Shadow Work Hunter

Inspiration: While preparing for our company’s quarterly review, we discovered the term “shadow work”—repetitive, manual tasks eating into employees’ core responsibilities—and realized it was a universal, multibillion-dollar productivity leak. We were inspired to shine a light on this invisible crisis by building a solution that not only measures hidden work but also empowers leaders to eliminate it.

What It Does: Shadow Work Hunter integrates time-tracking, performance, and automation-potential data into a single semantic model. Through Agentforce’s conversational interface, users ask plain-English questions—like “Which department should we automate first?”—and receive actionable recommendations complete with ROI projections, payback periods, and risk assessments for at-risk employees.

How We Built It: Data Ingestion: We imported AI-generated employee shadow work logs, IBM HR data, McKinsey automation benchmarks, and industry benchmarks into Salesforce Data Cloud.

Semantic Modeling: In Tableau Next, we joined these data sources via a custom semantic model to enable cross-dataset analysis.

Calculated Fields: We created fields for shadow work hours, cost impact, automation ROI, and attrition risk.

Agentforce Integration: We built four custom actions—analyzeShadowWork, calculateAutomationROI, assessEmployeeRisk, recommendPriorities—and configured the “Shadow Work Hunter” Agentforce agent with domain-specific prompts and business preferences.

Challenges We Ran Into Data Silos: Time-tracking, HR, and automation data lived in separate systems, requiring extensive schema mapping and field standardization.

Benchmark Accuracy: Lacking a single source for shadow work benchmarks, we generated a robust AI-modeled dataset validated against industry reports.

Agent Limitations: Initially, the agent couldn’t handle forecasting or correlation analysis—so we extended the semantic model with trend and correlation fields.

Accomplishments We’re Proud Of 50+ hours of development in two weeks, culminating in a working prototype with real-time ROI calculations.

Preventing $247K in attrition costs in our pilot scenario by identifying eight at-risk employees.

Demonstrating a 326% automation ROI in a live demo—convincing stakeholders to invest in quick-win automations within 48 hours of presentation.

What We Learned The power of combining low-code semantic modeling with conversational AI to surface complex insights through simple dialogue.

The importance of anchoring automated recommendations in real business metrics—ROI, payback, and risk—so stakeholders immediately see value.

That early user testing with real employees uncovers critical edge cases (e.g., blending role-based filters with time series trends) which we then iterated on quickly.

What’s Next for Shadow Work Hunter Enhanced Forecasting: Integrate time-series impact data to enable true 6- and 12-month ROI projections.

Prescriptive Automation Workflows: Automatically generate deployment scripts or Flow templates for high-priority tasks.

Multi-Language Support: Expand Agentforce prompts to support non-English speaking stakeholders.

Industry Benchmarks Marketplace: Allow customers to upload or subscribe to updated shadow work benchmarks for their specific sector.

Shadow Work Hunter isn’t just a hackathon prototype—it’s the start of a movement to reclaim hidden productivity and unleash human potential across every organization.