AI adoption is advancing rapidly. According to Semarchy, 74% of businesses are investing in AI this year, eager to unlock efficiency, innovation, and competitive advantages. But ambition alone is not enough.
The paradox? While AI promises data-driven decision-making, nearly all organizations (98%) say poor AI data quality is undermining success. Data isn’t just an AI input — it’s the foundation. And weak foundations lead to unreliable results.
Think of AI as a sportscar driving over a crumbling bridge. Powerful, advanced, and capable of high performance, but ultimately at risk of failure if its underlying structure is weak. No matter how sophisticated AI models are, without trusted, well-integrated data, they will generate flawed insights, biased decisions, and costly mistakes.
Our research reveals a gap between AI ambition and execution, posing risks to businesses that fail to prioritize data readiness. You can get the full report here.
In this accompanying blog, we’ll explore:
- Key findings from the research which expose AI’s data challenges
- How poor data quality derails AI-driven decision-making
- Practical steps senior leaders can take to build AI-ready data
The data paradox: big AI budgets, fragile data foundations
Globally, businesses are pouring billions into AI — but many are doing so on an unstable foundation. More than half (52%) of organizations will dedicate at least 10% of their tech budget to AI this year, showing a strong push toward AI-powered transformation. Yet only 46% of leaders trust the data powering their AI models.
This disconnect is what makes AI a high-risk investment without proper data management. AI doesn’t think for itself. It mirrors the quality of the data it ingests. If that data is siloed, inconsistent, or riddled with duplicate records, AI will scale those problems rather than solve them.
Our research identifies three key AI and data quality challenges blocking success:
- Compliance constraints (27%) – Regulatory uncertainty making AI governance difficult.
- Duplicate records (25%) – Mismatched, conflicting data leading to inaccurate insights.
- Poor integration (21%) – AI trained on fragmented data, failing to see the full picture.
A surgeon wouldn’t work with outdated X-rays — the risk of misdiagnosis is enormous! The same applies to using AI without high-quality, well-governed data. If organizations don’t solve their AI data quality issues upfront, they risk increasing project costs, regulatory exposure, and executive mistrust in AI-generated decisions.
Why poor AI data quality is a silent killer
Flawed, inconsistent, or low-quality data flows directly into AI algorithms, affecting everything from recommendations to customer insights to risk modeling.
And businesses are already feeling the impact:
- 22% of AI projects are delayed due to insufficient data pipelines.
- 21% report operational inefficiencies caused by inaccurate AI outputs.
- 20% of organizations experience increased costs from fixing AI-related mistakes.
- 19% face compliance issues when AI fails to meet data security and governance requirements.
- 19% say trust in AI-generated insights is deteriorating
Crucially, if business leaders don’t trust the data, they won’t trust the AI, creating a cycle of ambiguity and hesitation, rather than adoption and innovation.
Who owns AI strategy? Leadership disconnects stall progress
If AI is critical, who ensures its success?
Leadership responsibility is scattered, and this fragmentation is slowing progress. According to Semarchy’s research:
- 38% of CIOs consider AI their domain but focus more on infrastructure than data quality.
- 30% of CTOs push AI forward from a technology perspective.
- CDOs — despite their responsibility for data strategy — are among the least likely to own AI (only 15%).
This misalignment creates confusion. When AI leadership is unclear, data management suffers.
Yet, fewer than 7% of organizations have a cross-functional team responsible for AI strategy, including functions such as operations, finance, compliance, and marketing. This leadership vacuum means businesses risk accelerating AI deployment without aligning business and technical objectives, leading to bad data, flawed implementations and wasted investments.
How to improve data quality for AI success: a 6-step playbook
For AI to deliver real, scalable business value, organizations must move beyond experimentation to execution. That means ensuring data is structured, governed, and accessible before AI models go into production.
To achieve this, business leaders should focus on six critical areas:
1. Understand and catalog your data before AI begins.
AI models rely on context-rich, well-documented data, yet most businesses operate with limited visibility into their own data assets.
Semarchy’s data profiling, cataloging, and lineage tracking tools provide organizations with a deep understanding of what data they have, where it lives, and who owns it, creating a clear foundation for AI-driven insights.
2. Unify enterprise data into a single source of truth.
AI trained on fragmented, inconsistent data will generate conflicting outputs. Just as a GPS requires a complete map to provide accurate directions, AI requires a unified, 360-degree view of enterprise data to make reliable predictions.
Semarchy’s master data management (MDM) solution eliminates silos, consolidating critical data into AI-ready golden records that ensure AI models work from a consistent, structured foundation.
3. Ensure data is clean, high-quality, and bias-free.
Garbage in, garbage out — AI magnifies data flaws rather than fixing them. Without automated stewardship, AI models may introduce systemic bias, produce misleading insights, or require costly retraining.
Semarchy automates data validation, enrichment, and cleansing, reducing redundancy, improving completeness, and ensuring AI-driven decisions are accurate, fair, and actionable.
4. Move data to AI models efficiently and securely.
AI models need fast, structured, and well-integrated data to function effectively. Slow, fragmented pipelines lead to stale, irrelevant insights that hinder real-time decision-making.
Semarchy’s built-in Extract, Load, Transform (ELT) capabilities enable real-time movement of trusted, governed data across enterprise systems, ensuring AI models are always working with current, relevant inputs.
5. Scale AI responsibly with compliance and governance in mind.
With AI regulations such as the EU’s AI Act still evolving, businesses can’t afford to take a wait-and-see approach. Without proper governance, AI models risk non-compliance, privacy breaches, and reputational damage.
Semarchy embeds audit trails, role-based access controls, and enterprise-grade data governance frameworks, helping businesses scale AI confidently while staying compliant with emerging laws and ethical guidelines.
6. Make AI-ready data accessible beyond IT teams.
AI shouldn’t be confined to technical experts alone. Many organizations struggle to democratize data, limiting AI’s value to a handful of teams.
Semarchy enables business users, analysts, and decision-makers to access AI-ready data securely. This ensures AI-driven insights are collaborative, aligned with real business needs, and accessible across the enterprise — not just locked within IT.
Is neglecting AI and data quality a gamble you can afford to make?
Organizations that fail to prioritize data integrity and governance will watch their AI investments falter. Those with a strategic data mindset will be the ones that turn AI ambition into real-world success.
Semarchy’s expertise in MDM and AI-ready data governance enables businesses to close the gap between AI potential and execution. Leading enterprises trust Semarchy unify data, ensure AI-quality records, and build AI strategies on a foundation of trust and compliance.
Want to see how AI-ready data powers real business results? Request a demo of the Semarchy Data Platform today.
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