Why you need this
The hardest part of AI adoption isn't learning the technology—it's figuring out where to apply it. Teams waste months exploring AI without clear use cases, or they copy competitors without understanding if those applications fit their needs.
The problem: "We should use AI" is not a strategy. Without concrete use cases relevant to your industry and role, AI remains an abstract concept rather than a practical tool that delivers ROI.
This library solves that. It provides 100 specific, actionable AI use cases organized by industry, department, and skill level—giving you a curated menu of proven applications to inspire your AI strategy.
Perfect for:
- Business leaders developing AI strategies
- Department heads identifying automation opportunities
- Innovation teams exploring AI applications
- Consultants advising clients on AI adoption
What's inside
100 Use Cases Across Industries & Functions
By Industry:
- Retail & E-commerce: Personalized recommendations, inventory forecasting, chatbot support, dynamic pricing, review analysis
- Healthcare: Clinical documentation, patient triage, drug discovery, medical imaging analysis, appointment scheduling
- Financial Services: Fraud detection, credit scoring, portfolio optimization, compliance monitoring, customer insights
- Manufacturing: Predictive maintenance, quality control, supply chain optimization, demand forecasting, safety monitoring
- Professional Services: Document analysis, proposal generation, research automation, client communications, billing optimization
- Education: Personalized tutoring, assignment grading, curriculum development, student support, accessibility tools
- Media & Entertainment: Content creation, audience analysis, recommendation engines, script development, ad targeting
By Department:
- Marketing: Campaign ideation, copy generation, SEO optimization, A/B testing, customer segmentation, social media automation
- Sales: Lead scoring, email outreach, objection handling, proposal writing, CRM data enrichment, call analysis
- Customer Support: Ticket categorization, chatbot responses, knowledge base search, sentiment analysis, quality assurance
- Engineering: Code generation, bug detection, documentation, code review, test case creation, architecture planning
- HR: Resume screening, job description writing, onboarding automation, employee sentiment analysis, training content
- Finance: Expense categorization, financial modeling, report generation, anomaly detection, forecasting
By Implementation Complexity:
- Low-hanging fruit (quick wins): Email drafting, meeting notes, brainstorming, proofreading, basic research
- Medium complexity: Content generation, data analysis, customer service automation, code assistance
- High complexity: Custom models, predictive analytics, autonomous decision-making, enterprise integrations
Each Use Case Includes:
Problem Statement: What pain point does this solve?
AI Solution: How AI addresses the problem
Tools & Technologies: Specific AI tools that enable this use case (e.g., ChatGPT, Claude, Copilot, specialized platforms)
Implementation Steps: High-level roadmap to deploy
Success Metrics: How to measure ROI
Difficulty Rating: Beginner, Intermediate, or Advanced
Real-World Example: Company or scenario demonstrating the use case
Common Pitfalls: What goes wrong and how to avoid it
Special Sections:
Cross-Functional Use Cases:
- Applications that benefit multiple departments
- How to prioritize high-impact opportunities
- Building internal AI champions network
Industry-Specific Deep Dives:
- Unique applications for regulated industries
- Compliance considerations
- Competitive advantage opportunities
Emerging Use Cases:
- Cutting-edge applications to watch
- Future trends and opportunities
- When to wait vs. when to experiment
How to use it
- Strategic planning — Identify top 3-5 use cases aligned with business goals
- Department workshops — Review relevant use cases with teams to spark ideas
- ROI analysis — Estimate impact and effort for prioritization
- Proof of concept — Start with quick wins before tackling complex implementations
Example use case
Use Case: AI-Powered Meeting Notes & Action Items
Industry: All (universal application)
Department: All teams
Difficulty: Beginner
Problem: Hours wasted manually writing meeting notes; action items get lost; unclear accountability
AI Solution: Tools like Otter.ai, Fireflies, or ChatGPT analyze meeting transcripts to:
- Generate structured meeting summaries
- Extract and assign action items
- Identify key decisions and next steps
- Search past meetings by topic
Tools: Otter.ai, Fireflies.ai, Microsoft Teams with Copilot, Zoom AI Companion
Implementation:
- Choose tool based on existing meeting platform
- Enable recording with participant consent
- Review and edit AI summaries for accuracy
- Share notes and track action items in project management tool
Success Metrics: Hours saved per week, action item completion rate, meeting effectiveness scores
ROI: ~5 hours saved per week per person in meeting-heavy roles
Pitfall: Forgetting to get consent before recording meetings (privacy/legal issue)
Want to go deeper?
This library provides ideas. For implementing AI use cases effectively:
- Guide: AI at Work Basics — Foundational AI skills for any use case
- Resource: AI Decision Framework — Evaluate which use cases to pursue
- Resource: AI Onboarding Kit — Train teams to execute use cases
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Share with your organization
- Adapt for industry-specific strategy
- Use in presentations and workshops
Just include this attribution:
"AI Use Case Library" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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