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

~25000

Employees

$15B

Revenue

Technology & Digital Experience

Industry

San Jose, U.S.

Headquarters

About the organization

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

Global Skill Standardization
Standardize talent and skill frameworks across global tech functions.
Adapt to AI Skill Shifts
Adapt to rapidly evolving AI skill demands and emerging job families.
Build the Right Hubs
Identify and prioritize scalable global hubs for AI-focused talent growth.
Evaluate Academic Ecosystems
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.
01
Global Talent Trend Analysis
Mapped global profiles and job descriptions to identify evolving AI skills, emerging roles, and future competency needs across key functions.
02
Generative AI Workforce Impact Study
Evaluated AI’s impact on workloads and skill gaps, defining reskilling pathways for transitioning roles into AI-driven capabilities.
03
AI Hub Prioritization & Location Strategy
Benchmarked global markets on talent maturity, cost, and ecosystem depth to identify scalable AI hubs for expansion.
04
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.
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
AICPA SOC2 TYPE 2
SOC 2
GDPR Logo
GDPR
ISO 27001 Logo
ISO
EAIGG Logo
EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

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.

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.

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.

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.

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.

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.

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.

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