Leveraging LLMs for Business Operations and Process Improvement

Introduction

Like all exciting new technologies, it is alluringly easy to find problems that the shiny new tool can solve. For organizations with limited resources, sometimes the only choice (and maybe smartest) that can be made is to use such new technology to frame or re-frame existing problems and opportunities in a new cost and ROI light. This post aims to provide the background and an evaluation methodology for AI-based solutioning that organizations can use to navigate what adds value and what likely does not.

The Tipping Point

LLMs represent threshold crossings that fundamentally change what’s economically viable:

Quality Threshold: Previous NLP required extensive domain-specific training, custom models, and often produced unusable outputs. LLMs cross the “good enough for real work” threshold out of the box.

Generalization Threshold: Earlier systems were narrow – a chatbot for customer service couldn’t help with document analysis. LLMs are the first general-purpose language tools that work across domains without retraining.

Complexity Threshold: Previous systems could handle simple, structured tasks. LLMs can handle multi-step reasoning, ambiguous context, and creative synthesis that previously required human judgment.

Opportunities

We can describe two categories of opportunities available to us with the advent of LLMs.

Category 1: Previously Possible, Now Economically Viable

Enterprise Knowledge Management

Before: Required massive indexing projects, specialized search tools, knowledge engineers
Now: Natural language queries across all company data with instant, contextual answers

Customer Service Automation

Before: Rule-based chatbots, decision trees, extensive scripting for each scenario
Now: Handle complex, multi-turn conversations without pre-scripting every path

Document Processing & Analysis

Before: OCR + rules engines + human review for complex documents
Now: Direct extraction, summarization, and analysis of nuanced content at scale

Content Localization & Translation

Before: Human translators + lengthy review cycles + cultural adaptation
Now: Context-aware translation that maintains tone, brand voice, and cultural nuance

Category 2: Newly Practical/Possible with LLMs

Conversational Interfaces for Complex Software

Replace training-intensive enterprise software with natural language interaction
Enable non-technical users to perform complex data analysis or system operations

Real-time Synthesis Across Disparate Data Sources

Combine structured data, documents, emails, and external sources into coherent insights
Generate executive briefings that pull from dozens of systems automatically

Personalized Content Generation at Individual Scale

Create unique, contextually relevant content for each customer/employee/situation
Mass customization of communications, training, and experiences

Ambient Intelligence in Workflows

Systems that understand context from natural work patterns and proactively assist
Convert meeting discussions directly into project plans, follow-ups, and documentation

Evaluation Methodology for AI Implementation

Stage 1: Opportunity Identification & Prioritization

Current State Mapping
1. Audit existing pain points in knowledge work, customer interaction, and decision-making processes
2. Identify current technology costs (licensing, maintenance, personnel) for Category 1 opportunities
3. Map workflows that currently require significant human intervention or are avoided due to cost/complexity
Initial Filtering Matrix Rate each opportunity (1-5 scale):

Factor	Rating
Business impact (revenue/cost impact)
Frequency of occurrence
Current cost/effort to execute
Data availability and quality
Stakeholder buy-in likelihood

Stage 2: Deep Dive Assessment

Category-Specific Analysis
1. For Category 1 (Better Economics):
  1. Calculate current total cost of ownership
  2. Benchmark existing solution performance/satisfaction
  3. Identify switching costs and integration complexity
2. For Category 2 (Newly Possible):
  1. Define success metrics and measurement approach
  2. Assess organizational readiness for new capabilities
  3. Evaluate change management requirements
Technical Feasibility Assessment

Factor	Assessment	Notes
Data requirements: availability, quality, privacy constraints
Integration complexity with existing systems
Security and compliance implications
Required expertise and training

Stage 3: Build vs. Buy Decision Framework

Capability Analysis
1. Favor BUILD when:
  1. Core competitive differentiator
  2. Unique data or domain requirements
  3. High customization needs
  4. Strong internal AI/development capability
  5. Long-term strategic asset
2. Favor BUY when:
  1. Commodity/standard business function
  2. Proven vendor solutions exist
  3. Need rapid deployment
  4. Limited internal capability
  5. Non-core business process
ROI Modeling
1. Category 1 ROI Formula:
  1. Cost savings = (Current solution cost – New solution cost) × frequency
  2. Add productivity gains and quality improvements
  3. Subtract implementation and switching costs
2. Category 2 ROI Formula:
  1. Value creation = New revenue/opportunities enabled
  2. Efficiency gains = Time/cost savings from new capabilities
  3. Risk mitigation value (harder to quantify but important)

Stage 4: Pilot Design & Validation

Pilot Scoping
1. Define MVP scope with clear success criteria
2. Identify test users and measurement methodology
3. Set timeline and resource requirements
4. Plan scaling approach if successful
Risk Assessment
1. Technical risks (performance, integration)
2. Business risks (adoption, ROI)
3. Vendor risks (for buy decisions)
4. Mitigation strategies for each

Revolutionary technology doesn’t come along that often, so it’s hard to navigate something with as large potential ramifications as this latest generation of AI does, especially given a situation in which you might already be resource-constrained. For the opportunities that are industry destroying and creating, no ROI forecast will help you with that. For the aspects that are more measurable in existing validated problems, this framework can at least help with assessing those.