Strengthening AI & Cybersecurity Talent Strategy for a Leading Finland-Based $25B Global Telecommunications Enterprise

Discover how Draup strengthened AI and cybersecurity workforce strategy for a $25B global telecom leader.

78,000+

Employee Count

$25 billion

Revenue

Telecommunications & Network Infrastructure

Industry

Espoo, Finland

Headquarters

About the Company

A global telecommunications and network infrastructure leader delivering mobile networks, IP networking, cloud solutions, and optical connectivity across 100+ countries. With AI-driven automation and cybersecurity reshaping telecom, the organization sought data-backed talent intelligence to guide workforce expansion.

The Core Challenges

Assess global talent supply and demand dynamics for AI and cybersecurity across eight priority countries, identifying growth trends and demand-supply gaps.

Map traditional and emerging skill clusters in both domains, distinguishing sunrise capabilities from continuing skillsets to guide hiring and upskilling priorities.

Benchmark the organization's AI and cybersecurity workforce positioning against major tech and telecom peers across talent size, growth rate, and hiring intensity.

Identify scalable, cost-efficient global talent hotspots to diversify sourcing beyond saturated tier-1 hubs and reduce competitive hiring pressure.

The Solution

Electronics Engineer

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

The image is for representational purpose only.

Global AI & Cybersecurity Talent Market Analysis

Mapped quarter-over-quarter talent demand trends for both AI and cybersecurity from 2022 to 2025, analyzing supply growth, demand-supply ratios, and year-over-year shifts across the US, India, Canada, France, Poland, Romania, Portugal, and Finland.

Skill Cluster & Gap Diagnostics

Identified high-growth sunrise skills—including RAG, generative modeling, prompt engineering, Zero Trust Architecture, and DevSecOps—alongside continuing traditional capabilities, quantifying share-of-volume shifts in the organization's hiring patterns.

Peer Benchmarking & Competitive Intelligence

Benchmarked the organization against six major tech peers and six telecom peers on talent supply, year-over-year growth, hiring demand, and skill mix across both AI and cybersecurity functions.

Location Strategy & Talent Hotspot Identification

Evaluated global talent hotspots by mapping workforce concentration, peer hiring focus, talent growth rates, and cost dynamics to identify diversified sourcing opportunities beyond saturated markets.

Outcome

Clear AI & Cybersecurity Workforce Positioning

Gained a unified view of how the organization compares to tech and telecom peers in talent size, growth trajectory, and skill mix—establishing that AI talent was at par with peers while cybersecurity required focused capability building.

Strategic Location Diversification

Identified India and Canada as high-growth, favorable markets for scaling AI and cybersecurity teams, with Portugal and Poland validated as emerging hubs for workforce diversification and early-career talent sourcing.

Future-Ready Skill Architecture

Mapped clear upskilling priorities in sunrise areas—DevSecOps, Zero Trust Architecture, Responsible AI, Conversational AI, and RAG—while reinforcing the need to maintain traditional cybersecurity foundations.

Data-Backed Hiring & Upskilling Roadmap

Delivered an integrated intelligence framework combining skill gap analysis, peer investment tracking, and location-level insights to guide targeted hiring, early-career recruitment programs, and internal reskilling initiatives.

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.