How to Evaluate Whether AI Makes Sense for Your Business Before Writing Code

### The Illusion of "AI Everywhere" In the current technological landscape, executive teams face immense pressure to "add AI" to every workflow. However, after evaluating dozens of production systems, our engineering team consistently finds that roughly 40% of requested AI features are better, faster, and cheaper to solve using deterministic algorithms, structured relational database queries, or simple heuristic rules. Before investing engineering capital into training custom models or wiring complex LangChain agents, leadership must run through a rigorous feasibility evaluation. --- ### 1. The Deterministic vs. Probabilistic Test Ask this fundamental question: **Does the task require genuine semantic interpretation, or is it governed by explicit business rules?** - **Choose Deterministic Software**: If the input has a fixed structure (e.g., extracting values from standardized CSVs, calculating tax brackets, syncing inventory counts), classical backend code in TypeScript or Python is 100x faster, 100% predictable, and costs £0 in token inference fees. - **Choose Applied AI**: If the input is unstructured (e.g., messy multi-page vendor PDF contracts, customer support emails with varied sentiment, unstandardized medical notes), machine learning and generative LLM extraction provide immense leverage. --- ### 2. The Data Grounding & Accessibility Audit Even the most advanced reasoning models produce hallucinations when starved of domain context. An AI feature is only as strong as the underlying retrieval infrastructure. - Where does the source data live? - Is it updated in real-time or batch intervals? - Does user querying require strict role-based access control (RBAC)? (i.e. User A must never retrieve documents owned by User B). --- ### 3. Unit Economics & Token Feasibility A prototype that costs £0.04 per request seems negligible on a developer's machine. But scaled to 50,000 monthly operational transactions, that translates to £2,000/month in recurring API overhead before server hosting. Engineers must calculate: 1. **Context Window Payload**: Can we use semantic chunking or small embedding models to avoid passing 50-page documents into expensive frontier LLMs? 2. **Model Tiering**: Can a fast, lightweight model (like Gemini Flash or Claude 3.5 Haiku) perform the extraction, reserving frontier models only for edge-case reasoning? 3. **Prompt Caching**: Are system prompts cached at the API gateway layer to cut input token fees by 50%? --- ### 4. Designing Human-in-the-Loop Thresholds In critical business operations (invoicing, legal compliance, medical records), 95% accuracy is unacceptable if the 5% error causes catastrophic compliance failures. Production AI systems must implement **confidence scoring**: - Confidence > 98%: Automated straight-through processing. - Confidence ≤ 98%: Task automatically routed to an internal review dashboard with highlighted source citations for one-click human verification. --- ### Summary Checklist Before approving an AI development sprint: 1. Confirm the problem cannot be solved reliably with classical code. 2. Verify access to clean, labeled or unstructured ground-truth data. 3. Model monthly inference costs under peak volume. 4. Establish clear accuracy benchmarks and human fallback routes.