AI Features and How They Benefit Users
Real examples of how generative AI features make products faster, easier to use, and more valuable for your users.
Workflow Automation & Agentic Tasks
Agentic AI can perform sequences of actions on behalf of your users. The key is designing clear guardrails so the automation is trustworthy and users stay in control.
What this looks like in practice
Within a single application: A CRM where incoming leads are automatically enriched, scored, and routed to the right rep with a draft outreach email ready to review.
Across applications and services: An agentic application that evaluates whether or not KYC checks need to be performed on a new customer added to a CRM system, that performs those checks via a third-party KYC service when they are required, and that automatically inserts the results of those KYC checks, including risk scores, into the new customer record in the CRM system, without human intervention.
Conversational Interfaces & In-App Assistants
Give users a way to ask questions, get help, or take action in plain language. In-app AI assistants can guide users through complex workflows, surface relevant information from your data, and reduce the need to search documentation or contact support.
What this looks like in practice
A project management tool where users can ask “What are the open blockers on this sprint?” and get a direct answer drawn from live data.
Content Intelligence
AI is exceptionally good at making sense of large volumes of semi-structured textual data, and structured alphanumeric data. You can use it to automatically summarize long documents or threads, categorize incoming content without manual tagging, and enrich records with structured metadata extracted from unstructured input.
What this looks like in practice
An employee feedback platform that automatically categorizes and prioritizes manager feedback so leaders see the themes that need attention without having to read every response.
A reporting feature that allows your user to ask “When am I most likely to purchase garden supplies?” or “In which month in 2024 did I spend the most money with you?”
Semantic Search & Smart Discovery
Traditional search fails when users don’t know the exact keyword. Semantic search understands intent and meaning, surfacing results that match what the user is actually looking for. Pair it with smart filtering and personalized recommendations and you move from a search box to a discovery engine.
What this looks like in practice
A knowledge base where a user types “how do I handle a refund edge case” and surfaces the right policy article even though it’s titled something different.
Personalization & Adaptive Experiences
AI can analyze behavior patterns and surface the right content, features, or next steps for each individual user. This ranges from simple recommendation engines to fully adaptive interfaces that prioritize what a user is most likely to need based on their history and context.
What this looks like in practice
A learning platform that adjusts the sequence and difficulty of content based on how a user has performed and engaged over time.
Data Insights & Natural Language Analytics
Not every user is a data analyst, but every user wants answers. Natural language querying lets non-technical users ask questions of your data in plain English and get clear answers, without needing to navigate a complex dashboard.
What this looks like in practice
A financial platform where a business owner types “which product line had the highest margin last quarter” and gets a direct answer with supporting context.
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