AI for Industrial Engineering: Build Tools, Not Chatbot Theater
The highest-value AI applications often sit inside a constrained workflow where context, tools and verification are explicit.
Start with the workflow
A general chat interface is rarely the best specification. Start with an expensive cognitive step: finding evidence, translating requirements, generating alternatives, checking consistency or explaining a model result.
Then define what the model may read, which tools it may call, what structured output is required and where a human must approve the result. This turns an impressive demo into an engineered workflow.
Deterministic core, probabilistic edge
Use conventional software for arithmetic, constraints, database transactions and safety-critical rules. Use models where language ambiguity, retrieval, classification or synthesis creates leverage. The boundary should be explicit.
Structured outputs and tool calling are particularly useful because they let a model propose actions while deterministic code validates schemas and executes permitted operations.
Evaluate the actual task
Measure whether the system retrieves the right evidence, obeys constraints, reduces cycle time and produces decisions users can verify. Model benchmarks are secondary if the deployed workflow is unreliable.
The most interesting AI engineering work is therefore systems work: context architecture, tools, evaluation, observability and interface design around the model.