By Sunil Senan, Senior Vice President and Global Head of Data, Analytics and AI at Infosys
AI may be today’s transformative technology and tomorrow’s competitive engine, but its success hinges on the quality of the data powering it. Fragmented, inconsistent or poorly accessible data can limit the potential of even the most advanced AI systems. Before any meaningful AI implementation can take shape, organizations must ensure their data is well-organized, reliable and aligned with their broader AI objectives. In other words, the data needs to be AI-ready.
Recent research shows that organizations that fail to distinguish between AI-ready data operations and traditional data management run the risk of compromising their AI initiatives. In fact, Gartner has predicted that by 2026, 60 percent of AI projects lacking proper AI-ready data foundations will be prematurely abandoned.
This should serve as an effective warning. AI initiatives rarely fail because the algorithms are flawed; they fail because the data they rely on is flawed. This highlights the urgent need to rethink data management for the AI era.
What is AI-ready Data?
AI-ready data is trustworthy, interpretable and actionable without requiring any heavy manual fixing for each new use case.
It moves beyond traditional clean data to support high-stakes decisions at scale. Its key characteristics include:
- Complete, accurate and consistent datasets with minimal gaps or contradictions to avoid model errors
- Strong metadata that reinforces business meaning
- Richly tagged, searchable, unstructured, content so it contributes insights rather than sitting idle
- Clear end-to-end lineage showing data flow and transformations for debugging and trust
- Structuring optimized for AI-driven decisions, including inference speed, cost efficiency and relevance
- Centrally accessible and properly labeled – designed for easy discovery and reuse
- Built-in privacy, security and compliance controls to ensure responsible use of sensitive information
- Regular refresh cycles to capture evolving behavior and intent for predictive and personalized use cases
Together, these elements create a reliable and well-governed foundation that allows AI to deliver accurate, timely and business-relevant outcomes.
Unfortunately, AI ambition has outpaced data reality in many organizations. A recent Gartner survey found that 63 percent of organizations either do not have, or are unsure if they have, the right data management practices for AI.
This reality raises an important question: how can organizations prepare their data for AI?
How to Prepare Your Data for AI
1. Start from the AI use case:
Define a clear business-led AI use case and work backward to the data required. Bring together product owners, SMEs and technical teams to agree on what relevant data truly means and exclude what does not matter. Ensure consistent core business metrics so that teams do not generate conflicting outputs.
2. Explore and profile your data:
Conduct data exploration to identify patterns, gaps, outliers and biases that could mislead models. Use data profiling to surface poor-quality fields before they affect training or inference.
3. Clean, blend and wrangle:
Remove duplicates, fix errors, resolve inconsistencies and manage missing values. Blend data from the necessary sources, then structure it in the formats AI models consume – whether events, features or documents.
4. Structure and document with metadata:
Apply consistent formats, schemas and relationships that support AI workloads. Invest in metadata management and cataloguing so that teams can quickly understand each field’s meaning, lineage and usage. Enrich metadata with more business and enterprise context to help AI interpret the data accurately.
5. Govern with business context:
Embed security, privacy, compliance and ethical oversight. Treat data governance as a cross-functional practice in which business and technical teams regularly review definitions, dependencies and model feedback. Include human-in-the-loop mechanisms to reinforce trust and allow experts to correct outputs along the way.
6. Build continuous validation into pipelines:
Embed validation across data workflows – freshness checks, quality rules, drift detection and regression testing for critical datasets and models. Feed these insights back into pipeline logic to address issues before they escalate.
7. Design for access, security, and reuse:
Centralize AI-relevant data on platforms with appropriate access controls. Make data secure and private by default, while ensuring authorized teams can discover and reuse it easily.
8. Keep data dynamic, not static:
Refresh data regularly so that it stays aligned with business behaviors and model needs. Continuously refine features, labels and structures as use cases evolve. Treat AI-readiness as an ongoing capability rather than a one-time exercise.
9. Build Knowledge Corpus
AI systems rely heavily on knowledge such as enterprise standards, business processes and standard operating procedures. Traditionally in the human first world, this knowledge is not digitized and that is a huge roadblock for AI systems to deliver value. Digitizing and managing knowledge corpus is critical in the AI first world.
AI-first means data-first
In today’s market, becoming an AI-first enterprise is no longer optional – it is a competitive necessity. Achieving it begins with taking control of the data that fuels AI. Organizations that invest in trustworthy, well-governed, AI-ready data give their models the foundation they need to perform reliably at scale. If organizations want AI to deliver measurable, enterprise-wide impact, the work must begin with the data. This is not just a technical necessity, it is a strategic enabler for unlocking business value at scale.
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ABOUT THE AUTHOR
Sunil Senan is Senior Vice President and Global Head of Data, Analytics and AI at Infosys. In this role, he works closely with Infosys’s strategic clients on their data & analytics led digital transformation initiatives. He is passionate about how data & analytics is creating economic impact in society and how enterprises and governments can engage in driving this transformation. He has written the “Data economy in Digital times” paper articulating how the new data economy presents a set of new possibilities for enterprises, governments to serve their citizens and consumers.
