The Missing Layer : Decision Intelligence
Every major function has intelligence infrastructure:
Finance → Financial systems
Marketing → Attribution analytics
Sales → CRM & forecasting
Hiring does not.
Until now.
Talent Decisions AI introduces a decision intelligence layer that:
• Structures judgment
• Quantifies trade-offs
• Preserves transparency
• Reduces cognitive bias
• Scales across teams and roles
This layer sits between applicants and final decision makers — improving the quality of judgment without removing human control.
Academic Provenance
The founder studied decision sciences at the London School of Economics, training under pioneers of MCDA and later working with the university spin-off commercializing early decision modelling platforms.
This is not HR theory.
This is formal decision science.
The Science
Built on Decision Science (MCDA)
Talent Decisions AI is grounded in Multi-Criteria Decision Analysis (MCDA) — used when:
• Objectives conflict
• Trade-offs must be explicit
• Decisions are high stakes
• Outcomes must be defensible
Instead of asking:
“Who do we like best?”, MCDA asks:
“Which candidate best satisfies what matters most for this role — and why?”
10+ Years of Proof (Before AI)
For over a decade, this methodology powered the non-AI platform used in:
• Junior, mid-level, and senior executive hiring
• Cross-industry leadership searches
• Board-level decision processes
• High-stakes roles with low margin for error
Delivered outcomes included:
• Clear role definitions
• Explicit criteria weighting and decision analysis
• Transparent candidate comparisons
• Documented, defensible decisions
• Timelines reduced by up to 50%
Bias was reduced not by intention — but by structure.