How to get your data ready for generative AI

As generative AI moves from experimentation to expectation, many organizations are realizing that success depends less on tools and more on having the right data strategy for AI. Without a clear approach to how data is structured, governed and validated, generative AI initiatives often produce confident outputs that are difficult to trust or defend.

Generative AI applications such as ChatGPT are already reshaping business operations, from content creation and customer support to analytics and decision support. Many organizations are seeing real productivity gains and new capabilities. At the same time, they are encountering technical, legal, privacy and strategic challenges tied directly to data readiness.

To unlock the full value of generative AI and reduce risk along the way, leaders need to focus on how their data is prepared, protected and applied. The following steps outline how to build and sustain a data strategy for generative AI that supports real business outcomes.

Step 1: Build a strong data foundation for generative AI

A reliable data foundation is the starting point for any successful generative AI initiative. This includes improving data availability, governance, quality and context so AI models can operate effectively and responsibly.

• Improving data availability: To fully leverage generative AI, organizations need their data assets to be accessible and well-organized. This often begins with uncovering “dark data,” information that exists across emails, contracts, documents, images or archives but is rarely used.

  Ingesting this data into AI-enabled environments can unlock new insights and efficiencies. Diverse data sources such as customer records, transactional data, legal documents and internal communications allow generative AI applications to identify patterns and relationships that would otherwise remain hidden.

• Enacting data governance: Strong data governance is essential for ethical and compliant use of generative AI. Organizations need clear guardrails defining how data can be accessed, used and shared by AI systems.

  Governance practices help address privacy concerns, reduce bias and ensure that data used by AI models is accurate and consistent. Without governance, generative AI can amplify existing data issues rather than solve them.

• Ensuring data quality: Generative AI is only as reliable as the data it is trained on and retrieves from. Inaccurate, incomplete or outdated data leads to outputs that appear confident but lack credibility.

  Organizations should prioritize data accuracy, completeness, timeliness and consistency. The familiar principle of “garbage in, garbage out” applies even more strongly in AI-driven environments where errors can scale quickly.

• Adding data annotations and metadata: Metadata gives generative AI the context it needs to interpret data correctly. This includes tags, labels, lineage, quality indicators and usage constraints.

Investing in metadata improves how AI models understand content, leading to more relevant and accurate outputs. It also supports governance and validation as data is reused across multiple applications.

• Curating new data sources: Some generative AI use cases require data organizations do not currently have. This may include external market data, industry research, web content or third-party datasets. Establishing a disciplined approach to sourcing and managing these data supplies helps organizations expand AI capabilities while maintaining control and quality.
• Validating AI-generated content: As organizations begin to generate content and insights using generative AI, they must also validate what those systems produce. AI models can generate information that sounds plausible but is factually incorrect or incomplete.

Policies and review processes should be established to ensure AI-generated outputs are appropriately labeled, reviewed and validated before they are used in decision-making or shared externally.

Step 2: Identify generative AI use cases and required data

A data strategy for generative AI becomes practical when it is tied to clear use cases. Leaders should identify where generative AI can meaningfully support business objectives, then prioritize data preparation accordingly.

Common use cases include:

• Customer service automation: Generative AI can automate responses to common customer inquiries, handle routine requests and support service teams. This requires access to high-quality customer data, call center records and product information.
• Personalized content and recommendations: Organizations are already using generative AI to tailor content, recommendations and messaging. Success depends on integrating customer data, feedback and sentiment analysis to understand preferences and behavior.
• Content creation and marketing support: Generative AI can assist with marketing and sales content, social media posts and blogs. Effective use requires curated data sources such as market research, competitive insights and industry trends.
• Fraud detection and compliance monitoring: Generative AI can analyze large volumes of data to identify patterns associated with fraud or noncompliance. This use case relies on transactional data, regulatory guidance and historical incident data.
• Training and decision support: Generative AI is increasingly used to support employee training and executive decision-making. Simulations, scenario modeling and knowledge retrieval require access to training materials, historical decisions and strategic objectives.

Step 3: Balance data security, accessibility and compliance

A successful data strategy for generative AI must balance security with accessibility. Encryption, access controls and regular backups are critical for protecting sensitive information.

At the same time, data needs to be centralized and accessible enough to support AI workflows. Cloud-based platforms often provide the flexibility and scalability required to manage data for generative AI initiatives.

Organizations must also understand the legal and regulatory implications of using generative AI. This includes reviewing the terms of AI models, protecting intellectual property and complying with privacy regulations. Clear guidelines help address ethical concerns and reduce risk as AI use expands.

Step 4: Operationalize and sustain your data strategy for generative AI

With foundational elements in place, organizations need to operationalize and sustain their data strategy for generative AI. This goes beyond initial preparation and focuses on long-term alignment with business goals.

• Reinforce objectives and priorities: As generative AI use evolves, leaders should regularly revisit objectives and use cases to ensure they remain aligned with business priorities and risk tolerance.
• Maintain data integration and alignment: New use cases often require new data sources and integrations. Ongoing coordination helps ensure the right data is available as AI applications expand.
• Ensure infrastructure readiness: Generative AI can place new demands on systems and architecture. Organizations may need to update databases, APIs and integration layers to support performance and scale.
• Develop skills and capabilities: Teams need the skills to work effectively with generative AI. Training and upskilling help employees understand how to use AI outputs appropriately and recognize limitations.
• Measure outcomes and adjust: Defining metrics and key performance indicators allows leaders to evaluate the impact of generative AI initiatives. Metrics may include cost savings, customer satisfaction, risk reduction or revenue growth. Regular evaluation helps refine strategy over time.

Conclusion

Generative AI has already begun to transform how organizations operate, compete and make decisions. But the technology delivers value only when it is supported by a clear and intentional data strategy for generative AI.

By strengthening data foundations, aligning use cases with business goals and sustaining governance over time, leaders can reduce risk and move forward with greater confidence.

In an environment shaped by uncertainty and constant what-ifs, data readiness is no longer just a technical requirement. It is a business imperative.

How Wipfli can help

Wipfli’s technologists help organizations align data, analytics and AI initiatives with business strategy.

From data integration and governance to AI readiness and risk considerations, our teams support leaders as they move from experimentation to execution.

Learn more about Wipfli’s data and analytics services or our AI services for mid-market companies.