Post Syndicated from Maddie Presland original https://www.backblaze.com/blog/your-ai-strategy-is-only-as-strong-as-your-data-foundation/
Two-thirds of enterprise leaders see significant potential in integrating AI models with their proprietary data. Yet only 22% feel confident their current IT infrastructure could actually support new AI applications. That gap—between ambition and capability—is where most AI investments stall, budgets balloon, and promising projects quietly get shelved.
The problem isn’t the models, the change management, or even the cost. It’s the underlying data foundation.
The conversation that never happens
The problem is not that either conversation is wrong. It is that they happen separately.
Most organizations run two parallel conversations about AI, and they rarely meet in the same room until a deployment runs into trouble.
The first happens at the strategy level: executives identify use cases, project ROI, approve budgets, and set timelines.
AI is treated as a technology question with business outcomes.
The second happens further down the stack: infrastructure teams make decisions about where data lives, how it moves, and who can access it.
Storage is treated as a cost question, divorced from strategy.
This separation is intuitive. Different people work on it. Different timelines apply. Different success metrics matter. But the separation is a liability disguised as organizational structure.
Two-thirds of executives leading infrastructure efforts say they are excluded from key AI decision-making conversations. Every model selected, application built, and workflow redesigned depends on the same thing: a data foundation that either supports the strategy or constrains it.
That foundation has to be built alongside the strategy, not bolted on as an afterthought.
The cost of misalignment
There are consequences to this misalignment. When data infrastructure decisions get made separately from AI strategy, the result is predictable: AI teams discover too late that the data they need is fragmented, inaccessible, or poorly governed. Infrastructure teams optimize for cost without knowing what future AI workloads will require. Business leaders fund use cases without validating whether the data foundation can support them.
And more specifically, a company building an internal tool to surface insights from customer support transcripts needs audio and text data organized, labeled, and retrievable before the tool can work. A company developing an AI-powered product for external customers needs guardrails in place for data provenance, consent, and version control before model selection matters. Both depend on data that is governed and accessible before any model enters the picture.
Yet according to Gartner’s survey of data management leaders, 63% of organizations either don’t have or aren’t sure they have the right data management practices for AI. Gartner’s projection is stark: through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.
Each abandoned project represents sunk cost, such as engineering hours and vendor contracts.
And yet, the pattern repeats because the problem is structural. Strategy teams set direction without validating that infrastructure can support it. Infrastructure teams make decisions without understanding what the strategy actually requires.
Neither group is wrong; they’re simply operating from different information, different incentives, and different success criteria.
What changes when AI strategy becomes business strategy
When organizations treat AI strategy and data strategy as the same conversation, the outcomes shift. Three things happen:
- Infrastructure decisions get made with strategic context. Where data lives, how it moves, what it costs—these have become capability questions rather than cost optimization questions. AI development depends on iteration: moving data between tools, environments, teams, and models. A storage provider that charges $90 per TB for egress can become architecturally limiting, penalizing the high-frequency data movement that accelerates AI development. When CFOs understand that the storage decision determines whether the organization can iterate quickly or iterate slowly, the conversation changes.
- AI initiatives get funded with data readiness built in. BCG’s research on future-built companies found that top performers define AI programs with ambitious cost and revenue targets set at the executive level and hold teams accountable to near-term results. What they also have in common: they’ve established governance structures, inventoried their data assets, and planned infrastructure before deployment pressure arrives.
- Accountability shifts. According to McKinsey’s 2025 State of AI survey, 28% of organizations using AI report that their CEO is responsible for overseeing AI governance—the policies, processes, and controls that determine how AI is developed and deployed. McKinsey’s analysis found that CEO oversight of AI governance is one of the factors most correlated with meaningful bottom-line impact from AI use. When executive leadership owns both strategy and governance, the two stay aligned.
A companion resource to help you make data decisions
AI strategy cannot succeed as a standalone initiative. It depends on the organization’s ability to make data available, portable, governed, and cost-effective at the exact moments teams need it. For leaders, that means treating infrastructure not as a downstream implementation detail, but as part of the strategy itself.
Read the ebook, Navigating Multimodal Dataset Economics, to get the guide on making decisions about the AI datasets at your organization, and how interoperable-by-design object storage is critical for multimodal AI datasets.
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