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    What is Generative AI?

    Generative AI (GenAI) helps organizations quickly turn data into useful outputs like reports, insights, or even workflow suggestions. By learning from existing data, it reduces manual effort and makes advanced analytics more accessible.

    Expanded Definition

    Generative AI (GenAI) is a branch of artificial intelligence that uses machine learning models, most commonly large language models (LLMs) and generative adversarial networks (GANs), to create new outputs based on existing data.

    Unlike traditional analytics that focus on prediction or classification, GenAI produces original results:  From financial summaries and marketing copy to drug compound simulations. Gartner notes that by 2026, over 80% of enterprises will have used GenAI APIs or deployed GenAI-enabled applications in production, up from less than 5% in 2023.

    How Generative AI is Applied in Business & Data

    Generative AI is rapidly becoming a productivity multiplier across industries.

    • Finance teams use it to automate report generation and scenario modeling.
    • Retailers apply it to generate personalized product descriptions at scale.
    • Supply chain operations simulate demand fluctuations and optimize inventory planning.

    In the data domain, GenAI speeds up analytics by suggesting queries, creating repeatable workflows, and explaining results in plain language.

    How Generative AI Works

    Generative AI is trained on large sets of data and learns the patterns, structures, and relationships within that information. Once trained, it can create new outputs that mirror those patterns.

    This allows organizations to ask questions in plain language and quickly receive outputs that previously required hours of manual effort.

    Use Cases

    • Data Prep and Analysis: Automating repetitive data cleaning tasks and recommending transformations.
    • Business Reporting: Generating executive-ready summaries that highlight key metrics.
    • Customer Engagement: Creating personalized marketing campaigns that adapt to audience behavior.
    • Product Innovation: Accelerating design by simulating variations of prototypes.

    Industry Examples

    • Healthcare: Generate synthetic patient data for research while maintaining privacy compliance.
    • Banking: Automating compliance reports and fraud detection summaries.
    • Manufacturing: Simulating supply chain stress tests under different economic scenarios.
    • Public Sector: Creating accessible summaries of complex regulatory data for faster decision-making.

    FAQs

    Q: How is Generative AI different from traditional AI?
    Traditional AI often classifies or predicts based on patterns. Generative AI creates new output—text, images, simulations—that didn’t exist in the training data.

    Q: What are the risks of using Generative AI?
    Risks include data privacy concerns, intellectual property issues, and “hallucinations,” where AI produces incorrect but plausible-sounding outputs. Strong governance and human oversight are essential.

    Q: How does Alteryx make GenAI safe for business use?
    Through AiDIN, Alteryx provides controlled, auditable AI experiences. Users benefit from automation and speed while retaining transparency, compliance, and data security.

    Further Resources

    Sources and References

    Synonyms

    • GenAI
    • Generative Artificial Intelligence
    • AI-Powered Generation

    Related Terms

     

    Last Reviewed:

    October 2025

    Alteryx Editorial Standards and Review

    This glossary entry was created and reviewed by the Alteryx content team for clarity, accuracy, and alignment with our expertise in data analytics automation. Learn more about our editorial standards.