The usefulness of AI in decision making, automating tasks, and yielding insights hinges on the data it consumes. The quality of the data or sources that AI ingests is directly related to its success.
About 85% of AI initiatives fail due to bad or insufficient quality data. Employers collect vast amounts of data, but the quality of the data remains questionable.
Workforce data is distributed across payroll, HR, learning, and collaboration systems. This creates inconsistencies, duplicates, and even gaps that undermine AI projects before they take off.
Human Capital Management (HCM) systems take care of harmonizing and managing employee data. These platforms offer structural data that enable AI systems to delivery valuable insights. For CIOs, HCMs are not merely tools; they are foundational platforms for AI-ready enterprises.
Why Clean Data Matters to Stakeholders?
AI systems deliver value only when it is fed with clean, structured, and accessible data. Without good data foundations, organizations would see little to no measurable return.
Workforce data must be accurate, well-regulated, and optimized. High-quality, well-governed data helps AI tools to generate usable insights.
Poor data foundations are one of the key barriers to AI adoption. 83% of senior leaders agreed that this factor hinders the progress of AI adoption.
Collaboration between HR and IT teams is necessary to standardize, enrich, and secure data. This ensures that the data is AI-ready.
For people-centric workflows, clean data is needed for
Decision-making
Removal of bias
Performance management
Talent development, and more.
What are the Data Problems CIOs Face Today?
An astonishing 81% of businesses are plagued by AI data quality, threatening both the ROI of AI investments and business stability in general. Big companies in particular are fearful: 77% predict that these poor data problems will create significant crises.
Distrust in data undermines BI and AI. When decisions are predicated on dashboards based on dubious data, digital transformation comes to a halt. Without proper data governance, its analytics strategies can falter and erode confidence across the board.
According to S&P Global research, 42% of firms drop most of their AI projects before they go live. This is an unmistakable indication that pilot exhaustion is real and data-related.
Business success stories show the impact quality data has on the ROI of AI implementation. AI won't replace bad data; it will magnify it.
Companies cannot even provide simple answers such as "How many customers do we actually have?" because data resides in discrete silos. On the other hand, organisations that have strong data foundations are already capturing AI benefits.
How to Convert Raw Data into AI-Ready Data?
AI thrives on clean, credible data. Here's how businesses achieve that:
Reduce Errors and Bias
Removal of missing values, duplicates, and inconsistencies will allow AI models to learn from data that is accurate and trustworthy. Decisions made by AI are subsequently more accurate and fair, as clean data helps in reducing noise and bias.
Validate, Standardize, Enrich
Refine datasets so that the AI program uses data that is accurate, clean, consistent, and complete. For AI to create meaningful insights, it must be fed with structured data, enhanced with metadata such as skills taxonomies, performance ratings, and role hierarchies.
Systems Integration
The HCM should integrate seamlessly with systems such as Finance, Payroll, and Learning & Development, so that AI models are fed with consistent data.
What Are The Consequences Of Incomplete Data Foundations?
Bad data not only makes AI solutions fail but also constitutes a great strategic risk for the business. When bad data goes in, AI models give one-sided, untrustworthy, or biased results. Instead of correcting, they end up compounding errors.
Take Zillow's AI-based house valuation model. It failed because the model was based on old, naive, and mismatched data, which did not reflect market changes in real-time. Housing prices in the Zestimates were continuously off from the market value, causing losses of over $500 million by the time the company shut down its iBuying business.
Problems with data integrity translate into regulatory breaches, financial losses, and reputational damage. Poor data quality results in losses amounting to a whopping US$12.9 million per year for organizations.
As businesses scale AI, disparate or isolated data undermines stakeholder confidence, converting strategic efforts into liability risks.
Why HCM Systems Are the Building Blocks for AI HR Transformation?
The Human Capital Management (HCM) systems provide dependable data that is a foundation for any AI-powered processing.
Unified Data Model: Top HCM systems keep the data clean, consistent, and available across the enterprise. A single source for clean and organized data enhances AI and ML.
System of Record for Workforce Data: HCM includes employee identity, roles, skills, and organisational hierarchy in the identity record for all employees. These cover the entire HR life cycle, right from hiring to performing, and reduce inconsistency and duplication.
Standardized Data Entry and Validation: HCM cuts down on mistakes made when entering data by hand by using standard fields and rules for checking data. It makes sure data is complete by checking that all required fields are filled out.
Data Cleansing Audits: Some HCM platforms have tools that detect and fix inaccurate or incomplete data. Regular data audits get rid of old information and duplicates.
Data Governance and Stewardship: HCM systems make sure that data is well-governed and that data ownership is clear. CIOs can set rules for how to collect and use data correctly.
Data Integration: HCM solutions that work with different systems to get rid of data silos. Tracking the lineage of data helps keep things clear by showing where the data came from.
Automated Compliance: Monitoring and ensuring that data is always accurate and consistent reduces compliance risks. Secure data access further ensures that workforce data isn't misused.
With HCM as a regulated and organized framework, CIOs are able to future-proof their technology landscapes to deliver scalable AI, agility, and sustainable ROI.
How Darwinbox Ensures AI-Ready Data?
Darwinbox converts workforce data into clean, compliant, and AI-ready information. It equips CIOs with intelligent validation, governance, insights into data, and out-of-the-box integrations.
Centralised Hub with Stringent Data Governance
Darwinbox safeguards data integrity via automated validation, cleaning, de-duplication, and labelling at the point of entry. These steps ensure AI models work based on credible and bias-free employee data.
Advanced Skills Ontologies & AI-Driven Intelligence
With a skills-ontology-based framework, Darwinbox facilitates more intelligent talent matching, internal mobility, and career pathing. Such a framework effectively visualizes not only what skills there are, but also how they relate to one another and develop. The platform, consisting of Darwinbox Sense and PROSE (People's Relational and Organizational Semantic Engine), provides predictive insights, personalized learning, career suggestions, skill mapping, and attrition predictions based on high-quality data.
Low-Code Integrations and Security-First Extension Tools
Darwinbox Studio comes with a simple, low-code integration dashboard, with more than 100 out-of-the-box connectors and 50+ templates. It has enterprise-grade security with endpoint-level access control, AES 256 and PGP encryption, and end-to-end auditability. Darwinbox Amplify empowers teams to build feature updates, cross-module processes, and UI-level automations within a secure, limited-code environment. In-built governance safeguards ensure that the extensions remain stable and compliant.
Conclusion
Enterprises shouldn't implement AI just because everyone else is doing it. According to a recent MIT study, 95% of generative AI initiatives never bring about significant business results, but instead get stuck due to subpar integration and fractured foundations. The foundation for any AI model is clean, high-quality data.
CIOs have a critical part to play in making workforce data clean, well-structured, governed, and well-integrated through HCM systems. Thus, they set the stage for AI that's robust, scalable, and transformative.
Darwinbox is a strategic partner that provides a centralized platform with automated data validation, enriched skills taxonomy, deep integration, and governance controls. It empowers CIOs with the foundation they require to deliver true AI value from clean data.
Visit Darwinbox and learn how it can help your organization build a strong AI-ready data foundation.
