5 Signs Your Data Is Ready for AI

Artificial intelligence is one of the most transformative technologies in a generation. In fact, according to the 2025 AI & Data Leadership Executive Benchmark Survey, a staggering 98% of data and AI leaders say their organizations are increasing their investments in data and AI this year—up from 82% in 2024. And adoption is growing, too. The number of organizations running AI in full-scale production is up 19% year over year, while those in earlier stages of production have increased by 22%.

As AI investment and adoption continue to skyrocket, the need for high-quality, trustworthy data has never been more critical. But when it comes to AI in data management, how do you know if your data is up to the task? These telltale signs can help you determine whether your data is prepared to support your AI initiatives effectively. 

5 Signs Your Data is AI-Ready

1. Your organization has a single, agreed-upon customer count‍

AI requires data that is consistent and complete. A good way to gauge the quality of your data is by asking the question, “How many customers do we have?” Then, compare the answers you receive from different constituents across the organization. If the responses are consistent, then your customer data is likely in pretty good shape. If they’re not, then you have some customer master data management (MDM) work to do to assess and improve the quality of your data. 

2. It’s easy to fix errors in your data

No company has data that is 100% error-free. But when it comes to AI, the better the data quality, the more reliable the insights. If your business has a process in place to continuously clean your data, you’re one step closer to having the high-quality data needed to power AI applications.

3. You’ve established a data enrichment program

Data enrichment is the process of enhancing existing internal datasets with information that is generated from additional, trustworthy, outside data sources. And it’s a core piece of the puzzle when it comes to ensuring your data is complete and up-to-date.

4. You have a process in place for users to fix bad data

Collaboration and user feedback are equally important when it comes to ensuring the quality of your data. Without input from a cross-section of stakeholders, including the data team and end users, the data that powers your AI or integrates with LLMs runs the risk of being misaligned with real-world needs—leading to misguided insights, or worse, insights that are flat out wrong (i.e., “hallucinations).

5. You trust your data‍

Arguably the most critical factor in determining whether your data is AI-ready is a simple one: Can you trust it? If the answer is no, then stop what you are doing, take a step back, and embark on a journey to assess, improve, review, and operationalize your data.

Bridging the Gap to AI-Ready Data

As AI continues to transform the business landscape, high-quality, trustworthy data has become more important than ever. But if you need to improve your data, or if you are unsure if your data makes the cut for powering AI, analytics, and confident decision-making, then download our ebook The MDM Journey: From Trusted Data to Operationalization. In it, you’ll discover what the MDM journey is, how to assess where you are, and where you should go next.

By following the MDM journey and assessing your data against the readiness indicators above, you not only reduce risk but also position your business to move with greater confidence into an AI-driven future.