LLMs and Generative AI in the enterprise.
Inspire, develop, and guide a winning organization.
Understand the unique values and behaviors of a successful organization.
Create visible workflows to achieve well-architected software.
Understand and use meaningful data to measure success.
Integrate and automate quality, security, and compliance into daily work.
An on-demand learning experience from the people who brought you The Phoenix Project, Team Topologies, Accelerate, and more.
Learn how to enhance collaboration and performance in large-scale organizations through Flow Engineering
Learn how making work visible, value stream management, and flow metrics can affect change in your organization.
Clarify team interactions for fast flow using simple sense-making approaches and tools.
Multiple award-winning CTO, researcher, and bestselling author Gene Kim hosts enterprise technology and business leaders.
In the first part of this two-part episode of The Idealcast, Gene Kim speaks with Dr. Ron Westrum, Emeritus Professor of Sociology at Eastern Michigan University.
In the first episode of Season 2 of The Idealcast, Gene Kim speaks with Admiral John Richardson, who served as Chief of Naval Operations for four years.
Exploring the impact of GenAI in our organizations & creating business impact through technology leadership.
DevOps best practices, case studies, organizational change, ways of working, and the latest thinking affecting business and technology leadership.
The debate over in-office versus remote work misses a fundamental truth: high-performing teams succeed based on how they’re organized, not where they sit.
Leaders can help their organizations move from the danger zone to the winning zone by changing how they wire their organization’s social circuitry.
The values and philosophies that frame the processes, procedures, and practices of DevOps.
This post presents the four key metrics to measure software delivery performance.
June 26, 2024
In a recent presentation at the 2024 Enterprise Technology Leadership Summit Virtual Europe, Brian Scott and Daniel Neff, principal architects at Adobe, shared their experience developing a generative AI governance strategy for one of the world’s largest software companies. As more organizations grapple with the challenges and opportunities presented by AI, the insights from Adobe’s journey provide valuable lessons for executives looking to responsibly integrate this transformative technology.
With the rapid advancement of generative AI, companies face a delicate balancing act. On one hand, there is a strong desire among developers to leverage AI to enhance their work and drive innovation. On the other hand, organizations must navigate the potential risks and ensure responsible deployment. This is where a robust AI governance strategy becomes critical.
To address this challenge, Scott and Neff developed the A-F Framework, a standardized approach to evaluating and managing AI use cases across the enterprise. This single artifact ensures that all stakeholders, from the requesting team to legal, security, and privacy, are aligned throughout the review process.
The framework captures six key data points for each AI use case:
The A-F Framework, developed by Brian Scott and Dan Neff at Adobe, is a standardized approach to evaluating and managing generative AI use cases across an enterprise. It captures six key data points for each AI use case, ensuring that all stakeholders, from the requesting team to legal, security, and privacy, are aligned throughout the review process. The framework consists of the following components:
By capturing this information upfront, the A-F Framework enables a streamlined review process and ensures that all relevant stakeholders have the necessary context to assess the risks and benefits of each use case. For example, a possible use case could be:
Once a use case is submitted, stakeholders assign a risk score based on factors such as the audience (internal vs. external), input data sensitivity (public vs. private), and the nature of the objective (summary vs. actionable). This risk score serves as an early warning system, helping the requesting team understand the level of scrutiny their use case will face and allowing reviewers to prioritize their efforts.
Drawing inspiration from DevOps principles, Scott and Neff emphasized the importance of continuous improvement in AI governance. They recommended a three-pronged approach:
By iterating on their governance process, Adobe has been able to strike a balance between innovation and responsibility, enabling teams to move quickly while ensuring appropriate oversight.
Throughout their journey, Scott and Neff encountered several challenges and gleaned valuable lessons:
As generative AI continues to evolve, the need for effective governance will only grow. Adobe’s experience highlights the importance of proactive, collaborative, and adaptable approaches to managing this powerful technology. By sharing their insights and seeking input from the broader community, Scott and Neff are contributing to the development of best practices that will benefit organizations across industries.
For executives embarking on their own AI governance journeys, the key takeaways are clear: engage stakeholders early, establish clear frameworks and processes, prioritize based on risk, and continuously refine your approach. By doing so, you can harness the transformative potential of generative AI while mitigating the risks and ensuring responsible deployment at scale.
Watch the full presentation in our video library here.
Sign up for the next Enterprise Technology Leadership Summit here.
Managing Editor at IT Revolution working on publishing books and guidance papers for the modern business leader. I also oversee the production of the IT Revolution blog, combining the best of responsible, human-centered content with the assistance of AI tools.
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