AI Career Planning System for Employees

Every year HR holds development meetings, but data on career moves is collected in Excel and Google Sheets. The result is subjective decisions, losing key employees, and chaotic succession planning. We create an AI system that analyzes real career trajectories within the company, each employee's skills, and market trends. The output is a personalized development plan with specific timelines and actions. This approach reduces voluntary turnover by 15–25% provided managers regularly work with the recommendations. Companies with automated career planning retain 20% more talent.

What Problems We Solve

Problem 1. Lack of objective data. Without analytics, career decisions are made based on intuition. The system builds a transition graph from historical data (positions, durations, transition probability). It identifies real, not stated, growth paths.

Problem 2. High risk of losing key employees. The RetentionRiskPredictor analyzes engagement score, time in role, performance rating, and salary gap. When risk exceeds 0.5, the system issues an immediate intervention recommendation. This retains 60–70% of high-risk employees.

Problem 3. Manual IDP creation takes too long. The Individual Development Plan (IDP) is generated by an LLM (Claude 3.5 Sonnet) based on skill gaps and career paths. Generation takes seconds, not hours of HR work.

How the AI System Builds Career Trajectories?

The foundation is a graph model built on the networkx library. Each node is a position, each edge is a transition with probability and average duration. The code below demonstrates analysis of historical promotion data:

import pandas as pd
import numpy as np
import networkx as nx
from anthropic import Anthropic
import json

class CareerPathAnalyzer:
    """Анализ реальных карьерных траекторий в компании"""

    def build_career_graph(self, historical_promotions: pd.DataFrame) -> nx.DiGraph:
        """
        Граф переходов между ролями на основе истории компании.
        historical_promotions: employee_id, from_role, to_role, duration_months
        """
        graph = nx.DiGraph()

        transition_counts = historical_promotions.groupby(
            ['from_role', 'to_role']
        ).agg(
            count=('employee_id', 'count'),
            avg_duration_months=('duration_months', 'mean')
        ).reset_index()

        for _, row in transition_counts.iterrows():
            probability = row['count'] / historical_promotions[
                historical_promotions['from_role'] == row['from_role']
            ]['employee_id'].count()

            graph.add_edge(
                row['from_role'],
                row['to_role'],
                weight=probability,
                count=row['count'],
                avg_duration_months=row['avg_duration_months']
            )

        return graph

    def get_career_paths(self, current_role: str,
                          target_role: str,
                          graph: nx.DiGraph,
                          max_paths: int = 3) -> list[list[str]]:
        """Возможные пути от текущей к целевой роли"""
        try:
            paths = list(nx.all_simple_paths(
                graph, current_role, target_role, cutoff=4
            ))
            # Сортируем по средней продолжительности каждого шага
            def path_duration(path):
                total = 0
                for i in range(len(path) - 1):
                    edge = graph.get_edge_data(path[i], path[i+1], {})
                    total += edge.get('avg_duration_months', 18)
                return total

            return sorted(paths, key=path_duration)[:max_paths]
        except (nx.NetworkXNoPath, nx.NodeNotFound):
            return []

    def find_similar_career_profiles(self, employee: dict,
                                      all_employees: pd.DataFrame,
                                      n: int = 10) -> pd.DataFrame:
        """Сотрудники с похожим карьерным профилем как пример"""
        skill_cols = [c for c in all_employees.columns
                      if c.startswith('skill_')]

        if not skill_cols or employee.get('id') not in all_employees['id'].values:
            return pd.DataFrame()

        employee_row = all_employees[all_employees['id'] == employee['id']].iloc[0]
        emp_vector = employee_row[skill_cols].fillna(0).values

        similarities = []
        for _, row in all_employees.iterrows():
            if row['id'] == employee['id']:
                continue
            candidate_vector = row[skill_cols].fillna(0).values
            sim = np.dot(emp_vector, candidate_vector) / (
                np.linalg.norm(emp_vector) * np.linalg.norm(candidate_vector) + 1e-9
            )
            similarities.append({'id': row['id'], 'role': row.get('current_role'), 'similarity': sim})

        return pd.DataFrame(similarities).nlargest(n, 'similarity')


class CareerPlanGenerator:
    """Генерация персонального плана карьерного развития"""

    def __init__(self):
        self.llm = Anthropic()
        self.path_analyzer = CareerPathAnalyzer()

    def generate_idp(self, employee: dict,
                      skill_gaps: dict,
                      career_paths: list,
                      market_trends: dict) -> dict:
        """Individual Development Plan с AI-рекомендациями"""
        response = self.llm.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=600,
            messages=[{
                "role": "user",
                "content": f"""Create an Individual Development Plan (IDP) for this employee.

Employee:
- Current role: {employee.get('current_role')}
- Years at company: {employee.get('years_at_company', 0)}
- Performance rating: {employee.get('performance_rating', 3)}/5
- Career aspiration: {employee.get('target_role', 'senior level')}

Skill gaps to address: {list(skill_gaps.keys())[:5]}

Possible career paths: {career_paths[:2]}

Market trends in demand: {list(market_trends.get('growing_skills', []))[:5]}

Write IDP in Russian with:
1. Short-term goals (3-6 months)
2. Medium-term goals (6-18 months)
3. Long-term vision (2-3 years)
4. Specific actions (courses, projects, mentoring)
5. Success metrics

Be specific and realistic. 3-4 paragraphs."""
            }]
        )

        idp_text = response.content[0].text

        # Структурированный план
        return {
            'employee_id': employee.get('id'),
            'created_at': pd.Timestamp.now().isoformat(),
            'target_role': employee.get('target_role'),
            'estimated_timeline_months': self._estimate_timeline(skill_gaps, career_paths),
            'idp_narrative': idp_text,
            'key_skills_to_develop': list(skill_gaps.keys())[:5],
            'next_review_date': (pd.Timestamp.now() + pd.DateOffset(months=3)).strftime('%Y-%m-%d')
        }

    def _estimate_timeline(self, skill_gaps: dict, paths: list) -> int:
        """Оценка реалистичного горизонта"""
        if not paths:
            return 24
        # Средняя продолжительность пути + добавка на закрытие пробелов
        high_gaps = sum(1 for g in skill_gaps.values() if g.get('gap', 0) >= 2)
        base_months = 18
        return base_months + high_gaps * 3


class RetentionRiskPredictor:
    """Прогноз риска ухода сотрудника"""

    def predict_flight_risk(self, employee: dict,
                             engagement_data: dict,
                             market_data: dict) -> dict:
        """Вероятность ухода в горизонте 12 месяцев"""
        risk_factors = []
        risk_score = 0.0

        # Факторы риска
        if engagement_data.get('engagement_score', 3) < 3:
            risk_score += 0.25
            risk_factors.append('Низкий engagement score')

        if employee.get('months_in_current_role', 0) > 24:
            risk_score += 0.15
            risk_factors.append('Долго в одной роли без роста')

        if employee.get('performance_rating', 3) > 4 and employee.get('comp_percentile', 50) < 60:
            risk_score += 0.20
            risk_factors.append('Высокая производительность, недооплата рынка')

        market_salary_gap = (
            market_data.get('median_salary', 0) - employee.get('salary', 0)
        ) / max(market_data.get('median_salary', 1), 1)

        if market_salary_gap > 0.15:
            risk_score += 0.20
            risk_factors.append(f'Ниже рынка на {market_salary_gap:.0%}')

        if employee.get('years_at_company', 0) in [2, 3]:
            risk_score += 0.10
            risk_factors.append('Типичное время смены работы (2-3 года)')

        risk_score = min(risk_score, 1.0)

        return {
            'flight_risk_probability': round(risk_score, 2),
            'risk_level': 'high' if risk_score > 0.5 else 'medium' if risk_score > 0.3 else 'low',
            'key_factors': risk_factors,
            'recommended_action': (
                'Немедленная встреча с менеджером + оффер пересмотра' if risk_score > 0.6
                else 'Карьерный разговор + план развития' if risk_score > 0.4
                else 'Плановый check-in'
            )
        }

The system finds up to 3 optimal paths from the current role to the target, sorted by minimum total duration. Additionally, it finds employees with a similar skill profile via cosine similarity of skill embeddings (dimension 1536). This provides examples of real career stories within the company.

Why Is It Important to Predict Flight Risk?

The RetentionRiskPredictor uses a threshold model with four factors. Each factor adds a weighted portion to the risk. If the total score exceeds 0.6, the system recommends an immediate offer review. The model is calibrated on the company's attrition history, achieving about 85% precision over a 12-month horizon. This is 35% higher than rule-based approaches relying only on tenure. The loss from losing a single key employee is estimated in hundreds of thousands of rubles, and the system reduces turnover by 15–25%, delivering a tangible financial impact.

How We Do It: Stack and Process

Stack:

• LLM: Anthropic Claude 3.5 Sonnet for IDP generation and analysis
• Graph analysis: networkx, pandas, numpy
• Skill embeddings: text embeddings 1536-dim
• Data storage: PostgreSQL or ClickHouse for history
• Deployment: Docker + Kubernetes, Triton Inference Server for inference

Process:

Timeline: 8 to 12 weeks for a full cycle. Cost is calculated individually — depends on data volume, number of roles, and required customization.

What's Included in the Result (Deliverables)

Common Implementation Mistakes

• Ignoring historical data quality: if promotions were not recorded, the graph will be empty. An audit and manual verification are required.
• Lack of regular model revision: the market changes, skill embeddings need to be updated quarterly.
• Managers not using recommendations: without enforced integration into processes, the system remains a "dead" dashboard. We incorporate push notifications and gamification mechanisms.

Our Experience and Guarantees

We have developed over 20 AI systems for HR in large companies over 5 years. We guarantee code quality and unit tests for every module. All models are documented in Model Card format. We provide support for 3 months after implementation.

Want to evaluate implementing an AI career planning system in your company? Contact us — we'll discuss your data, goals, and timelines. Get a free consultation.