The AI Feature Baseline Has Shifted
In 2023, shipping an LLM feature was a differentiator. In 2025, it's table stakes for most product categories. Users now expect apps to understand natural language, surface relevant information proactively, and reduce repetitive manual work. The question is no longer whether to add AI. It's which AI features create genuine user value vs. which ones add complexity for its own sake.
At Kastling, AI feature work is now among our most common engagements. We design, build, and launch these features into production. Here is what actually ships and what actually retains users.
AI Features Worth Building
1. Natural Language Search and Filtering
If your app has a database of any kind (listings, documents, transactions, contacts), natural language search is now achievable in days, not months. Using semantic embeddings (OpenAI, Cohere, or open-source models), you can let users search with real questions rather than exact keyword matches.
Example: "Show me all invoices from last quarter that were paid late" instead of a filter panel with six dropdowns.
Implementation complexity: Medium. 3–7 days with a vector database (Pinecone, Weaviate, pgvector).
2. Smart Draft Generation
Anywhere your app asks a user to write something (an email, a description, a summary, a proposal), offer a draft. The AI doesn't replace the user's voice; it eliminates the blank-page problem.
This works particularly well in: CRM tools, content platforms, e-commerce product listings, and any B2B workflow app.
Implementation complexity: Low. 1–3 days with a basic GPT prompt and a UI affordance.
3. Automated Summarisation
For apps that accumulate content over time (messages, notes, analytics, activity logs), on-demand summarisation reduces cognitive load. "What happened this week?" is a question that should return a 3-sentence answer, not a scroll through 200 events.
Implementation complexity: Low to medium. 2–5 days.
4. Intelligent Onboarding and Contextual Help
Instead of a static help doc, ship a context-aware assistant that knows where the user is in the product and what they are trying to do. This is not a full AI chatbot. It's a targeted help layer with guardrails.
Studies suggest that apps with contextual help reduce time-to-first-value by 25–40% vs. static onboarding flows.
Implementation complexity: Medium. 5–10 days.
5. Anomaly Detection and Proactive Alerts
If your app processes data (financial, operational, behavioural), surface anomalies before the user has to discover them manually. "Your churn rate increased 12% this week, mostly among users who haven't completed onboarding" is more valuable than a dashboard the user has to read every day.
Implementation complexity: Medium to high. 1–3 weeks depending on data model complexity.
6. Personalisation at Scale
Rather than static feature flags and A/B tests, use behavioural signals to adapt the UI and content to each user's workflow. Which features does this user actually use? What's their typical session pattern? Surface what's relevant; hide what isn't.
Implementation complexity: High. 2–4 weeks. This is a larger architectural investment.
AI Features Not Worth Building Yet
- AI-generated images in your product. Unless your product is explicitly creative, this adds complexity without user value.
- Voice interfaces. Interesting for specific domains (healthcare, hands-free workflows), unnecessary for most B2B SaaS.
- Custom fine-tuned models. Fine-tuning is expensive and rarely worth it when prompt engineering with a foundation model achieves the same result.
How to Scope Your First AI Feature
Start with the one place in your product where a user does the most manual, repetitive, or ambiguous work. That's your first AI integration. Ship it. Measure whether it reduces time-on-task or increases activation. Then expand.
FAQ
Q: What's the fastest AI feature to ship?
A GPT-powered text generator (draft, summarise, expand) in any text input. 1–3 days with a clean API integration.
Q: Do I need to train my own model?
Almost certainly not. Foundation models from OpenAI, Anthropic, or open-source alternatives handle 95% of use cases with good prompt engineering. Train your own model only if you have proprietary data that genuinely differentiates the output.
Q: How much does running AI features cost?
At typical startup usage levels, LLM API costs run $50–$500/month. This becomes meaningful at scale. Design prompts to be efficient, because token costs add up with high-frequency usage.
Q: Are users comfortable with AI in B2B apps?
Yes, increasingly. The main concern is data privacy (what data is sent to the model?) and accuracy (can users trust the output?). Address both transparently in your UI.