LLM Strategy & Use Case Prioritization

1. AI Readiness Assessment

Before selecting a single use case, organizations need an honest evaluation of their current capabilities across four dimensions: data maturity, technical infrastructure, organizational culture, and talent. Skipping this step is the most common source of delayed or abandoned LLM projects.

The Four-Pillar Readiness Framework

Each pillar is scored on a 1 to 5 scale. Organizations scoring below 3 on any pillar should address that gap before committing to production LLM deployments. A total score below 12 (out of 20) indicates the organization should start with low-risk pilot projects rather than enterprise-wide initiatives.

from dataclasses import dataclass
from typing import Dict

@dataclass
class ReadinessAssessment:
    """Four-pillar AI readiness scoring framework."""
    data_maturity: int          # 1-5 scale
    tech_infrastructure: int   # 1-5 scale
    org_culture: int           # 1-5 scale
    talent: int                # 1-5 scale

    def total_score(self) -> int:
        return (self.data_maturity + self.tech_infrastructure
                + self.org_culture + self.talent)

    def weakest_pillar(self) -> str:
        scores = {
            "data_maturity": self.data_maturity,
            "tech_infrastructure": self.tech_infrastructure,
            "org_culture": self.org_culture,
            "talent": self.talent,
        }
        return min(scores, key=scores.get)

    def recommendation(self) -> str:
        total = self.total_score()
        weakest = self.weakest_pillar()
        if total >= 16:
            return "Ready for enterprise LLM initiatives"
        elif total >= 12:
            return f"Proceed with pilots; strengthen {weakest}"
        else:
            return f"Address {weakest} before committing budget"

# Example assessment for a mid-size fintech company
assessment = ReadinessAssessment(
    data_maturity=4,
    tech_infrastructure=3,
    org_culture=2,
    talent=3
)
print(f"Total: {assessment.total_score()}/20")
print(f"Weakest: {assessment.weakest_pillar()}")
print(f"Recommendation: {assessment.recommendation()}")

2. Use Case Identification

Effective use case identification starts from business pain points, not from technology capabilities. The goal is to find problems where LLMs provide a meaningful advantage over existing solutions (rule-based systems, traditional ML, manual processes) and where the organization has the data and infrastructure to support the solution.

The Use Case Discovery Workshop

A structured two-hour workshop with cross-functional stakeholders (engineering, product, operations, compliance) is the most reliable way to surface high-value use cases. The workshop follows four phases:

1. Pain Point Inventory (30 min): Each stakeholder lists the top three processes that consume the most time, produce the most errors, or frustrate customers the most.
2. LLM Fit Screening (20 min): Filter each pain point through a checklist: Does it involve natural language? Is the output subjective or variable? Would a human expert need context and judgment?
3. Data Availability Check (20 min): For each surviving candidate, assess whether training data, evaluation data, and production data pipelines exist or can be built within 4 weeks.
4. Impact Estimation (30 min): Estimate the annual cost of the current process and the expected improvement (time saved, errors reduced, revenue generated).

from dataclasses import dataclass, field
from typing import List

@dataclass
class UseCase:
    """Structured representation of a candidate LLM use case."""
    name: str
    department: str
    pain_point: str
    involves_language: bool
    data_available: bool
    annual_cost_current: float   # USD per year
    expected_improvement: float  # fraction, e.g., 0.40 = 40%
    complexity: str              # "low", "medium", "high"

    def estimated_annual_value(self) -> float:
        return self.annual_cost_current * self.expected_improvement

    def passes_screening(self) -> bool:
        return self.involves_language and self.data_available

# Workshop output: candidate use cases
candidates = [
    UseCase("Customer ticket routing", "Support",
            "Manual triage takes 8 min per ticket",
            involves_language=True, data_available=True,
            annual_cost_current=420_000, expected_improvement=0.55,
            complexity="low"),
    UseCase("Contract review assistant", "Legal",
            "Lawyers spend 60% of time on routine clauses",
            involves_language=True, data_available=True,
            annual_cost_current=800_000, expected_improvement=0.35,
            complexity="high"),
    UseCase("Image defect detection", "Manufacturing",
            "Visual inspection is slow and error-prone",
            involves_language=False, data_available=True,
            annual_cost_current=300_000, expected_improvement=0.50,
            complexity="medium"),
]

# Filter and rank
viable = [uc for uc in candidates if uc.passes_screening()]
ranked = sorted(viable, key=lambda uc: uc.estimated_annual_value(), reverse=True)

for uc in ranked:
    print(f"{uc.name}: ${uc.estimated_annual_value():,.0f}/yr value, {uc.complexity} complexity")

3. Prioritization Frameworks

After identifying viable use cases, you need a systematic way to decide which to pursue first. The two most effective frameworks for LLM prioritization are the Value-Complexity Matrix and the RICE scoring model adapted for AI projects.

Value-Complexity Matrix

Plot each use case on a two-by-two matrix with estimated annual value on the Y-axis and implementation complexity on the X-axis. The four quadrants provide clear action guidance:

AI-Adapted RICE Scoring

from dataclasses import dataclass

@dataclass
class RICEScore:
    """RICE scoring adapted for LLM use cases.

    Reach:      Number of users/processes affected per quarter
    Impact:     Expected improvement (0.25=low, 0.5=medium, 1.0=high, 2.0=massive)
    Confidence: Data availability and technical feasibility (0.0 to 1.0)
    Effort:     Person-months to deliver MVP
    """
    name: str
    reach: int
    impact: float
    confidence: float
    effort: float

    def score(self) -> float:
        return (self.reach * self.impact * self.confidence) / self.effort

use_cases = [
    RICEScore("Ticket routing",     reach=50000, impact=1.0, confidence=0.9, effort=2.0),
    RICEScore("Contract review",    reach=2000,  impact=2.0, confidence=0.6, effort=6.0),
    RICEScore("Internal knowledge", reach=5000,  impact=1.0, confidence=0.8, effort=3.0),
    RICEScore("Code generation",    reach=500,   impact=2.0, confidence=0.7, effort=4.0),
]

ranked = sorted(use_cases, key=lambda uc: uc.score(), reverse=True)
for uc in ranked:
    print(f"{uc.name:20s}  RICE = {uc.score():>10,.0f}")

4. Building the Business Case

A business case for an LLM initiative must answer four questions that executives care about: What is the problem? What is the proposed solution? What will it cost? What will it return? The structure below has been tested across dozens of enterprise AI proposals.

# Business Case Template (structured as a Python dict for automation)

business_case = {
    "title": "AI-Powered Customer Ticket Routing",
    "problem": {
        "description": "Manual ticket triage takes 8 min per ticket across 200K annual tickets",
        "annual_cost": 420_000,
        "pain_metrics": {
            "avg_first_response_time_hrs": 4.2,
            "misroute_rate": 0.18,
            "csat_score": 3.2,
        },
    },
    "solution": {
        "approach": "LLM classifier with RAG over knowledge base for routing",
        "model_strategy": "Fine-tuned small model (Llama 3.1 8B) for classification",
        "human_in_loop": "Confidence threshold: auto-route above 0.85, human review below",
    },
    "costs": {
        "development_one_time": 120_000,   # 2 engineers x 3 months
        "infrastructure_annual": 36_000,   # GPU inference + vector DB
        "maintenance_annual": 24_000,      # 0.5 FTE ongoing
    },
    "returns": {
        "labor_savings_annual": 231_000,   # 55% of current cost
        "csat_improvement": "3.2 -> 4.1 (projected)",
        "first_response_time": "4.2 hrs -> 0.5 hrs",
    },
    "timeline": {
        "phase_1_pilot": "Weeks 1-6: MVP with 10% traffic",
        "phase_2_scale": "Weeks 7-12: Full rollout with monitoring",
        "phase_3_optimize": "Months 4-6: Fine-tune, reduce human review",
    },
}

# Calculate payback period
total_year1_cost = (business_case["costs"]["development_one_time"]
                    + business_case["costs"]["infrastructure_annual"]
                    + business_case["costs"]["maintenance_annual"])
annual_savings = business_case["returns"]["labor_savings_annual"]
payback_months = (total_year1_cost / annual_savings) * 12

print(f"Year 1 total cost: ${total_year1_cost:,.0f}")
print(f"Annual savings: ${annual_savings:,.0f}")
print(f"Payback period: {payback_months:.1f} months")

5. Common Failure Modes

Understanding why LLM projects fail is as important as knowing how to succeed. Research across enterprise AI initiatives reveals consistent patterns of failure that can be anticipated and mitigated.

6. Building an AI Roadmap (6 to 18 Months)

An AI roadmap is not a Gantt chart of model training tasks. It is a phased plan that aligns technical milestones with business outcomes, organizational capability building, and risk management. The three-phase approach below provides a proven structure.