Learning Objectives

After completing this lecture, you will be able to:

• Understand the key concepts of building an ai use case: from idea to business case in a government context
• Participate in structured workshop activities with real-world scenarios
• Connect building an ai use case: from idea to business case to your agency's AI initiatives
• Identify next steps for applying these concepts in your role

Key Topics Covered

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Problem definition, solution design, cost-benefit analysis, risk assessment

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The one-page AI use case template

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Government context for building an ai use case: from idea to business case

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Practical applications and next steps

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 building an ai use case: from idea to business case is essential for responsible, effective government AI adoption.

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TRANSCRIPT: Building an AI Use Case: From Idea to Business Case

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Chapter: 2

What you will learn:

• Identifying potential AI use cases in government
• Developing problem statements and success criteria
• Building compelling business cases for AI projects
• Cost-benefit analysis for AI investments
• Stakeholder engagement and buy-in
• Moving from concept to approval and funding

Many government organizations want to adopt AI but struggle with "what should we do?" There are hundreds of potential applications. Agencies need a systematic way to identify high-value use cases, evaluate feasibility, build business cases, and secure funding.

This lecture teaches a structured approach to use case development: from initial problem identification through business case development and approval.

WHY THIS MATTERS FOR GOVERNMENT

Not every AI application is valuable. Some promise efficiency but deliver minimal savings. Some require investment far exceeding benefits. Some address symptoms rather than root problems.

A systematic use case development process ensures resources are invested in high-value initiatives. It forces clear thinking about problems, solutions, and expected benefits. It builds stakeholder alignment. It supports budget justification.

IDENTIFYING USE CASES

Where to look for opportunities:

• High-volume, repetitive processes: Tasks done thousands of times annually are good AI candidates
• Costly manual processes: Where staff spend significant time
• Error-prone processes: Where mistakes happen regularly and have consequences
• Time-sensitive processes: Where faster response would provide value
• Data-intensive processes: Where pattern recognition could add value
• Inconsistent processes: Where judgment varies and consistency would help

PROBLEM STATEMENT

Before proposing an AI solution, clearly define the problem:

• What is the current state? How is the process done now?
• What are the pain points? What doesn't work well?
• What's the impact? How do these pain points affect the organization and constituents?
• What's the root cause? Why does this problem exist?
• What's the scope? How big is the problem (how many cases, how much cost)?

BUSINESS CASE COMPONENTS

A compelling business case includes:

• Problem statement: Clear description of the problem
• Proposed solution: How AI would address the problem
• Expected benefits: Quantified improvements (faster processing, reduced errors, cost savings, improved outcomes)
• Implementation approach: How the solution would be implemented
• Cost estimate: Total investment required (development, implementation, ongoing)
• Timeline: When benefits would be realized
• Risks and mitigations: What could go wrong and how would you address it
• Governance: How the system would be governed and monitored

COST-BENEFIT ANALYSIS

• One-time costs: Development, implementation, training, infrastructure
• Ongoing costs: Maintenance, monitoring, updates, staff
• Benefits: Quantified (cost savings, efficiency gains) and qualitative (improved decision quality, citizen satisfaction)
• Payback period: How long until cumulative benefits equal investment
• ROI: Net benefit divided by investment

BUILDING STAKEHOLDER BUY-IN

Overview

• Identify stakeholders: Who would be affected by this system?
• Understand their concerns: What do they care about?
• Engage early: Get input before finalizing the plan
• Address concerns: Incorporate feedback
• Communicate benefits: Help stakeholders understand why change is good

PRACTICAL USE CASE 1: Benefits Processing Efficiency

Problem: Benefits applications take 45 days to process. Citizens wait for determinations. Staff handle repetitive data entry and document review.

Proposed solution: AI system to extract application data, perform initial eligibility screening, flag documents for human review.

Business case:

• Expected benefits: Reduce processing time to 15 days, reduce staff time by 30%, improve citizen satisfaction
• Costs: $200K development, $50K annual maintenance
• Timeline: 12 months development, benefits begin after go-live
• Payback period: Staff efficiency savings exceed costs in 18 months
• Risks: Accuracy concerns (mitigated by human review), implementation delays (mitigated by phased approach)

ANTI-PATTERNS AND MISUSE RISKS

Risk 1: Solving Wrong Problem

Developing business case for AI solution to problem that doesn't exist or has better non-AI solutions.

Avoid by: Clearly defining the problem first. Evaluating alternatives.

Risk 2: Unrealistic Benefits

Projecting benefits that won't materialize in practice.

Avoid by: Being conservative in benefit projections. Validating with actual pilots.

Risk 3: Hidden Costs

Underestimating ongoing costs of maintaining and updating systems.

Avoid by: Including realistic maintenance and monitoring costs.

Risk 4: Ignoring Risks

Not acknowledging potential problems or mitigation strategies.

Avoid by: Comprehensive risk assessment. Real contingency planning.

PRACTICE AND REFLECTION PROMPTS

Prompt 1: Use Case Identification

Brainstorm high-value AI use cases in your organization. Identify 3-5 potential opportunities.

Prompt 2: Problem Analysis

Pick one use case. Develop a detailed problem statement: current state, pain points, impact, root cause.

Prompt 3: Solution Design

For your problem, sketch an AI solution. What would the system do? How would it work?

Prompt 4: Business Case

Develop a preliminary business case. Estimate benefits. Estimate costs. Calculate rough ROI.

Prompt 5: Stakeholder Analysis

Identify stakeholders for your proposed system. What are their concerns? How would you address them?

KEY TAKEAWAYS

• Good use cases target high-volume, costly, or error-prone processes.
• Clear problem statements are foundational.
• Business cases must quantify benefits and costs realistically.
• Stakeholder engagement builds support for projects.
• Risk assessment and mitigation planning are essential.
• ROI and payback period help prioritize investments.

End of Transcript

Source: GOVT.CLUB

Visit: https://govt.club/learn/lectures/l2/226-building-an-ai-use-case-from-idea-to-business-case.html

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