sf-agent-framework
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AI Agent Orchestration Framework for Salesforce Development - Two-phase architecture with 70% context reduction
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# Training Needs Assessment Task
This task guides the systematic assessment of training needs across
organizations to design targeted, effective Salesforce learning programs.
## Purpose
Enable training specialists to:
- Identify skill gaps and training requirements
- Analyze organizational learning needs
- Align training programs with business objectives
- Prioritize training initiatives based on impact
- Design data-driven learning strategies
## Prerequisites
- Access to organizational performance data
- Understanding of business processes and objectives
- Stakeholder interview and survey capabilities
- Learning analytics tools and methodologies
- Knowledge of competency frameworks
## Training Needs Assessment Framework
### 1. Assessment Methodology
**Multi-Level Analysis Approach**
```yaml
Assessment_Levels:
Organizational_Level:
Focus: Strategic alignment and business impact
Methods: Executive interviews, performance analysis
Timeframe: Annual strategic review
Stakeholders: Leadership team, department heads
Department_Level:
Focus: Team performance and process efficiency
Methods: Manager interviews, workflow analysis
Timeframe: Quarterly assessments
Stakeholders: Department managers, team leads
Individual_Level:
Focus: Personal skill gaps and career development
Methods: Self-assessments, performance reviews
Timeframe: Ongoing individual assessments
Stakeholders: Individual contributors, supervisors
Role_Level:
Focus: Job-specific competencies and requirements
Methods: Job analysis, competency mapping
Timeframe: Role creation or major changes
Stakeholders: HR, role incumbents, managers
```
**Data Collection Framework**
```python
class TrainingNeedsAssessment:
def __init__(self, organization_config):
self.org_config = organization_config
self.assessment_methods = {
'quantitative': ['surveys', 'performance_metrics', 'usage_analytics'],
'qualitative': ['interviews', 'focus_groups', 'observations'],
'mixed_methods': ['360_reviews', 'competency_assessments', 'scenario_analysis']
}
def design_comprehensive_assessment(self, assessment_scope):
"""Design multi-method assessment approach"""
assessment_plan = {
'stakeholder_analysis': self.identify_key_stakeholders(assessment_scope),
'data_collection_methods': self.select_data_collection_methods(assessment_scope),
'timeline_and_milestones': self.create_assessment_timeline(),
'resource_requirements': self.calculate_resource_needs(),
'communication_strategy': self.design_communication_plan()
}
return assessment_plan
def identify_key_stakeholders(self, scope):
"""Identify and categorize stakeholders for assessment"""
stakeholder_categories = {
'primary_stakeholders': {
'end_users': {
'roles': ['sales_reps', 'service_agents', 'managers'],
'assessment_methods': ['surveys', 'interviews', 'usage_analytics'],
'key_questions': [
'Current Salesforce proficiency level',
'Daily usage patterns and challenges',
'Desired skills and capabilities'
]
},
'business_leaders': {
'roles': ['executives', 'department_heads', 'process_owners'],
'assessment_methods': ['structured_interviews', 'business_impact_analysis'],
'key_questions': [
'Business objectives and priorities',
'Performance gaps and improvement opportunities',
'ROI expectations from training'
]
}
},
'secondary_stakeholders': {
'support_functions': {
'roles': ['it_administrators', 'trainers', 'change_managers'],
'assessment_methods': ['expert_interviews', 'capability_assessments'],
'key_questions': [
'Current support capabilities',
'Training delivery preferences',
'Resource constraints and opportunities'
]
}
}
}
return stakeholder_categories
```
### 2. Competency Gap Analysis
**Competency Framework Development**
```yaml
Salesforce_Competency_Framework:
Technical_Competencies:
Platform_Basics:
Level_1_Novice:
- Basic navigation and layout understanding
- Simple record creation and editing
- Basic reporting and dashboard viewing
Level_2_Proficient:
- Advanced record management
- Custom field and page layout configuration
- Report building and customization
Level_3_Expert:
- Workflow and process automation
- Advanced reporting and analytics
- Integration and API understanding
Role_Specific_Skills:
Sales_Cloud:
- Lead management and conversion
- Opportunity pipeline management
- Quote and proposal generation
- Territory and account planning
Service_Cloud:
- Case management and resolution
- Knowledge base utilization
- Omni-channel functionality
- SLA monitoring and compliance
Business_Competencies:
Process_Understanding:
- End-to-end business process knowledge
- Cross-functional workflow comprehension
- Customer journey mapping
- Data quality and integrity awareness
Analytical_Skills:
- Data interpretation and analysis
- Performance metric tracking
- Trend identification and reporting
- Decision-making based on insights
```
**Gap Analysis Implementation**
```
Algorithm: Comprehensive Competency Gap Analysis
INPUT: target_population, competency_framework
PROCESS:
1. DEFINE gap_analysis_methods:
[self_assessment_surveys, manager_assessments, peer_evaluations,
practical_skill_tests, performance_data_analysis]
2. INITIALIZE gap_analysis_results = empty dictionary
3. FOR each individual in target_population:
a. ASSESS current_competencies using multiple methods
b. DETERMINE required_competencies based on role
c. CALCULATE competency_gaps (required - current)
d. PRIORITIZE development_areas by impact and urgency
e. CREATE recommended_learning_path
f. COMPILE individual_analysis
g. STORE in gap_analysis_results[individual.id]
4. AGGREGATE organizational_insights from individual results
5. RETURN complete analysis with individual and organizational insights
OUTPUT: comprehensive_competency_gap_analysis
```
```
Algorithm: Current Competency Assessment
INPUT: individual
PROCESS:
1. CONDUCT assessment using multiple methods:
- self_assessment: individual's self-evaluation
- manager_assessment: supervisor evaluation
- usage_analytics: system usage pattern analysis
- practical_evaluation: hands-on skill testing
2. COMBINE assessment_results using weighted average:
- Apply weights based on method reliability
- Calculate composite competency scores
3. RETURN competency_scores by skill area
OUTPUT: current_competency_assessment
```
```
Algorithm: Development Needs Prioritization
INPUT: individual, competency_gaps
PROCESS:
1. DEFINE prioritization_criteria with weights:
- business_impact: 0.4 (role_criticality, process_importance, customer_impact)
- skill_gap_size: 0.3 (current_vs_required_level, complexity_of_gap)
- learning_readiness: 0.2 (motivation_level, available_time, prior_experience)
- organizational_priority: 0.1 (strategic_alignment, resource_availability)
2. CALCULATE priority_scores for each competency gap
3. RANK development_priorities by composite score
4. RETURN prioritized development needs list
OUTPUT: prioritized_learning_development_plan
```
## Implementation Steps
### Step 1: Assessment Planning and Design
**Stakeholder Engagement Strategy**
```
Algorithm: Stakeholder Engagement Plan Design
INPUT: stakeholder_groups
PROCESS:
1. INITIALIZE engagement_plan = empty dictionary
2. FOR each group_name, group_info in stakeholder_groups:
a. CREATE group_strategy with:
- communication_approach selection
- participation_incentives design
- timing_and_scheduling optimization
- follow_up_strategy planning
b. SET engagement_plan[group_name] = group_strategy
3. RETURN complete engagement_plan
OUTPUT: targeted_stakeholder_engagement_plan
```
```
Algorithm: Communication Method Selection
INPUT: group_info
PROCESS:
1. DEFINE communication_methods by group type:
- executives:
primary: "one_on_one_interviews"
secondary: "executive_briefings"
format: "structured_discussion"
duration: "45-60 minutes"
- managers:
primary: "focus_groups"
secondary: "online_surveys"
format: "facilitated_discussion"
duration: "90 minutes"
- end_users:
primary: "online_surveys"
secondary: "brief_interviews"
format: "structured_questionnaire"
duration: "15-20 minutes"
2. GET group_type from group_info
3. RETURN appropriate communication_method (default: end_users method)
OUTPUT: selected_communication_approach
```
**Assessment Instrument Development**
```yaml
Assessment_Instruments:
Self_Assessment_Survey:
Purpose: Individual competency self-evaluation
Format: Online questionnaire
Duration: 20-25 minutes
Sections:
- Demographic information
- Current role and responsibilities
- Salesforce usage patterns
- Competency self-ratings
- Training preferences and constraints
- Career development goals
Manager_Assessment_Form:
Purpose: Supervisor evaluation of team member competencies
Format: Structured interview guide
Duration: 30-40 minutes per individual
Focus_Areas:
- Job performance in Salesforce-related tasks
- Observed skill gaps and strengths
- Development potential and readiness
- Business impact of skill improvements
Focus_Group_Guide:
Purpose: Group discussion on training needs and challenges
Format: Facilitated discussion
Duration: 90 minutes
Discussion_Topics:
- Current challenges and pain points
- Success stories and best practices
- Desired capabilities and outcomes
- Preferred learning methods and formats
- Organizational barriers and enablers
```
### Step 2: Data Collection and Analysis
**Multi-Method Data Collection**
```python
class DataCollectionOrchestrator:
def __init__(self, assessment_config):
self.config = assessment_config
self.data_sources = {}
def execute_data_collection_plan(self, collection_methods):
"""Execute comprehensive data collection across multiple methods"""
collection_results = {}
for method_name, method_config in collection_methods.items():
try:
if method_name == 'surveys':
results = self.conduct_survey_collection(method_config)
elif method_name == 'interviews':
results = self.conduct_interview_collection(method_config)
elif method_name == 'focus_groups':
results = self.conduct_focus_group_collection(method_config)
elif method_name == 'analytics':
results = self.collect_usage_analytics(method_config)
elif method_name == 'performance_data':
results = self.collect_performance_metrics(method_config)
collection_results[method_name] = results
except Exception as e:
collection_results[method_name] = {'error': str(e), 'status': 'failed'}
return collection_results
def analyze_usage_patterns(self, salesforce_analytics):
\"\"\"Analyze Salesforce usage data to identify training needs\"\"\"
usage_analysis = {
'login_frequency': self.analyze_login_patterns(salesforce_analytics),
'feature_adoption': self.analyze_feature_usage(salesforce_analytics),
'user_efficiency': self.analyze_task_completion_times(salesforce_analytics),
'error_patterns': self.analyze_user_errors(salesforce_analytics),
'support_requests': self.analyze_help_desk_tickets(salesforce_analytics)
}
training_implications = self.derive_training_implications(usage_analysis)
return {
'usage_patterns': usage_analysis,
'training_recommendations': training_implications
}
```
**Performance Gap Analysis**
```python
class PerformanceGapAnalyzer:
def __init__(self):
self.performance_metrics = [
'productivity_measures',
'quality_indicators',
'efficiency_ratios',
'compliance_rates'
]
def analyze_performance_gaps(self, performance_data, competency_data):
\"\"\"Correlate performance gaps with competency deficiencies\"\"\"
gap_analysis = {}
for individual_id, perf_data in performance_data.items():
individual_gaps = {
'performance_metrics': perf_data,
'competency_scores': competency_data.get(individual_id, {}),
'correlation_analysis': self.correlate_performance_competency(
perf_data, competency_data.get(individual_id, {})
),
'priority_training_areas': self.identify_priority_areas(
perf_data, competency_data.get(individual_id, {})
)
}
gap_analysis[individual_id] = individual_gaps
organizational_patterns = self.identify_organizational_patterns(gap_analysis)
return {
'individual_analysis': gap_analysis,
'organizational_patterns': organizational_patterns
}
def correlate_performance_competency(self, performance_data, competency_data):
\"\"\"Identify correlations between performance issues and skill gaps\"\"\"
correlations = []
# Example correlation analysis
if performance_data.get('sales_conversion_rate', 0) < 0.15: # Below 15%
if competency_data.get('opportunity_management', 0) < 3: # Below proficient
correlations.append({
'performance_issue': 'low_sales_conversion',
'related_competency': 'opportunity_management',
'correlation_strength': 'high',
'training_recommendation': 'Advanced opportunity management workshop'
})
return correlations
```
### Step 3: Needs Analysis and Prioritization
**Training Needs Synthesis**
```python
class TrainingNeedsSynthesizer:
def __init__(self):
self.synthesis_framework = {
'individual_needs': 'personal_development_requirements',
'team_needs': 'collective_skill_building',
'organizational_needs': 'strategic_capability_development'
}
def synthesize_training_needs(self, assessment_results):
\"\"\"Synthesize comprehensive training needs from multiple data sources\"\"\"
synthesis_results = {
'priority_training_areas': self.identify_priority_areas(assessment_results),
'target_audiences': self.segment_target_audiences(assessment_results),
'learning_modalities': self.recommend_learning_approaches(assessment_results),
'resource_requirements': self.estimate_resource_needs(assessment_results),
'success_metrics': self.define_success_measures(assessment_results)
}
return synthesis_results
def create_training_roadmap(self, synthesized_needs):
\"\"\"Create prioritized training implementation roadmap\"\"\"
roadmap = {
'immediate_priorities': {
'timeline': '0-3 months',
'focus': 'critical_gaps_and_quick_wins',
'training_initiatives': self.identify_immediate_initiatives(synthesized_needs)
},
'short_term_goals': {
'timeline': '3-6 months',
'focus': 'core_competency_building',
'training_initiatives': self.identify_short_term_initiatives(synthesized_needs)
},
'medium_term_objectives': {
'timeline': '6-12 months',
'focus': 'advanced_skills_and_specialization',
'training_initiatives': self.identify_medium_term_initiatives(synthesized_needs)
},
'long_term_vision': {
'timeline': '12+ months',
'focus': 'strategic_capability_advancement',
'training_initiatives': self.identify_long_term_initiatives(synthesized_needs)
}
}
return roadmap
```
## Success Criteria
✅ Comprehensive stakeholder engagement completed ✅ Multi-method data
collection executed ✅ Competency gaps identified and analyzed ✅ Performance
correlations established ✅ Training needs prioritized and synthesized ✅
Training roadmap developed ✅ Resource requirements estimated ✅ Success metrics
defined and baselined