Why Data Quality is the Make-or-Break Factor for AI Success

Semarchy’s Craig Gravina offers commentary on why data quality is the make-or-break factor for AI success. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI has rapidly ascended the ranks to become one of today’s top investment priorities, yet the sobering reality is that most organizations can’t trust the data powering their AI initiatives.

According to a recent survey of 1,050 senior business leaders across the US, UK, and France, only 46 percent express confidence in the quality of their data. This lack of trust in data quality represents the Achilles’ heel of many promising AI strategies, underscoring a critical truth: without trustworthy data, even the most sophisticated AI initiatives risk falling short of their potential.

The Data Confidence Gap

The widespread lack of trust doesn’t emerge in isolation—it’s the product of systemic organizational challenges. For instance, many companies continue to rely on siloed legacy systems, making it difficult to consolidate and verify data accuracy across the enterprise. Further exacerbating the problem is unclear ownership of data, translating into fragmented accountability. Without clear lines of accountability, organizations inevitably struggle to establish clear standards and practices for data quality.

Alarmingly, governance frameworks designed around AI data usage remain extremely limited, with fewer than 7 percent of organizations surveyed having a dedicated AI governance committee in place. This absence of governance opens doors to risks such as data misuse, quality degradation, and ethical or compliance breaches.

Employee behavior can compound these challenges: the survey found that nearly half of employees (47 percent) use external or non-private AI environments to perform tasks involving sensitive company data. This practice significantly increases the likelihood of data leakage, inconsistency, and diminished trust.

Internal misalignment is another contributing factor. The research highlighted a disconnect between technical and business stakeholders around the urgency and readiness for AI implementation. Chief Technology Officers (CTOs), for example, typically perceive AI projects as more urgent priorities than Chief Data Officers (CDOs). Until this gap is bridged, businesses will likely continue to struggle to build the trusted foundation of quality data required for AI success.

Use Case Example

A mid-market financial services provider embarked on an ambitious AI project aimed at improving customer analytics and driving targeted marketing campaigns. But rather than achieving rapid insights, the analytics initiative stalled. The reason? Customer information existed across six legacy systems, resulting in inconsistent data formats and duplicate records.

Data science teams wasted weeks on cleansing, standardizing, and de-duplicating critical customer data before even beginning to train the AI models. These lengthy processes delayed the project timeline, causing leadership to question the value of their AI investment.

Recognizing the underlying data quality issues, the company took decisive action by establishing centralized governance and rolling out a unified data model. With clear standards and ownership firmly defined, project timelines contracted, and output quality improved significantly.

A Roadmap to Data Confidence

To strengthen their confidence in data quality and unlock the full potential of AI, businesses must adopt a structured approach to data management. Here are five essential best practices to establish reliable foundations for AI-driven strategies:

Establish Joint Ownership Between Business & IT

Data quality isn’t solely an IT responsibility; it requires active participation and clear accountability from teams who produce, manage, and consume data across the organization. To establish joint ownership, encourage close alignment between decision-makers, such as CTOs, CDOs, and business executives, to agree on what “good” data looks like.

Create a Unified Data Model

Data silos are AI readiness’ greatest adversary. Eliminate this threat by introducing data standardization and harmonization practices to create consistency across business units.

Implement Proactive Data Governance

Effective data governance goes beyond basic compliance—it relies heavily on how organizations assess training data, ensure transparency, and reduce AI bias. Before scaling AI projects, establish trust in the data by deploying automated data validations, role-based access controls, and lineage tracking.

Secure Data Usage Across AI Tools

Closely monitor when and how employees use generative AI tools, as many users currently rely on unapproved external platforms, exposing sensitive or unvetted company information. To limit or eliminate this practice, establish clear AI usage policies while providing secure internal platforms that deliver powerful AI outputs without compromising data security or integrity.

Start Small but Design for Scale

Launch AI projects within defined business domains, such as marketing or finance, with centrally managed, high-quality datasets. Build early success stories—underpinned by an agile and scalable data infrastructure— to drive broader adoption.

Don’t Risk Falling Behind

Good data quality isn’t optional; it’s the fuel powering every successful AI initiative. Organizations can’t afford to adopt a “wait and see” approach to governance or hope that poor-quality data might still yield high-quality results. Those who neglect this essential investment will be forced to watch from a distance as forward-thinking competitors race ahead.

As enterprise analytics continue to evolve toward AI-driven, real-time, and democratized capabilities, organizations that establish strong foundations of data trust and governance will be best prepared to capitalize on change for a competitive advantage. Therefore, leaders who prioritize data quality as a strategic imperative today will inevitably win the AI race tomorrow.