GenAI Readiness & Use Case Prioritization

Why Readiness Matters

Many AI initiatives stall before production. In May 2024, Gartner reported that only ~48% of AI projects make it into production on average. The primary culprit? Organizations that jump to solutions before assessing whether they're ready to execute.

According to Gartner's 2025 research, only 7% of CFOs report seeing high ROI from AI in finance—despite significant investment. The gap isn't the technology. It's readiness: data, talent, infrastructure, and organizational willingness to change.

The Readiness Reality

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Workplace Scenario: The GenAI Task Force

You are on a "GenAI Task Force" assessing project ideas submitted by different departments. The team has received proposals including:

1. Employee FAQ Assistant – A GenAI assistant to answer common HR, IT, and policy questions using internal documentation
2. Customer Email Drafting – GenAI tool to help support agents draft responses to customer inquiries
3. Meeting Summarization – Automated summaries and action items from meeting transcripts
4. Sales Call Prep – GenAI assistant that summarizes account history and suggests talking points before customer calls
5. Contract Analysis – Extract key terms and flag risks in vendor contracts

Constraints:

• Budget for only 2-3 initiatives this year
• No dedicated AI/ML team (business users will be primary operators)
• Some departments have well-organized documentation; others don't
• Leadership wants visible wins within 6 months

Your task: Prioritize these proposals using a structured framework.

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The GenAI Readiness Checklist

Before prioritizing use cases, honestly assess your organizational readiness. Complete this interactive checklist:

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Use Case Prioritization Framework

The 2x2 Scoring Model

Evaluate each use case on two dimensions:

Value Score (1-10):

• Strategic alignment with company priorities
• Potential financial impact (cost savings or revenue)
• Customer or employee experience improvement
• Risk mitigation value

Feasibility Score (1-10):

• Data availability and quality
• Technical complexity
• Change management required
• Regulatory or compliance considerations

Prioritization Matrix

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Mini-Case: Lessons from the Field

Case A: The Ambitious Failure

A professional services firm launched a GenAI "knowledge assistant" to help consultants find relevant past work. Results:

• Knowledge base was outdated—60% of documents hadn't been reviewed in 3+ years
• No clear policies on data handling—confidential client info was being pasted into prompts
• The assistant hallucinated project names and client details
• Project paused after 3 months due to compliance concerns

What went wrong: No readiness assessment. The knowledge base wasn't ready, and governance wasn't in place.

Case B: The Staged Success

A software company started with a focused pilot:

• Chose internal IT helpdesk as the first use case (low risk, clear documentation)
• Spent 2 weeks curating and validating the IT knowledge base
• Built a proof of concept in 30 days
• Demonstrated 40% reduction in tickets reaching human agents
• Used success to secure funding for customer-facing expansion

What went right: Started with a well-scoped pilot where they had readiness—clean documentation, low-risk audience. Built credibility before scaling.

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Interactive: Prioritize the GenAI Task Force Proposals

Given the constraints (limited budget, no dedicated AI team, 6-month timeline), drag these proposals into priority order:

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Completion: Your Use Case Prioritization

Complete a Use Case Prioritization Template for your context:

Step 1: List Use Cases

Identify at least one potential AI use case from your work area.

Step 2: Score Each Use Case

Classification (based on your scores):

• Quick Win — High Value + High Feasibility (do first)
• Strategic Bet — High Value + Low Feasibility (invest carefully)
• Experiment — Low Value + High Feasibility (learn cheaply)
• Avoid — Low Value + Low Feasibility (deprioritize)

Provide brief justification for each score.

Step 3: Identify Prerequisites

For your top-priority use case, identify one prerequisite that must be addressed first:

• Example: "We need to clean up and consolidate our HR policy documentation before launching the FAQ assistant."
• Example: "We need Legal sign-off on acceptable use policies before any customer-facing GenAI deployment."
• Example: "We need to establish data handling guidelines—what can and can't be pasted into prompts."

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Key Takeaways

• Readiness assessment prevents wasted investment—many GenAI pilots fail to scale
• GenAI readiness includes: approved tools, clean knowledge bases, governance policies, and user skills
• Use a structured framework to evaluate value and feasibility
• Quick wins (internal, low-risk) build credibility; customer-facing use cases require more preparation
• Always identify prerequisites—especially data handling policies and knowledge base cleanup
• BCG cautions organizations not to treat GenAI as only 'low-hanging fruit'—value requires consistent, scaled deployment

Sources

• Gartner Survey Finds Generative AI Is Now the Most Frequently Deployed AI Solution in Organizations, May 2024 (Gartner)
• 5 Steps CFOs Can Take to Maximize ROI From AI Initiatives, Oct 2025 (Gartner)
• GenAI Can Revolutionize ERP Transformations, Apr 2025 (BCG)

Next Steps

In the next module, we'll dive deeper into Data Literacy for GenAI—understanding knowledge base quality, data privacy in prompts, and how to verify AI outputs. This expands on the readiness concepts introduced here.