Should We Drive AI Adoption Before Fixing Our Data?

Organisations face a difficult dilemma: Should they accelerate AI adoption despite imperfect data, or should they wait until their data is fully optimised?

The instinctive response is to fix data first. After all, AI relies on clean, structured, and reliable data to deliver results. However, this approach comes with risks. Data perfection is an endless pursuit, and waiting too long to address every data challenge can stall AI initiatives—leaving organisations lagging behind competitors who take a more pragmatic approach.

The reality is, AI and data quality must evolve together. The organisations that succeed in AI transformation are not the ones that delay adoption but those that strategically integrate AI while continuously improving their data foundations.

The Challenges of “Fixing Data First”

Every organisation faces data quality issues. While resolving these challenges is important, insisting on perfect data before AI deployment presents three major risks:

1. Fixing Data Isn’t a Priority for the Business

Data governance programmes struggle to secure funding because they don’t deliver immediate revenue or visible quick wins. As a result, they are often deprioritised in favour of more tangible initiatives.

2. Waiting to Fix Data Could Mean Falling Behind

By the time data issues are fully addressed, competitors will have already implemented AI, gaining operational efficiencies and market advantage. In a fast-moving environment, waiting for perfection can be a costly mistake.

3. “Fix Our Data” Lacks Clear Definition

The phrase is too vague to act on effectively. Without a structured framework, data governance efforts can become resource-intensive, slow-moving, and misaligned with business goals.

A Pragmatic Approach: AI and Data Quality Together

Rather than treating AI adoption and data governance as separate projects, organisations should take a structured approach that allows them to balance immediate AI-driven impact with long-term data improvement.

1. Use AI to Drive Data Governance and Quality

Instead of postponing AI initiatives, businesses should establish a data improvement workstream alongside AI deployment. AI can actively contribute to better data management rather than waiting for a “clean slate.”

2. Develop an Optimisation Roadmap

A structured roadmap ensures AI and data quality improvements align with strategic objectives. Key steps include:

• Assessing AI initiatives by data dependency → Identify which projects require high-quality data and which can proceed immediately.
• Creating a dependency matrix → Prioritise AI deployments based on existing data readiness and impact potential.
• Balancing quick wins with foundational improvements → Ensure short-term AI implementation does not compromise long-term data integrity.

Senior leaders, eager for AI-driven efficiencies, may push for rapid implementation. A structured approach ensures AI adoption aligns with corporate goals while maintaining realistic expectations about data readiness.

3. Leverage AI to Improve Data Quality

AI is not just dependent on clean data—it can also enhance data integrity. AI-driven tools can:

• Identify inconsistencies and missing values
• Auto-correct and cleanse records
• Detect anomalies and errors
• Automate data categorisation and labelling

By using AI as part of data management, organisations can refine data while implementing AI, rather than delaying transformation efforts.

Conclusion: AI and Data Should Evolve Together

The notion that “AI can’t work until data is fixed” is outdated. A layered approach—one that unlocks AI’s value now while progressively improving data quality—is the most effective path forward.

It’s not about rewiring the entire house before installing smart lighting. Businesses can modernise where it makes sense while strengthening the foundation over time.