Implementing an effective data readiness plan for AI insight and productivity

Artificial intelligence is transforming how companies of all sizes and in all industries do business. Effective AI implementation and integration can deliver rapid gains in efficiency, insight and productivity—but data is the key. Having actionable, accessible, trusted and secure data is essential to AI success, and a data readiness strategy is a critical first step in that effort.   

According to the RSM Middle Market AI Survey 2025: U.S. and Canada, 91% of total respondents said their organization uses AI, either formally or informally, in business practices. However, data was listed as a common barrier to AI success, as 41% of respondents who experienced AI implementation issues expressed concerns about data quality, making that the top problem companies faced.

An effective data approach directly correlates to getting the most value from AI investments. Therefore, before deploying AI initiatives, companies should take a step back and ask a few questions about their data. These include:

• How ready is my data to support AI initiatives?
• Is my data governed properly?
• What practical steps can be taken to improve data readiness?

Capitalizing on incremental AI data readiness

On the surface, data readiness can seem like a daunting challenge, but it does not have to be. Rather than tackling data readiness across your entire data estate, evaluating and addressing data readiness in conjunction with individual AI use cases is a streamlined strategy for success.

You can identify an individual AI use case and focus data readiness efforts on the associated prerequisites, especially gaps that are blocking the data readiness for the chosen use case to make governed data available. From there, you can address targeted data challenges that arise during the development cycle and ultimately deliver an AI solution grounded in high-quality data and measurable outcomes.

In doing so, you not only develop a successful AI use case with powerful results, but also resolve data issues within that specific scope and potentially uncover problems that may exist in other areas of the organization. This incremental approach to data readiness is firmly linked to delivering the AI use case and is thus delivering value. It also delivers data readiness value for the enterprise in the form of a slice of governed data that is now available for broader use across the enterprise.

With one AI use case successfully addressed, you can then move on to the next use case and very specifically and exclusively determine what trustworthy data is needed to support it and repeat the above exercise of working through the prerequisite data needs and blockers to govern the data required for the second use case.

Over time, as more AI use cases are worked in this way, data readiness is being built throughout the enterprise, providing a growing set of governed data for other operational and analytic uses.

Identifying and prioritizing use cases

AI encompasses a vast set of solutions, and companies should identify and prioritize their specific goals and understand what AI means for each of them. To start that process, you need to establish organizational alignment and a process to determine what use cases mean the most to the business. This will help you to start pinpointing what readiness measures are necessary for those efforts.

For example, companies can target AI to provide several business improvements from an automation or predictive perspective, introducing new efficiencies and insights. Those automation opportunities, predictive analytics and modeling use cases should first and foremost be driven by your business strategy and align with what you’re trying to accomplish.

AI use cases can also originate where an organization is experiencing issues. Perhaps the customer experience may require streamlining, inventory management may need attention or fraudulent activity may be a concern in specific areas of the business. Additionally, AI use cases can emerge in response to industry trends or by identifying new opportunities for growth. AI solutions can help address these matters, but effective and dependable data is necessary for success.

Common data challenges

When implementing an AI solution, data challenges can emerge in many ways, from accessibility and accuracy to quality and security.

In some cases, companies do not understand the current state of their data. Users can feed an AI model or an AI agent, but if they do not understand or trust what information is going in, they certainly will not understand what is coming out. Organizations need to ensure they have a readiness checklist for prioritized AI use cases to confirm data access, availability and quality, based on agreed-upon thresholds to support those AI strategies.

Data protection and privacy concerns also commonly emerge when information is not securely labeled, or if it is classified incorrectly. In these situations, companies risk exposing sensitive data and intellectual property or violating data security regulations by revealing customer or employee personally identifiable information.

How do you know if you have a problem?

If a company has a data issue in AI development and deployment, it often becomes apparent very quickly when a lack of standard inputs and calculations emerge and outputs do not yield the desired insights or productivity gains. Unfortunately, these issues result in extended timelines and increased costs, emphasizing the importance of initial standardization, data quality and alignment of data that feeds into AI models.

Often, companies don't understand where all of their data resides, or they simply don't trust their data. Those scenarios indicate immediate red flags; they mean that companies will almost certainly experience a data issue. When the data is pulled together, multiple competing elements can seem to represent the same thing, results may not be reliable and, once again, development time and costs will continue to grow.

Further, in some cases, results for an AI model may not be compelling or conclusive. That may be because data elements do not represent what they appear to be because they were named or categorized in an inconsistent or nonstandardized manner. If that’s the case, those data elements need to be updated within the model, which leads to additional development time. In the end, after going through multiple cycles, users still may not be confident in the results after adjustments, and outputs may not be relevant for their initial intended business purpose. 

A detailed assessment of your AI data readiness

Prior to initiating an AI project, your organization can evaluate your data readiness efforts to improve your data foundation by undergoing a data strategy assessment. That process delivers an in-depth analysis of your data environment, providing an overview of any current data needs as well as a future roadmap that outlines how your data can ignite successful development of your defined AI use cases.

In just a few weeks, an assessment can identify and resolve your current AI data readiness concerns while encouraging data optimization for future AI use cases with outcomes including:

• Establishing a defined data and analytics foundation
• Introducing improved decision making through reliable and accessible data
• Creating a more effective, data-driven culture