How Midmarket CIOs Can Build AI-Ready Data Without Driving Up AI Costs
Organizations that skip data readiness before building AI are likely to rack up remediation costs later.
Getting data wrong in AI is expensive. An IBM study found that over one-quarter of organizations lose more than $5 million annually to poor data quality, and those losses scale with AI investments. An analysis of more than 2,400 enterprise AI initiatives tracked by Pertama Partners found that organizations that skip data readiness before building AI are likely to rack up remediation costs later.
The colossal consequence of running AI on bad data foundations came up in the most recent episode of the Ready.Set.Midmarket! podcast with Steve Leslie, CEO of Quadbridge, who argued that poor data governance is one of the key drivers of AI failures in the midmarket.
Here are some of the lessons CIOs can draw from that episode, as well as best practices for building quality data pipelines for AI without overstretching your budget.
Start With One AI Use Case—Or Data Costs Will Spiral
One of the most common budget traps in midmarket AI projects is attempting to make all data AI-ready at once. Leslie described this on the podcast, noting that midmarket AI adoption tends to be “active but informal,” with teams experimenting across multiple areas without a strategic framework guiding where to focus.
That informal spread is what turns data preparation into an open-ended cost. A more effective approach, as recommended by BCG’s Build for the Future research, is to redesign a specific workflow from end to end rather than layering AI on top of existing processes. When organizations do this, they are significantly more likely to generate measurable AI value, the study said.
In practical terms, that means selecting one use case with a defined business outcome and cleaning only the data it requires. If the deployment is a support triage tool, for example, the data that needs governing could come from ticket history and the knowledge base. If it is a demand forecasting model, the scope may narrow to sales records and inventory data. With this in place, the data work stays contained, the cost stays justifiable, and the governance framework built for that first use case becomes reusable when the second one arrives.
Why Cleaning Existing Data Beats Buying New AI Platforms
CIOs under pressure to deliver AI results often respond by purchasing new platforms, but the more cost-effective move for most midmarket teams is to clean and standardize the data they already have. DBT Labs' 2026 State of Analytics Engineering Report found that ambiguous data ownership remains a challenge for 41 percent of organizations and that poor data quality is still the single most cited obstacle to analytics and AI performance. This point is reinforced by Deloitte in its 2026 Regulatory Outlook on AI and Data, which suggests that legacy systems and fragmented architectures have left data inconsistent and siloed, while also pushing the argument that governance is foundational to the transparency and explainability that regulators increasingly expect.
Based on that report, some steps that control cost include assigning named owners to priority datasets, establish basic quality rules, eliminating duplicate records, and documenting lineage for the data feeding AI systems. These are low-friction moves that improve AI performance without adding licensing costs or integration overhead.
Bake Data Governance Into AI Deployments—Or Pay For It Later
One of the fastest ways to drive up AI costs is to build governance as a separate project that runs on its own timeline. Ron Reiter, CTO at Sentra, told the Ready.Set.Midmarket! podcast that data security posture needs to be in place by the time the AI program goes live because tools like enterprise AI assistants can instantly expose sensitive information buried in unstructured repositories.
Building classification and access controls directly into the deployment work stream avoids the expense of retrofitting them later.
It also means the governance framework from the first use case carries over to the next at incremental cost, which is how midmarket teams keep the per-deployment price declining instead of compounding.
Consolidation Is Becoming A Data Cost-Control Strategy For AI
Every additional tool in the data stack adds integration overhead, licensing cost, and governance complexity, all of which drive up the total cost of making data AI-ready.
Based on MES Computing’s midmarket CIO priorities for 2026, boards and executive teams are looking for evidence of consolidation over tool sprawl, with delivery measured by whether AI capability is built on platforms that support multiple needs within a single operating model.
CIOs who focus on consolidating around fewer, better-governed platforms cut integration spend, eliminate redundant licensing, and reduce the hours teams spend reconciling conflicting data across disconnected systems.