Transforming global AI talent strategy for the leading San Jose based digital design enterprise

Discover how Draup enabled this $15 billion design software leader to build an AI-ready workforce and scale innovation worldwide

25,000+

Employee Count

$15 Bn

Revenue

Technology & Digital Experience

Industry

San Jose, U.S.

Headquarters

About the Company

A global pioneer in digital experience technologies, creative software, and AI-driven content solutions, the company empowers millions of users and enterprises worldwide. With operations across 40+ countries and a rapidly expanding AI product ecosystem, the company continues to redefine how digital content is created, managed, and delivered.

The Core Challenges

Standardize talent and skill frameworks across global tech functions.

Adapt to rapidly evolving AI skill demands and emerging job families.

Identify and prioritize scalable global hubs for AI-focused talent growth.

Build a unified model for evaluating universities and research ecosystems.

The Solution

Artificial Intelligence Engineer

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

The image is for representational purpose only.

Global Talent Trend Analysis

Mapped global profiles and job descriptions to identify evolving AI skills, emerging roles, and future competency needs across key functions.

Generative AI Workforce Impact Study

Evaluated AI’s impact on workloads and skill gaps, defining reskilling pathways for transitioning roles into AI-driven capabilities.

AI Hub Prioritization & Location Strategy

Benchmarked global markets on talent maturity, cost, and ecosystem depth to identify scalable AI hubs for expansion.

University & Ecosystem Benchmarking

Assessed universities on graduate readiness and research strength to solidify partnerships and build sustainable AI talent pipelines.

Outcome

AI Workforce Strategy

A unified model integrating AI readiness, talent analytics, and hiring competitiveness across global markets.

Strategic Hub Identification

Vancouver, Toronto, and Bucharest emerged as high-potential, scalable AI centers.

Future Ready Skill Framework

Clear reskilling and upskilling pathways enable seamless transitions into AI-centric roles.

Stronger Academic Partnerships

Deepened partnerships with top AI universities to secure long term, innovation aligned talent pipelines.

Powered by unparalleled global labor & market data

23K+

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.