AI Use Case Prioritization: Selecting High-Impact Projects

I wrote “AI Use Case Prioritization: Selecting High-Impact Projects” to share practical, production-minded guidance on this topic.

AI Use Case Prioritization Framework

from dataclasses import dataclass
from typing import List, Dict
import numpy as np

@dataclass
class AIUseCase:
    name: str
    description: str
    business_value: float  # 1-10
    feasibility: float  # 1-10
    data_readiness: float  # 1-10
    strategic_alignment: float  # 1-10
    risk_level: float  # 1-10 (higher = more risk)
    estimated_cost: float
    estimated_timeline_months: int

class UseCasePrioritizer:
    def __init__(self, weights: Dict = None):
        self.weights = weights or {
            "business_value": 0.30,
            "feasibility": 0.20,
            "data_readiness": 0.15,
            "strategic_alignment": 0.20,
            "risk_adjusted": 0.15
        }

    def score_use_case(self, use_case: AIUseCase) -> Dict:
        """Calculate priority score for use case."""
        # Risk-adjusted score (invert risk)
        risk_adjusted = 10 - use_case.risk_level

        scores = {
            "business_value": use_case.business_value,
            "feasibility": use_case.feasibility,
            "data_readiness": use_case.data_readiness,
            "strategic_alignment": use_case.strategic_alignment,
            "risk_adjusted": risk_adjusted
        }

        weighted_score = sum(
            scores[factor] * weight
            for factor, weight in self.weights.items()
        )

        return {
            "use_case": use_case.name,
            "individual_scores": scores,
            "weighted_score": weighted_score,
            "quadrant": self.determine_quadrant(use_case)
        }

    def determine_quadrant(self, use_case: AIUseCase) -> str:
        """Determine strategic quadrant."""
        high_value = use_case.business_value >= 7
        high_feasibility = use_case.feasibility >= 7

        if high_value and high_feasibility:
            return "Quick Wins"  # Do first
        elif high_value and not high_feasibility:
            return "Strategic Bets"  # Invest carefully
        elif not high_value and high_feasibility:
            return "Low Hanging Fruit"  # Easy but less impactful
        else:
            return "Deprioritize"  # Consider later

    def prioritize_portfolio(self, use_cases: List[AIUseCase]) -> Dict:
        """Prioritize portfolio of use cases."""
        scored = [self.score_use_case(uc) for uc in use_cases]

        # Sort by weighted score
        ranked = sorted(scored, key=lambda x: x["weighted_score"], reverse=True)

        # Group by quadrant
        by_quadrant = {}
        for item in scored:
            quadrant = item["quadrant"]
            if quadrant not in by_quadrant:
                by_quadrant[quadrant] = []
            by_quadrant[quadrant].append(item)

        return {
            "ranked_list": ranked,
            "by_quadrant": by_quadrant,
            "recommendations": self.generate_recommendations(ranked, by_quadrant)
        }

    def generate_recommendations(self, ranked: List, by_quadrant: Dict) -> List[str]:
        """Generate portfolio recommendations."""
        recommendations = []

        # Quick wins first
        quick_wins = by_quadrant.get("Quick Wins", [])
        if quick_wins:
            recommendations.append(
                f"Start with {len(quick_wins)} quick win(s): {', '.join(q['use_case'] for q in quick_wins[:3])}"
            )

        # Strategic bets need careful planning
        strategic = by_quadrant.get("Strategic Bets", [])
        if strategic:
            recommendations.append(
                f"Plan carefully for {len(strategic)} strategic bet(s) - high value but complex"
            )

        return recommendations

Systematic prioritization ensures AI investments focus on highest-impact opportunities.\n\n## Takeaways\n\nAdd a concise, personal takeaway and recommended next steps here.\n