - Est. 3 weeks, $30K investment, $120K annual savings

STRATEGIC INITIATIVES (High Value, High Effort):
─────────────────────────────────────────────────────────────────────────────
1. [Process 1] - Est. 16 weeks, $250K investment, $600K annual savings
2. [Process 2] - Est. 12 weeks, $180K investment, $450K annual savings

═══════════════════════════════════════════════════════════════════════════════

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Skill 2: Process Mining Analysis (/aai-process-mine)

Purpose

Analyze event logs to discover actual process flows and identify optimization opportunities.

Process Mining Capabilities

Capability	Description	Output
Discovery	Automatically discover process models from logs	Process map
Conformance	Compare actual vs designed process	Deviation report
Enhancement	Identify bottlenecks and improvements	Optimization recommendations

Process Mining Metrics

Metric	Formula	Target
Throughput Time	End timestamp - Start timestamp	Minimize
Cycle Efficiency	Value-add time / Total time	>80%
Rework Rate	Rework loops / Total cases	<5%
Automation Rate	Automated steps / Total steps	Maximize
First-Time-Right	Cases without rework / Total cases	>95%

Output Format

═══════════════════════════════════════════════════════════════════════════════
PROCESS MINING ANALYSIS
Process: [Process Name]
Analysis Period: [Start Date] to [End Date]
Cases Analyzed: [N]
═══════════════════════════════════════════════════════════════════════════════

PROCESS DISCOVERY:
─────────────────────────────────────────────────────────────────────────────
[ASCII Process Flow Diagram]

Start → Task A → Task B → Decision → Task C → End
                    ↓              ↗
                Task D → Task E →─┘
                    ↓
                Rework Loop (15%)

PROCESS METRICS:
─────────────────────────────────────────────────────────────────────────────
│ Metric              │ Current  │ Benchmark │ Gap    │ Status │
├─────────────────────┼──────────┼───────────┼────────┼────────┤
│ Avg Throughput Time │ 4.5 days │ 2.0 days  │ -2.5d  │ 🔴     │
│ Cycle Efficiency    │ 45%      │ 80%       │ -35%   │ 🔴     │
│ Rework Rate         │ 15%      │ 5%        │ +10%   │ 🟠     │
│ Automation Rate     │ 20%      │ 70%       │ -50%   │ 🔴     │
│ First-Time-Right    │ 72%      │ 95%       │ -23%   │ 🟠     │
─────────────────────────────────────────────────────────────────────────────

BOTTLENECKS IDENTIFIED:
─────────────────────────────────────────────────────────────────────────────
1. Task B: Avg wait time 1.8 days (resource constraint)
2. Decision Point: 25% cases require escalation
3. Task D-E Loop: 15% rework rate

AUTOMATION OPPORTUNITIES:
─────────────────────────────────────────────────────────────────────────────
• Task A: 95% rule-based, RPA candidate
• Task C: Document processing, IDP candidate
• Decision: 60% can be automated with business rules

═══════════════════════════════════════════════════════════════════════════════

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Skill 3: RPA Feasibility Study (/aai-rpa-feasibility)

Purpose

Assess technical and business feasibility of RPA implementation.

Feasibility Assessment Dimensions

Dimension	Weight	Criteria
Technical Fit	30%	System compatibility, data structure, stability
Business Value	30%	Cost savings, time savings, quality improvement
Implementation Risk	20%	Complexity, change management, dependencies
Strategic Alignment	20%	Digital strategy, scalability, future-proofing

RPA Business Case Template

═══════════════════════════════════════════════════════════════════════════════
RPA FEASIBILITY STUDY
Process: [Process Name]
Date: [Date]
═══════════════════════════════════════════════════════════════════════════════

CURRENT STATE:
─────────────────────────────────────────────────────────────────────────────
• Volume: [X] transactions/month
• FTEs Involved: [X]
• Avg Processing Time: [X] minutes/transaction
• Error Rate: [X]%
• Annual Cost: $[X]

PROPOSED RPA SOLUTION:
─────────────────────────────────────────────────────────────────────────────
• Bot Type: Attended / Unattended
• Platform: [UiPath / Automation Anywhere / Blue Prism]
• Bots Required: [X]
• Automation Coverage: [X]%

FINANCIAL ANALYSIS:
─────────────────────────────────────────────────────────────────────────────
COSTS:
│ Item                    │ Year 0   │ Year 1   │ Year 2   │ Year 3   │
├─────────────────────────┼──────────┼──────────┼──────────┼──────────┤
│ Software Licenses       │ $50,000  │ $30,000  │ $30,000  │ $30,000  │
│ Development             │ $80,000  │ $20,000  │ $10,000  │ $10,000  │
│ Infrastructure          │ $15,000  │ $5,000   │ $5,000   │ $5,000   │
│ Support & Maintenance   │ -        │ $15,000  │ $15,000  │ $15,000  │
├─────────────────────────┼──────────┼──────────┼──────────┼──────────┤
│ Total Costs             │ $145,000 │ $70,000  │ $60,000  │ $60,000  │

BENEFITS:
│ Item                    │ Year 0   │ Year 1   │ Year 2   │ Year 3   │
├─────────────────────────┼──────────┼──────────┼──────────┼──────────┤
│ Labor Savings           │ -        │ $180,000 │ $200,000 │ $220,000 │
│ Error Reduction         │ -        │ $25,000  │ $30,000  │ $35,000  │
│ Faster Processing       │ -        │ $15,000  │ $20,000  │ $25,000  │
├─────────────────────────┼──────────┼──────────┼──────────┼──────────┤
│ Total Benefits          │ -        │ $220,000 │ $250,000 │ $280,000 │

NET BENEFIT:              ($145,000) │ $150,000 │ $190,000 │ $220,000 │
CUMULATIVE:               ($145,000) │ $5,000   │ $195,000 │ $415,000 │

─────────────────────────────────────────────────────────────────────────────
ROI (3 years): 186%
Payback Period: 11 months
NPV (10%): $312,000

RECOMMENDATION: ✅ PROCEED

═══════════════════════════════════════════════════════════════════════════════

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Skill 4: Agent Design Blueprint (/aai-agent-design)

Purpose

Design AI agents and multi-agent systems for autonomous task execution.

Agent Architecture Components

┌─────────────────────────────────────────────────────────────────────────────┐
│                         AI AGENT ARCHITECTURE                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐                     │
│  │   PERCEIVE  │ →  │    REASON   │ →  │     ACT     │                     │
│  │   (Input)   │    │   (Decide)  │    │   (Output)  │                     │
│  └─────────────┘    └─────────────┘    └─────────────┘                     │
│        ↑                   │                   │                            │
│        │                   ↓                   │                            │
│        │            ┌─────────────┐            │                            │
│        │            │   MEMORY    │            │                            │
│        │            │  (Context)  │            │                            │
│        │            └─────────────┘            │                            │
│        │                   ↑                   │                            │
│        └───────────────────┴───────────────────┘                            │
│                        FEEDBACK                                             │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Agent Types

Type	Description	Use Cases
Reactive	Responds to current input only	Simple automation, alerts
Deliberative	Plans ahead, reasons about goals	Task planning, scheduling
Learning	Improves from experience	Personalization, optimization
Collaborative	Works with other agents	Complex workflows, orchestration

Multi-Agent System Design

┌─────────────────────────────────────────────────────────────────────────────┐
│                      MULTI-AGENT SYSTEM                                     │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│                    ┌─────────────────┐                                      │
│                    │  ORCHESTRATOR   │                                      │
│                    │     AGENT       │                                      │
│                    └────────┬────────┘                                      │
│                             │                                               │
│         ┌───────────────────┼───────────────────┐                          │
│         │                   │                   │                          │
│  ┌──────┴──────┐    ┌──────┴──────┐    ┌──────┴──────┐                    │
│  │  RESEARCH   │    │   ANALYSIS  │    │   ACTION    │                    │
│  │   AGENT     │    │    AGENT    │    │   AGENT     │                    │
│  └─────────────┘    └─────────────┘    └─────────────┘                    │
│         │                   │                   │                          │
│         └───────────────────┴───────────────────┘                          │
│                             │                                               │
│                    ┌────────┴────────┐                                      │
│                    │   SHARED MEMORY │                                      │
│                    │   & KNOWLEDGE   │                                      │
│                    └─────────────────┘                                      │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Agent Design Template

═══════════════════════════════════════════════════════════════════════════════
AI AGENT DESIGN BLUEPRINT
Agent Name: [Name]
Purpose: [Description]
Date: [Date]
═══════════════════════════════════════════════════════════════════════════════

AGENT PROFILE:
─────────────────────────────────────────────────────────────────────────────
Name:           [Agent Name]
Type:           [Reactive / Deliberative / Learning / Collaborative]
Role:           [Primary function]
Autonomy Level: [1-5 scale]

CAPABILITIES:
─────────────────────────────────────────────────────────────────────────────
INPUTS (Perceive):
• [Input 1]: [Description, format, source]
• [Input 2]: [Description, format, source]

REASONING (Decide):
• [Decision 1]: [Logic, conditions, rules]
• [Decision 2]: [Logic, conditions, rules]

OUTPUTS (Act):
• [Action 1]: [Description, target, format]
• [Action 2]: [Description, target, format]

TOOLS AVAILABLE:
─────────────────────────────────────────────────────────────────────────────
• [Tool 1]: [Description, when to use]
• [Tool 2]: [Description, when to use]
• [Tool 3]: [Description, when to use]

GUARDRAILS:
─────────────────────────────────────────────────────────────────────────────
• [Constraint 1]: [What the agent cannot do]
• [Constraint 2]: [Required approvals]
• [Constraint 3]: [Escalation triggers]

═══════════════════════════════════════════════════════════════════════════════

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Skill 5: Automation ROI Calculator (/aai-roi)

Purpose

Calculate comprehensive ROI for automation initiatives.

ROI Components

Cost Components:

Category	Items
Software	Licenses, subscriptions
Development	Design, build, test, deploy
Infrastructure	Servers, cloud, network
Operations	Support, maintenance, monitoring
Change Management	Training, communication

Benefit Components:

Category	Items
Labor Savings	FTE reduction, redeployment
Time Savings	Faster processing, reduced wait
Quality Improvement	Error reduction, consistency
Compliance	Audit trail, accuracy
Revenue	Faster time-to-market, capacity

ROI Calculation

ROI = (Total Benefits - Total Costs) / Total Costs × 100%

Payback Period = Initial Investment / Annual Net Benefits

NPV = Σ (Net Benefit_t / (1 + r)^t) - Initial Investment

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Skill 6: Bot Inventory Manager (/aai-bot-inventory)

Purpose

Manage and monitor RPA bot portfolio.

Bot Inventory Template

{
  "bot_id": "BOT-001",
  "name": "Invoice Processing Bot",
  "status": "production",
  "type": "unattended",
  "platform": "UiPath",
  "process": "Accounts Payable",
  "owner": "Finance Team",
  "deployed_date": "2025-06-15",
  "last_run": "2026-02-01T23:00:00Z",
  "metrics": {
    "transactions_processed": 15420,
    "success_rate": 98.5,
    "avg_processing_time_sec": 45,
    "errors_last_30_days": 23
  },
  "schedule": "Daily 23:00-06:00",
  "dependencies": ["SAP", "Email Server", "SharePoint"],
  "documentation": "/docs/bots/invoice-bot.md"
}

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Skill 7: Hyperautomation Roadmap (/aai-hyperauto)

Purpose

Plan organization-wide hyperautomation journey.

Hyperautomation Maturity Model

Level	Name	Characteristics
1	Task Automation	Individual task automation, RPA pilots
2	Process Automation	End-to-end process automation, scaling RPA
3	Intelligent Automation	AI/ML integration, IDP, decision automation
4	Autonomous Operations	Self-optimizing processes, minimal human intervention
5	Enterprise Intelligence	Organization-wide automation fabric, continuous learning

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Skill 8: Agent Guardrails Designer (/aai-guardrails)

Purpose

Design safety guardrails and constraints for AI agents.

Guardrail Categories

Category	Description	Examples
Capability Limits	What agent can/cannot do	No financial transactions >$10K
Knowledge Boundaries	Information access limits	No PII access without approval
Action Constraints	Restricted actions	No external communications
Escalation Rules	When to involve humans	Uncertainty >20%, edge cases
Audit Requirements	Logging and traceability	All decisions logged

Guardrail Template

═══════════════════════════════════════════════════════════════════════════════
AGENT GUARDRAILS SPECIFICATION
Agent: [Agent Name]
Version: [X.X]
Date: [Date]
═══════════════════════════════════════════════════════════════════════════════

CAPABILITY GUARDRAILS:
─────────────────────────────────────────────────────────────────────────────
ALLOWED:
✅ Read customer data for authorized purposes
✅ Generate reports and summaries
✅ Schedule meetings within business hours
✅ Send notifications to internal users

PROHIBITED:
❌ Delete any data
❌ Access financial systems directly
❌ Communicate with external parties
❌ Make decisions affecting >$10,000

ESCALATION TRIGGERS:
─────────────────────────────────────────────────────────────────────────────
• Confidence score <80%: Escalate to human review
• Request involves PII: Require manager approval
• Error rate >5%: Pause and alert operations
• Unknown request type: Escalate to supervisor agent

HUMAN-IN-THE-LOOP REQUIREMENTS:
─────────────────────────────────────────────────────────────────────────────
• All final decisions affecting customers require human approval
• Weekly review of agent actions by process owner
• Quarterly audit of guardrail effectiveness

═══════════════════════════════════════════════════════════════════════════════

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Key Frameworks Referenced

Framework	Description
Automation Anywhere CoE	Center of Excellence model
UiPath Operating Model	RPA governance framework
Gartner Hyperautomation	Hyperautomation framework
IEEE P2863	Organizational AI governance

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Integration with Other Skills

Skill	Integration
/cps-budget	Fee calculation for AAI engagements
/dig-maturity	Link automation to digital maturity
/aig-guardrails	AI governance for agents
/doc-gen	Generate automation documentation

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Service Line: AAI (Agentic AI & Intelligent Automation) Version: 1.0 Last Updated: 2026-02-02