The News: On July 11, IBM announced that watsonx, the company’s enterprise-ready AI and data platform, has begun to roll out for general availability. The platform is made up of three products – watsonx.ai, watsonx.data, and watsonx.governance. Components of watsonx.ai and watsonx.data are live and have been covered by Futurum Group analysts in recent research notes: IBM watsonx.ai and .data Go GA, watson.ai Leverages Foundation Models to Accelerate AI Application Development, and IBM watsonx.data is Now Generally Available as Part of Major watsonx Announcement. The focus of this note is on watsonx.governance, which will be available later in 2023.
Here are the pertinent details:
- watsonx.governance is a product designed to enable enterprises to direct, monitor, and manage the AI activities of the organization. It addresses three challenges:
- How does an organization operationalize AI with confidence?
- How does an organization manage risk and protect reputation?
- How does an organization comply with growing regulations?
- Operationalizing AI with confidence: AI is typically handled by experts in silos, which is not scalable. Organizations build too many models without clarity, monitoring, or cataloging. There is generally a lack of transparency for explainable workflows across the model lifecycle and there is a lack of automated processes for scale.
- How watsonx.governance addresses this: Governs across the AI lifecycle by automating and consolidating tools, applications, and platforms. If something changes in models, all that information is automatically collected for audit trail through testing and diagnostics.
- Managing AI risk and organization reputation: Enterprises must understand risks of AI in every use, case by case. Emerging best practices are for organizations to build an AI ethics governance framework, including establishing an ethics committee or board to oversee. This approach is a highly manual process.
- How watsonx.governance addresses this: Automate workflows to better detect fairness, bias, and drift. Automated testing to ensure compliance to an enterprise’s standards and policies throughout the AI’s lifecycle.
- Complying with growing AI regulations: More than 700 AI regulations have been proposed globally. Most are umbrella regulations that are not overly specific. Most companies do not understand how to comply.
- How watsonx.governance addresses this: By translating growing regulations into enforceable policies within the company. The solution breaks down the requirements in the regulation and builds controls.
Read the full watsonx GA announcement on the IBM website.
IBM watsonx.governance Tackles AI Risk Management
Analyst Take: AI risk management is foundational and critical to operationalizing AI. Enterprises will learn this either the hard way, through ignoring it, or the easier way, by embracing it. IBM is in a great position to help enterprises navigate AI risk management. Following are the key takeaways related to IBM’s strategic move in this space.
IBM understands the importance of AI risk management. IBM is well positioned to help enterprises operationalize AI. They are one of the AI pioneers and an innovator in the AI space. Along with a handful of other companies, IBM has thought about and worked with AI for many years. That experience comes into play when thinking about how to operationalize AI, and what it takes to be successful with AI. IBM has gone through this process – they have had the time to think about what AI is, and they have had time to experiment with how to use it. With the benefit of that experience, they understand AI risk and the AI lifecycle.
IBM’s watsonx.governance is unique. IBM’s approach to AI is full stack and watsonx.governance is a unique, differentiated element. watsonx includes comprehensive products for AI models and managing data, both of which face competing products. At this time, there are not competing products to watsonx.governance.
Challenges in the soft areas – people, processes, and lawmakers. watsonx.governance is ambitious. The strengths are in the vision and blueprint for automating model management/audit trail and for automating workflows to better detect fairness, bias, and drift and for automating testing throughout the AI lifecycle. The challenges for the product will be that some outputs are dependent on people and processes – for instance, will an organization construct a competent, effective AI ethics governance framework and committee? If AI regulations are vague, will the product be able to build good enough company enforceable policies? As the product moves through further testing and general availability, these questions might be answered in the affirmative or we might also see a shift in product features. Either way, an automated process to AI risk management is a very good thing.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other insights from The Futurum Group:
IBM watsonx.ai and .data Go GA
watson.ai Leverages Foundation Models to Accelerate AI Application Development
IBM watsonx.data is Now Generally Available as Part of Major watsonx Announcement
Author Information
Based in Tampa, Florida, Mark is a veteran market research analyst with 25 years of experience interpreting technology business and holds a Bachelor of Science from the University of Florida.
