Our AI governance framework
Integrating AI into your roadmap requires a strategic approach. To navigate effectively, there are four essential steps your organization must take. These steps form the framework of a systematic, repeatable approach to AI.
Define the use case
Identify and understand data
Document models and results
Verify and monitor
By following these steps, you can harness the full potential of AI, driving innovation and achieving significant competitive advantages.
Before you start anything, you need to know what you’re doing. The first step in your AI journey is to clearly define your use case. Knowing the intended purpose of your AI model — and where it will be deployed — should always be your first step.
A well-defined use case serves several purposes. It clarifies why the AI model is necessary, outlines the specific problems it aims to solve, and details the type of data it will utilize. Additionally, it provides a clear vision for the desired outcomes and the business value. More importantly, it helps your stakeholders and reviewers make informed decisions about how to move forward.
Business context: Develop a well-documented use-case description that includes an analysis of the business value, the business policies the model may impact, and a list of business owners and their respective responsibilities.
Legal, ethics, and compliance: Assess whether the model will handle sensitive or private information, such as personal identifiable information (PII). Understand and document any specific regulations that may impact your AI model, along with risk assessments to ensure compliance and ethical considerations are addressed.
Data usage: Clearly outline the data required for the model, including what data will be used as input, how the model will be trained, and the nature of the output data.
By addressing these key areas, you’ll ensure your AI roadmap is grounded in a thorough understanding of the broader context before you invest any resources into building an AI model.
An AI governance solution that provides a single location to document AI use cases and collaborate with a wide range of stakeholders will help you keep track of AI use cases across the project lifecycle.
Trust is everything in data and AI. It’s why the old adage — "Garbage in, garbage out" — still holds true in our AI era. It explains why once you’ve defined your AI use cases, you need to take a close look at your data.
But how? Most organizations face an intractable challenge. Fragmented governance tethers control and visibility to specific data systems, sources or compute platforms and prevents companies from scaling their data and AI use cases safely. Data exists in pockets across apps, multiple public and private clouds, and on-prem, creating blind spots for what data exists, context for what it means and who has access.
The news doesn’t get better the longer you wait. As data estates become more complex and LOBs spin up more use cases, this problem will only grow more complicated, while AI multiplies the risk of unreliable and noncompliant use. The disconnect extends to people as most systems offer no way to bring business users to access data and give it meaning.
It’s why the cornerstone of any successful AI initiative is a deep understanding of the data your model will leverage. It includes understanding the nature of your data, as well as ensuring your compliance with all relevant laws and
regulations. And it’s why delivering trusted data for AI models starts with implementing the right data governance strategy. Rigorous guardrails will ensure you can operationalize AI workflows and processes to deliver trusted data.
To operationalize successfully, an enterprise data catalog is key. A game-changer for data scientists, a data catalog streamlines discovery and understanding of data across sources. The inclusion of a user-friendly data marketplace, in addition to a data catalog, helps data scientists find and access data in a fraction of the time compared to traditional methods of consulting stakeholders.
Data quality is another pivotal factor in the success of your AI initiatives. The active monitoring of data pipelines using advanced data quality and observability tools is crucial. These tools help quickly identify and resolve problems before they reach downstream outputs, such as your AI models. Finally, implementing clear data privacy policies ensures only authorized users can access specific datasets, reducing the risk of inappropriate data usage and reinforcing the integrity of your AI program.
It’s time to build. With a well-defined use case and high-quality data to feed your model, your focus shifts to building the AI model. It’s crucial to document every detail during this process, including model outputs and challenges faced.
This step is where data scientists will focus most of their time. They’ll document, trace and track the model, associated data products and usage. Comprehensive documentation is vital for model analysis and reporting. Data lineage is particularly essential in this phase; it ensures that you have clarity on the origin of the data, any transformations to it, and how and where outputs are used.
This is especially useful in highly regulated industries, like financial services, where regulators may demand to see how data is being used.
In this step, your primary goal is to get initial results.
Once you land on a model that passes scrutiny, you’re ready for the hard step of moving into production.
Data Citizens Dialogues Podcast
The final step isn’t really a final step. It’s important to remember that AI governance is not a one-time effort. Once your model is ready for production, it’s vital you continually monitor results and revisit the legal and compliance requirements as new AI- and data-specific regulations are always coming into play.
Verifying model performance: Prior to full-scale deployment, it’s critical to verify that the AI model acts as intended. Verification is a quality check, confirming that the model meets technical and business expectations
Putting the model into production: Moving the AI model from a controlled testing environment into production is a big step. It involves integrating your model into your operational environment where it will start affecting real-world decisions. You’ll also trace and document the flow of data through the AI system to understand how data is transformed and used in decision-making, which is crucial for troubleshooting and compliance
Ongoing monitoring for data quality and compliance: Monitoring is vital to detect and address performance issues, data drifts or unexpected behavior. It involves tracking model output for accuracy, bias, and adherence to regulatory and ethical standards. You’ll also vigilantly protect sensitive data, adhering to privacy regulations and ethical standards, especially as the model interacts with new datasets
Retraining the model as needed: AI models are not set-and-forget tools. They require periodic retraining to incorporate new data, new regulations, and new technologies. Retraining is crucial to ensuring the model’s accuracy and relevance
By following this step with an emphasis on data quality, data lineage, and data privacy, you can ensure your AI models remain relevant, robust, and compliant, as well as capable of adapting to new challenges and ensuring long-term effectiveness.
