AI is Screening AI-Generated CVs. Here's How We Fix It.

Candidates use AI to write CVs. Organisations use AI to screen them. Nobody knows what the candidate is actually capable of.

This is the AI-enabled recruiting crisis. And it is not a technology problem. It is a data problem.

The current paradigm uses AI to find meaning in meaningless unstructured text. NLP, keyword matching, sentiment analysis, all attempting to extract insight from data that was never designed to carry structured meaning. The same failed paradigm undermines People Analytics.

The fundamental limitation is simple: you cannot reliably extract what was never systematically captured.

We need to flip this entirely.

DRL 7 protocols make the flip possible. Structured collection protocols produce candidate data designed for intelligent consumption. AI stops interpreting noise and starts modelling intelligence. The shift delivers what AI-enabled recruitment always promised but never achieved: genuine capability matching, Quality of Hire prediction, team fit analysis, and culture alignment from the moment of application.

The Use Case

Lumenai is launching a formal use case under the Data Maturity Matters DRL 7 Use Case Initiative:

External Recruitment: Structured protocols producing DRL 7 mature candidate data from first contact

Internal Talent Pipeline: The same data flowing forward into the employee lifecycle, powering mobility, succession, and workforce intelligence

This is not about replacing your existing systems. It is about putting mature data inside them.

Why Join

The market is flooded with vendor promises positioning solutions as AI-enabled Workforce Intelligence. Most cannot deliver because they never address the underlying data architecture.

Understanding what DRL 7 data actually looks like is the core value of this initiative.

• Transparent methodology.

• Open-source standards.

• Collectively building awareness of the actual shape of the problem.

Launch Event

Oxford University: 2nd February 5-7pm GMT Face to Face Event

We are kicking off this research at Oxford Edge, Oxford University. The session will introduce the theoretical foundations, present the Total Data Quality Management (TDQM) and Data as a Product Manager (DPM) protocols adapted for external recruitment and internal talent matching, and open the conversation for collaborative validation from student career services and graduate recruitment programmes.

Places are limited. Contact antonia@lumenai.tech to register your interest.

Partner With Us

We are actively building out the research roadshow and seeking partners for the next phases.

Universities: We are looking for university partners to host roadshow sessions, contribute to methodology development, and explore applications within student career services and graduate recruitment programmes. If your institution is interested in examining how DRL 7 protocols apply to your context, whether preparing students for a transformed recruitment landscape or rethinking how you match graduates to opportunities, we want to hear from you.

Organisations: We are seeking organisations willing to pilot DRL 7 protocols within live Talent Acquisition and internal talent environments. Pilot partners will work directly with the research team to implement structured collection protocols, generate evidence for methodology refinement, and measure outcomes. This is an opportunity to shape the standards before they become industry benchmarks.

The results of this research will be published in our academic paper and through the Data Maturity Matters consortium as open-source standards, maintaining academic rigour whilst ensuring practical applicability. Partners contribute to transparent foundations upon which the entire research community can build.

Get Involved

Contact: antonia@lumenai.tech