What You'll Learn
This guide answers the questions executives ask most when evaluating AI investments:
• How do LLMs actually work?
Understand the fundamentals without needing a technical background (~5 min)
• What automation patterns exist?
Learn when to use workflows vs. agents vs. agentic systems (~7 min)
• What will this cost and what ROI can I expect?
Calculate real costs and understand fixed vs. variable expenses (~5 min)
• What are the risks and how do I mitigate them?
Understand hallucinations, context limits, and practical solutions (~5 min)
Total reading time: ~20 minutes. Use the navigation on the left to skip to any section.
Text In, Text Out
"What is the capital of France?"
"The capital of France is Paris."
That's the entire interface: text in, text out. This simple interface is why LLMs are so versatile for business:
Input can be...
Customer emails, contracts, reports, database queries, or images
Output can be...
Summaries, classifications, responses, or instructions for other systems
Result...
Easy integration into any business process without complex APIs
You don't need to understand neural networks or transformers to use LLMs effectively. You just need to understand what they can do—and that comes from how they're trained.
Four Key Training Aspects
Modern LLMs that power applications like ChatGPT are trained to excel in multiple dimensions
Statistical Likelihood
Trained on massive datasets (web, books, research) to generate text based on patterns observed in the data.
Human Preference
Fine-tuned with human feedback to produce responses that people find helpful, accurate, and appropriate.
Instruction Following
Specifically trained to understand and follow instructions, making them useful for task completion.
Tool Calling
Enhanced to interact with external tools and APIs, extending capabilities beyond text generation.
The Three Flavors of AI Automation
Understanding these three patterns helps you evaluate opportunities and make build vs. buy decisions
Which Pattern Fits Your Problem?
Choose Workflows when...
You have well-defined steps, need predictability, and want full control over the process
Choose Agents when...
User needs vary, tasks require exploration, and you need flexibility over rigid control
Choose Systems when...
Problems span multiple domains, require specialized expertise, and justify the complexity
AI Workflows: Code in Control
In workflows, you design each step. LLMs are tools called at specific points for text generation or analysis. This example shows invoice processing automation.
System receives PDF invoice via email or upload
Receive Invoice
CodeSystem receives PDF invoice via email or upload
Extract Data
LLMLLM extracts structured information from invoice
Validate Data
CodeCode checks extracted data against business rules
Classify Category
LLMLLM categorizes expense (Office Supplies, Travel, etc.)
Route Decision
LogicCode routes based on amount and category
Generate Summary
LLMLLM creates human-readable summary for approval
Send Notification
CodeSystem sends email with summary to approver
Key Characteristics
Predictable
Every execution follows the same path you designed
Controllable
You control exactly when and how LLMs are used
Testable
Easy to test each step independently
Production Considerations
In production workflows, include error handling for when validation or LLM steps fail. Build in retry logic with improved prompts, and human-in-the-loop escalation for edge cases that automated validation catches.
AI Agents: LLM in Control
In agent systems, the LLM makes decisions about what to do next. The LLM can maintain conversations, call tools, and adapt to user needs. Here are two examples of agents in action.
How ChatGPT Works
Each response is generated by feeding the entire conversation history (including system instructions) into the LLM
Starting state: System prompt and user message in chat history
Step 1 of 8 • Auto-playing
Chat History
You are a helpful assistant that provides concise, accurate answers.
What is machine learning?
The conversation grows with each exchange. The LLM sees the full history every time, allowing it to maintain context and coherence.
AI Agents with Tool Calling
LLMs can be enhanced to call external tools, enabling them to query databases, generate visualizations, and perform complex multi-step tasks
To measure profitability by SKU, I'll pull sales and cost data from the database.
Manual control • Click play to resume
You are a data analysis assistant.
- Use query_database(sql) to fetch data
- Use run_regression(sql, formula) for statistical modeling
- Use make_chart(vega_spec) to visualize results
Always explain your reasoning, show steps clearly, and ensure outputs are accurate and interpretable.
I want to understand product profitability by SKU.
Here's the profitability analysis:
Can you create a visualization to compare profitability by SKU?
Here's the visualization comparing profitability by SKU. SKU-C shows the highest profit margin at 38%. What would you like to explore next?
query_database()
make_chart()
By combining LLMs with tool calling, AI agents can break down complex tasks, execute code, query databases, and provide rich, interactive responses.
Agentic Systems: Coordinated Intelligence
Multiple specialized agents collaborate on complex tasks. Each agent has domain expertise and tools. An orchestrator coordinates the work and synthesizes findings.
"Analyze potential acquisition of TechCorp Inc."
User requests comprehensive M&A due diligence analysis
Why Agentic Systems?
Specialized Expertise
Each agent has domain-specific training, tools, and context
Parallel Processing
Multiple agents work simultaneously on different aspects
Complex Problem Solving
Handles multi-domain problems that single agents can't solve
When This Pattern Pays Off
Agentic systems make sense when the problem has these characteristics:
✅Good Fit When:
- •Task requires 3+ distinct areas of expertise (legal + financial + compliance)
- •Each domain needs specialized tools or data sources
- •Volume justifies 6-12 month implementation timeline
- •Manual process currently requires multiple teams/handoffs
❌Wrong Choice When:
- •A single agent with 2-3 tools would work
- •Process is linear and predictable (use workflow)
- •You haven't proven ROI with simpler patterns first
- •You lack internal expertise to maintain complex systems
Example: M&A due diligence on a $50M acquisition—replaces 4 weeks of work across legal, financial, risk, and market research teams. Justifies the complexity.
Where LLMs Add Business Value
Explore high-ROI use cases across business functions. Each is tagged with its recommended automation pattern and implementation complexity.
Start with Quick Wins
For fastest ROI, start with high ROI + low complexity use cases like document processing, ticket routing, or resume screening. Build momentum before tackling complex implementations.
Document Processing
High ROI, Low Complexity
Ticket Classification
High ROI, Low Complexity
Resume Screening
High ROI, Low Complexity
Understanding Costs & ROI
LLMs use token-based pricing. Calculate the true costs and compare to time savings to understand your ROI.
Configure Your Scenario
Your ROI Analysis
⚠️ Remember: Variable Costs Are Only Part of the Picture
The calculator above shows variable costs (API fees that scale with usage). But successful AI implementations also require fixed costs for implementation and maintenance.
💰Variable Costs (Ongoing)
- •API fees per token (shown above)
- •Infrastructure costs (if self-hosting)
- •Scale linearly with usage
🔧Fixed Costs (One-Time + Maintenance)
- •Understanding use cases and requirements
- •Development, testing, and integration
- •Prompt engineering and fine-tuning
- •Ongoing monitoring, iteration, and support
- •Training employees on new workflows
Rule of thumb: For simple workflows, fixed costs might equal 3-6 months' worth of variable costs. For complex agentic systems, fixed costs can equal 12-24 months' worth of variable costs. Plan accordingly.
Understanding the Economics
Token-Based Pricing
You pay per token (roughly 4 characters). Longer inputs/outputs cost more. Calculator assumes 60/40 input/output split—adjust for your use case.
Right-Sizing Models
Use the smallest model that works. Small models are 10-100x cheaper than large ones.
Start Simple
Begin with simple workflows to minimize fixed costs and prove ROI before scaling.
The Hallucination Problem
LLMs can generate plausible-sounding but incorrect information. Understanding this limitation is critical for deployment decisions.
Try It: Same Prompt, Different Answers
What year was the Eiffel Tower completed?
In real usage, LLMs might occasionally generate any of these responses. Click to see examples.
The Eiffel Tower was completed in 1889.
Even simple factual questions can produce incorrect answers. Click "Generate Response" multiple times to see variations.
Why Hallucinations Happen
Pattern Matching, Not Knowledge
LLMs generate text that sounds right based on patterns they've seen, not actual facts they "know." Think of it like autocomplete on steroids—it predicts what words should come next, not what's true.
Gaps in Training
If the correct information wasn't in the training data—or if contradictory information was— the model will guess. And confident-sounding guesses are often more convincing than admitting uncertainty.
Trained to Be Helpful
Models are trained to provide answers, not to say "I don't know." Sometimes being helpful and confident is prioritized over accuracy—a design trade-off you need to account for in your deployment.
Mitigation Strategies
Risk Tolerance Framework
Low-Stakes: Acceptable
Marketing copy, brainstorming, first drafts. Errors are easily caught and inconsequential.
Medium-Stakes: Mitigate
Customer support, research, analysis. Require grounding, verification, or human review.
High-Stakes: Human-in-the-Loop
Legal, medical, financial decisions. Never trust LLM alone. Always require expert human review.
Context Windows: Your Working Memory
LLMs have a limited "context window"—the amount of text they can process at once. This affects what you can do and how much it costs.
What Are You Processing?
Fixed Overhead
Context Window Usage
169K tokens remaining for additional output
Cost Impact
Larger context = higher cost. This request uses 27K input tokens. At $1.25/1M tokens (GPT-5), that's $0.0338 per request.
Strategies for Large Documents
Summarization
Summarize long documents in chunks, then process summaries. Works for initial filtering.
Chunking
Break documents into sections, process individually. Best when questions target specific sections.
RAG (Retrieval)
Search for relevant sections first, then only send those to LLM. Most efficient for large knowledge bases.
Context Limits by Automation Tier
Process large batches by chunking. Each chunk is independent.
Conversation history fills up. May need to summarize or truncate older messages.
Agents pass information via handoffs and summaries, not full context sharing.