Decision Guide: Is GenAI Right for Your Use Case?
Overview
Not every problem is best solved by Generative AI. Before investing in a GenAI solution, use this guide to stress-test your use case and ensure you’re choosing the right tool for the job.
This guide helps you answer four questions:
- Should you use AI at all? (vs. process improvement, traditional software, or hiring)
- Should you use GenAI specifically? (vs. classical ML, rules-based systems, or search)
- Should you build or buy?
- Is your organization ready?
Question 1: Should You Use AI at All?
Before jumping to AI, verify that the problem isn’t better solved by simpler means.
The “Simpler Solution” Checklist
Run through this checklist. If any item is true, consider the alternative first:
| Check | If True, Consider |
|---|---|
| The process is broken due to unclear ownership or incentives | Process redesign, not AI |
| The task takes hours because of tool friction, not cognitive work | Better tooling or automation |
| The data doesn’t exist or is poor quality | Data strategy first |
| The problem affects <5 people and <10 hours/week | Not worth the investment |
| A simple rule or template would solve 80% of cases | Rules-based automation |
| The task requires physical presence or sensory judgment | Wrong modality for AI |
If none of these apply and the problem involves significant knowledge work at scale, continue to Question 2.
Question 2: Should You Use GenAI Specifically?
GenAI (large language models, diffusion models, etc.) excels at specific tasks. Make sure your use case matches.
GenAI Strengths vs. Alternatives
| Task Type | Best Approach |
|---|---|
| Open-ended text generation, summarization, Q&A | GenAI (LLM) |
| Image/video/audio generation | GenAI (diffusion/multimodal) |
| Classification with labeled training data | Classical ML (faster, cheaper, more accurate) |
| Structured data prediction | Classical ML or statistical modeling |
| Exact pattern matching (emails, invoices) | Rules-based or regex automation |
| Database queries and reporting | SQL + BI tools |
| Semantic search over documents | GenAI (RAG) or hybrid search |
| Real-time decision-making (<50ms latency) | Classical ML or rules |
GenAI Fit Score
Rate your use case on these dimensions (1 = low, 5 = high):
| Dimension | Score | Why It Matters |
|---|---|---|
| Requires natural language understanding | /5 | Core GenAI strength |
| High variability in inputs/outputs | /5 | GenAI handles variance well |
| Benefits from context and reasoning | /5 | Differentiating capability |
| Large volume of cases (>1K/month) | /5 | Justifies infrastructure cost |
| Humans currently do this task | /5 | Confirms cognitive load |
| Acceptable latency >2 seconds | /5 | GenAI is slower than rules |
Score interpretation:
- 20–30: Strong GenAI fit — proceed
- 12–19: Moderate fit — validate with a small pilot
- <12: Weak fit — explore alternatives
Question 3: Should You Build or Buy?
Once you’ve confirmed GenAI is the right approach, decide on the delivery model.
The Build vs. Buy Framework
| Factor | Favor Build | Favor Buy |
|---|---|---|
| Proprietary data advantage | Yes — custom model benefits | No — generic data, off-shelf fine |
| Deep workflow integration | Yes — needs custom embedding | No — standalone tool works |
| Vendor lock-in risk | High — critical business function | Low — easy to switch |
| Team AI capability | High — can build and maintain | Low — better to outsource |
| Speed to value required | No — can take time | Yes — need results in weeks |
| Unique/novel use case | Yes — no existing solutions | No — mature vendor landscape |
| Budget | >$200K available | <$200K or preference for OpEx |
Common Delivery Models
| Model | Description | Best For |
|---|---|---|
| Buy SaaS | Subscribe to an AI product (Jasper, Intercom AI, GitHub Copilot) | Commodity use cases, fast start |
| Buy + Configure | Deploy a platform and configure for your workflows (Microsoft Copilot, Salesforce Einstein) | Enterprise with existing vendor relationships |
| Build on API | Use OpenAI/Anthropic/Gemini APIs to build custom solution | Unique workflows, proprietary data |
| Fine-tune/Custom | Fine-tune an open-source model on your data | Highest control, high volume, regulated industries |
| On-premises | Run models locally (LLaMA, Mistral) | Strict data sovereignty requirements |
Question 4: Is Your Organization Ready?
The best technology fails without organizational readiness. Assess your readiness honestly.
Readiness Assessment
Score each dimension 1–5:
Data Readiness
| Item | Score | |—|—| | Relevant data exists and is accessible | /5 | | Data quality is sufficient (accurate, complete, current) | /5 | | Data governance and privacy requirements are understood | /5 | | Data pipelines can feed the AI system | /5 | | Data Readiness Score | /20 |
People Readiness
| Item | Score | |—|—| | Executive sponsorship is confirmed | /5 | | A product owner / AI champion is identified | /5 | | End users are motivated to adopt AI tools | /5 | | Legal/compliance team is engaged | /5 | | People Readiness Score | /20 |
Technical Readiness
| Item | Score | |—|—| | Engineering team has AI/ML experience | /5 | | Cloud infrastructure is in place | /5 | | Security and access controls can be extended | /5 | | Monitoring and observability capabilities exist | /5 | | Technical Readiness Score | /20 |
Interpreting Your Readiness Score
| Total Score (60 max) | Recommendation |
|---|---|
| 45–60 | Strong readiness — proceed with full deployment |
| 30–44 | Moderate readiness — start with a focused pilot |
| 15–29 | Low readiness — address gaps before investing |
| <15 | Not ready — foundational work needed first |
The Go/No-Go Decision Matrix
Combine your GenAI Fit Score and Readiness Score:
Low Readiness High Readiness
(< 30) (≥ 30)
┌────────────────┬────────────────┐
High GenAI Fit │ Build │ Go — Full │
(≥ 20) │ Readiness │ Deployment │
│ First │ │
├────────────────┼────────────────┤
Low GenAI Fit │ Stop — │ Reconsider │
(< 20) │ Wrong │ Approach │
│ Approach │ │
└────────────────┴────────────────┘
Red Flags: When to Walk Away
Stop the project (or pause until resolved) if any of these apply:
- The ROI is only positive in the optimistic scenario. A project must pencil out in the realistic case.
- There is no measurable baseline. You can’t prove value without measurement.
- Legal/compliance approval is blocked. Don’t build something that can’t launch.
- The champion has left the organization. AI projects without internal advocates fail.
- The underlying data doesn’t exist. No data = no AI value.
- The budget doesn’t cover the full 18-month runway. Underfunded AI projects die slowly and expensively.
Making the Final Decision
If you’ve worked through all four questions and the answers are positive, you have a strong foundation for a GenAI investment. Use the following summary template:
Use Case: [Name]
Business Problem: [1-sentence description]
GenAI Fit Score: [X/30]
Readiness Score: [X/60]
Estimated Break-Even: [Month X]
3-Year ROI (Realistic): [X%]
Biggest Risk: [Top risk and mitigation]
Recommendation: [Go / Pilot / Wait / Stop]
Next Steps
- Use Case Library → — See how your use case compares to benchmarks
- Business Case Template → — Turn your analysis into a stakeholder presentation
- Interactive Calculator → — Model your final numbers