AI Governance Checklist: Is Your Business AI-Ready?

Artificial intelligence (AI) is a transformative force reshaping the very fabric of organizations across the globe. By 2030, AI is expected to automate nearly 30% of work hours in the U.S., driving efficiency, innovation, and growth (McKinsey). But with this transformation comes significant disruption. While AI will create millions of new jobs (Forbes), it will also redefine roles, requiring organizations to adapt swiftly or risk being left behind. 

How can organizations prepare to capitalize on this awesome opportunity? Here at Alation, we have created a checklist to ensure your organization is ready to deploy safe, compliant and effective AI initiatives.

For AI models to produce reliable predictions, they must be trained on high-quality data that is easily discoverable and trusted across the organization. For this reason, AI readiness hinges on a mature data culture, which ensures that AI initiatives are supported by accurate, well-understood, and properly governed data. A data intelligence platform plays a crucial role in this ecosystem by centralizing metadata and enabling efficient data management, ultimately facilitating AI readiness.

If your organization can confidently check off these boxes, you're on the right path to becoming AI-ready. In the sections below, we’ll dive deeper into each of these elements and provide actionable steps to ensure your organization is poised to thrive in the AI-driven future.

Is your data easily accessible and discoverable?

• Can you find the governed data assets you need to feed your AI/ML models?

• Can you access the relevant SMEs who know this data best?

• Can you see relevant metadata about each asset to inform your usage?

For data scientists and AI practitioners, the ability to find the right, governed data assets quickly is crucial. These experts also need details on the quality, relevance, and context of the data, which can significantly impact the model's performance and accuracy.  By reducing the time spent on manual data exploration, leaders can empower data scientists to dedicate more time to model development and optimization. Furthermore, the ability to uncover and access diverse datasets—often spread across various sources—can lead to more comprehensive and robust AI solutions, ultimately driving innovation and improving outcomes.

Search and discovery are at the heart of Alation’s Data Intelligence Platform. Alation acts as a single source of truth for data teams to find datasets and relevant metadata needed for models. Alation’s Intelligent Search capabilities streamline this process by providing a single, unified platform where data scientists can self-serve the datasets they need, along with metadata that adds context. This eliminates the time-consuming task of sifting through various data sources, ensuring that only the most relevant and compliant data is used in model training.  

Additionally, Alation supports collaboration among cross-functional teams, allowing data scientists to work more efficiently by learning from the collective knowledge stored within the platform. This ultimately leads to faster model development cycles and more robust AI solutions.

Is your data governed?

• Do you have a robust data governance framework in place? 

• Is your governance framework aligned with your ethical standards?

• Are the appropriate regulations documented?

• Does your framework safeguard data quality?

• Does it safeguard privacy, and tag PII data appropriately? 

• Does it ensure data security? 

A robust data governance framework is the cornerstone of AI readiness, particularly as organizations mature in their data culture. High-quality, well-governed data enables AI models to deliver accurate, reliable, and unbiased results, which is critical in today’s data-driven landscape. Higher data quality means superior outputs. Stronger privacy means avoiding regulatory concerns, reputational damage or the the loss of trust from prospects and customers. 

As organizations evolve, their ability to effectively manage data privacy, security, and quality becomes a competitive advantage, enabling them to build AI systems that are not only compliant with regulations but also aligned with ethical standards. Organizations need to start to invest in these areas to build a strong foundation. Without the foundation, AI initiatives risk becoming unstable, leading to mistrust and stunted innovation. Thus, data governance is not just a regulatory requirement; it’s an enabler of a mature, AI-ready data culture that can drive sustained business success.

Alation accelerates an organization's journey toward AI readiness by embedding strong data governance practices into every stage of the data lifecycle. Our platform’s active metadata management ensures that data is not only discoverable and understood but also governed with precision, supporting the maturation of a data culture that’s prepared for AI. 

With Alation, data scientists and AI practitioners gain confidence in the data they use, thanks to clear lineage, consistent quality checks, and rigorous privacy controls. Moreover, Alation fosters collaboration across the enterprise, making data governance a collective effort that enhances overall AI governance. In doing so, we empower organizations to harness AI with trust and integrity, ultimately driving innovation in a responsible and scalable manner. 

Are your AI products documented?

• Are you prepared for impending AI regulatory changes and auditing processes? 

• Are you tracking the datasets that your models are ingesting?

• Are you tracking AI model outputs?

As AI technologies become increasingly integral to business operations, the importance of AI governance cannot be overstated. Understanding the upstream and downstream lineage of AI governance is important both for internal but also external auditing. Effective AI governance ensures that organizations are not only compliant with current and future regulatory requirements but also prepared for the rigorous auditing processes that accompany AI deployment. This involves tracking and documenting every aspect of the AI lifecycle, from data ingestion to model output, to ensure transparency, accountability, and ethical integrity. 

With the complexity of AI models and the potential risks they pose, documentation and transparency is essential for mitigating risks, maintaining trust, and driving sustainable innovation in a rapidly evolving regulatory landscape. Once AI models are made available within the organization, maintaining confidence in their performance relies on robust metadata, lineage, and governance controls. If AI performance comes into question, the same data lineage and provenance information can be leveraged to update, replace, or enhance the original training data, ensuring ongoing compliance and effectiveness.

Are your models documented?

• Are you able to collaborate and communicate with teams crafting models across your organization? 

• Are AI regulations documented?

• Does your AI Governance Council have visibility into model documentation? 

As AI becomes more embedded in business operations, the formation of AI governance councils is becoming increasingly common. These councils play a critical role in overseeing the ethical and responsible deployment of AI technologies across organizations. However, for these councils to be effective, there needs to be seamless collaboration and communication about the AI models in use. This includes understanding the models' purposes, their data inputs, and how they align with the organization’s ethical standards and regulatory requirements. As these councils rise in prominence, it is essential that all stakeholders, from data scientists to business leaders, can effectively communicate and collaborate on AI models to ensure transparency, accountability, and alignment with strategic goals.

Are you ready for the next step of AI? 

• Do your current tools and technologies support scalable AI/ML operations? 

• Are your tools flexible enough to adapt to evolving data and business needs?

• Are you involving non-technical experts (like sociologists) in AI design to ensure human-centricity?

In today’s rapidly evolving technological landscape, the data and AI stack must be more than just robust –- it needs to be highly interoperable and flexible. As new AI software and development tools emerge, organizations require a stack that seamlessly integrates these innovations without disrupting existing operations. This adaptability is crucial for enabling rapid experimentation, where different AI and machine learning models can be tested, optimized, and deployed at scale. A flexible stack ensures that data flows efficiently across different systems, facilitating the continuous learning and improvement cycles fundamental to staying competitive in a dynamic market.

In conclusion, AI readiness is not just about the tools you use; it’s about fostering a culture where data is trusted, well-governed, and easily discoverable. By assessing your data culture maturity you can determine if your organization is truly prepared to leverage AI for innovation and long-term success. With a mature data culture, you can confidently navigate the complexities of AI and drive meaningful business outcomes.

Organizations aiming to harness generative AI face two key obstacles: a lack of expertise and the need to ensure accurate outputs. Watch this webinar, Building Trust in AI: Best Practices for AI Governance, from IDC's Stewart Bond, to learn how to prepare your AI initiatives for success.

Curious to learn more about getting AI-ready? Take the data culture maturity assessment to get started.