Transforming Non-Tech Workforce Strategy for a $40B+ Global Ride-Sharing Leader

Discover how Draup helped a global mobility leader map GenAI's impact on non-tech roles, skills, and reskilling pathways

30,000+

Employee Count

$40+ billion

Revenue

Mobility & Technology

Industry

San Francisco, California

Headquarters

About the Company

A global mobility enterprise operating across 70+ countries in ride-hailing, delivery, and logistics, seeking to understand GenAI's impact on non-tech workforce roles and reskilling needs.

The Core Challenges

Assess how GenAI would reshape workloads across Customer Service, Sales, and Operations — clarifying automation exposure, augmentation zones, and human-led responsibilities for each role.

Map emerging skill requirements and proficiency progression across entry, mid, and senior levels for AI-augmented non-tech job families.

Distinguish fully automated, augmented, and negligible-impact tasks to guide role redesign, workforce planning, and investment prioritization.

Define structured reskilling pathways and emerging skill contextualization to prepare non-tech professionals for AI-transformed workflows.

The Solution

Customer Success Consultant

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

The image is for representational purpose only.

GenAI Workload Evolution Analysis

Mapped GenAI's impact across Customer Service, Sales (Account Reps, Managers), and Operations (Strategy & Ops Managers, Senior Ops Managers, Consumer Growth Leads, Senior Researchers) detailing before and after.

Automation vs. Augmentation Modeling

Classified tasks across all non-tech functions into fully automated, AI-augmented, and negligible-impact categories, providing granular visibility into which responsibilities shift to AI and which remain human-led.

Skill Proficiency & Career Path Mapping

Built structured skill proficiency maps across entry, mid, and senior levels for each function, identifying associated tools, platforms, and capability progressions aligned to AI-augmented workflows.

Emerging Skill Contextualization

Contextualized emerging skills by comparing before-GenAI and after-GenAI application, quantifying business impact across metrics such as resolution speed, forecast accuracy, and research throughput.

Outcome

Clear Workload Transformation Roadmap

Established a structured, role-by-role view of how GenAI reshapes workloads across Customer Service, Sales, and Operations - enabling proactive workforce planning and role redesign.

Precision Automation & Augmentation Zoning

Distinguished fully automated, augmented, and human-led tasks across all non-tech functions, providing leadership with a defensible framework for investment prioritization and capability planning.

Future-Ready Skill Architecture

Delivered comprehensive skill proficiency maps across seniority levels, with associated tools and platforms, enabling targeted development programs and structured career pathing for AI-augmented roles.

Quantified Business Impact of Emerging Skills

Contextualized emerging skills with measurable outcomes - including 25–45% faster resolution in Customer Service, improved forecast in Sales, and 2–4× research throughput in Ops grounding reskilling investments in tangible business value.

Powered by unparalleled global labor & market data

26K+

Skills

1B+

Professionals

5700+

Locations

1.6M+

Peer Group Companies

1B+

Job Descriptions

4M+

Career Paths

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.