How to Design an AI System for Your Business
Choosing a model is only one of the decisions involved in building an AI system. The harder work is defining the problem, deciding what information the system can use, what it is allowed to do, and how you will know whether it works. This page brings together the Enterprise Intelligence Architecture working model that developed across 11 articles and the essays that examine each decision in depth.
The five decisions that emerged from the series
What emerged from these 11 articles is not a technology stack but five connected decisions. Model choice should follow from those decisions, not lead them. This is a working model rather than a finished methodology or scoring system, and it may change as the research develops.
- 01
Business result
Which decision or workflow are we trying to change, what is the current method, and what would success look like?
- 02
Break down the work
Which steps require finding information, applying a rule, predicting, deciding, choosing a path, or taking action?
- 03
Allocate responsibility
Where do the roles of information, rules, models, conventional software, and people begin and end?
Articles that develop this decision04. Does Enterprise AI Really Need Fine-Tuning?05. When Do You Actually Need an AI Agent?06. When Does Enterprise AI Actually Need RAG?09. When Is One AI Model Enough?10. How Should You Route Requests Across Multiple AI Models?11. What Makes an AI System Specific to Your Business? - 04
Controlled access and action
What information, tools, and operations can the system access; what may it recommend, prepare, or do; and where is meaningful approval required?
Articles that develop this decision06. When Does Enterprise AI Actually Need RAG?07. How Much Authority Should You Give an AI System? - 05
Evidence
What evidence separates a model output, a completed task, and a result for the business?
Articles that develop this decision08. Is Your AI System Actually Delivering Business Results?
Model choice is the result of these five decisions, not their starting point. RAG, an agent, a second model, a router, or human approval belongs in the system only when it solves a clear need that a simpler design cannot meet.
Which question are you trying to answer?
You do not need to read these articles in order. Start with the decision your project currently faces, then move backward or forward when the next question appears.
Are you solving the right problem?
Before debating models or architecture, establish what the system is actually supposed to improve.
Are you starting the project by debating which model to use?
Start With the Business Problem, Not the AI Model
Why model selection should not be the first decision in an AI project, and what must be defined about the business outcome, action boundary, and evidence first.
Read the article →Does this process actually need AI?
When Does Process Automation Actually Need AI?
Lead routing, financial disclosures, and bank integrations show where AI adds value—and where rules or conventional software are the better choice.
Read the article →Is the information and evidence needed for this decision sufficient today?
Is Your Data Ready for AI? Start With the Decision It Must Support
Whether data is sufficient depends on the decision it must support, how current it needs to be, and how the outcome can be verified.
Read the article →How will the system do the work?
Separate where information comes from, which steps may change, and where the system is allowed to act.
Is supplying information enough, or must the model learn a new pattern?
Does Enterprise AI Really Need Fine-Tuning?
When is providing a document enough, when does RAG help, and when does fine-tuning actually make sense? The decision starts with information, rules, and patterns.
Read the article →Are the steps known in advance, or must the system choose its path?
When Do You Actually Need an AI Agent?
Customer support, lead routing, REDAR, and incident diagnosis show the practical difference between software-defined workflows and AI agents.
Read the article →Where does the required information live, and how often does it change?
When Does Enterprise AI Actually Need RAG?
When should enterprise AI use RAG, direct context, a database query, or an API? A practical way to choose the right source for each question.
Read the article →Will the system only make a recommendation, or carry out a real action?
How Much Authority Should You Give an AI System?
When should an AI system prepare a recommendation, and when should it be allowed to execute a real transaction? A practical boundary for authority, approval, and verification.
Read the article →Is the added complexity earning its place?
Additional models and routing layers matter only when they create a measurable difference in quality, cost, or risk.
Are you measuring a good answer, a completed task, or a business result?
Is Your AI System Actually Delivering Business Results?
A good model response, a completed task, and a measurable business result are three different outcomes. Here is how to evaluate each one.
Read the article →What concrete difference would a second model make?
When Is One AI Model Enough?
When should an AI system begin with one model, and what improvement in quality, cost, speed, or risk would justify adding another?
Read the article →Are explicit routing rules enough, or do you really need a router?
How Should You Route Requests Across Multiple AI Models?
When an AI system uses several models, should requests be assigned by explicit rules, predictive routing, cascades, or fallback paths?
Read the article →Does company-specific value come from the model or from the whole system?
What Makes an AI System Specific to Your Business?
A company-specific AI system is more than a model trained on company data. The real difference often lives in data, rules, tools, permissions, and measurement.
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