Making Data Ready for AI

Posted on:
September 19, 2025
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Scott Bekker
Moderator & Editor

TL;DR - Article Summary

Most companies mistakenly believe their data is AI-ready, but a significant gap exists between leadership and IT on data quality. This "bad data"—which is often inaccurate, inconsistent, and siloed—hinders AI's potential, undermines customer experience, and costs companies valuable time. True AI readiness requires a cultural shift where data is a shared, strategic asset.

Most companies think their data is AI-ready—but the reality is far messier. From leadership blind spots to broken customer experiences, this piece unpacks what “excellent data” really means, why it matters, and how to fix the disconnect. If AI is the engine, data is the fuel. Is yours combustible or clean? ⛽

Though most businesspeople agree that good data fuels good AI, a significant disconnect persists between leadership and IT professionals about how prepared their data truly is. Informatica’s research reveals that over two-thirds of leaders rate their data quality highly, compared to just one-third of senior managers. This gap underscores a deeper issue: Many organizations believe they’re AI-ready when they’re not.

Customer data is omnipresent—used across finance, sales, marketing, service, and product development. But excellence in data isn’t just about availability; it’s about trust, consistency, and shared understanding. When teams operate from a unified dataset, meetings aren’t bogged down by conflicting analyses or assumptions. Instead, data becomes a strategic asset that can challenge corporate myths—like the airline example where gold loyalty members outperformed platinum ones in revenue per mile.

📉 What Bad Data Really Costs

AI thrives on clean, complete, and accessible data. But when even one attribute—accuracy, consistency, completeness, standardization, integration, or usability—is compromised, the entire system suffers. Bad data is often siloed, duplicated, outdated, or hard to interpret. It slows down operations, undermines customer interactions, and forces teams to spend more time cleaning than innovating.

The metaphor “a man with one watch knows the time; a man with two is never sure” captures the confusion caused by duplicate or conflicting datasets. Systems that rely on batch processing miss real-time signals, and without unified customer context—order history, sentiment, complaints—touchpoints feel disconnected. Instead of delivering value, teams waste time debating whose data is correct.

Informatica’s findings show that 69% of IT teams rate their data quality as excellent or very good, while only 51% of business leaders agree. This disparity stems from differing perspectives: IT sees infrastructure progress—pipelines and storage—while business teams struggle with usability and access. The result is a disconnect between technical capability and practical application.

✅ Defining AI-Ready Data

AI readiness isn’t just a buzzword—it’s a rigorous standard. Informatica identifies 11 attributes grouped into four categories:

  • Clean and Correct: 🧼 Accuracy, consistency, completeness, standardization
  • Integrated and Unified: 🔗 Seamless data consolidation across systems
  • Timely and Relevant: ⏰ Real-time access and contextual relevance
  • Accessible and Usable: 🔍 Findable, understandable, and actionable data

If your marketing team can’t quickly launch a targeted campaign based on location and product usage, or your service team can’t access full customer context across channels, your data isn’t AI-ready. The top three challenges cited in the research were inaccurate, inconsistent, and non-integrated data—barriers that prevent AI from delivering real business value.

🤝 Bridging the Business-IT Divide

True AI readiness requires cultural and operational transformation. Successful companies treat data as a shared asset—everyone owns it. Business teams must become fluent in foundational data concepts, collaborate with IT, and focus on outcomes that drive customer trust and loyalty. Meanwhile, IT leaders must embed themselves in business strategy, align metrics with business goals, and measure success by customer impact, not just technical delivery.

Six steps can accelerate this transformation:

  1. Break down silos: Create a single source of truth across departments
  2. Build infrastructure around business problems: Focus on customer pain points
  3. Choose flexible solutions: Adapt to evolving needs
  4. Align metrics: Share goals across teams, and use a common set of yardsticks.
  5. Promote data literacy: Pilot projects with measurable outcomes
  6. Empower employees: Treat customer data as a foundation, not a technical asset

Ultimately, organizations that thrive with AI aren’t the ones with the biggest budgets or flashiest tech—they’re the ones that organize around customer data, enabling every employee to act with full context. AI readiness isn’t a technical checkbox; it’s a strategic commitment to data excellence and customer experience.

🚀 The Informatica Advantage

Informatica empowers businesses to harness the full potential of AI by transforming fragmented, inconsistent data into a unified, trustworthy foundation for intelligent decision-making. Through its Intelligent Data Management Cloud (IDMC), Informatica integrates, cleanses, and governs data across hybrid environments—making things accessible, accurate, and AI-ready. Its CLAIRE AI engine automates metadata management, while tools like CLAIRE Copilot and CLAIRE GPT simplify complex data tasks through natural language interfaces. Whether optimizing customer experience, accelerating analytics, or deploying generative AI, Informatica ensures that data is not just available—but usable, responsible, and strategically aligned with business goals.

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