How AI concretely improves web projects
How AI concretely improves web projects Link to heading
Artificial intelligence is already transforming many web projects. Here are some concrete uses I’ve recently implemented, with their results and limitations.
Before AI: recurring bottlenecks Link to heading
In traditional projects, we often find:
- Too much time spent on repetitive low-value tasks
- Strong dependence on humans to sort, classify or produce content
- Difficulties in exploiting certain data
Simple and effective use cases Link to heading
I tested AI as an assistant on specific tasks:
- Identify the intention of a message
- Automatically reformulate content
- Suggest actions or generate standard responses
The goal: save time, structure exchanges, and help teams focus on what matters.
What it changes concretely Link to heading
With these tools:
- Teams work faster
- Responses are more consistent
- Repetitive processes are automated
Technologies vary according to context: model choice, prompting, human validation. These tools don’t replace, they amplify.
Other possible applications Link to heading
These approaches also apply to:
- Generate or enrich product sheets
- Automatically classify documents
- Automate internal tasks (reporting, synthesis, etc.)
Three limitations to keep in mind Link to heading
- Reliability: AI can make mistakes, proofreading is essential
- Confidentiality: attention to data management
- Costs: reasonable, but to monitor according to usage and volume
How to explain it simply Link to heading
I often explain AI as a fast and tireless assistant, but one that needs to be guided with precision. It doesn’t replace an expert, but can save them precious time.
What’s next? Link to heading
I’m increasingly interested in LLM agents: AIs capable of acting in tools, while remaining supervised. This opens new possibilities for web products.
If you’re exploring AI in a project, I’d love to exchange ideas!