FAQs

  • An AI-driven system is a system that uses artificial intelligence (AI) as its core technology to make decisions, automate processes, or provide insights. These systems rely on machine learning, deep learning, natural language processing (NLP), or other AI techniques to function.

  • Automation – Reduces human effort by performing tasks intelligently.

    Data-Driven Decision Making – Uses AI algorithms to analyze data and suggest the best course of action.

    Continuous Learning – Improves over time by learning from new data.

    Predictive & Adaptive Capabilities – Forecasts trends and adapts to changing conditions.

    Human-Augmented Intelligence – Assists humans by providing insights and recommendations.

  • An AI chatbot is software that uses natural-language to converse with people, answer questions, and complete tasks through chat (web, mobile, email, SMS, Slack, etc.).

    How it works

    • Understand: parses the user’s message (intent, entities, context).

    • Decide: picks an answer or action (rules/workflows, ML, or a large language model).

    • Act/Reply: returns a response, triggers APIs (create a ticket, look up an order), and remembers context.

    Main types

    • Rule/FAQ bots: button flows or keyword/intents; reliable but limited.

    • LLM/GPT bots: free-form, more natural; often paired with RAG to ground answers in your data.

    • Agentic bots: can call tools/APIs, follow multi-step plans, and log outcomes.

    Common uses

    Customer support, IT/HR helpdesk, sales assist, knowledge search, BI explainers (“what does this KPI mean?”), DSS copilots (run a what-if, generate a recommendation, ex. Github Co-pilot).

    Benefits: 24/7 responses, faster response, consistent answers, data capture for process improvement

  • An AI virtual assistant is a software agent that understands natural language (text or voice) and gets things done for you—answering questions, automating workflows, and taking actions via connected apps and APIs.

    What it does

    • Understands & remembers context across conversations.

    • Calls tools/APIs (e.g., calendar, CRM, helpdesk, BI) to fetch data or perform tasks.

    • Proactive help: reminders, alerts, follow-ups based on rules or events.

    • Omnichannel: web, mobile, chat apps, email, voice.

    How it differs from a chatbot

    • Chatbots mainly answer; virtual assistants answer and act (schedule a meeting, create a ticket, run a BI query, kick off a DSS scenario).

    Common uses

    • IT/HR helpdesk, sales ops, scheduling and travel, BI “explain this KPI,” DSS copilots (what-ifs, recommendations), customer support triage

  • A RAG (Retrieval-Augmented Generation) system pairs a search step with a generative model so answers are grounded in your own documents/data, not just the model’s memory.

    How it works:

    1. Index your content (vectors + keywords, with metadata).

    2. Retrieve the most relevant chunks for a user query.

    3. Augment the model’s prompt with those chunks.

    4. Generate an answer (ideally with citations) and log sources.

    Why it’s used: fresher, verifiable answers; fewer hallucinations; faster to adapt than fine-tuning.

    Where it fits: policy/FAQ portals, product docs, BI/DSS explainers (“what does this KPI mean?”), internal knowledge bases.

  • A Business Intelligence (BI) system is a stack of tools plus processes that turns raw data into trusted, actionable insights for people across the business.

    What it does

    • Collects & integrates data from source systems (ex. CRM, ERP, web).

    • Models & governs it into a single, consistent view (semantic layer, security).

    • Analyzes & visualizes via dashboards, self-serve queries, reports, and alerts.

    • Distributes insights to the right roles at the right time (web, email, embedded into applications).

    Typical components
    Data sources → ETL/ELT → warehouse/lakehouse → semantic models → BI tool (Qlik/Power BI/Tableau) → dashboards/KPIs → governance & audit.

    Outcomes

    • Faster, better decisions; shared KPIs; root-cause analysis; operational efficiency; auditability.

  • A Decision Support System (DSS) is software that helps people make better, faster decisions—especially when problems are complex or only partly structured.

    What it includes

    • Data layer: curated data from warehouses/lakehouses plus live feeds

    • Model layer: analytics/ML, optimization, simulation, rules

    • Interaction layer: scenarios, what-ifs, goal-seek, recommendations with explanations

    • Governance: decision logs, approvals, auditability, guardrails

    Examples

    • Pricing & promotions, staffing/scheduling, credit limits, fraud interventions.

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    BI vs. DSS

    • BI: Describes & explains what happened/why.

    • DSS: Explores what could happen/what to do (what-if, optimization).

  • Utilizes historical data to forecast what would happen (scores, probabilities, forecasts). Source: IBM

  • Utilizes models (e.g., LLMs) to create new content—text, images, code, etc. Source: (IBM)

  • Utilizes AI agents that can plan, use tools, and take actions toward a goal with limited supervision. Source: Mckinsey & Company