Прикладная автоматизация: ИИ как операционный слой

An AI tool becomes an operational layer when it no longer supplements a process but carries it: the work flows through it, and timing and quality depend on it. IPIIR's applied automation line examines where that shift is justified and where the tool should remain an auxiliary utility beside a person.

The line maintains an open catalogue of patterns for integrating off-the-shelf AI tools, large language models included: how work is divided between model, rules and human, where review is mandatory, and what the arrangement does to data infrastructure and to process SLAs. Anti-patterns — configurations that reliably fail — are documented alongside.

Because failures are rarely written up, so organisations keep repeating the same mistakes. A postmortem at IPIIR reconstructs one project: what was planned, what happened, and which decision produced the outcome. Such accounts give the field material that vendor case studies do not contain.

Those that describe the process itself: cost per operation, cycle time, share of rework, and the total cost of ownership of the solution. The line separates them from vanity metrics — the number of model calls, or the count of tools formally deployed — which rise whether or not the process improved.