Adaptive Learn Agent

Details

Adaptive Learn Agent is a net-new enterprise AI agentic workflow designed to transform corporate upskilling and professional development. By combining intelligent coaching, dynamic learning space curation, and immersive role-play simulation, the product bridges the gap between current job roles and future career trajectories.

Client

Cornerstone OnDemand

Scope of Ownership

End-to-end UX/UI architecture, Conversational UI design, System interaction models

Cross-Functional Partners

User journey maps, conversational design frameworks, interactive prototypes, and iteration frameworks based on early customer co-validation.

Year

2026

The problem

Fragmented legacy learning platforms lacked the proactive guidance and contextual intelligence required to actively coach employees toward career growth.

Prior to this initiative, enterprise learning tools were passive repositories rather than active partners in career progression. Employees faced a baseline state where discovering relevant training required navigating 4 to 5 disjointed legacy pages, resulting in an average manual search and enrollment time of 35 minutes per session. Furthermore, there was no safe, natural environment to practice complex workplace scenarios, leading to an alarmingly low module completion rate of just 22% for soft-skills training and stagnant employee upskilling velocity.

The process

A human-centered, iterative methodology bridging complex AI capabilities with intuitive enterprise UX patterns.

We approached the problem through a rigorous multi-phase framework, tracking leading indicators such as prototype task-completion rates and initial session drop-off:

Discovery & Leading Indicators: Partnered with research to map psychological hurdles, tracking a target initial task-success rate of over 85% during early usability tests to ensure low cognitive friction.
Value Stream Mapping (VSM): Analyzed existing training workflows, successfully identifying and eliminating 6 redundant manual steps and system handoffs between discovery and practice.
Iterative Design & Prototyping: Refined conversational flows, smart bar interactions, and multi-modal feedback loops to minimize input hesitation.
Testing & Co-Validation: Partnering closely with pilot customers ahead of General Availability (GA), measuring user initiation velocity as a key leading indicator—tracking how quickly users began speaking upon launching a role-play session (which improved from a baseline 18-second delay to under 3 seconds after introducing intuitive prompt cues).

The solution

A unified, AI-driven coaching ecosystem featuring conversational role-play simulations and dynamic learning space curation.

The architecture centers on an intuitive AI agent experience designed to cut cognitive load and streamline user engagement:

Intelligent Learning Space Curation: Automatically builds personalized learning hubs based on role and aspirations, slashing navigation depth from 4 clicks down to 1 unified smart bar search.
Verbal Role-Play Simulation: Enables natural, voice-driven scenario practice where users can role-play workplace dynamics and receive automated grading against custom criteria in under 60 seconds.
Targeted Gap Analysis & Recommendations: Synthesizes data from learning spaces and simulation performance to proactively suggest critical skills, targeting a 50% increase in relevant skill adoption.

Impact & outcomes

Cycle Time Reduction:

Cut the end-to-end skill discovery and enrollment cycle time by 78% across pilot enterprise cohorts.

Engagement Lift:

Drove a projected 3.4x increase in active soft-skill practice completion rates compared to legacy learning management systems (moving from the 22% baseline to a target 75% adoption rate).

Operational Efficiency:

Successfully eliminated 4.5 hours of administrative overhead per employee annually by automating gap analysis and grading rubrics.

Pre-GA Validation Milestone:

Maintained a 92% satisfaction score (CSAT) among early co-development pilot customers during live workflow testing.

Selected Projects

Global Search

Redesign, End-to-end user experience, Interaction architecture, Design system componentization