Building an AI-enabled workforce for the future of an Italian aerospace, defense, and security company

Discover How Draup Helped a Defense & Security Leader Redefine Skill Architecture

60,000+

Employee Count

$19.2 Bn

Revenue

Aerospace, Defense & Security

Industry

Rome, Italy

Headquarters

About the Company

A leading European multinational in aerospace, defense, and digital engineering, the company designs advanced systems in aviation, security, and space technology. The aerospace engineering giant also integrates AI, simulation, and cyber-resilient digital engineering to enhance competitiveness and foster workforce innovation. This approach enhances competitiveness and fosters workforce transformation into a rapidly evolving technological landscape.

Before Draup

1

Evolving Job Architecture

Rapid skill shifts across AI, quantum computing, and digital engineering required redefining job role families and competencies.

2

Bridging the Skills-Roles Gap

Legacy engineering roles needed to evolve toward systems thinking, software-defined operations, and data-driven decision-making.

3

Future-Proofing Reskilling

The company’s Professional Training Center needed a measurable roadmap for reskilling and personalized learning.

4

Boosting Internal Mobility

The company sought to redeploy existing talent across geographies and business units instead of relying heavily on external hiring.

The Core Challenges

To anticipate future skill needs and align talent pipelines with next-generation technologies, the company needed a unified view of role evolution across engineering, avionics, and security.

To benchmark effectively and assess workforce readiness, the organization required greater transparency into the AI and digital talent ecosystems of leading aerospace and defense peers.

To modernize job architectures and align current roles with emerging capabilities, the company had to move beyond traditional, manual, and function-specific role mapping methods.

To diversify its workforce and enable global expansion, the company needed broader visibility into AI, software, and digital twin expertise hotspots beyond Europe.

The Solution

Avionics Engineer

JOB POSTING TIMELINE

CORE SKILLS

TALENT MARKET INSIGHTS

TECH STACK

CAREER PATHS

AI-Driven Skill Taxonomy Expansion

Utilized Draup's ontology to expand role-skill taxonomy, capturing emerging digital, cyber, and AI capabilities, and developed a Future Skills Library for strategic digital transformation.

Workforce Analytics Dashboard

Created a real-time skills intelligence dashboard, integrating HR data and global labor market trends, providing predictive insights on talent supply, automation impact, and learning priorities across geographies.

Personalized Learning & Career Pathing

AI-driven career path recommendations were implemented using the company’s internal data and Draup's global talent graph, with embedded learning modules tied to daily tasks and upskilling needs.

Personalized Learning & Career Pathing

AI-driven career pathing leveraged Draup’s talent graph, while the Reskilling Propensity Index (RPI) identified technicians with the highest potential to transition into engineering roles.

Outcome

Future-Ready Talent Architecture

Established a unified, analytics-driven technician-to-engineer career framework, enabling clear mobility pathways and reducing skill mismatches by ~25%.

Improved Hiring Efficiency & Cost Savings

Achieved ~20–25% savings per hire by converting internal technicians into engineering roles instead of relying on external recruitment.

Accelerated Workforce Planning

Enabled workforce-planning decisions to be made 30–40% faster through real-time insights on reskilling vs. hiring requirements.

Enhanced Mobility & Retention

Transparent career pathways and structured learning journeys led to a ~20% uplift in retention among technicians transitioning into engineering roles.

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.