While 85% of People Analytics and AI projects currently fail due to data quality issues, a breakthrough in human capability indexing is transforming how organisations capture and structure their people data, frictionlessly and at scale.
This thought leadership examines the landscape behind these widespread failures and reveals how the shift from subjective opinions and scraped big data to structured human capability indexing changes everything. The core challenges, inconsistent data capture methods, fragmented data architecture, and the absence of validated measurement frameworks, have kept organisations from realising returns on their analytics investments. Until now.
Lumenai's approach addresses these foundational problems by introducing a frictionless method for collecting structured human capability data. By replacing manager opinions and data mining with scientifically validated, standardised indexing, organisations can finally build the robust data architecture needed for Predictive People Analytics and AI to deliver consistent value. This isn't just about better data, it's about creating a sustainable foundation that transforms people analytics from a costly experiment into a reliable driver of business performance and human capital optimisation.
Thought Leadership
Workforce DNA: Why Your AI Is Only as Good as Your Workforce Data
When implementing AI solutions, many organisations overlook a fundamental principle: AI systems can only be as effective as the workforce data that informs them. Just as a building needs a solid foundation, AI requires high-quality data to deliver meaningful results.
Workforce data functions as the DNA of your organisation, it contains the essential information that defines how your workforce operates and competes. Without properly structured, objective data, even the most advanced AI models cannot accurately identify talent, analyse skill gaps, or generate cost savings. This is because AI learns patterns from historical data; if that data is flawed, the resulting analyses will be equally flawed.