Learning Objectives

After completing this lecture, you will be able to:

• Understand the key concepts of workflow analysis: finding ai opportunities in a government context

• Participate in structured workshop activities with real-world scenarios

• Connect workflow analysis: finding ai opportunities to your agency's AI initiatives

• Identify next steps for applying these concepts in your role

Key Topics Covered

• Mapping your processes
• Identifying repetitive, data-heavy, time-consuming tasks
• The AI opportunity matrix

Why This Matters for Government

Government agencies face unique challenges when it comes to AI adoption. This lecture addresses these challenges head-on by providing analysts, project leads, team supervisors with the knowledge and frameworks needed to navigate AI in the public sector responsibly and effectively.

As part of the L2 (AI Practitioner) curriculum, this lecture builds on the foundational principle that every AI system in government ultimately serves citizens. Whether you are working with AI tools daily or setting strategy for your agency, understanding workflow analysis: finding ai opportunities is essential for responsible, effective government AI adoption.

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TRANSCRIPT: Workflow Analysis: Finding AI Opportunities

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What you will learn: How to map workflows systematically; identifying characteristics that make tasks suitable for AI (repetitive, data-heavy, high volume); the AI opportunity matrix; how to evaluate whether an AI solution makes sense; prioritizing opportunities based on impact and feasibility.

You now understand how AI works. But where in your agency should you use it? This lecture teaches you to look at your workflows systematically, identify where AI could help, and evaluate whether those opportunities are worth pursuing.

By the end, you'll be able to walk through your agency, identify AI opportunities, and build a prioritized list of use cases to pursue.

WHY THIS MATTERS FOR GOVERNMENT

Not every task is suitable for AI. Some tasks are better suited for process improvement, automation, or human work. The challenge is identifying which tasks would genuinely benefit from AI and which would waste resources.

A systematic approach to workflow analysis prevents you from pursuing bad ideas and helps you identify high-impact opportunities.

WORKFLOW MAPPING

Before you can identify AI opportunities, you must understand your current workflows. Map the workflow:

• What are the steps involved?

• Who performs each step?

• How long does each step take?

• What information is used at each step?

• What are the failure points?

• What would happen if a step was skipped or done wrong?

Document this clearly. You might create flowcharts, timelines, or written descriptions.

CHARACTERISTICS THAT MAKE TASKS AI-SUITABLE

Tasks with these characteristics are good candidates for AI:

Characteristic 1: Repetitive. The same type of task is done many times. AI can learn patterns and automate repetition.

Characteristic 2: Data-heavy. The task involves analyzing large amounts of data. AI is good at this.

Characteristic 3: Rule-based but with exceptions. The task follows general rules but has edge cases. AI can handle the general cases and flag exceptions.

Characteristic 4: High volume. There's significant volume, so even small efficiency gains multiply.

Characteristic 5: Well-defined success criteria. You can clearly define what a correct answer looks like.

Characteristic 6: Exists in training data. For AI to learn, patterns must exist in historical data or similar examples.

Counter-characteristics (warning signs):

Counter-sign 1: Requires real-time adaptation. The task requires understanding constantly changing context. AI might struggle.

Counter-sign 2: Requires nuanced human judgment. The task involves understanding subtle social, ethical, or contextual factors. AI might oversimplify.

Counter-sign 3: Involves physically interacting with the world. AI can't physically manipulate things.

Counter-sign 4: Requires understanding rare events. If you only have 10 examples of something and need to detect it, AI will struggle.

Counter-sign 5: Would cause serious harm if wrong. If an error causes significant damage, you need very high accuracy, which might not be achievable.

THE AI OPPORTUNITY MATRIX

Plot opportunities on a matrix:

• X-axis: Impact (low to high). How much would automating this task improve operations?

• Y-axis: Feasibility (low to high). How feasible is it to build an AI system for this task?

Prioritize high-impact, high-feasibility opportunities. Don't pursue low-feasibility opportunities even if they're high-impact. Don't pursue low-impact opportunities.

EVALUATING SPECIFIC OPPORTUNITIES

For a candidate opportunity, ask:

• What is the task? Be specific.

• What is the volume? How many times is this task done weekly/monthly?

• What's the current cost (staff time)? How many hours per week do staff spend on this?

• What's the potential AI cost savings? If AI automates 50% of the task, what time is saved?

• What's the implementation cost? How much would it cost to build, train, and deploy the AI system?

• What's the payback period? If the system costs $100k and saves $50k annually, payback is 2 years.

• What's the risk if the system fails? If the AI makes a mistake, what happens?

• What's the human oversight cost? Does a human need to review every output?

PRACTICAL USE CASES

Use Case 1: Workflow Analysis for Permit Applications

A city planning department receives 500 permit applications per month. The process:

• Intake: Check application completeness (2 hours per app)

• Initial review: Determine if application meets basic requirements (1.5 hours per app)

• Specialist review: Route to appropriate specialist based on permit type (0.5 hours per app)

• Detailed review: Specialists review applications in detail (varies widely, 10-40 hours per app)

• Decision: City staff make final decision (1 hour per app)

Analysis:

—Steps 1-3 are repetitive, rule-based, good candidates for AI

—Step 4 requires significant human judgment; less suitable

—Step 5 requires human decision; not suitable

Opportunity 1: AI for completeness checking (Step 1)

—Volume: 500 apps/month = 1,000 hours saved annually if successful

—Feasibility: High (clear rules about what makes an app complete)

—Impact: High

—Recommended: Pursue

Opportunity 2: AI for basic requirement checking (Step 2)

—Volume: 750 hours saved annually

—Feasibility: High

—Impact: Medium-High

—Recommended: Pursue after opportunity 1

Opportunity 3: AI-assisted specialist assignment (Step 3)

—Volume: 250 hours saved annually

—Feasibility: Medium (requires learning assignment rules)

—Impact: Medium

—Recommended: Pursue only if opportunities 1-2 succeed first

Use Case 2: Workflow Analysis for Compliance Monitoring

An environmental agency monitors whether facilities comply with regulations. Currently:

• Data comes from self-reports and inspections (manual entry)

• Data is analyzed manually to identify non-compliance (weekly, staff review data)

• Non-compliance notices are sent (staff write custom notices)

Analysis:

—Step 1: Could AI improve data entry accuracy? Possibly, but main value is in downstream steps

—Step 2: AI could automatically flag non-compliance patterns, saving analysis time

—Step 3: AI could draft notices (humans review before sending)

Opportunity: AI for compliance monitoring

—Current process: 20 hours/week of staff analysis time

—Potential savings: 80% reduction if AI can flag 80% of non-compliance

—Implementation cost: Low-medium (API to data sources, analysis rules)

—Payback: <6 months

—Recommended: High priority

PRACTICE AND REFLECTION PROMPTS

Prompt 1: Workflow Mapping

Pick one workflow in your agency. Map it: what are the steps, who does each, how long does each take? Document this clearly.

Prompt 2: Characteristic Assessment

For that workflow, which tasks have AI-suitable characteristics? Which have counter-characteristics?

Prompt 3: Opportunity Matrix

Create a 2x2 matrix (impact vs. feasibility) and plot the tasks from your workflow analysis on it. Which are high-priority candidates?

Prompt 4: Detailed Evaluation

For your highest-priority candidate opportunity, answer the evaluation questions: volume, cost savings, implementation cost, payback period, risk if it fails.

Prompt 5: Comparison

Compare two opportunities from your agency. Which would you pursue first? Why?

KEY TAKEAWAYS

• Systematic workflow analysis identifies where AI could help.

• Repetitive, data-heavy, rule-based, high-volume tasks with well-defined success criteria are good candidates.

• The AI opportunity matrix (impact vs. feasibility) helps prioritize opportunities.

• High-impact, high-feasibility opportunities should be pursued first.

• Evaluation should consider volume, cost savings, implementation cost, payback period, and risk.

• Not every workflow can be improved with AI. Some are better suited to other improvements.

You've completed Lecture 2.2.1. You now know how to analyze workflows and identify AI opportunities. Next lecture (2.2.2: Prompt Engineering Mastery—Structured Prompts), we shift focus. You'll learn how to use AI effectively through careful prompt engineering.

Government AI CLUB Certification Program

Level 2: AI Ready | Workflow Analysis: Finding AI Opportunities | Lecture 2.2.1

A GOVT.CLUB initiative.

<- 2.1.8 Emerging AI Capabilities: Agents, Reasoning, and Tools 2.2.2 Building an AI Use Case: From Idea to Business Case ->

Start Your CLUB Certification

This lecture is part of L2: AI Practitioner—40 hours of comprehensive government AI training.

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Related Lectures

L2 2.2.2—Building an AI Use Case: From Idea to Business Case 60 min - Workshop

L2 2.2.3—Prompt Engineering Mastery: Structured Prompts 60 min - Hands-On Lab

L2 2.2.4—Prompt Engineering Mastery: Chain-of-Thought and Few-Shot 60 min - Hands-On Lab

Frequently Asked Questions

What will I learn in Workflow Analysis: Finding AI Opportunities?

In this 60 min workshop lecture, you will Mapping your processes. Identifying repetitive, data-heavy, time-consuming tasks. The AI opportunity matrix

What level is Workflow Analysis: Finding AI Opportunities?

This is a Level 2 (AI Practitioner) lecture, part of Chapter 2.2 \u2014 Applied AI for Government. It is designed for analysts, project leads, team supervisors.

How long is lecture 2.2.1?

Lecture 2.2.1 (Workflow Analysis: Finding AI Opportunities) takes 60 min. It is delivered as a workshop format.

Do I need prerequisites for Workflow Analysis: Finding AI Opportunities?

This lecture is part of L2 (AI Practitioner). Prerequisites: L1 Certification.

What is the CLUB Certification?

CLUB (Community Leading Unified Benchmarks) is a maturity-based AI certification for government professionals with 5 levels (L1-L5), 215 lectures, and 25 chapters aligned with NIST AI RMF, OMB, and GAO frameworks.