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03

AI Enablement

FlagshipConcept / Pilot Proposal

Learning Beyond the Course

Designing an AI-enabled reinforcement system for post-training support and performance readiness

Company
Intuit
My role
Learning Strategy & AI Enablement Lead
Status
Concept / Pilot Proposal
  • AI Enablement
  • Learning & Capability Transformation
  • Performance Support
01

Context

Learning cannot stop when the course ends—especially when the work keeps changing.

TurboTax Experts support customers during a fast-moving tax season where call drivers, customer questions, workflows, and support needs can shift quickly. Formal training builds foundational knowledge, but Experts may still need timely reinforcement and access to relevant resources as they encounter real customer situations.

I explored how AI could extend learning beyond formal training and provide contextual support closer to the moment of need.

At a glance

  • Company: Intuit
  • Category: AI Enablement
  • Role: Learning Strategy & AI Enablement Lead
  • Focus: Post-training reinforcement, performance support, AI-enabled workflows
02

The Challenge

The challenge wasn't simply providing more training. Experts needed a way to reconnect with the right learning and support at the right time, based on what was happening in the business.

Key needs

  • Reinforcing critical learning after formal training
  • Responding to changing customer call drivers
  • Surfacing relevant resources without requiring Experts to search for them
  • Providing timely refreshers, tips, and reminders
  • Creating a pathway from digital support to human coaching when needed

What if learning could respond to the work instead of waiting for the learner to return to a course?

03

My Role

I developed the concept and strategy for an AI SmartSupport Companion designed to extend learning into the flow of work.

My work included

  • Developing the business case for AI-enabled post-training reinforcement
  • Defining the SmartSupport Companion experience and capabilities
  • Designing contextual reinforcement and messaging patterns
  • Connecting support to current call-driver data
  • Developing trigger and personalization logic
  • Creating a representative use case around rejected tax returns
  • Defining pathways between AI support, learning resources, and human coaching
  • Exploring technology and data requirements
  • Identifying proposed success measures
  • Recommending a pilot and phased rollout approach
04

The Approach

From

  • Course-based reinforcement
  • Learner-initiated search
  • Generic reminders
  • Static resource libraries
  • Training disconnected from changing call drivers
  • Support separated from learning

To

  • Contextual reinforcement
  • Proactive support
  • Call-driver-informed nudges
  • Resources surfaced closer to the moment of need
  • Learning connected to current business conditions
  • AI support with pathways to human coaching
05

The Solution

A reinforcement system triggered by the work.

The proposed SmartSupport Companion used business and learning signals to determine when support might be useful and what type of support to provide.

Proposed model

  1. 01

    Detect

    Identify relevant signals such as current call drivers, training status, new resources, or Expert feedback.

  2. 02

    Match

    Connect the signal to relevant training, guidance, job aids, tips, or support.

  3. 03

    Nudge

    Deliver a contextual reminder, refresher, resource, or recommendation.

  4. 04

    Support

    Give the Expert quick access to assistance closer to the flow of work.

  5. 05

    Escalate

    Provide a pathway to a Lead or Manager when additional human support is needed.

06

Visual Evidence

Concept artifacts

Conceptual AI Coach chat mockup surfacing the week's top call driver, a three-minute refresher guide, and options for a cheat sheet or coach support.
AI-Enabled Performance Support Concept. A conceptual AI Coach surfaces timely reinforcement based on a current call driver and provides pathways to additional learning or human support.

  1. Signals

    Call drivers · Training status · New resources · Expert feedback

  2. Identify need

  3. Match support

    Refresher · Reminder · Tip · Resource · Check-in

  4. Deliver nudge

  5. Expert response

  6. Reinforce or escalate

    Continue support · Connect to Lead/Manager

Examples

  • Call driverRefresher
  • Incomplete trainingReminder
  • New resourceResource alert
  • Need helpHuman support
Contextual Reinforcement Logic. Business, learning, and engagement signals determine when to surface relevant reinforcement, resources, reminders, or human support.

Conceptual model

Business signals

Call drivers

Learning signals

Training status · Learning activity

  1. SmartSupport Companion

  2. Contextual support

    Refreshers · Tips · Resources · Reminders · Check-ins

  3. Expert

  4. Human support

    Learning · Coach · Manager

Expert engagement and response feed back into the system, informing what support is surfaced next.

Connected Support Ecosystem. The concept connects business signals, learning activity, contextual reinforcement, Expert interaction, and human support within a responsive performance-support model.
07

Proposed Measures

The pilot was designed to test whether AI-enabled reinforcement could strengthen access to support, learning follow-through, performance readiness, and actionable measurement.

  • Access to Support

    Surface relevant resources closer to the moment of need.

  • Learning Follow-Through

    Encourage Experts to complete or revisit relevant learning.

  • Performance Readiness

    Help Experts prepare for frequently occurring customer issues.

  • Actionable Signals

    Use engagement, learning, sentiment, and performance signals to understand what support is working.

Proposed measures for evaluating a pilot; not reported outcomes.

08

What This Demonstrates

  • AI Enablement
  • Learning & Capability Transformation
  • Performance Support Strategy
  • Systems Thinking
  • Workflow Design
  • Measurement Strategy
  • Cross-Functional Solution Concepting

“The opportunity wasn't to create more learning. It was to make learning more responsive to the work.”

— Samta Chowdhary
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