The 5 use cases with proven ROI
After implementing AI in more than 15 companies of different sizes, these are the patterns with the most predictable return:
- First response in customer service. An LLM trained on the internal knowledge base can handle 60-80% of queries without human intervention, with response times of seconds rather than hours.
- Document analysis and extraction. Contracts, invoices, medical reports, technical data sheets. Current models (GPT-4o, Claude Sonnet) extract, classify and summarize with 90-95% accuracy on well-structured texts.
- Personalized content generation at scale. Product descriptions, follow-up emails, adapted commercial proposals. It doesn't replace the copywriter: it makes them 5-10 times more productive.
- Anomaly detection in data. Unusual transactions, spikes in operational metrics, churn patterns. Supervised ML models have detection rates far superior to manual rules.
- AI-assisted code for development teams. Tools like Cursor or GitHub Copilot reduce implementation time by 20-35% according to studies from Microsoft and Stripe.
When NOT to implement AI (and what makes projects fail)
The most frequent mistake is implementing AI where there isn't enough data or where the process isn't sufficiently defined. An LLM cannot automate a process that humans don't perform consistently either.
Situations where AI tends to fail: processes without documentation or historical data, cases where the decision has legal consequences without explicit human oversight, products where the client is specifically paying for human interaction, and companies that expect AI to substitute strategy rather than execute it.
How to assess whether your company is ready for AI
Three questions to make the assessment before committing budget:
- Do I have enough data? For supervised ML you need at least hundreds of labeled examples. For LLMs, you need structured documentation of your process or domain.
- Is the process well defined? If you can't write the rules for how to do it manually, a model won't learn them.
- Is there someone on the team who can oversee and maintain the system? AI is not a set-and-forget project: it requires ongoing review and improvement.
The implementation process that works
The approach we've validated in real projects: start with a pilot in production rather than a prototype in a test environment, choose the use case with the highest data volume and most measurable impact, involve the team that will use the system from the design phase (not from delivery), and measure the result against a human baseline before scaling.
Frequently asked questions
Depends on the use case. An LLM API integration (OpenAI, Anthropic) for customer service can cost between €5,000 and €15,000 including development, prompt engineering and testing. A custom ML system with proprietary data can cost between €20,000 and €80,000. Monthly operating costs are low: between €50 and €500 in API consumption for an average SME.
For 90% of business use cases, API models (GPT-4o, Claude Sonnet, Gemini) are superior in cost-benefit to proprietary models. Fine-tuning only makes sense when there's a very high volume of domain-specific data and a need to reduce API costs at scale. In most SMEs, customization via prompt engineering and RAG produces equivalent results.
In well-designed projects with a specific use case, ROI is typically seen between the second and fourth month of operation. The critical factors are clarity of the business objective, not 'implement AI' but 'reduce first response time by 70%', and team adoption from day one.