Human-AI Orthogonality: The Art of Perfect Complementarity

# Example: Code Generation Domain Separation
class CodeGenerationOrthogonality:
    """Clear domain separation for AI code generation"""

    # AI Domain: Pattern Recognition & Generation
    AI_RESPONSIBILITIES = {
        'boilerplate_generation': 'Generate repetitive code structures',
        'pattern_matching': 'Suggest code patterns based on context',
        'syntax_completion': 'Complete language-specific syntax',
        'library_suggestions': 'Recommend appropriate libraries',
        'code_formatting': 'Apply consistent formatting rules'
    }

    # Human Domain: Logic & Judgment
    HUMAN_RESPONSIBILITIES = {
        'business_logic_design': 'Define core business requirements',
        'architecture_decisions': 'Choose system architecture patterns',
        'security_validation': 'Verify security implications',
        'performance_optimization': 'Optimize for specific use cases',
        'final_approval': 'Make go/no-go decisions'
    }

    # Shared Domain: Collaborative Validation
    COLLABORATIVE_RESPONSIBILITIES = {
        'code_review': 'AI suggests, human validates and approves',
        'testing_strategy': 'AI generates tests, human defines test cases',
        'documentation': 'AI drafts, human reviews and contextualizes'
    }

# AI-Human Workflow Sequencing
class WorkflowOrthogonality:
    """Define temporal sequences for AI-human collaboration"""

    def code_development_sequence(self):
        """Standard sequence for code development"""
        return [
            {'phase': 'requirements_gathering', 'owner': 'human', 'duration': '2-4 hours'},
            {'phase': 'initial_code_generation', 'owner': 'ai', 'duration': '5-15 minutes'},
            {'phase': 'logic_validation', 'owner': 'human', 'duration': '30-60 minutes'},
            {'phase': 'test_generation', 'owner': 'ai', 'duration': '10-20 minutes'},
            {'phase': 'test_review', 'owner': 'human', 'duration': '15-30 minutes'},
            {'phase': 'integration_testing', 'owner': 'collaborative', 'duration': '1-2 hours'},
            {'phase': 'final_approval', 'owner': 'human', 'duration': '15-30 minutes'}
        ]

    def bug_resolution_sequence(self):
        """Sequence for handling bugs and issues"""
        return [
            {'phase': 'issue_detection', 'owner': 'ai', 'trigger': 'automated_monitoring'},
            {'phase': 'impact_assessment', 'owner': 'human', 'duration': '15-30 minutes'},
            {'phase': 'root_cause_analysis', 'owner': 'collaborative', 'duration': '30-90 minutes'},
            {'phase': 'solution_generation', 'owner': 'ai', 'duration': '10-30 minutes'},
            {'phase': 'solution_validation', 'owner': 'human', 'duration': '30-60 minutes'},
            {'phase': 'implementation', 'owner': 'collaborative', 'duration': '1-4 hours'},
            {'phase': 'verification', 'owner': 'human', 'duration': '30-60 minutes'}
        ]

# Who's responsible for approving this AI-generated function?
def calculate_customer_discount(customer_data, purchase_history, seasonal_factors):
    """AI-generated discount calculation - but who validates the business logic?"""
    # Complex calculation logic here...
    # Gray Zone: Is this correct? Who decides?
    # Human reviewer: "Looks complex, AI must know what it's doing"
    # AI: "I generated it based on patterns, but I don't understand business context"
    pass

# AI-generated API endpoint
@app.route('/api/user-data/<user_id>')
def get_user_data(user_id):
    """AI-generated endpoint - security implications unclear"""
    # AI focused on functionality, not security
    # Human assumes AI considered security
    # Result: Potential security vulnerability
    user = database.get_user(user_id)  # No authorization check!
    return jsonify(user.to_dict())

# AI suggested this optimization
def process_large_dataset(data):
    """AI-optimized data processing"""
    # AI optimized for code elegance, not production performance
    # Human didn't question the approach
    # Result: Works in development, fails in production
    return [complex_operation(item) for item in data]  # Memory explosion with large datasets

# Comprehensive Task Mapping Tool
class DevelopmentTaskMapper:
    def __init__(self):
        self.task_categories = {
            'creative_design': {
                'owner': 'human',
                'rationale': 'Requires creativity, intuition, and business understanding',
                'tasks': [
                    'system_architecture_design',
                    'user_experience_design',
                    'business_logic_specification',
                    'creative_problem_solving',
                    'stakeholder_communication'
                ]
            },
            'pattern_recognition': {
                'owner': 'ai',
                'rationale': 'Excels at pattern matching and repetitive tasks',
                'tasks': [
                    'code_pattern_suggestion',
                    'boilerplate_generation',
                    'syntax_completion',
                    'library_recommendations',
                    'code_formatting'
                ]
            },
            'analytical_validation': {
                'owner': 'human',
                'rationale': 'Requires domain expertise and critical thinking',
                'tasks': [
                    'business_logic_validation',
                    'security_review',
                    'performance_analysis',
                    'compliance_checking',
                    'risk_assessment'
                ]
            },
            'computational_analysis': {
                'owner': 'ai',
                'rationale': 'Benefits from AI\'s computational power',
                'tasks': [
                    'code_complexity_analysis',
                    'dependency_impact_analysis',
                    'test_coverage_calculation',
                    'performance_profiling',
                    'static_code_analysis'
                ]
            },
            'collaborative_refinement': {
                'owner': 'collaborative',
                'rationale': 'Benefits from both AI generation and human judgment',
                'tasks': [
                    'code_review_process',
                    'test_case_development',
                    'documentation_creation',
                    'debugging_sessions',
                    'optimization_iterations'
                ]
            }
        }

    def generate_task_matrix(self):
        """Generate comprehensive task ownership matrix"""
        matrix = {}
        for category, details in self.task_categories.items():
            for task in details['tasks']:
                matrix[task] = {
                    'owner': details['owner'],
                    'category': category,
                    'rationale': details['rationale']
                }
        return matrix

    def identify_gray_zones(self, current_practices):
        """Identify tasks with unclear ownership"""
        gray_zones = []
        task_matrix = self.generate_task_matrix()

        for task, ownership in current_practices.items():
            if ownership == 'unclear' or ownership == 'both':
                recommended = task_matrix.get(task, {})
                gray_zones.append({
                    'task': task,
                    'current_status': ownership,
                    'recommended_owner': recommended.get('owner', 'needs_analysis'),
                    'rationale': recommended.get('rationale', 'Requires detailed analysis')
                })

        return gray_zones

# Enhanced RACI Matrix for AI Teams
class HybridRACIMatrix:
    def __init__(self):
        self.roles = {
            'H': 'Human Lead',
            'AI': 'AI Assistant', 
            'C': 'Collaborative',
            'S': 'System/Automated'
        }

        self.responsibilities = {
            'R': 'Responsible - Does the work',
            'A': 'Accountable - Ultimately answerable',
            'C': 'Consulted - Provides input',
            'I': 'Informed - Kept informed',
            'V': 'Validates - Reviews and approves work',
            'G': 'Gates - Has veto power'
        }

    def create_development_raci(self):
        """Create RACI matrix for development activities"""
        return {
            'requirements_analysis': {
                'Human Lead': 'A,R',
                'AI Assistant': 'C',
                'Stakeholders': 'C,I',
                'System': 'I'
            },
            'code_generation': {
                'Human Lead': 'A,V',
                'AI Assistant': 'R',
                'Code Reviewer': 'C',
                'System': 'I'
            },
            'business_logic_validation': {
                'Human Lead': 'A,R,G',
                'Domain Expert': 'C,V',
                'AI Assistant': 'I',
                'System': 'I'
            },
            'security_review': {
                'Security Lead': 'A,R,G',
                'Human Lead': 'C',
                'AI Assistant': 'C',
                'Automated Security Tools': 'R,I'
            },
            'performance_optimization': {
                'Human Lead': 'A,R',
                'AI Assistant': 'C',
                'Performance Tools': 'R,I',
                'System': 'R'
            },
            'final_deployment_approval': {
                'Human Lead': 'A,G',
                'Technical Lead': 'R,V',
                'AI Assistant': 'I',
                'Automated Tests': 'C'
            }
        }

    def validate_raci_completeness(self, raci_matrix):
        """Ensure every task has clear accountability"""
        issues = []

        for task, assignments in raci_matrix.items():
            # Check for exactly one Accountable
            accountable_count = sum(1 for role, resp in assignments.items() if 'A' in resp)
            if accountable_count != 1:
                issues.append(f"{task}: Must have exactly 1 Accountable role (found {accountable_count})")

            # Check for at least one Responsible
            responsible_count = sum(1 for role, resp in assignments.items() if 'R' in resp)
            if responsible_count == 0:
                issues.append(f"{task}: Must have at least 1 Responsible role")

            # Check for Gate keeper on critical tasks
            if task in ['business_logic_validation', 'security_review', 'final_deployment_approval']:
                gate_count = sum(1 for role, resp in assignments.items() if 'G' in resp)
                if gate_count == 0:
                    issues.append(f"{task}: Critical task must have a Gatekeeper")

        return issues

# Gray Zone Detection System
class GrayZoneDetector:
    def __init__(self, team_data, ai_usage_logs):
        self.team_data = team_data
        self.ai_logs = ai_usage_logs

    def detect_gray_zones(self):
        """Identify areas with unclear human-AI boundaries"""
        gray_zones = []

        # Analyze decision-making patterns
        decision_conflicts = self.analyze_decision_conflicts()

        # Check for duplicated work
        work_duplications = self.find_work_duplications()

        # Identify dropped responsibilities
        dropped_tasks = self.find_dropped_tasks()

        # Analyze communication gaps
        communication_gaps = self.analyze_communication_gaps()

        return {
            'decision_conflicts': decision_conflicts,
            'work_duplications': work_duplications,
            'dropped_tasks': dropped_tasks,
            'communication_gaps': communication_gaps,
            'total_issues': len(decision_conflicts) + len(work_duplications) + len(dropped_tasks) + len(communication_gaps)
        }

    def analyze_decision_conflicts(self):
        """Find instances where both human and AI made contradictory decisions"""
        conflicts = []

        for log_entry in self.ai_logs:
            if log_entry.get('type') == 'decision_override':
                conflicts.append({
                    'task': log_entry['task'],
                    'ai_decision': log_entry['ai_decision'],
                    'human_override': log_entry['human_decision'],
                    'timestamp': log_entry['timestamp'],
                    'resolution_time': log_entry.get('resolution_time'),
                    'impact': log_entry.get('impact_level')
                })

        return conflicts

    def find_work_duplications(self):
        """Identify tasks performed by both human and AI"""
        duplications = []

        # Group activities by task and time window
        task_groups = {}

        for log_entry in self.ai_logs:
            task_key = f"{log_entry['task']}_{log_entry['timestamp'].date()}"
            if task_key not in task_groups:
                task_groups[task_key] = []
            task_groups[task_key].append(log_entry)

        # Find tasks with both human and AI involvement
        for task_key, entries in task_groups.items():
            human_entries = [e for e in entries if e['actor_type'] == 'human']
            ai_entries = [e for e in entries if e['actor_type'] == 'ai']

            if len(human_entries) > 0 and len(ai_entries) > 0:
                # Check if they worked on the same subtask
                human_subtasks = set(e['subtask'] for e in human_entries)
                ai_subtasks = set(e['subtask'] for e in ai_entries)

                overlapping_subtasks = human_subtasks.intersection(ai_subtasks)
                if overlapping_subtasks:
                    duplications.append({
                        'task': task_key.split('_')[0],
                        'date': task_key.split('_')[1],
                        'overlapping_subtasks': list(overlapping_subtasks),
                        'wasted_effort_estimate': self.calculate_wasted_effort(human_entries, ai_entries)
                    })

        return duplications

# AI Gatekeeping Policy Engine
class AIGatekeepingPolicies:
    def __init__(self):
        self.policies = {
            'code_generation': {
                'autonomous_threshold': {
                    'max_lines': 50,
                    'max_complexity': 3,
                    'no_external_dependencies': True,
                    'no_security_implications': True
                },
                'human_approval_required': {
                    'business_logic_changes': True,
                    'database_schema_changes': True,
                    'api_contract_changes': True,
                    'security_related_code': True,
                    'performance_critical_sections': True
                },
                'automatic_rejection': {
                    'deprecated_patterns': True,
                    'known_security_vulnerabilities': True,
                    'licensing_violations': True
                }
            },
            'code_review': {
                'autonomous_approval': {
                    'formatting_only_changes': True,
                    'documentation_updates': True,
                    'test_additions': True
                },
                'human_review_required': {
                    'logic_changes': True,
                    'dependency_additions': True,
                    'configuration_changes': True
                }
            },
            'debugging': {
                'autonomous_fixes': {
                    'syntax_errors': True,
                    'import_statements': True,
                    'formatting_issues': True
                },
                'collaborative_required': {
                    'logic_errors': True,
                    'performance_issues': True,
                    'integration_problems': True
                }
            }
        }

    def evaluate_ai_action(self, action_type, context):
        """Determine if AI can proceed autonomously"""
        if action_type not in self.policies:
            return {'allowed': False, 'reason': 'Unknown action type'}

        policy = self.policies[action_type]

        # Check for automatic rejection criteria
        if 'automatic_rejection' in policy:
            for criterion, enabled in policy['automatic_rejection'].items():
                if enabled and self.check_criterion(criterion, context):
                    return {
                        'allowed': False,
                        'reason': f'Automatic rejection: {criterion}',
                        'requires_human': True
                    }

        # Check for human approval requirements
        if 'human_approval_required' in policy:
            for criterion, enabled in policy['human_approval_required'].items():
                if enabled and self.check_criterion(criterion, context):
                    return {
                        'allowed': False,
                        'reason': f'Human approval required: {criterion}',
                        'requires_human': True,
                        'escalation_priority': self.get_escalation_priority(criterion)
                    }

        # Check autonomous thresholds
        if 'autonomous_threshold' in policy:
            for criterion, threshold in policy['autonomous_threshold'].items():
                if not self.meets_threshold(criterion, threshold, context):
                    return {
                        'allowed': False,
                        'reason': f'Exceeds autonomous threshold: {criterion}',
                        'requires_human': True
                    }

        # Check for autonomous approval
        if 'autonomous_approval' in policy:
            for criterion, enabled in policy['autonomous_approval'].items():
                if enabled and self.check_criterion(criterion, context):
                    return {
                        'allowed': True,
                        'reason': f'Autonomous approval: {criterion}',
                        'requires_human': False
                    }

        # Default to requiring human review
        return {
            'allowed': False,
            'reason': 'Default policy: human review required',
            'requires_human': True
        }

    def check_criterion(self, criterion, context):
        """Check if a specific criterion is met"""
        # Implementation would check context against specific criteria
        # This is a simplified example
        criterion_checkers = {
            'business_logic_changes': lambda ctx: 'business_logic' in ctx.get('modified_areas', []),
            'security_related_code': lambda ctx: any(sec in ctx.get('code', '') for sec in ['password', 'token', 'auth', 'security']),
            'syntax_errors': lambda ctx: ctx.get('error_type') == 'syntax',
            'deprecated_patterns': lambda ctx: any(pattern in ctx.get('code', '') for pattern in ['deprecated_function', 'old_api'])
        }

        checker = criterion_checkers.get(criterion)
        return checker(context) if checker else False

# Orthogonality Feedback System
class OrthogonalityFeedbackLoop:
    def __init__(self):
        self.feedback_channels = {
            'daily_standups': {
                'frequency': 'daily',
                'participants': ['humans', 'ai_system_reports'],
                'focus': 'identify_immediate_conflicts'
            },
            'weekly_retrospectives': {
                'frequency': 'weekly', 
                'participants': ['team_members', 'ai_usage_analysts'],
                'focus': 'analyze_collaboration_patterns'
            },
            'monthly_orthogonality_reviews': {
                'frequency': 'monthly',
                'participants': ['team_leads', 'ai_specialists', 'domain_experts'],
                'focus': 'strategic_responsibility_adjustments'
            },
            'quarterly_policy_updates': {
                'frequency': 'quarterly',
                'participants': ['leadership', 'ai_governance_committee'],
                'focus': 'update_gatekeeping_policies'
            }
        }

    def collect_collaboration_metrics(self):
        """Collect metrics on human-AI collaboration effectiveness"""
        return {
            'conflict_resolution_time': self.measure_conflict_resolution(),
            'decision_clarity_score': self.measure_decision_clarity(),
            'work_duplication_rate': self.measure_work_duplication(),
            'task_completion_efficiency': self.measure_completion_efficiency(),
            'team_satisfaction_score': self.measure_team_satisfaction(),
            'ai_utility_score': self.measure_ai_utility()
        }

    def generate_feedback_report(self, metrics):
        """Generate actionable feedback for improving orthogonality"""
        recommendations = []

        if metrics['conflict_resolution_time'] > 60:  # minutes
            recommendations.append({
                'issue': 'Slow conflict resolution',
                'recommendation': 'Clarify decision-making hierarchy and gatekeeping policies',
                'priority': 'high'
            })

        if metrics['work_duplication_rate'] > 0.15:  # 15%
            recommendations.append({
                'issue': 'High work duplication',
                'recommendation': 'Review and refine RACI matrix, improve task assignment clarity',
                'priority': 'medium'
            })

        if metrics['team_satisfaction_score'] < 7:  # out of 10
            recommendations.append({
                'issue': 'Low team satisfaction with AI collaboration',
                'recommendation': 'Conduct workshops on AI capabilities and limitations',
                'priority': 'high'
            })

        return {
            'metrics_summary': metrics,
            'recommendations': recommendations,
            'next_review_date': self.calculate_next_review_date(),
            'action_items': self.generate_action_items(recommendations)
        }

# Real-world RACI Matrix Example
DEVELOPMENT_RACI = {
    'feature_requirements_gathering': {
        'Product Owner': 'A,R',
        'Development Lead': 'C,V', 
        'AI Assistant': 'I',
        'Stakeholders': 'C,I'
    },

    'initial_code_architecture': {
        'Senior Developer': 'A,R,G',
        'AI Assistant': 'C',
        'Team Lead': 'V',
        'Junior Developers': 'I'
    },

    'boilerplate_code_generation': {
        'AI Assistant': 'R',
        'Developer': 'A,V',
        'Code Standards Bot': 'C',
        'Team': 'I'
    },

    'business_logic_implementation': {
        'Domain Expert': 'A,V,G',
        'Developer': 'R',
        'AI Assistant': 'C',
        'QA': 'I'
    },

    'security_code_review': {
        'Security Lead': 'A,G',
        'Security Tools': 'R',
        'Developer': 'C',
        'AI Assistant': 'C',
        'Team': 'I'
    },

    'performance_optimization': {
        'Performance Engineer': 'A,R',
        'Monitoring Tools': 'R,I',
        'AI Assistant': 'C',
        'Developer': 'C,V'
    },

    'final_code_approval': {
        'Tech Lead': 'A,G',
        'Senior Developer': 'V',
        'Automated Tests': 'R',
        'AI Quality Checks': 'C'
    }
}

# Orthogonality Success Metrics
class OrthogonalityKPIs:
    def __init__(self, team_data, ai_logs, performance_data):
        self.team_data = team_data
        self.ai_logs = ai_logs
        self.performance_data = performance_data

    def calculate_core_metrics(self):
        """Calculate the essential orthogonality metrics"""
        return {
            # Clarity Metrics
            'responsibility_clarity_score': self.measure_responsibility_clarity(),
            'decision_ambiguity_rate': self.measure_decision_ambiguity(),
            'escalation_efficiency': self.measure_escalation_efficiency(),

            # Efficiency Metrics
            'work_duplication_percentage': self.measure_work_duplication(),
            'task_completion_velocity': self.measure_completion_velocity(),
            'context_switching_overhead': self.measure_context_switching(),

            # Quality Metrics
            'human_ai_error_attribution': self.measure_error_attribution(),
            'collaborative_output_quality': self.measure_output_quality(),
            'feedback_loop_effectiveness': self.measure_feedback_effectiveness(),

            # Satisfaction Metrics
            'team_collaboration_satisfaction': self.survey_team_satisfaction(),
            'ai_utility_perception': self.measure_ai_utility_perception(),
            'cognitive_load_reduction': self.measure_cognitive_load()
        }

    def measure_responsibility_clarity(self):
        """Measure how clearly responsibilities are defined and understood"""
        # Survey team on responsibility clarity
        clarity_responses = self.team_data.get('responsibility_clarity_survey', [])

        if not clarity_responses:
            return 0

        # Scale: 1-10, where 10 is "completely clear"
        average_clarity = sum(clarity_responses) / len(clarity_responses)

        # Adjust for consistency (lower std dev = higher score)
        import statistics
        consistency_factor = max(0, 10 - statistics.stdev(clarity_responses))

        return (average_clarity + consistency_factor) / 2

    def measure_decision_ambiguity(self):
        """Calculate rate of ambiguous decisions requiring clarification"""
        total_decisions = len(self.ai_logs.get('decisions', []))
        ambiguous_decisions = len([
            d for d in self.ai_logs.get('decisions', [])
            if d.get('required_clarification') or d.get('was_overridden')
        ])

        return (ambiguous_decisions / total_decisions) * 100 if total_decisions > 0 else 0

    def measure_work_duplication(self):
        """Calculate percentage of work performed by both human and AI"""
        total_tasks = len(self.performance_data.get('completed_tasks', []))
        duplicated_tasks = len([
            task for task in self.performance_data.get('completed_tasks', [])
            if task.get('human_work_time', 0) > 0 and task.get('ai_work_time', 0) > 0
            and task.get('task_type') not in ['collaborative', 'review']
        ])

        return (duplicated_tasks / total_tasks) * 100 if total_tasks > 0 else 0

    def measure_completion_velocity(self):
        """Measure task completion speed with orthogonal vs non-orthogonal workflows"""
        orthogonal_tasks = [
            task for task in self.performance_data.get('completed_tasks', [])
            if task.get('workflow_type') == 'orthogonal'
        ]

        non_orthogonal_tasks = [
            task for task in self.performance_data.get('completed_tasks', [])
            if task.get('workflow_type') == 'traditional'
        ]

        if not orthogonal_tasks or not non_orthogonal_tasks:
            return 0

        orthogonal_avg_time = sum(task['completion_time'] for task in orthogonal_tasks) / len(orthogonal_tasks)
        traditional_avg_time = sum(task['completion_time'] for task in non_orthogonal_tasks) / len(non_orthogonal_tasks)

        # Return improvement factor (values > 1 indicate orthogonal is faster)
        return traditional_avg_time / orthogonal_avg_time if orthogonal_avg_time > 0 else 0

    def generate_kpi_dashboard(self):
        """Generate a comprehensive KPI dashboard"""
        metrics = self.calculate_core_metrics()

        dashboard = {
            'overall_orthogonality_score': self.calculate_overall_score(metrics),
            'metric_breakdown': metrics,
            'trend_analysis': self.analyze_trends(metrics),
            'recommendations': self.generate_recommendations(metrics),
            'benchmarks': self.compare_to_benchmarks(metrics)
        }

        return dashboard

    def calculate_overall_score(self, metrics):
        """Calculate overall orthogonality health score (0-100)"""
        weights = {
            'responsibility_clarity_score': 0.25,
            'decision_ambiguity_rate': -0.20,  # Lower is better
            'work_duplication_percentage': -0.15,  # Lower is better
            'task_completion_velocity': 0.15,
            'team_collaboration_satisfaction': 0.25
        }

        score = 0
        for metric, weight in weights.items():
            value = metrics.get(metric, 0)

            # Normalize negative metrics
            if weight < 0:
                normalized_value = max(0, 100 - value)
            else:
                normalized_value = min(100, value * 10)  # Assuming metrics are 0-10 scale

            score += normalized_value * abs(weight)

        return min(100, max(0, score))

# Typical scenario before implementing orthogonality
class CustomerRecommendationEngine:
    """Everyone worked on everything - chaos ensued"""

    def generate_recommendations(self, customer_id):
        # AI generated this function
        # Developer modified it without understanding ML logic
        # Product manager requested changes to business rules
        # Data scientist optimized algorithm
        # Result: Nobody understood the final code
        pass

# Clear orthogonal separation
class CustomerRecommendationEngine:
    """Clear separation of concerns and responsibilities"""

    def __init__(self):
        # AI Domain: Pattern recognition and data processing
        self.ai_engine = AIRecommendationCore()

        # Human Domain: Business logic and validation
        self.business_rules = BusinessRulesValidator()
        self.quality_assurance = HumanQualityGate()

    def generate_recommendations(self, customer_id):
        """Orthogonal workflow: AI processes, human validates"""

        # Step 1: AI generates raw recommendations
        raw_recommendations = self.ai_engine.process_customer_data(customer_id)

        # Step 2: Human-defined business rules filter results
        filtered_recommendations = self.business_rules.apply_filters(
            raw_recommendations, customer_id
        )

        # Step 3: Human quality gate for final approval
        final_recommendations = self.quality_assurance.validate_and_approve(
            filtered_recommendations, customer_id
        )

        return final_recommendations

   AI_DOMAIN = {
       'data_processing': 'Raw data analysis and pattern recognition',
       'recommendation_generation': 'Initial suggestion creation',
       'performance_monitoring': 'System performance tracking'
   }

   HUMAN_DOMAIN = {
       'business_logic_definition': 'Define what makes a good recommendation',
       'quality_validation': 'Final approval of recommendations',
       'customer_experience_design': 'How recommendations are presented'
   }

   def escalation_policy(recommendation_context):
       if recommendation_context.confidence < 0.8:
           return "human_review_required"
       elif recommendation_context.involves_new_customer:
           return "senior_approval_needed"
       elif recommendation_context.revenue_impact > 10000:
           return "business_stakeholder_approval"
       else:
           return "auto_approve"

class RegulatoryCompliantRiskAssessment:
    """Orthogonal design for regulatory compliance"""

    def __init__(self):
        # AI Domain: Data analysis and pattern detection
        self.risk_analyzer = AIRiskPatternAnalyzer()

        # Human Domain: Regulatory interpretation and final decisions
        self.compliance_validator = ComplianceExpert()
        self.risk_committee = RiskDecisionCommittee()

    def assess_loan_risk(self, application):
        """Two-phase orthogonal risk assessment"""

        # Phase 1: AI Analysis (Explainable)
        ai_analysis = self.risk_analyzer.analyze_application(application)
        risk_factors = ai_analysis.get_explainable_factors()

        # Phase 2: Human Interpretation and Decision
        compliance_review = self.compliance_validator.review_factors(risk_factors)

        if compliance_review.requires_committee_review:
            final_decision = self.risk_committee.make_decision(
                ai_analysis, compliance_review, application
            )
        else:
            final_decision = self.compliance_validator.make_decision(
                ai_analysis, application
            )

        return final_decision

class CreativeGameDevelopment:
    """Orthogonal separation of creative and technical concerns"""

    def __init__(self):
        # AI Domain: Technical optimization and boilerplate
        self.code_optimizer = AICodeOptimizer()
        self.performance_analyzer = AIPerformanceAnalyzer()

        # Human Domain: Creative vision and player experience
        self.creative_director = CreativeDirector()
        self.ux_designer = UserExperienceDesigner()
        self.game_designer = GameDesigner()

    def develop_game_feature(self, feature_concept):
        """Creative-first development with AI optimization"""

        # Step 1: Human creative vision
        creative_vision = self.creative_director.define_vision(feature_concept)
        ux_requirements = self.ux_designer.design_experience(creative_vision)
        game_mechanics = self.game_designer.define_mechanics(ux_requirements)

        # Step 2: AI technical implementation
        technical_architecture = self.code_optimizer.design_architecture(game_mechanics)
        optimized_code = self.code_optimizer.generate_code(technical_architecture)

        # Step 3: Human creative validation
        creative_review = self.creative_director.review_implementation(
            optimized_code, creative_vision
        )

        # Step 4: Iterative refinement
        if not creative_review.meets_vision:
            constraints = creative_review.get_constraints()
            refined_code = self.code_optimizer.refine_with_constraints(
                optimized_code, constraints
            )
            return refined_code

        return optimized_code

# Resistance Pattern: Developers hoarding complex tasks
class DeveloperResistancePattern:
    """Common behaviors when developers feel threatened by AI"""

    def __init__(self):
        self.resistance_behaviors = {
            'task_hoarding': 'Claiming AI-suitable tasks as "too complex" for automation',
            'ai_dismissal': 'Finding reasons why AI suggestions are always wrong',
            'process_sabotage': 'Creating unnecessarily complex approval processes',
            'skill_gatekeeping': 'Arguing that only senior developers can review AI code'
        }

    def identify_resistance_signals(self, team_behavior):
        """Detect resistance patterns in team dynamics"""
        signals = []

        # Check for task hoarding
        if team_behavior.get('ai_task_rejection_rate') > 0.6:
            signals.append({
                'type': 'task_hoarding',
                'evidence': 'High rejection rate for AI-suitable tasks',
                'intervention': 'Demonstrate AI success in low-risk scenarios'
            })

        # Check for skill gatekeeping
        if team_behavior.get('ai_review_bottleneck'):
            signals.append({
                'type': 'skill_gatekeeping', 
                'evidence': 'Only senior members review AI code',
                'intervention': 'Create training programs for all team members'
            })

        return signals

def address_developer_concerns(team_context):
    """Framework for addressing developer resistance"""
    return {
        'career_path_clarity': {
            'ai_enhanced_roles': 'Show how AI enhances rather than replaces developer skills',
            'specialization_opportunities': 'Create new specialization paths (AI prompt engineering, AI code review)',
            'mentorship_roles': 'Position senior developers as AI integration mentors'
        },
        'gradual_exposure': {
            'start_with_wins': 'Begin with tasks developers dislike (boilerplate, documentation)',
            'showcase_success': 'Publicly celebrate successful AI-human collaborations',
            'measure_impact': 'Show concrete productivity improvements'
        },
        'skill_development': {
            'ai_literacy_training': 'Invest in AI understanding for all team members',
            'cross_training': 'Rotate team members through different AI collaboration roles',
            'external_validation': 'Bring in industry experts to validate the approach'
        }
    }

class ManagementResistancePattern:
    """Managerial concerns about AI integration"""

    def __init__(self):
        self.management_fears = {
            'loss_of_control': 'Cannot direct or evaluate AI work using traditional methods',
            'accountability_confusion': 'Unclear who is responsible when AI makes mistakes',
            'performance_measurement': 'Existing KPIs do not capture AI-human collaboration',
            'resource_allocation': 'Difficulty justifying AI tool costs vs. traditional development'
        }

    def create_management_comfort_framework(self):
        """Address management concerns with orthogonal clarity"""
        return {
            'control_mechanisms': {
                'clear_escalation_paths': 'Define when decisions escalate to human management',
                'ai_audit_trails': 'Implement logging for all AI decisions and modifications',
                'performance_dashboards': 'Create management-friendly metrics for AI collaboration'
            },
            'accountability_structure': {
                'human_ultimate_responsibility': 'Humans always accountable for business outcomes',
                'ai_decision_attribution': 'Clear tracking of AI vs human contributions',
                'error_handling_protocols': 'Defined processes for AI mistake resolution'
            },
            'roi_demonstration': {
                'pilot_project_metrics': 'Start with measurable, time-boxed pilots',
                'comparative_analysis': 'Show AI-assisted vs traditional development outcomes',
                'cost_benefit_tracking': 'Transparent tracking of AI tool ROI'
            }
        }

class OrthogonalityChangeManagement:
    """Systematic approach to organizational change for AI integration"""

    def phase_1_foundation(self):
        """Build foundation for orthogonal adoption"""
        return {
            'leadership_alignment': {
                'executive_briefing': 'Present business case for orthogonal AI integration',
                'champion_identification': 'Identify and empower internal advocates',
                'resource_commitment': 'Secure dedicated time and budget for transition'
            },
            'team_preparation': {
                'current_state_assessment': 'Use assessment template to baseline current practices',
                'pain_point_documentation': 'Catalog existing frustrations with AI integration',
                'success_criteria_definition': 'Establish clear, measurable success metrics'
            },
            'communication_strategy': {
                'transparent_roadmap': 'Share implementation timeline and expected changes',
                'regular_updates': 'Weekly progress communications to all stakeholders',
                'feedback_channels': 'Create safe spaces for concerns and suggestions'
            }
        }

    def phase_2_pilot_implementation(self):
        """Controlled rollout with selected teams"""
        return {
            'pilot_team_selection': {
                'criteria': 'Choose teams with high AI readiness and low change resistance',
                'size': 'Start with 5-8 person teams for manageable scope',
                'duration': '6-8 week pilots with clear success metrics'
            },
            'support_structure': {
                'dedicated_coach': 'Assign experienced AI integration specialist',
                'weekly_check_ins': 'Regular progress reviews and obstacle removal',
                'peer_learning': 'Cross-team sharing of challenges and solutions'
            },
            'measurement_focus': {
                'baseline_establishment': 'Measure current productivity and satisfaction',
                'progress_tracking': 'Weekly measurement of orthogonality KPIs',
                'story_collection': 'Document specific success stories and lessons learned'
            }
        }

class BaselineBootstrapper:
    """Establish initial measurements for teams new to AI orthogonality"""

    def __init__(self):
        self.baseline_categories = {
            'current_state_proxies': 'Use existing metrics as starting points',
            'rapid_assessment_tools': 'Quick measurement techniques for immediate baselines',
            'retrospective_analysis': 'Analyze past projects to establish historical baselines'
        }

    def establish_baseline_without_historical_data(self, team_context):
        """Create baseline measurements for teams starting fresh"""

        # Week 1: Rapid Assessment
        rapid_baseline = {
            'responsibility_clarity': self.conduct_team_survey(
                'Rate clarity of current AI vs human responsibilities (1-10)'
            ),
            'decision_speed': self.measure_current_decision_patterns(
                'Track all decisions for one week, noting delays and confusion'
            ),
            'work_duplication': self.identify_current_overlaps(
                'Document instances where both human and AI work on same tasks'
            )
        }

        # Week 2: Historical Analysis
        historical_proxy = {
            'code_review_time': 'Average PR review time over last 3 months',
            'bug_attribution': 'Percentage of bugs from AI-generated vs human code',
            'team_satisfaction': 'Most recent team satisfaction survey results'
        }

        # Week 3: Comparative Baseline
        comparative_baseline = {
            'industry_benchmarks': 'Compare against published industry standards',
            'internal_benchmarks': 'Compare against non-AI teams in same organization',
            'vendor_benchmarks': 'Use AI tool vendor-provided baseline metrics'
        }

        return {
            'immediate_baseline': rapid_baseline,
            'historical_proxy': historical_proxy,
            'comparative_context': comparative_baseline,
            'measurement_plan': self.create_ongoing_measurement_plan()
        }

    def create_ongoing_measurement_plan(self):
        """Establish sustainable measurement practices"""
        return {
            'daily_metrics': ['Decision delays', 'AI suggestion acceptance rate'],
            'weekly_metrics': ['Work duplication instances', 'Escalation frequency'],
            'monthly_metrics': ['Team satisfaction', 'Overall orthogonality score'],
            'quarterly_metrics': ['Strategic alignment', 'ROI measurement']
        }

class OrganizationalConflictResolver:
    """Handle conflicts between optimal AI design and existing structures"""

    def resolve_structural_conflicts(self, optimal_design, current_structure):
        """Navigate conflicts between ideal and practical implementation"""

        conflict_types = {
            'reporting_hierarchy': {
                'problem': 'AI responsibilities cross traditional team boundaries',
                'solution': 'Create matrix responsibility model with clear escalation',
                'example': 'AI code review requires both technical and domain expertise'
            },
            'skill_distribution': {
                'problem': 'Current team lacks skills for optimal role separation',
                'solution': 'Implement gradual skill development with interim compromises',
                'example': 'Only one person understands AI well enough to review suggestions'
            },
            'budget_constraints': {
                'problem': 'Optimal design requires tools or training not in current budget',
                'solution': 'Phased implementation prioritizing highest-impact changes',
                'example': 'Advanced AI tools cost more than current budget allows'
            },
            'cultural_resistance': {
                'problem': 'Team culture conflicts with required collaboration patterns',
                'solution': 'Culture change program alongside technical implementation',
                'example': 'Individualistic culture resists collaborative AI workflows'
            }
        }

        return self.create_compromise_framework(optimal_design, current_structure, conflict_types)

    def create_compromise_framework(self, optimal, current, conflicts):
        """Create practical implementation plan that addresses conflicts"""
        return {
            'immediate_actions': 'Changes that can be implemented within current constraints',
            'medium_term_evolution': 'Gradual progression toward optimal design',
            'long_term_vision': 'Ultimate goal with timeline for full implementation',
            'success_milestones': 'Specific checkpoints to measure progress',
            'contingency_plans': 'Alternative approaches if primary plan encounters resistance'
        }