Prioritization in AI Product Development: The Art of Strategic No

class ProjectLoad:
    def __init__(self):
        self.potential_projects = [
            "Implement transformer architecture",
            "Build real-time inference pipeline", 
            "Create data labeling platform",
            "Optimize model for edge deployment",
            "Develop explainability dashboard",
            "Refactor feature engineering pipeline",
            "Implement A/B testing framework",
            "Build model monitoring system",
            "Create automated retraining pipeline",
            "Develop custom loss functions"
        ]
        
    def calculate_completion_rate(self, projects_attempted):
        capacity = 100  # Team capacity units
        effort_per_project = 30  # Average effort units
        context_switching_cost = 5 * (projects_attempted - 1)
        
        actual_capacity = capacity - context_switching_cost
        completion_rate = min(1.0, actual_capacity / (projects_attempted * effort_per_project))
        
        return {
            'projects_attempted': projects_attempted,
            'completion_rate': completion_rate,
            'projects_completed': int(projects_attempted * completion_rate)
        }

# Results:
# 2 projects: 95% completion = 2 completed
# 5 projects: 60% completion = 3 completed  
# 10 projects: 20% completion = 2 completed

def calculate_daily_productivity(tasks_per_day):
    work_hours = 8
    deep_work_per_task = 2  # Hours needed for meaningful progress
    switch_cost = 0.38  # Hours per switch (23 minutes)
    
    switch_time = (tasks_per_day - 1) * switch_cost
    available_time = work_hours - switch_time
    
    productivity = min(1.0, available_time / (tasks_per_day * deep_work_per_task))
    
    return {
        'tasks': tasks_per_day,
        'switch_time': switch_time,
        'productive_time': available_time,
        'effectiveness': productivity
    }

# 1 task: 100% effectiveness
# 3 tasks: 76% effectiveness
# 5 tasks: 47% effectiveness

class TechnicalDebt:
    def __init__(self):
        self.debt_items = []
        
    def add_rushed_feature(self, feature_name, corners_cut):
        future_cost = corners_cut * 3  # Each shortcut costs 3x to fix later
        self.debt_items.append({
            'feature': feature_name,
            'immediate_time_saved': corners_cut,
            'future_time_cost': future_cost,
            'net_cost': future_cost - corners_cut
        })
        
    def calculate_total_debt(self):
        return sum(item['net_cost'] for item in self.debt_items)

class ProjectPrioritizer:
    def calculate_ice_score(self, project):
        # Impact: Business value delivered
        impact = self.estimate_impact(project)
        
        # Confidence: Probability of success
        confidence = self.estimate_confidence(project)
        
        # Ease: Inverse of effort required
        ease = 10 - self.estimate_effort(project)
        
        return (impact * confidence * ease) / 100
    
    def estimate_impact(self, project):
        """1-10 scale based on user value"""
        metrics = {
            'users_affected': project.get('users', 0) / 1000000,
            'revenue_impact': project.get('revenue', 0) / 100000,
            'strategic_value': project.get('strategic', 5)
        }
        return min(10, sum(metrics.values()))
    
    def estimate_confidence(self, project):
        """1-10 scale based on technical feasibility"""
        factors = {
            'prior_art_exists': 3,
            'team_has_expertise': 3,
            'data_available': 2,
            'infrastructure_ready': 2
        }
        return sum(factors.values() if project.get(key) else 0 
                  for key in factors.keys())
    
    def estimate_effort(self, project):
        """1-10 scale of resource requirements"""
        return project.get('weeks_required', 1)

# Q4 2024 NOT-DOING List

## Features We're Not Building
- Multi-language support (revisit Q2 2025)
- Custom model architectures (use pre-trained)
- Real-time video processing (batch is sufficient)
- Mobile app (web works fine)

## Optimizations We're Skipping
- Sub-50ms latency (100ms meets SLA)
- 99.99% uptime (99.9% is acceptable)
- Perfect accuracy (85% provides value)

## Technologies We're Not Adopting
- Latest transformer variant (current works)
- Kubernetes migration (Docker Compose scales)
- GraphQL API (REST is sufficient)

class TeamWorkflow:
    def __init__(self, team_size):
        self.team_size = team_size
        self.max_wip = 2  # Maximum projects in progress
        self.active_projects = []
        
    def can_start_new_project(self):
        return len(self.active_projects) < self.max_wip
    
    def start_project(self, project):
        if not self.can_start_new_project():
            raise Exception("Complete current projects first")
        
        self.active_projects.append(project)
        project.status = "active"
        project.team_allocation = self.team_size / self.max_wip

import calendar

class DefensiveCalendar:
    def __init__(self):
        self.schedule = {
            'monday': {'9-12': 'Deep Work', '1-3': 'Meetings', '3-5': 'Code Review'},
            'tuesday': {'9-12': 'Deep Work', '1-5': 'Deep Work'},
            'wednesday': {'9-12': 'Deep Work', '1-3': 'Meetings', '3-5': 'Planning'},
            'thursday': {'9-5': 'No Meeting Day - Deep Work Only'},
            'friday': {'9-12': 'Deep Work', '1-3': 'Learning', '3-5': 'Cleanup'}
        }
        
    def can_schedule_meeting(self, day, time):
        day_schedule = self.schedule.get(day, {})
        for time_slot, activity in day_schedule.items():
            if time in time_slot and 'Deep Work' in activity:
                return False
        return True

class CapacityPlanning:
    def __init__(self, team_hours_per_week):
        self.total_capacity = team_hours_per_week
        self.planned_allocation = 0.9
        self.buffer_allocation = 0.1
        
    def allocate_work(self):
        return {
            'planned_work': self.total_capacity * self.planned_allocation,
            'urgent_requests': self.total_capacity * self.buffer_allocation,
            'description': 'Reserve 10% for unexpected but critical work'
        }

def project_audit():
    projects = load_all_projects()
    
    # Score each project
    for project in projects:
        project.score = calculate_ice_score(project)
    
    # Keep only top 3
    projects.sort(key=lambda x: x.score, reverse=True)
    keep = projects[:3]
    defer = projects[3:8]
    cancel = projects[8:]
    
    return {
        'keep': keep,
        'defer': defer,  # Explicitly postpone
        'cancel': cancel  # Explicitly stop
    }

class ProfessionalNo:
    def __init__(self):
        self.templates = {
            'not_now': """
                This is an interesting opportunity. However, we're currently 
                focused on {current_priority} which directly impacts {metric}.
                Can we revisit this in {timeframe}?
            """,
            
            'not_us': """
                This falls outside our team's core expertise in {our_expertise}.
                Team {other_team} might be better positioned to deliver value here.
            """,
            
            'not_valuable': """
                After analysis, this would deliver approximately {small_value} 
                in value while requiring {large_effort} in effort.
                Should we explore alternative approaches?
            """,
            
            'offer_alternative': """
                Instead of {requested_solution}, could {simpler_solution} 
                address 80% of the need with 20% of the effort?
            """
        }

## Q4 Prioritization Framework

### What We're Optimizing For:
1. Model accuracy (target: 90%+)
2. System latency (target: <100ms)
3. User retention (target: 70%+)

### What We're Accepting:
1. Limited feature set (core features only)
2. Manual processes (automation later)
3. Single language support (English only)

### The Tradeoff:
By focusing on fewer features, we can deliver:
- 3x faster time to market
- 2x improvement in quality
- 90% reduction in bugs

class PersonalProductivity:
    def __init__(self):
        self.daily_priorities = []
        
    def plan_day(self):
        # Maximum 3 priorities
        if len(self.daily_priorities) > 3:
            self.daily_priorities = self.daily_priorities[:3]
            
        # One must be completion-focused
        self.ensure_completion_task()
        
    def ensure_completion_task(self):
        has_completion = any('complete' in task or 'finish' in task 
                           for task in self.daily_priorities)
        if not has_completion:
            self.daily_priorities[2] = "Complete [specific deliverable]"

class FocusMetrics:
    def calculate_team_focus_score(self):
        metrics = {
            'project_completion_rate': 0.3,  # Weight
            'wip_limit_adherence': 0.2,
            'context_switches_per_day': 0.2,
            'deep_work_hours_percentage': 0.3
        }
        
        score = sum(self.measure(metric) * weight 
                   for metric, weight in metrics.items())
        return score
    
    def measure(self, metric):
        # Returns 0-1 score for each metric
        measurements = {
            'project_completion_rate': self.completed / self.started,
            'wip_limit_adherence': self.days_under_limit / self.total_days,
            'context_switches_per_day': max(0, 1 - (self.switches / 10)),
            'deep_work_hours_percentage': self.deep_hours / self.total_hours
        }
        return measurements.get(metric, 0)