"Add AI" isn't a feature, it's a category — and most of it isn't relevant to most apps. Here's where it's genuinely earned its place in real projects, versus where it's usually just noise.
Where it helps: personalization/recommendation engines, which genuinely improve engagement in e-commerce and content apps once there's enough behavioral data to train on. Support automation, when scoped to the repetitive 20% of questions (order status, password resets) with a clean handoff to a human for the rest. Fraud/anomaly detection in fintech and e-commerce, where pattern-based detection catches things rule-based systems miss. Image recognition for visual search and product matching in e-commerce, which has matured past "demo feature" territory.
Where it's usually a checkbox: chatbots bolted onto apps without enough support volume to justify one; "AI-powered" features added to a pitch deck without a clear user problem attached; predictive features built before there's enough historical data to predict anything meaningfully.
The actual question worth asking before any AI feature: what specific decision or task does this improve for the user, and do we have enough real data to make it reliable? If the honest answer is "not yet," it's worth waiting.
We integrate AI where the data and use case genuinely support it, not by default — happy to pressure-test a feature idea against this list.
Brancosoft | 📞 +91 9999321509 | ✉️ nirdesh.verma@brancosoft.co.in
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