Data Strategy: Building a Foundation for AI
By enforcing data quality and governance, organizations can lay the necessary foundation for trusted artificial intelligence solutions that scale.
IN THIS ARTICLE
Even as organizations across industries race to adopt artificial intelligence solutions, many leaders feel like they are running in place, with pilots and proofs of concept stalling and lasting enterprise value proving elusive. Often, the culprit for this stagnation is data. While enterprises are generating and storing more information than ever before, their data environments are frequently fragmented, outdated and inconsistent, providing a poor foundation for AI tools that depend on accurate, up-to-the-minute information to make recommendations and take actions on behalf of users and customers.
To build a modern data foundation that supports enterprise AI, organizations must give AI tools the necessary context to make sense of business information, as well as appropriate access to data stores. Governance is also critically important, not only for reducing security and compliance risks but also for ensuring that AI tools are operating with trusted data that will yield accurate outputs. A trusted partner such as CDW can help organizations outline and implement a data strategy that supports AI success.
CDW can help you unlock the power of your data.
- Governance Enables Trusted AI
- Financial Governance for AI
- An AI-Ready Data Roadmap
When many people hear the word “governance,” they automatically think about restrictions that control risk and satisfy compliance requirements. But especially for AI, governance has increasingly become about activation. According to a 2026 report from Drexel University, 42% of leaders cite “improved readiness for AI” as a top outcome of their data governance programs, while 39% cite “improved quality of AI outcomes.”
While poor data governance can slow down AI programs, effective governance provides the trusted data, ongoing observability and strong management needed for AI success.
TRUST: AI cannot produce trustworthy results without trustworthy data; if an AI tool is trained on flawed data, then it will inherit those flaws and create outputs that reflect the poor quality of the inputs. The problem is greater than an occasional inaccuracy. When employees and leaders learn that their enterprise AI solutions cannot always be trusted, they may be reluctant to use the tools at all out of fear that they will produce faulty conclusions and offer poorly informed recommendations. And if AI agents are allowed to take autonomous actions based on inaccurate data, the problem can become much more serious.
OBSERVABILITY: Data observability is the continuous monitoring and diagnosis of the health and reliability of an organization’s data and data pipelines. The goal of observability is to help organizations detect, investigate and prevent data problems — such as late, incomplete or malformed data — before they can corrupt dashboards, applications or AI outputs. This preventive function is especially important in the age of AI, when a “fix it later” approach can leave significant risks unaddressed. The value of observability is obvious, but in practice, organizations often fail to emphasize it. According to Drexel University, only 31% cite data observability as a top priority for improving data integrity.
MANAGEMENT: Organizations must enforce guardrails for data quality, security and regulatory compliance, without creating roadblocks that slow down AI efforts. Governance should be designed around two complementary layers: a strategic layer that establishes enterprise policies around compliance, privacy and risk and a tactical layer that embeds controls into data pipelines. Today, technical elements of governance, such as data quality, lineage and logging, can often be automated. Users represent an often overlooked source of governance information. Because these users have an intimate understanding of the data, they can identify when something looks wrong. Organizations that let employees easily surface anomalies will give themselves an advantage as they seek to implement effective governance.
Click Below To Continue Reading
Historically, data systems have been designed around the assumption that there is a human at the end of the process, someone who would use the stored data to make decisions and take action. Organizations still need to design their data foundations to be usable by humans, but they now must ensure that these systems are able to work with generative and agentic AI solutions as well.
Modern data foundations for enterprise AI should offer essential context around data, give AI tools access to trusted data wherever they require it, and increase agency for line-of-business users to leverage AI to help them be more productive and proficient at their jobs.
CONTEXT: AI success depends on context as much as it does raw data. For example, AI agents need to understand how data from enterprise systems correlates to what is happening in the real world, and how that information affects customer service, delivery, safety and other practical considerations. In its report, Drexel University notes that 96% of organizations are adding context to their data with location intelligence and third-party data enrichment. “Organizations that successfully build a reliable, contextual understanding of their business environment while addressing privacy, quality and integration challenges position themselves to extract maximum value from AI investments,” the report’s authors write.
ACCESS: Modern architectures should allow organizations to deliver trusted data wherever AI applications require it. This is easier said than done. According to the Drexel report: “The data required to power Agentic AI is often hard to access, scattered across hybrid and legacy systems, incomplete or outdated, lacking context, non-compliant and expensive to manage.” Organizations can break down these silos by creating cross-functional teams spanning areas such as data, security and infrastructure to ensure that data systems are serving the needs of the entire enterprise. Rather than merely moving data from one point to another, these teams must create reliable processes that allow trusted data to reach the workloads that need it.
USER AGENCY: It is important to remember that the entire point of creating a modern data foundation is not to facilitate the adoption of new technologies, but rather to help employees achieve the goals of the business. A modern data foundation should increase agency for the employees who use it, giving line-of-business users (not just IT and data teams) the tools and trusted data they need to make decisions and act quickly. According to McKinsey, 65% of organizations identified as AI high-performers have defined processes to determine how and when model outputs need human validation to ensure accuracy (compared with just 23% of other respondents).
As AI and token consumption scale, traditional financial planning and budgeting models often struggle to keep pace. For a time, some organizations publicly pursued “tokenmaxxing” strategies, complete with leaderboards that incentivized AI consumption. However, many of those same organizations are now trying to control AI costs, especially as vendors have moved away from flat-fee pricing models.
Today, CFOs have become central players in AI and data strategy, tasked with managing cost volatility, vendor dependency and infrastructure tradeoffs. Leaders can control AI costs by taking these steps:
INVESTMENT PRIORITIZATION: By strategically prioritizing their efforts, organizations can turn their AI spending into a series of deliberate choices, rather than defaulting to the most expensive option. Different AI use cases require differing investments: Some high-volume uses, such as document classification and routine summarization, have moderate quality requirements, while other initiatives demand high-level capability but at low volume. Designing a tiered strategy early beats retrofitting one after the AI bill becomes unmanageable. Success isn’t about using more; it’s about making better use of the tools available.
COST VISIBILITY AND OPTIMIZATION: Cost visibility starts with a simple truth: Every interaction with an AI model has a measurable cost behind it, even when bundled tools or enterprise licensing make it invisible. As organizations increase their use of AI, token costs compound, driven by reasoning depth, context windows, tool-use chains and multicall agentic workflows. This growth is often faster than budgets anticipate, but once organizations can see consumption by workload, they can optimize it: routing simple tasks to lighter models, reserving premium models for complex reasoning, and building token awareness into budgeting, performance evaluation and governance before costs become unmanageable.
AI/CLOUD FINOPS: FinOps brings financial accountability to the structural growth of AI costs. As agentic workflows drive compounding increases in token costs, organizations need continuous collaboration among their finance, engineering and business teams to avoid after-the-fact bill shock. This requires teams to treat tokens like any metered resource. They should be tracked by workload, budgeted proactively and tied to ROI. A tiered strategy that is implemented before costs get out of control gives finance and technical teams the shared visibility they need to forecast spending, evaluate performance and manage usage, which helps to ensure they derive the intended value from their token use.
BUDGET OWNERSHIP AND CAPACITY PLANNING: Budget ownership and capacity planning replace guesswork with accountability. As token costs increase, organizations need clearly defined owners who can track consumption by workload rather than by head count. A tiered strategy that matches high-volume, moderate-quality tasks to lighter models and reserves higher-level capabilities for complex reasoning must be implemented upfront, long before budget overruns. By planning capacity proactively against actual workload demands, organizational leaders can keep spending predictable as adoption scales.
GOVERNANCE AND CONTROLS: Governance and controls enable organizations to manage AI cost growth before it becomes structural. AI allows users to build workflows without traditional development resources, but this process requires oversight to prevent runaway consumption and maintain output quality. Effective controls enable requests to be routed deliberately, directing simpler tasks to lighter models while reserving premium models for complex reasoning. Organizations that build these controls into their budgeting, performance evaluation and usage governance processes can scale AI intentionally rather than reactively.
Click Below To Continue Reading
Minimum viable data governance (MVDG) offers a pragmatic, incremental path to embedding data governance from the start of data initiatives, setting a governance “floor” that ensures organizations incorporate essential concepts into their AI programs.
1. Data Stewardship: Ensure that valuable data assets are accurate, consistent and secure before they are used to make key decisions.
2. Data Quality: Avoid downstream mistakes and delays through automated quality checks and continuous monitoring that ensure data is complete, consistent and delivered in a timely fashion.
3. Data Privacy: Build privacy-by-design principles into workflows to safeguard sensitive information at every touchpoint and reduce the risk of exposure.
4. Data Security: Implement asset controls, encryption and regular audits to monitor data usage and protect data assets from unauthorized access or breaches.
5. Metadata Management: Enable data discovery and usability by implementing data dictionaries to capture and systematically organize information about each data asset, including lineage, classification tags and documentation.
Technology is moving too quickly for organizations to adhere to a rigid AI master plan. But by developing and following a flexible roadmap, leaders can ensure their teams are heading in the right direction.
ASSESS THE ENVIRONMENT: For AI environments to be successful over the long term, they must be built on a solid foundation. Organizations should start by assessing their data security and governance, their infrastructure and other factors that may hamstring their efforts. A trusted partner such as CDW can bring a more objective viewpoint to this process. CDW offers AI readiness assessments, data quality assessments, governance diagnostics and health checks that can help identify foundational problems that need to be fixed before organizations can successfully scale their AI initiatives.
MAP DATA TO BUSINESS VALUE: Enterprises that invest in strong data foundations will be better positioned to adopt future AI innovations. However, it is easy for organizations to get wrapped up in the hype of one new AI tool after another. Instead, leaders should map their data to business value and then prioritize improvements that will have the greatest business impact. After organizations complete an initial inventory of their data environments, they should identify the metrics that most directly impact revenue. By focusing their AI efforts on workflows involving this data, leaders are more likely to see a tangible ROI.
ITERATE AND OPTIMIZE: AI readiness is an ongoing operational discipline, not a one-time project. Data strategy must evolve along with an organization’s business priorities and changes in the technology landscape. A data strategy roadmap, therefore, should be iterative and adaptive, resembling agile development cycles more than a fixed multiyear plan. Whereas technology initiatives were once organized around three-year plans, leaders today are more focused on velocity than on establishing and implementing a semipermanent strategy. This change is due not only to a desire to accelerate time to market but also to a recognition that technology is simply changing too quickly for anyone to predict with any real accuracy what their organization’s needs will be several years in the future.
MODERNIZE IN PHASES: AI success comes from prioritization and phased execution, not from all-at-once modernization efforts. Rather than trying to clean up the entire data estate before taking action, organizations should first optimize their most business-critical data for AI. Then, they can continue to modernize over time, pursuing projects that are both high-impact and achievable. These modernization efforts should simultaneously retire technical debt and strengthen controls, creating an ever-improving data foundation as organizations implement new AI tools and workflows. CDW offers engagements and services to help at every step of the way, including data classification, FinOps assessments, full strategy engagements, and embedded architects and engineers.
CDW can help you with your data needs to create a path toward AI success.
CDW Experts
CDW Expert
