Skip to Main Content

  • Generative AI: Using artificial intelligence to make human impact. Learn how

  • Five AI-enabled capabilities every organization needs to transform. Start here

  • Publicis Sapient named market leader for cloud services. Upgrade now

DBT GPT ™

The Generative AI Risk Management Playbook

From Proof of Concept to Production: De-Risking Generative AI for Enterprise Success

Unlock the playbook

Executives: forget chasing the next big thing—become the next big thing

Did you know that the majority of generative AI proofs of concept fail to make it into production?

The good news is that, with the right approach, some generative AI first movers are bridging the gap between successful prototypes and impactful products.

Below is a practical guide that’s not based on generative AI in theory, but rather in reality—with strategies to overcome unexpected challenges and mitigate risk when taking generative AI prototypes into production, based on lived experience with real products created by top brands.

Inside the playbook:
Generative AI implementation challenges conquered

Timeframe traps: Learn how to slash generative AI development timelines

Unexpected costs: Discover strategies to minimize model costs, user adoption hurdles and hidden infrastructure upgrades

Silo buster: Eliminate inefficiencies and security risks with a clear roadmap to avoid siloed efforts and shadow IT

Access the full playbook to discover:

How to overcome common Gen AI project roadblocks and minimize risks across models, tech, customer experience, data and legal areas.

Generative AI risk mitigation strategies

Minimize risks across the entire generative AI lifecycle:

  • Model and technology:
    Choose the right model for accuracy, cost and speed. Implement rate limits and future-proof your tech stack

    Customer experience:
    Craft user-friendly prompts, leverage data for accuracy and prioritize human oversight for ethical interactions

    Customer safety:
    Prevent offensive outputs, identify vulnerabilities and ensure model outputs are safe and unbiased

  • Data protection:
    Develop responsible AI use guidelines, anonymize data and balance transparency with model confidentiality

    Legal and regulatory:
    Navigate high-risk categories, maintain detailed model documentation and ensure user and regulatory transparency

Case study
Marriott Homes & Villas success story

See how a leading brand built a vacation rental search using generative AI, allowing users to describe their dream getaway and find perfect matches.

Read the case study