Map your workforce capabilities, identify critical skill gaps, predict future talent needs, and optimize build vs. buy decisions using AI-powered skills intelligence and talent analytics.
Most organizations don't have accurate inventory of employee skills. Job titles don't reflect actual capabilities. Hidden talent and transferable skills go undiscovered, leading to external hiring when internal candidates exist.
Business strategy requires new capabilities (AI, cloud migration, data analytics) but organization lacks these skills. Scrambling to hire specialized talent takes 6-12 months, delaying strategic initiatives and competitive advantage.
Organizations spend €1,200+ per employee annually on training without knowing which skills to prioritize. Generic training programs don't address actual capability gaps. Poor ROI measurement means wasteful spend continues.
When skill gaps emerge, unclear whether to train existing employees or hire externally. Without data on training time, success rates, and hiring timelines, organizations make expensive suboptimal decisions.
Our machine learning platform maps workforce skills, identifies critical gaps, predicts future capability needs, recommends training or hiring strategies, and optimizes talent development ROI.
NLP models automatically extract skills from resumes, LinkedIn profiles, job descriptions, project assignments, certifications, and training records. Build comprehensive skills inventory for entire workforce. Standardize skill naming using industry taxonomies (O*NET, LinkedIn Skills Graph). Cluster related skills and identify skill adjacencies.
Compare current workforce skills vs. future requirements from business strategy, product roadmaps, technology adoption plans, and industry trends. Identify critical skill gaps with prioritization scoring. Predict skill obsolescence (declining demand for legacy technologies) and emerging skill needs (AI, cloud, security).
ML matching algorithms identify employees with adjacent skills who could transition to high-need roles with training. Recommend internal candidates for open positions before external recruiting. Calculate skill similarity scores and training requirements for role transitions. Enable skills-based talent marketplace.
Ready to map workforce capabilities and eliminate skill blindspots? Our platform identifies critical skill gaps and recommends optimal build vs. buy strategies.
For each skill gap, AI recommends whether to train existing employees or hire externally. Analyzes: training time and success probability, hiring market availability and timeline, cost comparison, strategic importance and urgency. Provides decision scorecard with recommendation confidence.
ML models predict training success probability for each employee-skill combination based on learning history, skill adjacency, and time availability. Recommend personalized learning paths with course sequences, expected duration, and success likelihood. Optimize training budget allocation across initiatives to maximize business impact.
Traditional workforce planning focuses on headcount and job titles. Skills-based approach is more granular and actionable. Benefits: Better hiring decisions - understand actual capability needs, not just job title requirements. Internal mobility - discover hidden talent and reduce external hiring. Strategic agility - quickly identify if you have skills for new initiatives. Training ROI - invest in high-impact skill development. Succession planning - identify critical skill dependencies and single points of failure.
Market context: LinkedIn reports skills required for jobs have changed 25% since 2015, and will change 40% by 2025. Half-life of technical skills is shrinking - from 5 years to 2.5 years for some technologies. Organizations must continuously map and update workforce capabilities to remain competitive. Companies with strong skills intelligence are 2.5x more likely to be talent innovators and 3x more likely to successfully execute digital transformation.
Comprehensive skills inventory requires multiple data sources: Resumes and CVs (initial hire skills, may be outdated), LinkedIn profiles (self-reported skills and endorsements), Job descriptions (required skills for current roles), Project assignments (actual work performed and tools used), Training records (courses completed and certifications earned), Performance reviews (manager assessments of skill proficiency), Code repositories (programming languages and frameworks used), Self-assessments (employee-reported skills and proficiency levels).
Named Entity Recognition (NER) models identify skill mentions in unstructured text. Challenges: Skills are contextual - "Python" could be programming language or snake. Synonyms and variations - "JavaScript" vs. "JS" vs. "Node.js". Implicit skills - resume mentions "Led data analysis project" implies SQL, Excel, visualization skills. Solutions: Pre-trained models (spaCy, BERT) fine-tuned on HR/job description corpus, skill taxonomies for standardization (map "React.js" and "ReactJS" to "React"), context understanding through sentence embeddings, validation through multiple data sources.
Raw skill extraction yields messy data - 50 variations of "project management" or "machine learning." Require standardized taxonomy: Use established frameworks (O*NET Skills Database, LinkedIn Skills Graph, ESCO European classification), create hierarchical structure (category → skill → specialization, e.g., "Programming → Python → Django"), define skill relationships (Python is prerequisite for Django, Java and C# are similar/transferable), maintain proficiency levels (awareness, working knowledge, proficient, expert). Taxonomy enables accurate gap analysis and talent matching.
Not all skills are equal - junior developer "knows Python" differently than senior engineer. Proficiency assessment methods: Self-assessment questionnaires (fast but biased - Dunning-Kruger effect), manager ratings (accurate but time-intensive), time-based inference (used Python for 5 years likely more proficient than 6 months), project complexity analysis (contributed to simple scripts vs. architected production systems), certification and training (AWS Certified Solutions Architect indicates proficiency level), skills tests and assessments (coding challenges, case studies). Hybrid approach combines multiple signals for accuracy.
Skills gap analysis compares supply (current workforce skills) vs. demand (future requirements):
Skills intelligence transforms learning and development from generic programs to targeted capability building: (1) Personalized learning paths - AI recommends skill development priorities for each employee based on career goals, current skills, and business needs. (2) Just-in-time training - Deliver training when needed for project or role transition, not generic annual programs. (3) Peer learning networks - Connect employees wanting to learn skills with internal experts who can mentor. (4) Training ROI measurement - Track skill acquisition, application to work, business impact, and retention outcomes. (5) Budget optimization - Allocate training spend to highest-impact skills aligned with strategy.
Example: Company identifies Python skill gap for data analyst team. Traditional approach: Send all 20 analysts to same Python bootcamp, €50K total cost, mixed results. AI-optimized approach: Skill assessment identifies 8 analysts with programming background (fast track), 7 with Excel/SQL (intermediate track), 5 non-technical (fundamental track). Personalized learning paths with different curricula and timelines. Budget €35K on differentiated training plus internal mentorship. Result: 85% proficiency achievement vs. 50% with one-size-fits-all, €15K cost savings, faster time-to-productivity.
A 2,800-person manufacturing company was undergoing digital transformation - implementing IoT sensors, predictive maintenance AI, and cloud-based operations management. Challenge: Workforce dominated by mechanical engineers and traditional factory workers with limited software, data science, or cloud skills. Initial plan: Hire 85 specialized engineers and data scientists externally at €110K average salary (€9.3M annual cost), estimated 12-18 month hiring timeline. Skills blindspot: Unknown which existing employees had transferable skills or learning potential.
We implemented an AI skills intelligence platform. NLP models extracted skills from employee resumes, project histories, training records, and self-assessments for all 2,800 employees. Built comprehensive skills inventory with proficiency levels. Gap analysis compared current capabilities vs. digital transformation requirements - identified needs for Python, machine learning, AWS, IoT platforms, data visualization. ML matching algorithms identified 127 employees with adjacent skills (mechanical engineers with CAD programming, industrial engineers with statistics background, IT staff with scripting experience) who could transition to digital roles with training.
Build vs. buy analysis recommended: Train 53 internal employees with high skill adjacency (Python for engineers with programming, ML for statisticians, AWS for IT staff). Personalized 4-6 month learning paths with courses, certifications, and project-based learning. Cost: €650K (training programs + backfill for learning time). Hire 32 specialized roles externally for critical skills with no internal candidates (senior ML architects, cloud solution architects). Results after 18 months: 62% internal fill rate (53 of 85 roles) vs. 0% in original plan. Time to capability reduced from 14 months to 6 months average (58% improvement) - training faster than hiring. Total cost: €3.5M (€650K training + €3.52M for 32 external hires) vs. €9.3M all-external plan = €5.8M gross savings. Training ROI: €5.1M saved minus €650K training cost = 7.8:1 return. Secondary benefits: Employee engagement increased 18 points (NPS) due to career development opportunities, retention improved 23% among trained employees (invested in their growth), innovation culture strengthened as workforce gained digital skills. Digital transformation delivered 9 months ahead of schedule, creating €12M additional revenue impact.
Accuracy depends on data quality and validation approach. Pure NLP extraction from resumes/profiles: 70-85% accuracy (misses context, implicit skills, outdated information). Multi-source approach (resumes + projects + training + manager input + self-assessment): 85-95% accuracy through cross-validation. Best practice: AI provides initial skills inventory (fast, scales to thousands of employees), human validation for critical roles or strategic skills. Hybrid approach: AI extracts skills, presents to employees for review/correction, employees add missing skills and update proficiency. This achieves 90%+ accuracy while saving 80% of time vs. manual assessment. Ongoing maintenance: Update skills as employees complete training, change roles, or work on new projects.
Self-assessment bias is real - Dunning-Kruger effect causes less skilled people to overestimate abilities. Mitigation strategies: (1) Calibration through multiple data sources - compare self-assessment to manager ratings, project work, certifications. Flag large discrepancies. (2) Behavioral anchors - define proficiency levels with concrete examples ("Expert: Can architect production systems, mentor others, debug complex issues" vs. vague "very skilled"). (3) Skills tests - for critical skills, validate with technical assessments or coding challenges. (4) Longitudinal tracking - if employee claims "Expert Python" but never uses it in work, likely inflated. (5) Peer validation - skill endorsements from colleagues who've worked together. (6) Manager review - managers approve or adjust team member skill profiles. Transparency: Employees understand skills data used for development and opportunity, not performance punishment, encouraging honesty.
Yes, though technical skills are easier to identify and measure. Soft skills and business competencies (leadership, communication, strategic thinking, customer empathy) are harder to extract from documents and assess objectively. Approaches for non-technical skills: (1) Structured competency frameworks - define specific behaviors for each skill level, (2) 360-degree feedback - assess soft skills through peer, manager, and direct report ratings, (3) Performance review analysis - NLP on review text to identify competency strengths/gaps, (4) Behavioral interview data - structured interviews assess competencies with scoring rubrics, (5) Project outcomes - leadership roles, presentation experience, cross-functional collaboration as skill proxies. Example: Sales organization mapping consultative selling skills, negotiation capabilities, CRM proficiency, industry knowledge. Marketing team analyzing digital marketing skills, analytics capabilities, content creation, brand strategy.
Skills change faster than job titles or roles - continuous updates important. Update frequency depends on workforce: High-change environments (tech, consulting): Quarterly updates for critical technical skills. Standard corporate: Semi-annual comprehensive updates, real-time updates for major events (training completion, certification, role change, major project). Stable industries: Annual updates sufficient. Automation helps: Auto-update skills when employee completes training course or earns certification, prompt employees quarterly to review and update skills profile, NLP continuously monitors project assignments and internal communications for emerging skills. Balance freshness vs. burden - don't overload employees with constant surveys, automate what's possible. Most organizations find semi-annual employee review + automated event-triggered updates optimal.
Implementation: 8-12 weeks for data integration, skills extraction, taxonomy setup, dashboard development. Quick wins within first quarter: Skills visibility (discover hidden talent), internal mobility opportunities (fill roles internally vs. external recruiting - first internal hire saves €20-50K). Measurable ROI at 6-12 months: Build vs. buy decisions avoiding unnecessary external hires (€200K+ per avoided senior hire), training ROI through targeted skill development (3-5:1 return typical), faster time-to-capability for strategic initiatives (revenue impact). Full maturity at 12-18 months: Skills-based talent marketplace, predictive skills gap forecasting, continuous learning culture, competitive advantage through strategic capability building. Typical ROI: 5:1 to 15:1 for mid-large companies (500+ employees) through avoided hiring costs, training optimization, and strategic agility. For 1,000-person company, even 5% improvement in talent decisions (€3M value at €60K avg salary) far exceeds platform investment.
Map workforce skills, identify critical gaps, optimize build vs. buy decisions, and align talent development with business strategy. Our team will assess your skills landscape and design a custom intelligence solution.
Based in Lund, Sweden • Serving businesses globally