Modernizing Workforce & Talent Strategy for a Hong Kong–Based World’s #1 PC Maker

How Draup Helped a Hong Kong–Based Global Tech Leader Rethink Its Workforce Approach

75,000+

Employee Count

$60 Bn

Revenue

Technology

Industry

Hong Kong

Headquarters

About the Company

The organization is a leading global technology enterprise operating across intelligent devices, infrastructure solutions, and advanced computing. Serving both consumer and enterprise markets, it delivers a broad portfolio of hardware, software, and integrated solutions that enable digital transformation at scale, underpinned by deep engineering heritage and a strong global footprint. The company plays a critical role in advancing innovation across personal computing, enterprise infrastructure, and emerging technology ecosystems while navigating rapid shifts driven by AI, automation, and geopolitical change.

Before Draup

1

Limited Visibility into Early Career & AI Talent

Inconsistent benchmarks across peers made it difficult to assess fresher, intern, and early-career hiring effectiveness.

2

Unclear AI Hiring & Skill Priorities

Varying levels of AI adoption across hardware and technology peers created uncertainty around optimal AI talent scale and skill mix.

3

Macroeconomic & Location Disruption

Tariffs, geopolitical shifts, and nearshoring trends disrupted established manufacturing and talent location strategies.

4

Lack of Executive-Ready Workforce Insights

Leadership lacks concise, data-driven inputs to guide organization design, hiring models, and long-term talent resilience.

The Core Challenges

Early-career hiring benchmarks varied across peers, limiting clarity on fresher intake, intern conversion, and diversity targets.

Engineering and hardware roles are increasingly blended with AI and software skills, complicating workforce planning.

Cost inflation and saturation in tier-1 hubs, combined with unproven tier-2 locations, pressured global location strategies.

Narrowing wage gaps for AI and engineering talent weakened traditional cost-arbitrage hiring models.

The Solution

Artificial Intelligence Engineer

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

Gen-Z & Fresh Graduate Talent Benchmarking

Benchmarked early-career hiring across global peers to define clear targets for fresher intake, intern conversion, diversity, and regional talent focus.

AI Talent & Demand Analysis Across Peers

Assessed AI talent supply, growth, and hiring intensity across industry peers to guide selective scaling and capability prioritization.

Macroeconomic & Workforce Strategy Lens

Evaluated the impact of geopolitical shifts, tariffs, and nearshoring to inform resilient, multi-hub global workforce location strategies.

Skills-Based Hiring, Productivity & Organization Design Transformation

Analyzed skills-based hiring, Generative AI–driven productivity gains, and organization design trends to modernize role definitions, workforce models, and recruitment practices.

Outcome

Data-Driven Early-Career Hiring Strategy

Established clear peer benchmarks for fresher intake ratios, intern hiring volumes, and conversion targets.

Competitive AI & Future-Ready Skill Positioning

Clarified where to selectively scale AI talent while defining pathways to transition traditional engineering roles into AI-augmented capabilities.

Resilient Global Location Strategy

Validated tier-2 hubs and multi-hub workforce models to mitigate geopolitical, cost, and concentration risks.

Executive-Ready Talent Playbook

Equipped HR and business leaders with a unified, insight-driven framework to align workforce strategy with technology disruption and macroeconomic change.

Powered by unparalleled global labor & market data

19K+

Skills

850M+

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.