Generative AI Risk Management in Practice: A Playbook for Enterprise Leaders

Generative AI is transforming the enterprise landscape, promising unprecedented gains in productivity, customer experience, and innovation. Yet, as organizations move from proof of concept (POC) to production, the journey is fraught with risk—from model selection and data security to customer safety and regulatory compliance. For business and technology leaders, the challenge is not just to innovate, but to do so responsibly, safely, and at scale. This playbook, grounded in Publicis Sapient’s real-world client work and internal deployments, offers a practical, step-by-step guide to de-risking generative AI and accelerating time to value.

Why Generative AI Projects Stall—and How to Move Forward

Many organizations can quickly spin up generative AI prototypes, but few successfully operationalize them. Common barriers include:

Longer-than-expected timeframes: Building a robust AI ecosystem can take months or even a year.
Unexpected costs: Model licensing, infrastructure upgrades, and user adoption can drive up expenses.
Siloed efforts and shadow IT: Decentralized experimentation leads to inefficiencies and security risks.
Stakeholder uncertainty: Black-box models and unclear ROI make it hard to secure buy-in.

The solution? A disciplined approach to risk management, governance, and cross-functional collaboration—supported by actionable frameworks and real-world lessons.

The Five Pillars of Generative AI Risk Management

1. Model and Technology Risk

Key Questions:

What business problem are we solving?
What are the technical requirements and constraints?
How will we monitor and manage model performance over time?

Best Practices:

Evaluate model performance and cost trade-offs early and often.
Design for portability and future-proofing from the start.
Monitor for rapid model updates and plan for ongoing evaluation.
Balance accuracy, speed, and cost to meet business needs.

Case in Point: In the development of the Homes & Villas by Marriott Bonvoy generative AI search tool, multiple models (including OpenAI’s GPT-3.5 and GPT-4) were evaluated for accuracy, cost, and hallucination rates. The team chose a cost-effective model for initial deployment, while documenting prompts for future upgrades—ensuring both immediate value and long-term flexibility.

2. Customer Experience Risk

Key Questions:

How do we ensure relevant, accurate, and unbiased outputs?
How do we design intuitive, frustration-free user experiences?

Best Practices:

Use prompt engineering to split complex requests and reduce hallucinations.
Embed additional context and structure into prompts for more accurate responses.
Provide pre-set options or suggestions to help users get relevant results.
Leverage human-centered design to make tools intuitive for non-experts.

Checklist:

[ ] Test for hallucinations and irrelevant outputs before launch
[ ] Offer guided prompts or filters to users
[ ] Continuously monitor user feedback and iterate

3. Customer Safety Risk

Key Questions:

What are the potential risks of misuse, bias, or harm?
How will we monitor and mitigate these risks?

Best Practices:

Identify and assess risks early in the design process.
Implement safeguards, guardrails, and monitoring systems (e.g., constitutional AI for self-assessment and revision).
Establish clear processes for user feedback and incident response.
Continuously evaluate and improve risk mitigation strategies.

Example: In AI-powered customer support, outputs are evaluated for harmful or unethical advice before being delivered to users, with models prompted to self-critique and revise responses as needed.

4. Data Security Risk

Key Questions:

What types of data will be used, and are they subject to privacy regulations?
How will we protect sensitive or personal data?

Best Practices:

Avoid using sensitive or personal data whenever possible.
Implement data masking, pseudonymization, and anonymization techniques.
Ensure transparency and obtain consent for data use.
Establish clear data retention, deletion, and incident response policies.

Checklist:

[ ] Review and update data privacy policies for generative AI
[ ] Use sandboxed environments for model training and inference
[ ] Partner with trusted technology providers for robust security

5. Legal and Regulatory Risk

Key Questions:

Are we operating in a high-risk category (e.g., healthcare, finance, critical infrastructure)?
How do we ensure compliance with evolving AI laws and regulations?

Best Practices:

Mitigate the use of generative AI in high-risk categories unless fully compliant.
Monitor and adapt to evolving AI laws and regulations (e.g., EU AI Act).
Collaborate with legal, compliance, and risk management teams from the outset.
Document processes and maintain transparency with users.

Cross-Functional Collaboration: The Engine of Safe AI Deployment

Generative AI success is not just a technology challenge—it’s an organizational one. The most effective programs are built on cross-functional teams that bring together strategy, product, experience, engineering, data, and risk management. This SPEED approach (Strategy, Product, Experience, Engineering, Data & AI) ensures that:

Risks are identified and addressed early
Solutions are designed with the end user in mind
Governance and compliance are embedded from day one

Checklist for Cross-Functional AI Teams:

[ ] Define clear roles and responsibilities
[ ] Establish regular communication and feedback loops
[ ] Involve risk and compliance early and often
[ ] Empower domain experts to shape use cases and guardrails

Accelerating Time to Value: Lessons from the Field

Case Study: Homes & Villas by Marriott Bonvoy

Publicis Sapient partnered with Marriott to launch a generative AI-powered search tool that lets customers search for vacation rentals based on experience, not just location. Key risk management steps included:

Evaluating multiple models for cost, accuracy, and hallucination rates
Using synthetic data to test for edge cases and bias
Grounding the model in non-sensitive data and clearly disclosing AI use to customers
Collaborating with infrastructure and security teams to ensure scalability and compliance

The result: a differentiated, low-risk customer experience that can be iterated and scaled as models and regulations evolve.

Actionable Frameworks and Checklists

Generative AI Risk Management Checklist:

[ ] Build a cross-functional team
[ ] Establish clear governance and ethical guidelines
[ ] Prioritize data security and privacy
[ ] Start with high-value, low-risk use cases
[ ] Invest in change management and upskilling
[ ] Monitor and measure outcomes
[ ] Plan for scalability and continuous improvement

Key Questions for Every AI Project:

What business problem are we solving?
What data do we need, and how will we protect it?
How will we measure success?
What are the risks, and how will we mitigate them?
Who needs to be involved?
How will we scale and adapt as technology and regulations change?

The Path Forward: Empower, Educate, and Evolve

Generative AI is not a one-and-done project—it’s an ongoing journey. The most resilient organizations are those that:

Foster a culture of responsible experimentation and learning
Empower employees to understand and manage AI risks
Continuously update governance frameworks as technology and regulations evolve

At Publicis Sapient, we’ve learned that the key to de-risking generative AI is not to eliminate risk, but to manage it intelligently—balancing innovation with safety, speed with governance, and ambition with accountability. By following this playbook, enterprise leaders can move from POC to production with confidence, unlocking the full value of generative AI while protecting their customers, their data, and their brand.

Ready to accelerate your generative AI journey?
Connect with Publicis Sapient’s AI and risk management experts to start building your roadmap to safe, scalable, and successful AI deployment.