AI Use Case Prioritization: Selecting High-Impact Projects

Not all AI use cases are equal. Here’s how to prioritize for maximum impact.

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.