Over the past two years almost every company has asked how to use artificial intelligence in its digital products. The risk is adding a chatbot just for the sake of it, with no real benefit for users. AI creates value when it solves a specific problem: it saves time, simplifies a complex task or personalises the experience in a way users actually notice.
Here are the most practical use cases for integrating AI into a business app, and how to approach the project sustainably.
Practical use cases
1. Natural language search
Instead of filters and menus, users type or say what they're looking for: "a waterproof jacket under €150", "unpaid invoices from March". Semantic search understands intent and returns relevant results even when the words don't match exactly.
2. Customer support and knowledge bases
An assistant grounded in company documents (using RAG, retrieval-augmented generation) answers frequent questions with verified information and hands over to a human when needed. It cuts repetitive tickets and provides instant answers around the clock.
3. Personalisation and recommendations
Content, products or journeys suggested based on user behaviour increase engagement and conversion, especially in e-commerce, media, fitness and learning apps.
4. Extracting data from documents and images
Snap a receipt, an ID document or a delivery note and get the data already filled into the right fields. It's one of the use cases with the fastest return in B2B apps and financial services.
5. Content generation and summarisation
Report summaries, draft messages, product descriptions, meeting notes: AI prepares a first draft that users review and approve, cutting time spent on repetitive work.
6. Process automation with AI agents
Agents that carry out multi-step actions, such as booking, updating a CRM or preparing a quote, integrated with company systems and always under user control for important operations.
How to approach an AI integration project
- Identify the problem: start from a task that is currently slow, costly or frustrating for users.
- Check the data: AI is only as useful as the data it can access. Quality, structure and permissions must be assessed first.
- Prototype quickly: a proof of concept of a few weeks tells you whether the use case really works.
- Design the experience: the interface must explain what the AI does, handle errors and leave users in control.
- Measure: time saved, resolution rate, conversions, cost per request.
Privacy, costs and reliability
Three aspects to consider from day one. Privacy: which data is sent to the models, where it's processed and how GDPR and the EU AI Act are respected. Costs: every model request has a cost that must be estimated on real volumes. Reliability: models can be wrong, so you need checks, cited sources and human handover in critical cases.
Read also: how AI is transforming digital agencies
AI in apps: frequently asked questions
Do you need to train a proprietary model?
Almost never. In most cases existing models are used, connected to company data through RAG and specific instructions. Custom training only makes sense for very particular needs.
Can AI be added to an existing app?
Yes. Many integrations run through the backend and require limited changes to the app, starting with a single feature and expanding gradually.
How soon can you see results?
A proof of concept takes 2-4 weeks; a production feature, with design, testing and monitoring, typically 2-3 months.
Conclusion
AI isn't a feature to bolt on but a tool for solving specific problems better than before. The projects that work start from a clear use case, solid data and a carefully designed experience. GlueGlue helps companies integrate AI into apps and platforms, from choosing the use case to going live.
Let's talk about your project: get in touch with the GlueGlue team
