UC Berkeley: Responsible Use of Generative AI – A Playbook for Product Managers and Business Leaders
This playbook offers product managers and business leaders strategies for using generative AI responsibly by addressing risks like data privacy, inaccuracy, and bias while enhancing transparency, compliance, and brand trust.
UC Berkeley: Responsible Use of Generative AI – A Playbook for Product Managers and Business Leaders
Summary of https://re-ai.berkeley.edu/sites/default/files/responsible_use_of_generative_ai_uc_berkeley_2025.pdf
A playbook for product managers and business leaders seeking to responsibly use generative AI (genAI) in their work and products. It emphasizes proactively addressing risks like data privacy, inaccuracy, and bias to build trust and maintain accountability.
The playbook outlines ten actionable plays for organizational leaders and product managers to integrate responsible AI practices, improve transparency, and mitigate potential harms. It underscores the business benefits of responsible AI, including enhanced brand reputation and regulatory compliance.
Ultimately, the playbook aims to help organizations and individuals capitalize on genAI's potential while ensuring its ethical and sustainable implementation.
- GenAI has diverse applications and is used for automating work, generating content, transcribing voice, and powering new products and features.
- Organizations can use different genAI models. These include off-the-shelf tools, enterprise solutions, or open models, which can be customized for specific needs and products.
- Adoption of genAI can lead to increased productivity and efficiency. Organizations that address the risks associated with genAI are best positioned to capitalize on the benefits. Responsible AI practices can foster a positive brand image and customer loyalty.
- There are key risks product managers need to consider when using genAI, especially regarding data privacy, transparency, inaccuracy, bias, safety, and security.
- There are several challenges to using genAI responsibly, including a lack of organizational policies and individual education, the immaturity of the industry, and the replication of inequitable patterns that exist in society.
Related Articles
Students as Agent Builders: How Role-Based Access (RBAC) Makes It Possible
How ibl.ai’s role-based access control (RBAC) enables students to safely design and build real AI agents—mirroring industry-grade systems—while institutions retain full governance, security, and faculty oversight.
AI Equity as Infrastructure: Why Equitable Access to Institutional AI Must Be Treated as a Campus Utility — Not a Privilege
Why AI must be treated as shared campus infrastructure—closing the equity gap between students who can afford premium tools and those who can’t, and showing how ibl.ai enables affordable, governed AI access for all.
Pilot Fatigue and the Cost of Hesitation: Why Campuses Are Stuck in Endless Proof-of-Concept Cycles
Why higher education’s cautious pilot culture has become a roadblock to innovation—and how usage-based, scalable AI frameworks like ibl.ai’s help institutions escape “demo purgatory” and move confidently to production.
AI Literacy as Institutional Resilience: Equipping Faculty, Staff, and Administrators with Practical AI Fluency
How universities can turn AI literacy into institutional resilience—equipping every stakeholder with practical fluency, transparency, and confidence through explainable, campus-owned AI systems.