The hidden cost of “good enough”: Why CIOs must rethink data risk in the AI era

In today’s cost-conscious climate, CIOs are being asked to achieve the impossible: deliver more innovation with fewer resources. Cut budgets. Freeze hiring. Delay critical upgrades. On the surface, it seems reasonable to scale back or delay long-range investments, especially in complex foundational projects like data management and unification.

But in 2025, there’s one truth that enterprise leaders can’t afford to ignore:

“Good enough” data is no longer good enough. Not for AI. Not for compliance. Not for growth.

As enterprises lean harder into AI to gain a competitive edge, the cost of poor data infrastructure is compounding. When CIOs defer data unification or settle for patchwork solutions, they don’t just create technical debt—they increase the likelihood of catastrophic AI misfires, regulatory risk, and lost revenue.

When “good enough” leads to data debt

When we talk to CIOs, we often hear a common refrain: “Our data is good enough for now.” It’s an understandable response in a climate of budget scrutiny and competing priorities. But what does “good enough” really mean—and is it truly enough for the demands of AI, automation, and modern digital operations? For many organizations, “good enough data” typically means relying on fragmented systems, outdated records, and manual processes patched together to meet immediate needs. It might work—until it doesn’t. As enterprises push toward faster, more intelligent, and more automated operations, this mindset becomes a hidden liability. Before exploring the risks, it’s important to define what “good enough” data looks like in practice.

“Good enough” data often carries significant invisible baggage. Inconsistent, siloed information creates inefficiency, errors, and exposure that many leaders underestimate. 

Consider these eye-opening figures about the toll of fragmented and low-quality data:

Productivity Drain: Knowledge workers spend nearly 30% of their workweek searching for information across fragmented systems – roughly 11.6 hours per week lost per employee that could have been spent on strategic work, according to a survey from Airtable and Forrester Consulting.

Direct Financial Losses: Poor data quality is a bottom-line problem. Gartner estimates it costs organizations an average of $15 million per year. “Good enough” data means decisions are often based on flawed or incomplete information, leading to revenue losses and extra costs to correct mistakes.

Compliance and Security Risks: Fragmented data also undermines security and compliance. Disconnected systems make it hard to enforce uniform controls. One analysis found 70% of organizations with data silos suffered a breach in the past two years; meanwhile, regulators issued €1.2 billion in GDPR fines in 2024. “Good enough” may work for basic operations, but it fails under scrutiny.

Tolerating subpar data has tangible consequences. It drives up costs, creates inefficiencies, increases risk exposure, and stifles innovation.  This is an increasingly common phenomenon that we have witnessed. Many companies are accumulating a significant and growing amount of data debt.  Just like technical debt, data debt builds up over time when short-term fixes, legacy systems, or fragmented processes prioritize speed over long-term data quality and governance. Many companies are now taking stock of their data debt as they try to implement AI or real-time analytics, and discovering that it’s the biggest bottleneck to becoming a data-driven, agentic enterprise.

CIOs must recognize the hidden costs of data debt and treat high-quality, unified data as a business imperative, not an afterthought.

AI initiatives derail without quality data

The promise of AI is captivating—automating decisions and uncovering insights—but it can quickly turn into a pitfall if the underlying data is unreliable. AI systems are fundamentally garbage in, garbage out.

Feeding “good enough” data into AI will yield questionable results. Indeed, studies confirm that many AI projects fail due to data issues.

According to Gartner, 85% of AI projects fail to deliver on their objectives, largely because of poor data quality and availability.  A 2024 Reltio survey found that only 20% of data leaders reported that more than half of their enterprise AI initiatives have been successful. Poor data quality was the primary reason for failed or stalled AI projects.

In short, most AI efforts don’t flounder because the algorithms are flawed; they fail because the data feeding them isn’t up to par. Even the most powerful AI models produce flawed results when information is fragmented, inaccurate, or outdated.

The latest wave of generative AI only heightens the urgency for better data. Enterprises are rushing to pilot generative AI projects, but many will stall. Gartner predicted that 30% of all generative AI projects will be abandoned at the proof-of-concept stage, often due to poor data quality or unclear value.

Without clean, unified data, these pilots tend to yield erratic outputs and minimal ROI, quickly causing leaders to hit pause.

CIOs recognize that data foundations are the gating factor for AI success. In survey after survey, lack of trusted, accessible data is cited as a top barrier to AI adoption. The lesson is clear: to unlock AI’s potential, organizations must first fix their data.

Building a unified data foundation

If the era of “good enough” data is over, what’s the path forward? It begins with a unified, high-quality data foundation. Modern cloud data platforms, such as Reltio’s Data Cloud, help CIOs tackle this challenge. Instead of one-off integrations or siloed cleansing projects, they provide an integrated approach to unify and cleanse enterprise data continuously.

These platforms combine data from every source into a single source of truth, using AI to match and merge records so all teams share a complete view of the business. They also embed continuous data quality into data flows, validating, standardizing, and enriching data in real time to ensure information stays consistent and trusted. They bake in governance and compliance controls (e.g., consent tracking and audit trails) to enforce privacy and security policies across all data. And being cloud-native, they easily scale to handle massive data volumes while delivering fast time-to-value. For example, Reltio reports that companies can use trusted, unified data in as little as 90 days.

No more “Good Enough”:  Make data excellence the strategy

Competing today demands data excellence, and CIOs must lead by refusing to accept fragmented, “good enough” data in favor of a single, trusted source of truth. Building an enterprise-wide data foundation requires effort and investment, but the payoff is transformative: AI projects that succeed, teams freed from tedious data wrangling, and compliance requirements met proactively instead of reactively.

It’s time for CIOs to show that better data means better business. Modern platforms like Reltio make unified, high-quality data achievable. By rethinking data risk today, CIOs ensure unseen data pitfalls won’t derail their organization’s AI ambitions, because in the AI era, trusted data is a strategic advantage. Organizations that realize this will leave “good enough” behind and race ahead with data-driven innovation.