From competency to enterprise impact: a decision architecture for talent development in AI-enabled work
Rajaraman KannanPurpose
Competency evidence alone can be insufficient for forward-looking talent decisions when AI materially redesigns work. This article introduces STEP-I (Systemic Talent Evolution and Performance Impact), a decision architecture for roles in transition.
Design/methodology/approach
A practice-informed synthesis based on the author’s capability-acceleration work in delivery centers and financial services, now applied at an organizational evaluation site.
Findings
Competency frameworks remain necessary for current-role readiness. During bounded AI-enabled redesign, decisions should also consider separate evidence of developmental capacity, enabling conditions and prospective contribution.
Research limitations/implications
STEP-I has not been validated. A longitudinal evaluation is planned and will be reported separately, testing whether the pillars yield distinguishable evidence, whether assessors judge consistently, whether development produces contribution, and under which conditions it is useful.
Practical implications
Leaders can use STEP-I alongside essential role criteria to differentiate development and reassess evidence, never as a single score or an override of lawful requirements.
Social implications
Making enablement, evidence quality and accountability explicit may support fairer decisions, provided capacity judgments carry trained assessment, rationale and appeal.
Originality/value
STEP-I is not a further catalog of competencies but an architecture separating present capability, developmental capacity, contextual enablement and prospective impact so they are never collapsed into one judgment.