Everyone's talking about generative AI. In your teams, some are already testing ChatGPT, Gemini, or Perplexity on their daily tasks. But one question remains paramount:
What AI use cases are really relevant to my job — and not just more gadgets?
Most organizations are still in this phase. at the stage of individual experimentation : a few POCs, isolated tests, prompts circulating on Teams… but few structured, measured, and even fewer industrialized use cases. The result: noise, risks, and a huge potential loss of value.
This article offers a clear and actionable method for deciding where AI makes sense in your business. The goal: to help you move from “we are testing AI” to “we know precisely where, why and how AI creates value in our businesses”.
Discover the guide in free reading or download it for free in PDF format 👇
Why has identifying AI use cases become a major strategic issue?
Generative AI is no longer a lab subject. It has already entered, sometimes discreetly, the daily lives of your employees.
They already use it… often without a frame.
- A study Deloitte of 2023 shows that 61% of employees Those who work on computers already use generative AI tools in their daily work, sometimes without their manager being informed.
- A Cybsafe study of 2024 indicates that 38% of workers share sensitive information using AI tools without their company knowing.
In other words: your company does not have a technology problem.
She has an problem of identifying, activating and structuring uses.
What constitutes a relevant AI use case for a profession?
A relevant AI use case has four fundamental attributes :
1- Measurable impact on a business indicator
It must contribute directly to one of the following levers:
- Saving time or productivity
- Quality improvement or error reduction
- Accelerated time-to-market
- Customer/employee satisfaction
- Cost optimization
- Strengthening compliance or reducing risks
🎯 Example : automate the writing of complex emails for customer service → reduction of response time.
2- Reasonable technical and legal feasibility
The use case must be able to be prototyped quickly and without high risk:
- Accessible data?
- Acceptable level of sensitivity?
- GDPR framework and manageable security?
- Alignment with existing tools?
🎯 Example : generate marketing briefs from non-sensitive internal information.
3- Natural adoption by the teams
A use case is relevant if the collaborators:
- understand its usefulness
- They easily integrate it into their routine,
- notice an immediate improvement in their work.
🎯 Example : automatically summarize Teams/Zoom meetings.
4- Multi-industry replicability
A good use case can be broken down as follows:
- for several teams,
- across several processes,
- with a gradual increase in power.
🎯 Example : generate text variations (emails, descriptions, scripts, content).
The 5 common mistakes in identifying AI use cases
1. Start with the tools rather than the business needs – “We could use ChatGPT to…” → Bad starting point.
2. Focus only on spectacular tasks – The biggest gains are often invisible: reporting, summarizing, preparing, analyzing.
3. Ignoring security and governance – Without a clear framework, no use case can be industrialized.
4. Entrust identification solely to data experts – The best use cases come jobs, not technicians.
5. Failure to document and capitalize – Without a shared framework, each team is “reinventing the wheel”.
The proven method for identifying your AI use cases : the 3×3 frame
This approach is used by organizations that successfully complete their AI transformation.
It combines business analysis, collective intelligence et feasibility assessment.
Step 1 – Map the business scope (3 key questions)
1. What are the time-consuming tasks? Repetitive, low value added, sources of friction.
2. What tasks require analysis, writing, and synthesis? Generative AI excels at these functions.
3. Where are errors, delays, and overloads observed? Key indicators of potential for improvement.
Step 2 – Mobilize the teams to generate ideas (3 levers)
🔥 1. Thematic AI Challenges : Each profession submits its pain points, needs, and automation ideas.
Result: rapid and massive sourcing of opportunities.
🧪 2. Promphthathons Group sessions to test, adjust and prototype quick business solutions.
Result: rapid proof of use, maximum engagement.
➤ Read the article on this topic
💬 3. Structured collective intelligence : Votes, comments, feedback, co-improvement.
Result: collaborative filtering and organic prioritization.
Step 3 – Evaluate and prioritize use cases (3 simple but powerful criteria)
| Criteria | Key question | Score |
| Impact | How much time, quality, or value was created? | 1-5 |
| Feasibility | Can we prototype quickly? Is data available? | 1-5 |
| Adoption | Would the teams actually use it? | 1-5 |
🎯 Priority = Impact × Feasibility × Adoption
How does Beeshake facilitate the identification of large-scale AI use cases?
Beeshake structure the adoption of generative AI in three stages: acculturate → activate → capitalize.
1. AI Acculturation Space
📚 Microlearning + career guides + AI charter
→ The teams understand when AI is useful et when it is not.
2. AI Challenges & Promptathons
💡 Massive idea sourcing + collective improvement
→ Use cases emerge from real business pain points.
3. Library of business prompts
📖 Evolving internal repository
→ We identify the most used prompts and recurring needs.
4. Needs mapping & use cases
📊 Classification by profession / impact / feasibility
→ Consolidated view for making informed decisions.
5. AI Ambassador Program
🏅 Trained and autonomous in-house experts
→ Accelerated field identification.
6. Adoption Measurement & Impact
📈 AI Usage, ROI, Maturity
→ The prioritized use cases are based on objective data.
7. Security & Governance
🔐 Validation workflow, GDPR, compliance
→ The selected use cases can be industrialized with complete confidence.
Conclusion – the key is not AI… but the method
Identifying truly relevant AI use cases is not a technological exercise.
It's a job of business diagnosis,collective intelligence and governance.
This is precisely what Beeshake enables: mobilizing your teams, structuring opportunities, prioritizing impact, and deploying AI at scale.
👉 Do you want to identify your AI use cases?
Book a discovery session
FAQ: Identifying AI Use Cases
Through the analysis of workplace irritants and repetitive tasks, the most powerful ideas come from the field.
Yes. The impact × feasibility × adoption framework works regardless of the size of the organization.
No: the best use cases are provided by the business users. Data experts then step in to validate feasibility.
By putting in place clear governance, a prompt repository and a capitalization system.
Ophélie André – Communications & Marketing Manager – Beeshake
Passionate about digital communication and marketing, Ophélie has worked in a variety of environments, honing her expertise in content strategy, digital marketing, and collaborative engagement. She enjoys devoting her energy and creativity to projects that bring people together, create meaning, and enhance the strength of the collective.