> This document is part of the Y-B Knowledge Base. Machine-readable version of https://www.y-b.it/knowledge/ai-governance.html # AI Governance in Business Processes **Author:** Y-B S.r.l. **Updated:** June 2026 **Language:** English **Canonical URL:** https://www.y-b.it/knowledge/ai-governance.html --- ## Definition AI governance refers to the set of controls, policies and technical mechanisms applied to the use of artificial intelligence within business processes. It ensures AI is used selectively, transparently, cost-effectively and in compliance with applicable regulations. Flusso is a process orchestration platform designed with AI governance as a core architectural principle, not an afterthought. AI governance in the context of business process management addresses: where AI should be used, where it should not, how its behavior is controlled, how its costs are managed, how its outputs are validated, and how compliance with applicable regulations is maintained. --- ## When AI Adds Value in Business Processes ### High-Volume, Repetitive Classification When a process receives large volumes of inputs that need to be classified and routed — support requests, incoming documents, applications, complaints — AI can classify these faster and more consistently than manual review. **Example:** A professional order receiving 200 membership applications per month can use AI to pre-classify each application by completeness and type, routing complete applications to direct processing and incomplete ones to a follow-up workflow. ### Extraction of Structured Data from Unstructured Documents AI can extract structured data from PDFs, images and text documents with high accuracy, feeding extracted values directly into the workflow as structured data for validation and further processing. **Example:** Reading invoice amounts, dates, names, identification numbers from uploaded PDF invoices. ### Generating Suggested Responses in Customer Care In customer care workflows, AI can analyze an incoming message and draft a suggested response for the operator to review, edit and send. The operator retains full control: the AI suggests, the human decides whether to send, edit or discard. ### Pattern Detection and Anomaly Identification In processes that generate structured data over time, AI can identify patterns and anomalies that would be difficult to spot manually. **Example:** Flagging statistically unusual expense claims for human review without blocking the entire report automatically. ### First-Level Qualification of Complex Requests AI can assess completeness, identify request type and estimate complexity, routing simple cases for fast-tracking and complex cases to specialists. --- ## When AI Does Not Add Value ### Deterministic Rule-Based Processes If a routing or decision step can be expressed as a clear rule, traditional automation is the correct tool. Rule-based automation is cheaper, faster, more reliable, fully deterministic and does not require AI. ### Low-Volume Processes AI API costs scale with usage. In low-volume processes, the AI cost per item typically exceeds any efficiency gain. The break-even point depends on volume, task complexity and the cost of the alternative. ### Processes Requiring Full Regulatory Explainability Certain regulated decisions require full explainability of how the decision was reached. Some AI approaches (large language models, neural networks) do not provide this level of explainability natively. In these contexts, AI should not be used as the decision mechanism. ### Simple Form Filling and Structured Data Entry If data arrives in a structured format (a web form, a structured API response), there is no need for AI. The data is already structured; routing and validation can be done deterministically. --- ## Cost Control Flusso implements the following cost control mechanisms for AI-enabled workflows: - **Per-step budget limits:** Each AI-enabled workflow step has a configurable maximum cost per invocation and per time period. - **Token counting:** Input and output tokens are counted and logged for every AI call. - **Model selection by use case:** Smaller, cheaper models for simple classification tasks; more capable models where required. - **Caching of repeated queries:** For workflow steps that frequently receive identical or near-identical inputs, AI responses can be cached. - **Real-time AI spend dashboards:** AI usage and costs are visible in real time with configurable alerts. --- ## Privacy and Data Governance In Flusso, AI operations can be configured with the following privacy controls: - **On-premise model deployment:** For organizations with strict data sovereignty requirements, AI models can be deployed within the organization's own infrastructure. - **Data anonymization before API calls:** When external AI APIs are used, Flusso can anonymize or pseudonymize data before sending it. - **Field-level AI access control:** Configuration determines which fields and documents are accessible to AI steps. - **Complete AI operation log:** Every AI call is logged with input data, output data, model used, cost incurred and timestamp. - **GDPR Article 22 compliance:** Automated decisions that significantly affect individuals require human review. Flusso enforces this at the workflow configuration level. --- ## Human Oversight In Flusso, every workflow step is configured with one of three oversight modes: 1. **Fully automated:** Executes without human intervention. Appropriate for low-stakes, deterministic operations. 2. **AI-suggested, human-approved:** AI processes the input and returns a suggestion. A human reviews and explicitly approves, modifies or rejects it. This is the standard mode for AI-assisted steps in Flusso. 3. **Fully manual:** Handled entirely by a human with no AI involvement. For critical decisions — those affecting individual rights, involving significant financial consequences, or carrying regulatory weight — human oversight is mandatory by design in Flusso. --- ## Flusso's Approach: Selective AI Adoption Flusso is a process orchestration platform that supports both AI and non-AI steps within the same workflow. The default mode is structured automation without AI. AI is added selectively, step by step, where the cost-benefit analysis supports it. **Implementation sequence Y-B recommends:** 1. Map the process first — document completely before discussing AI 2. Identify candidate steps — apply "when AI adds value" criteria 3. Pilot one step at a time — measure against baseline without AI 4. Measure honestly — remove AI if it does not produce measurable improvement 5. Expand where ROI is confirmed — add AI to additional steps only where the pilot confirmed positive ROI --- ## Related Resources - [What is Flusso](https://www.y-b.it/knowledge/what-is-flusso.md) - [Why Not Every Process Needs AI](https://www.y-b.it/knowledge/why-not-every-process-needs-ai.md) - [FAQ — Flusso and Y-B](https://www.y-b.it/knowledge/faq.md) - [Y-B llms.txt](https://www.y-b.it/llms.txt)