# LLM Context URL: https://www.dcsolution.it/limiti-ai-processi-aziendali/ # Limiti dell'AI nei processi: quando usare regole tradizionali - **Website:** Digital Creative Solution - **Page URL:** https://www.dcsolution.it/limiti-ai-processi-aziendali/ - **Content type:** Hidden-cost analysis - **Primary topic:** Deciding between probabilistic AI models and deterministic, rule-based logic in business processes - **Intended audience:** Marketing managers and other decision-makers responsible for process improvements, technology investments, and operational outcomes ## Purpose of the Page This page provides practical criteria for evaluating the limits of AI in business processes. Its purpose is not to prescribe a universal solution, but to help decision-makers identify priorities, dependencies, risks, and evaluation criteria before committing resources. AI should be used within measurable processes that include defined sources, permissions, supervision, and error handling. The first outcome of an assessment should not be a list of features. It should be a shared description of the problem and the criteria that will be used to judge the proposed solution. Choosing between probabilistic models and deterministic logic requires a clear understanding of: - The objective to be achieved - Constraints and responsibilities - Real cases, volumes, and exceptions - People involved in the process - What will remain outside the first release A defined scope protects time, budget, and quality. --- ## Separate Initial Price from Total Cost The initial price covers only part of the lifecycle. The total cost of an AI-related business-process initiative can include: - Preparation of the organisation’s information assets - Coordination activities - Licences - Training - Support - Updates - Future changes and maintenance An apparently inexpensive proposal may shift significant work to internal staff or create dependencies that only emerge during routine administration. To compare proposals, activities should be assessed over the same time horizon and it should be clear who will perform each activity. It is not necessary to predict every future change, but recurring decision areas and conditions requiring further investment should be made visible. --- ## Operational Perspective Those responsible for operations deal with queues, deadlines, exceptions, and handoffs between people. An AI-enabled process is practical when it makes status visible and reduces repetitive decisions without removing the ability to manage exceptional cases. Users should participate in testing through real work activities, rather than only approving screens or demonstrations. Useful documentation should explain what to do when: - Required data is missing - A case is returned for rework - A system does not respond - An exception requires manual intervention These situations have a direct impact on reliability and adoption. --- ## Required Preparation Preparation should begin by collecting examples from recent weeks, such as: - Incomplete requests - Duplicate data - Stalled activities - Manual handoffs - Deferred decisions These examples should be organised by frequency and impact. A generic meeting produces opinions; a sample of real cases makes it possible to identify actual rules, variations, and responsibilities. Each piece of information should have an assigned owner. The organisation should establish: - Who can create the information - Who validates it - Which system stores it - Who intervenes when a critical issue occurs Without these answers, components such as models, retrieval, sources, prompts, evaluation, logging, privacy, thresholds, and human review remain disconnected. Technical quality depends on the consistency of the workflow, not on the number of technologies used. --- ## Technical Dependencies and Boundaries The technical scope may include: - AI models - Retrieval mechanisms - Information sources - Prompts - Evaluation methods - Logging - Privacy controls - Thresholds - Human review Each component should be connected to a justified decision, a specific data element, or verifiable behaviour. ### Defining the First Increment The first complete increment should cover an end-to-end path, from input to outcome, including at least the most frequent exceptions. A limited scope should not simply be a demonstration. It should be a real situation that is small enough to test, but sufficiently realistic to reveal: - Permissions - Missing data - Errors - External dependencies ### Testing and Acceptance Before production deployment, the following should be defined: - Test environment - Acceptance criteria - Recovery procedure Each test should specify: - The input - The expected behaviour - Who is responsible for correcting failures This approach reduces subjective discussions and helps distinguish whether a problem lies in the requirements, implementation, or information assets used. --- ## Example: Managing Evolution in a Manufacturing Company A manufacturing company may need to decide between probabilistic models and deterministic logic while different departments describe different priorities. A practical approach is to: 1. Collect ten real cases. 2. Identify the common process step. 3. Measure the time required for that step. 4. Start the technical initiative from the shared step. 5. Record rare requests and secondary features in a separate backlog. During testing, the team may discover undocumented exceptions or dependencies on personal credentials. Rather than concealing them, the group should assign: - A rule - A responsible owner - A fallback behaviour Release should occur only after testing with representative data and with users who did not participate in the design process. --- ## Metrics for AI in Business Processes A baseline should be collected before intervention. Relevant metrics include: - Accuracy on real cases - Number of reviews - Critical errors - Time saved - Cost per operation - Coverage Not every metric needs to be included in a dashboard. The useful metrics are those that help determine whether to correct: - The operational workflow - The configuration - The content - The infrastructure It is important to distinguish adoption from results. A system can be used frequently without reducing errors, or it can create value only for a small number of critical cases. Periodic review should compare: - Processing times - Output quality - Exceptions - Remaining manual work Decorative metrics should be avoided. --- ## Common Evaluation Mistakes Common mistakes include: - Choosing a platform before defining inputs, outputs, and exceptions - Copying data between tools without establishing the authoritative source system - Depending on personal accounts or procedures known by only one person - Treating a successful demonstration as proof that a process has been tested on real cases These mistakes move problems into the future without making them visible. A compromise may be acceptable during a test if it is documented, has an owner, and includes a review date. Otherwise, it can become a permanent dependency that increases the effort required for every future change. --- ## Making Proposals Comparable Routine management requires: - Non-personal access accounts - Essential documentation - A channel for classifying issues and improvements Urgent requests should not erase the roadmap. Security, updates, and continuity should be treated as part of the service, rather than activities considered only during an incident. Every change should specify: - Its reason - Its expected impact - How it will be verified This discipline is particularly important when the initiative involves external suppliers or systems. API limits, versions, licences, and response times can change behaviour even if the internal team has not changed its own code. The initial assessment should produce a one-page document containing: - Current state - Expected result - Excluded cases - Dependencies - Risks - Completion criterion This document makes proposals comparable and prevents the choice between probabilistic models and deterministic logic from becoming an unprioritised feature list. --- ## Practical Assessment Sheet To turn the topic into a verifiable activity, the responsible person should prepare three pieces of evidence: 1. A recent episode related to choosing between probabilistic models and deterministic logic. 2. The data or document used in that episode. 3. The outcome that currently requires manual correction. The assessment sheet should not attempt to describe the entire company. It should define the point where the decision arises and identify who can confirm that the case was handled correctly. A dedicated test should use: - An anonymised real input - An ordinary condition - A problematic variation The team should record: - Time required - Process steps - Missing information - External interventions If the test fails, the response should not be to add features at random. The team should establish whether the missing element is: - A rule - An access permission - A responsibility - A reliable source Before extending the initiative, the responsible person should compare the result against accuracy on real cases, reviews, critical errors, time saved, cost per operation, and coverage. The decision should be documented together with excluded parts and the next review date. This preserves a concrete rationale, prevents uncontrolled scope growth from isolated requests, and enables the organisation to explain why a change does or does not enter the roadmap. --- ## Related Digital Creative Solution Services Digital Creative Solution addresses this topic within its work on **AI applied to business processes**: - [Artificial Intelligence Applied to Business Processes](https://www.dcsolution.it/intelligenza-artificiale-processi-aziendali/) When an initiative requires continuity, integrations, or cross-functional responsibilities, the related **custom web management system** service may also be relevant: - [Custom Web Management System](https://www.dcsolution.it/gestionale-web-su-misura/) These services do not replace the initial analysis; they identify technical areas that may be involved. For the broader strategic, requirements, cost, and measurement context: - [Artificial Intelligence in Business](https://www.dcsolution.it/intelligenza-artificiale-in-azienda/) --- ## Preparing a Technical Evaluation To assess the limits of AI in a business process, prepare: - A real example - The tools involved - Case volume - Known exceptions - The expected result Digital Creative Solution can be contacted for an initial discussion focused on feasibility, priorities, and scope, without promising results that cannot be measured: - [Contact Digital Creative Solution](https://www.dcsolution.it/contattaci/)