A retail chatbot reduces cart and checkout drop-off when it can answer the highest-friction product + purchase questions in seconds (especially total cost, delivery timing, returns risk, fit/sizing, availability, payment/trust) and then route shoppers to a single next step (size […]
To reduce SaaS time-to-value (TTV) during onboarding, define one measurable First Value Moment, then use an in-app chatbot to remove blockers on the shortest path to that moment: answer setup questions with source-backed help, deep-link users to the next step, […]
Yes, if the assistant is grounded in your documentation at answer time. The reliable pattern is: ingest your docs, retrieve the most relevant passages per question, and show citations so users can verify what the bot used (and so the […]
Use an AI agent when most questions are repetitive and speed/coverage matter. Use live chat when issues are high-risk, emotionally charged, or exception-heavy. A hybrid model (AI first, human escalation) is often the safest default when you have both volume […]
An enterprise-ready AI assistant should support enforceable federated SSO (SAML/OIDC), least-privilege RBAC, protected admin audit logs, and defenses against prompt injection and data leakage (especially in RAG). Validate these by running an admin-live demo plus prompt-injection and offboarding test cases, […]
AI web scraping (also called AI scraping, LLM-based scraping, or semantic scraping) is the use of automation to collect web content and AI models to extract and normalize that content into structured outputs (for example, JSON fields, tables, or categorized […]
Define AI accountability by assigning clear ownership, maintaining documented evidence like evaluation results and logs, and establishing escalation paths for incidents. Operationalize this lifecycle by enforcing traceability through citations and monitoring usage to prove system behavior and facilitate rapid remediation. […]
In banking, ethical generative AI use means preventing unfair treatment, protecting customer and supervisory data, avoiding IP misuse, reducing misinformation/deepfake harms, and ensuring clear accountability with audit trails. Approve GenAI only when it is governed, tested, monitored, and traceable to […]
Teams using generative AI should set rules that reduce harm and liability across fairness (bias), privacy/security, intellectual property, accuracy/misinformation, security/misuse, and accountability/transparency. Start by restricting sensitive inputs, verifying high-impact outputs, disclosing material AI involvement, and assigning a human owner for […]
Generative AI escalates cybersecurity risks by scaling social engineering and introducing application-layer vulnerabilities like prompt injection and data leakage in LLM apps. Mitigate these threats by defining data boundaries, enforcing least privilege for tools, and hardening output handling to prevent […]
