# Corporate AI FAQ: where to start, what to choose, what to avoid. - public LLM dossier This public dossier extends the FAQ page with structured fields, need signals, limits and links, without adding promises beyond the HTML page. ## Definition Each answer helps identify the next useful step. Artik Lab starts from a diagnostic conversation, reads process, data, constraints and responsibilities, then proposes the format that fits the client's real context. ## Public dataset - HTML: https://ar-tik.com/en/ai-business-faq.html - Markdown: https://ar-tik.com/en/ai-business-faq.md - JSON: https://ar-tik.com/data/faq.en.json ## Anti-cloaking principle The dossier reuses questions and answers visible on the HTML page and adds public fields for AI agents: audience, intent, need signals, risks and links. It contains no different offers, recognisable client cases or confidential information. ## Structured fields - id - locale - category / categoryLabel - question / shortAnswer / detailedAnswer - audience / searchIntent / searchQueries / needSignals - relatedServiceIds / relatedCourseIds / relatedAtlasAreas / relatedFaqIds - riskOrLimit / nextStep / urls ## FAQ repertoire ## Where to start ### Where should a company start if it has no defined AI project? Start from a process, not from a tool. The first task is choosing a recurring decision, visible cost or risk worth reducing. The first conversation clarifies whether the right step is consulting, a course, data analysis or a controlled prototype. - ID: start-without-project - Area: Where to start - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project, tools chosen before the process - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: Starting from the model or tool often creates isolated trials with no measurable return. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### How should the first process to improve with AI be chosen? Choose a frequent, observable process linked to a cost or delay. Good candidates include repeated emails, documents to read, priorities to assign or decisions arriving late. If the process is not observable, it should first be made clearer. - ID: start-first-process - Area: Where to start - Audience: managers and function leads - Search intent: initial orientation - Need signals: slow recurring decisions, repetitive manual work - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: Starting from the model or tool often creates isolated trials with no measurable return. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### Can an SME without an internal IT team start? Yes, if it starts from decisions, processes and skills before technology. Many initial activities do not require software development: process mapping, risk criteria, focused training and first-case selection matter first. Technical work arrives only when the scope is clear. - ID: start-pmi-no-it - Area: Where to start - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project, AI skills not aligned across roles - Related services: AI management consulting - Related courses: Role-Based AI Literacy & Responsible Use, Managing AI for mixed company teams - Risk or limit: Starting from the model or tool often creates isolated trials with no measurable return. - Next step: Choose a course or lab if the main need is transferring method to the team. ### Is it better to start from ChatGPT, software or a problem? It is better to start from the business problem and choose the tool later. A tool can help, but it does not decide goal, data, responsibility and success criteria. Artik Lab uses the first diagnosis to avoid isolated trials and connect AI to an operating result. - ID: start-tool-or-problem - Area: Where to start - Audience: managers and function leads - Search intent: path selection - Need signals: tools chosen before the process, informal and ungoverned AI use - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: Starting from the model or tool often creates isolated trials with no measurable return. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### How can a company know whether it is ready to use AI? Readiness depends on process, sponsor, minimum data and clear responsibility. The company does not need to be mature everywhere. It does need one concrete problem, people able to validate the result and a decision to improve. Otherwise training or mapping should come first. - ID: start-ai-readiness - Area: Where to start - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project, available data not yet assessed - Related services: AI management consulting - Related courses: Managing AI - Risk or limit: Starting from the model or tool often creates isolated trials with no measurable return. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## First conversation and method ### What should be prepared for the first conversation? Prepare a process, a material example and one decision to improve. Perfect documents are not required. Context, constraints, roles involved, available data and a description of what now takes too long or creates risk are enough. - ID: discovery-prepare - Area: First conversation and method - Audience: managers and function leads - Search intent: initial orientation - Need signals: interest in AI with no defined project, available data not yet assessed - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: A generic diagnosis is not enough to choose investment, responsibility and data. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### How long does the first conversation take? Usually 30-45 minutes are enough to understand the initial scope. The goal is not solving everything in the meeting, but separating need, constraints and next step. A course, consulting path, data analysis or prototype may follow. - ID: discovery-duration - Area: First conversation and method - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: none - Risk or limit: A generic diagnosis is not enough to choose investment, responsibility and data. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### What comes out of the initial diagnosis? It indicates the most sensible format and the risks to govern. The diagnosis may point to training, opportunity mapping, data validation, technical prototype or a temporary stop. Its value is avoiding wrong investment before committing time and budget. - ID: discovery-output - Area: First conversation and method - Audience: managers and function leads - Search intent: path selection - Need signals: interest in AI with no defined project, tools chosen before the process - Related services: AI management consulting - Related courses: none - Risk or limit: A generic diagnosis is not enough to choose investment, responsibility and data. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### Who should join the first conversation? At least someone who knows the process and someone who can decide priorities. Leadership, the involved function and an operating reference avoid partial readings. If data or systems are involved, IT or tool owners can also be useful. - ID: discovery-stakeholders - Area: First conversation and method - Audience: leaders and owners - Search intent: initial orientation - Need signals: slow recurring decisions, informal and ungoverned AI use - Related services: AI management consulting - Related courses: none - Risk or limit: A generic diagnosis is not enough to choose investment, responsibility and data. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### What happens after the first conversation? The next choice is whether to deepen, train, analyse data, build a prototype or stop. The conversation does not force a project. It turns a vague question into a practical choice with clearer scope, priorities, risks and control criteria. - ID: discovery-after-call - Area: First conversation and method - Audience: managers and function leads - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: none - Risk or limit: A generic diagnosis is not enough to choose investment, responsibility and data. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ## Costs, timing and ROI ### How much does an AI project cost? Cost depends on scope, data, risk, people involved and expected result. Before estimating, it must be clear whether the work is training, diagnosis, data analysis, prototype or system. A small well-bounded path is often more useful than a broad unmeasurable project. - ID: cost-ai-project - Area: Costs, timing and ROI - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: AI Business Case & ROI Sprint - Risk or limit: ROI should not be promised before knowing process, baseline, data and possible actions. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### How should ROI be evaluated for an AI project? Compare current cost, possible improvement and actions that can truly be taken. Before the model, baseline, KPIs and responsibility are needed. If AI produces a signal but nobody can act, value stays theoretical; if it changes a frequent decision, return can be estimated. - ID: cost-roi - Area: Costs, timing and ROI - Audience: leaders and owners - Search intent: path selection - Need signals: slow recurring decisions, available data not yet assessed - Related services: AI management consulting, Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: ROI should not be promised before knowing process, baseline, data and possible actions. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### How long does it take to see a first result? A first result can arrive in a few weeks if the scope is small and verifiable. The initial result may be a map, policy, adapted course, data test or minimal prototype. It is not always production; often it is a better decision on what to fund or avoid. - ID: cost-timing - Area: Costs, timing and ROI - Audience: managers and function leads - Search intent: operating evaluation - Need signals: repetitive manual work, slow recurring decisions - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: ROI should not be promised before knowing process, baseline, data and possible actions. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### Can the company start with a small project? Yes, it is usually better to start with a narrow measurable scope. A small case validates data, responsibility and value without excessive expectations. If it works, it expands; if it does not, learning happens before too much spending. - ID: cost-small-start - Area: Costs, timing and ROI - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: AI Business Case & ROI Sprint - Risk or limit: ROI should not be promised before knowing process, baseline, data and possible actions. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### How can spending on the wrong AI project be reduced? Define a stop criterion before the full investment. Each case should have hypotheses, KPIs, minimum data, responsibility and stop conditions. A negative verdict on data or process can be a good result when it avoids larger costs. - ID: cost-risk-reduction - Area: Costs, timing and ROI - Audience: managers and function leads - Search intent: risk management - Need signals: personal or confidential data involved, informal and ungoverned AI use - Related services: AI management consulting - Related courses: Operational AI Governance - Risk or limit: ROI should not be promised before knowing process, baseline, data and possible actions. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## Data, documents and privacy ### Does AI require already clean data? No, but the company must know which data exists, who understands it and its limits. Perfectly clean data rarely exists at the start. The first task can be assessing quality, coverage, errors and usefulness against the decision to improve. - ID: data-clean - Area: Data, documents and privacy - Audience: managers and function leads - Search intent: operating evaluation - Need signals: available data not yet assessed - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: Personal, regulated or confidential data require minimisation, access control and competent review. - Next step: Consider agentic data analysis when the signal must be validated before building. ### Can AI work on documents, emails and procedures? Yes, many cases start from text material already inside the company. Contracts, manuals, tickets, emails and procedures can become search, summaries, checks or drafts. Clear sources, permissions, human review and boundaries on what AI may do are required. - ID: data-documents - Area: Data, documents and privacy - Audience: managers and function leads - Search intent: operating evaluation - Need signals: repetitive manual work, critical knowledge concentrated in few people - Related services: Technical AI software - Related courses: AI course: managing documents with AI, Semantic search and AI knowledge bases - Risk or limit: Personal, regulated or confidential data require minimisation, access control and competent review. - Next step: Use the Atlas to recognise similar patterns before shaping the project. ### How can privacy risks with AI be avoided? Limit data, access, tools and allowed uses before experimenting. Proper management starts with data classification, minimisation, lawful basis, authorised accounts and specialist review when required. Policy must become practical behaviour. - ID: data-privacy - Area: Data, documents and privacy - Audience: leaders and owners - Search intent: risk management - Need signals: personal or confidential data involved, informal and ungoverned AI use - Related services: AI management consulting - Related courses: Secure AI at Work - Risk or limit: Personal, regulated or confidential data require minimisation, access control and competent review. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### Can confidential data or technical know-how be used? Yes, only with explicitly agreed boundaries, access and materials. Code, drawings, specifications, industrial data and expert knowledge are treated as intellectual property. Public examples use only anonymised descriptions that cannot identify the client. - ID: data-confidential - Area: Data, documents and privacy - Audience: technical and operations teams - Search intent: risk management - Need signals: personal or confidential data involved, fragile software, spreadsheets or systems - Related services: Technical AI software - Related courses: Secure AI SDLC - Risk or limit: Personal, regulated or confidential data require minimisation, access control and competent review. - Next step: Move to software development only when a verifiable system, tests and maintenance are needed. ### What happens if the data is not enough? The useful result may be knowing which data is missing and which investments to avoid. A model is not always built. Sometimes the best work is defining new data collection, changing the process or postponing automation until the signal becomes verifiable. - ID: data-not-enough - Area: Data, documents and privacy - Audience: managers and function leads - Search intent: operating evaluation - Need signals: available data not yet assessed - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: Personal, regulated or confidential data require minimisation, access control and competent review. - Next step: Consider agentic data analysis when the signal must be validated before building. ## AI management consulting ### When is AI management consulting needed? It is needed when priorities, governance, risk criteria or roadmap are missing. Consulting helps leadership decide where to use AI, where to stop, which skills to build and which first pilot may have measurable value. - ID: consulting-when - Area: AI management consulting - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project, informal and ungoverned AI use - Related services: AI management consulting - Related courses: Managing AI, Operational AI Governance - Risk or limit: Without internal sponsor and real decisions, consulting remains an unused map. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### What remains after AI management consulting? Criteria, opportunity map, policy, roadmap and first-pilot brief remain. The goal is not an inspirational presentation. The artifacts should help leadership and functions decide, communicate rules, assign responsibility and move to the next case with control. - ID: consulting-output - Area: AI management consulting - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: Operational AI Governance - Risk or limit: Without internal sponsor and real decisions, consulting remains an unused map. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### How should people already using AI informally be managed? Turn informal use into governed practice, not only prohibition. Shadow AI signals a real efficiency need. The company should distinguish allowed uses, excluded data, output checks and safe company paths to avoid losing useful energy. - ID: consulting-shadow-ai - Area: AI management consulting - Audience: managers and function leads - Search intent: risk management - Need signals: informal and ungoverned AI use, personal or confidential data involved - Related services: AI management consulting - Related courses: Secure AI at Work - Risk or limit: Without internal sponsor and real decisions, consulting remains an unused map. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### Is an internal sponsor needed to start? Yes, at least one person must decide priorities and validate results. The sponsor does not need to be technical. They must understand process value, involve the right people and authorise choices about data, timing and responsibility. - ID: consulting-sponsor - Area: AI management consulting - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: Managing AI - Risk or limit: Without internal sponsor and real decisions, consulting remains an unused map. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### When does a company need an AI policy? It is needed when use grows and data, tools or responsibilities are no longer clear. A useful policy is not abstract: it defines allowed cases, forbidden data, human review, accounts, escalation and criteria for moving from personal use to company use. - ID: consulting-policy - Area: AI management consulting - Audience: managers and function leads - Search intent: risk management - Need signals: informal and ungoverned AI use, personal or confidential data involved - Related services: AI management consulting - Related courses: Operational AI Governance, Secure AI at Work - Risk or limit: Without internal sponsor and real decisions, consulting remains an unused map. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## AI training and courses ### When does an AI course make sense? It makes sense when the main issue is transferring method and criteria to the team. A course fits when people already use AI tools differently, common rules are missing or practical examples must be brought into company roles and processes. - ID: training-when - Area: AI training and courses - Audience: managers and function leads - Search intent: path selection - Need signals: AI skills not aligned across roles - Related services: AI management consulting - Related courses: Role-Based AI Literacy & Responsible Use, Managing AI - Risk or limit: Generic training does not change work if examples, roles and rules remain distant from context. - Next step: Choose a course or lab if the main need is transferring method to the team. ### Are courses standard or adapted to the company context? The structure is stable, but examples, exercises and priorities are adapted. The DTR method recalibrates the path around processes, materials and participant questions. This avoids abstract lessons and makes it easier to turn training into operating practice. - ID: training-standard-custom - Area: AI training and courses - Audience: managers and function leads - Search intent: path selection - Need signals: AI skills not aligned across roles, repetitive manual work - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab, Role-Based AI Literacy & Responsible Use - Risk or limit: Generic training does not change work if examples, roles and rules remain distant from context. - Next step: Choose a course or lab if the main need is transferring method to the team. ### Is programming required to join the courses? No for managerial, introductory and operational paths. Programming is required only in technical courses. For leaders and business functions, the focus is on processes, prompts, review, risks, data and responsible-use criteria. - ID: training-programming - Area: AI training and courses - Audience: operational teams - Search intent: initial orientation - Need signals: AI skills not aligned across roles - Related services: AI management consulting - Related courses: Role-Based AI Literacy & Responsible Use, Managing AI for mixed company teams - Risk or limit: Generic training does not change work if examples, roles and rules remain distant from context. - Next step: Choose a course or lab if the main need is transferring method to the team. ### What remains after a corporate AI course? Materials, usage criteria, adapted examples and a view of promising processes remain. The course should not end with theory only. It should leave practical tools: checklists, exercises, review rules, reusable examples and questions for choosing next cases. - ID: training-output - Area: AI training and courses - Audience: managers and function leads - Search intent: operating evaluation - Need signals: AI skills not aligned across roles - Related services: AI management consulting - Related courses: Role-Based AI Literacy & Responsible Use, Operational AI Governance - Risk or limit: Generic training does not change work if examples, roles and rules remain distant from context. - Next step: Choose a course or lab if the main need is transferring method to the team. ### Who should be trained first? Usually sponsors, function leads and people already using AI should come first. The priority is not training everyone immediately. It is creating a core group able to recognise useful cases, check outputs, explain limits and transfer practices. - ID: training-who - Area: AI training and courses - Audience: leaders and owners - Search intent: initial orientation - Need signals: AI skills not aligned across roles, informal and ungoverned AI use - Related services: AI management consulting - Related courses: Managing AI, AI Adoption Manager / AI Champions - Risk or limit: Generic training does not change work if examples, roles and rules remain distant from context. - Next step: Choose a course or lab if the main need is transferring method to the team. ## Agentic data analysis ### When is agentic data analysis the right first step? It is right when a decision depends on signals hidden in data. If the company has histories, orders, tickets, sensors or KPIs but does not know which priorities emerge, analysis validates signal, limits and possible actions before building. - ID: data-analysis-when - Area: Agentic data analysis - Audience: managers and function leads - Search intent: path selection - Need signals: available data not yet assessed, slow recurring decisions - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: If the data contains no signal, forcing a model creates cost and false confidence. - Next step: Consider agentic data analysis when the signal must be validated before building. ### Does agentic data analysis replace Business Intelligence? No, it complements BI when indicators must become decisions. BI monitors known metrics and past trends. Agentic analysis looks for signals, anomalies, priorities or stop criteria connected to a concrete action. - ID: data-analysis-bi - Area: Agentic data analysis - Audience: managers and function leads - Search intent: initial orientation - Need signals: available data not yet assessed - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: If the data contains no signal, forcing a model creates cost and false confidence. - Next step: Consider agentic data analysis when the signal must be validated before building. ### What is the value of a negative data result? It is valuable because it avoids funding a weak model. Knowing that the signal is not present yet allows the company to change data collection, review the process or move budget to more mature cases. It is a useful management decision. - ID: data-analysis-negative - Area: Agentic data analysis - Audience: leaders and owners - Search intent: risk management - Need signals: available data not yet assessed - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: If the data contains no signal, forcing a model creates cost and false confidence. - Next step: Consider agentic data analysis when the signal must be validated before building. ### Which KPI is needed before analysing data? A KPI linked to a decision or action is needed, not just to a chart. Useful examples: order to chase, batch to check, customer to contact, shift to rebalance. The KPI should show whether analysis truly changes work. - ID: data-analysis-kpi - Area: Agentic data analysis - Audience: managers and function leads - Search intent: operating evaluation - Need signals: slow recurring decisions, available data not yet assessed - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Risk or limit: If the data contains no signal, forcing a model creates cost and false confidence. - Next step: Consider agentic data analysis when the signal must be validated before building. ### Do data-based decisions remain human? Yes, especially when they affect customers, people, quality, safety or risk. Analysis can rank priorities, suggest signals and explain limits. The decision remains under company responsibility, with human review and criteria agreed before operating use. - ID: data-analysis-human - Area: Agentic data analysis - Audience: managers and function leads - Search intent: risk management - Need signals: personal or confidential data involved - Related services: Agentic data analysis - Related courses: AI Output Quality & Human Review - Risk or limit: If the data contains no signal, forcing a model creates cost and false confidence. - Next step: Consider agentic data analysis when the signal must be validated before building. ## Technical software and automation ### When does it make sense to build technical AI software? It makes sense when a verifiable system is needed and standard tools are not enough. If the process includes calculations, expert rules, legacy data, integrations or critical checks, custom development may be needed. Requirements, tests and responsibilities must come first. - ID: software-when - Area: Technical software and automation - Audience: technical and operations teams - Search intent: path selection - Need signals: fragile software, spreadsheets or systems, critical knowledge concentrated in few people - Related services: Technical AI software - Related courses: AI Software Engineering - Risk or limit: Automating a poorly understood process only makes errors and ambiguity faster. - Next step: Move to software development only when a verifiable system, tests and maintenance are needed. ### What is the difference between simple automation and technical software? Automation connects steps; technical software embeds rules, tests and maintenance. If moving data between tools is enough, automation can be light. If calculations, checks, versions, audit and responsibility matter, a more robust system is needed. - ID: software-vs-automation - Area: Technical software and automation - Audience: technical and operations teams - Search intent: path selection - Need signals: repetitive manual work, fragile software, spreadsheets or systems - Related services: Technical AI software - Related courses: AI Operations - Risk or limit: Automating a poorly understood process only makes errors and ambiguity faster. - Next step: Move to software development only when a verifiable system, tests and maintenance are needed. ### Can legacy software be modernised with AI? Yes, but existing logic, data, constraints and risks must be understood first. AI can help read code, documentation or data, but modernisation requires audit, result comparison, regression tests and progressive migration. - ID: software-legacy - Area: Technical software and automation - Audience: technical and operations teams - Search intent: operating evaluation - Need signals: fragile software, spreadsheets or systems, critical knowledge concentrated in few people - Related services: Technical AI software - Related courses: AI Software Engineering - Risk or limit: Automating a poorly understood process only makes errors and ambiguity faster. - Next step: Move to software development only when a verifiable system, tests and maintenance are needed. ### What is the difference between a controlled prototype and production system? A prototype validates feasibility; production requires tests, security, maintenance and responsibility. A prototype can be small and isolated. A production system must handle real users, errors, data, permissions, logging, documentation and acceptance criteria. - ID: software-prototype-production - Area: Technical software and automation - Audience: technical and operations teams - Search intent: operating evaluation - Need signals: fragile software, spreadsheets or systems - Related services: Technical AI software - Related courses: Secure AI SDLC - Risk or limit: Automating a poorly understood process only makes errors and ambiguity faster. - Next step: Move to software development only when a verifiable system, tests and maintenance are needed. ### Does AI software need to integrate with company systems? Only when value requires operational continuity, updated data or repeated use. Not every prototype must integrate immediately. Integration becomes necessary when the system enters daily work and must respect permissions, data, traceability and maintenance. - ID: software-integration - Area: Technical software and automation - Audience: technical and operations teams - Search intent: operating evaluation - Need signals: fragile software, spreadsheets or systems, repetitive manual work - Related services: Technical AI software - Related courses: RAG Engineering for reliable AI systems, Secure AI SDLC - Risk or limit: Automating a poorly understood process only makes errors and ambiguity faster. - Next step: Move to software development only when a verifiable system, tests and maintenance are needed. ## AI applications Atlas ### Is the Atlas a catalog of ready-made products? No, it is a map of patterns for recognising process opportunities. Each card helps formulate better questions about data, results, value and controls. The real solution is designed only after context, constraints and company priorities are reviewed. - ID: atlas-catalog - Area: AI applications Atlas - Audience: managers and function leads - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI applications atlas - Related courses: AI Workflow Redesign Lab - Risk or limit: A public pattern should not be treated as a standard promise or ready solution. - Next step: Use the Atlas to recognise similar patterns before shaping the project. ### How should the Atlas be used to test whether a case makes sense? Find a similar pattern and compare data, result and human review. If a card resembles the company process, the next step is checking available material, decision to improve, risk and suitable format: course, consulting, analysis or software. - ID: atlas-how-use - Area: AI applications Atlas - Audience: managers and function leads - Search intent: initial orientation - Need signals: interest in AI with no defined project, repetitive manual work - Related services: AI applications atlas - Related courses: AI Workflow Redesign Lab - Risk or limit: A public pattern should not be treated as a standard promise or ready solution. - Next step: Use the Atlas to recognise similar patterns before shaping the project. ### Are Atlas examples recognisable client cases? No, they are anonymised and generalised patterns. Names, natural persons, internal projects, recognisable products or identifying detail combinations are not published. The goal is recognising opportunities, not exposing confidential cases. - ID: atlas-anonymized - Area: AI applications Atlas - Audience: leaders and owners - Search intent: risk management - Need signals: personal or confidential data involved - Related services: AI applications atlas - Related courses: none - Risk or limit: A public pattern should not be treated as a standard promise or ready solution. - Next step: Use the Atlas to recognise similar patterns before shaping the project. ### After finding an Atlas card, which service should be chosen? It depends on the main constraint: decision, skill, data or system. If management choice is missing, consulting fits; if skills are missing, training fits; if the doubt is in data, analysis fits; if an operating engine is needed, technical software fits. - ID: atlas-service-choice - Area: AI applications Atlas - Audience: managers and function leads - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI applications atlas - Related courses: AI Workflow Redesign Lab - Risk or limit: A public pattern should not be treated as a standard promise or ready solution. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ## Governance, risks and human review ### When is human review needed on AI outputs? It is needed whenever output affects decisions, customers, sensitive data or responsibility. Review is not a formality. It should define who checks, with which criteria, when to correct, when to reject output and when AI should not be used. - ID: governance-human-review - Area: Governance, risks and human review - Audience: managers and function leads - Search intent: risk management - Need signals: personal or confidential data involved, informal and ungoverned AI use - Related services: AI management consulting - Related courses: AI Output Quality & Human Review, Operational AI Governance - Risk or limit: Without human review, privacy and responsibility boundaries, AI use remains fragile. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### Who is responsible for an AI-assisted decision? Responsibility remains with the organisation and appointed people. AI may suggest, rank priorities or draft, but it should not become a responsibility gap. Roles, escalation, traceability and acceptance criteria are needed. - ID: governance-responsibility - Area: Governance, risks and human review - Audience: leaders and owners - Search intent: risk management - Need signals: personal or confidential data involved - Related services: AI management consulting - Related courses: Operational AI Governance - Risk or limit: Without human review, privacy and responsibility boundaries, AI use remains fragile. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### Are there activities AI should not do? Yes, some decisions must remain human or require strong supervision. Legal, HR, safety, health, credit, critical quality or sensitive-data decisions need careful classification. In some cases AI may prepare material, not decide. - ID: governance-red-zone - Area: Governance, risks and human review - Audience: managers and function leads - Search intent: risk management - Need signals: personal or confidential data involved - Related services: AI management consulting - Related courses: Secure AI at Work - Risk or limit: Without human review, privacy and responsibility boundaries, AI use remains fragile. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### How can AI output quality be controlled? Use explicit criteria, approved examples and cases where output must be rejected. Quality should not be judged by impression. Source, tone, completeness, critical errors, acceptance threshold and human review should be defined, especially for documents and external communication. - ID: governance-quality - Area: Governance, risks and human review - Audience: managers and function leads - Search intent: risk management - Need signals: repetitive manual work, personal or confidential data involved - Related services: AI management consulting - Related courses: AI Output Quality & Human Review - Risk or limit: Without human review, privacy and responsibility boundaries, AI use remains fragile. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## Internal adoption and teams ### How can team resistance to AI be managed? Clarify purpose, limits and practical benefit. People collaborate better when they understand what changes, what remains human and which activities become lighter. Training and cases close to real work reduce fear and confusion. - ID: adoption-resistance - Area: Internal adoption and teams - Audience: leaders and owners - Search intent: operating evaluation - Need signals: AI skills not aligned across roles, informal and ungoverned AI use - Related services: AI management consulting - Related courses: AI Adoption Manager / AI Champions - Risk or limit: Adoption fails when people do not understand purpose, limits and usage rules. - Next step: Choose a course or lab if the main need is transferring method to the team. ### Are internal AI champions needed? They help when use must move from individual experimentation to shared practice. AI champions collect cases, spread rules, flag risks and maintain continuity after training or consulting. They need a clear mandate and dedicated time. - ID: adoption-champions - Area: Internal adoption and teams - Audience: managers and function leads - Search intent: operating evaluation - Need signals: AI skills not aligned across roles - Related services: AI management consulting - Related courses: AI Adoption Manager / AI Champions - Risk or limit: Adoption fails when people do not understand purpose, limits and usage rules. - Next step: Choose a course or lab if the main need is transferring method to the team. ### How should internal AI adoption be measured? Measure changed processes, checked outputs and improved decisions, not only access. Counting licences or prompts is not enough. Better indicators include saved time, fewer errors, governed cases, trained people, applied policies and faster or more reliable decisions. - ID: adoption-measure - Area: Internal adoption and teams - Audience: managers and function leads - Search intent: operating evaluation - Need signals: slow recurring decisions - Related services: AI management consulting - Related courses: AI Business Case & ROI Sprint - Risk or limit: Adoption fails when people do not understand purpose, limits and usage rules. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### How can a course avoid remaining isolated? Connect it to real cases, sponsors, policy and next actions. After training, candidate processes should be collected, two or three controlled experiments chosen and responsibilities assigned. This turns the course into adoption, not a separate event. - ID: adoption-after-training - Area: Internal adoption and teams - Audience: managers and function leads - Search intent: operating evaluation - Need signals: AI skills not aligned across roles - Related services: AI management consulting - Related courses: AI Adoption Manager / AI Champions - Risk or limit: Adoption fails when people do not understand purpose, limits and usage rules. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## Choosing the right path ### When is consulting needed and when is a course enough? Consulting is needed for strategy decisions; a course is enough for method transfer. If the issue is choosing priorities, governance and roadmap, consulting fits. If scope is clear and the need is helping people work better, a course may be right. - ID: routing-consulting-course - Area: Choosing the right path - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project, AI skills not aligned across roles - Related services: AI management consulting - Related courses: Role-Based AI Literacy & Responsible Use - Risk or limit: Choosing the wrong format increases cost, frustration and unmanaged expectations. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### When should data analysis be done and when software developed? Analysis validates the signal; software builds a usable, maintainable system. If it is unclear whether data contains value, start with analysis. If value is clear and needs operation through tests, interfaces and integrations, move to software. - ID: routing-data-software - Area: Choosing the right path - Audience: managers and function leads - Search intent: path selection - Need signals: available data not yet assessed, fragile software, spreadsheets or systems - Related services: Agentic data analysis, Technical AI software - Related courses: AI Business Case & ROI Sprint - Risk or limit: Choosing the wrong format increases cost, frustration and unmanaged expectations. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### When should the FAQ be used and when the Atlas? The FAQ answers path questions; the Atlas shows application examples. If the question is which path to choose, the FAQ helps. If the question is where AI could help in a process, the Atlas offers patterns to compare. - ID: routing-atlas - Area: Choosing the right path - Audience: managers and function leads - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI applications atlas - Related courses: none - Risk or limit: Choosing the wrong format increases cost, frustration and unmanaged expectations. - Next step: Use the Atlas to recognise similar patterns before shaping the project. ### What if nobody truly owns the process? Before automation, ownership and decision criteria must be assigned. A process without an owner creates ambiguity even with AI. Consulting or a redesign lab helps clarify roles, steps, data and priorities. - ID: routing-no-clear-owner - Area: Choosing the right path - Audience: leaders and owners - Search intent: path selection - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: Choosing the wrong format increases cost, frustration and unmanaged expectations. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## AI limits ### When is AI not worth using? When data, responsibility, possible action or error tolerance are missing. If error is unacceptable, the process is too ambiguous or nobody can verify the result, it is better to stop, redesign or use simpler tools. - ID: limits-not-use - Area: AI limits - Audience: leaders and owners - Search intent: risk management - Need signals: personal or confidential data involved, available data not yet assessed - Related services: AI management consulting - Related courses: Operational AI Governance - Risk or limit: AI does not replace judgement, professional responsibility or data that does not exist. - Next step: Stop or postpone the case if sponsor, minimum data, responsibility or possible action are missing. ### How should AI errors and hallucinations be managed? Plan for them with sources, checks, approved examples and human review. AI can produce plausible but wrong answers. Usage limits, citable sources, real-case tests and rules against using unchecked outputs are required. - ID: limits-hallucination - Area: AI limits - Audience: managers and function leads - Search intent: risk management - Need signals: personal or confidential data involved - Related services: AI management consulting - Related courses: AI Output Quality & Human Review - Risk or limit: AI does not replace judgement, professional responsibility or data that does not exist. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ### Can AI fully automate a process? Only rarely: most cases need supervision or human intervention. Full automation is risky when data, exceptions, responsibility and quality are not stable. Often the best value is a controlled copilot, not a process without people. - ID: limits-total-automation - Area: AI limits - Audience: leaders and owners - Search intent: risk management - Need signals: informal and ungoverned AI use - Related services: AI management consulting - Related courses: Operational AI Governance - Risk or limit: AI does not replace judgement, professional responsibility or data that does not exist. - Next step: Stop or postpone the case if sponsor, minimum data, responsibility or possible action are missing. ### Do these FAQs replace legal, tax or specialist advice? No, they provide business orientation, not regulated specialist advice. When a case touches legal, tax, medical, financial or safety obligations, qualified professionals should review it. AI may prepare material, not replace specialist responsibility. - ID: limits-regulated-advice - Area: AI limits - Audience: leaders and owners - Search intent: risk management - Need signals: personal or confidential data involved - Related services: AI management consulting - Related courses: Secure AI at Work - Risk or limit: AI does not replace judgement, professional responsibility or data that does not exist. - Next step: Open a consulting path to clarify priorities, governance and roadmap. ## Before contacting Artik Lab ### How can Artik Lab be contacted about a case? Write to dtr@ar-tik.com with process, goal and main constraints. The message can be short: business area, problem, available material, people involved and urgency. The first reply clarifies whether a diagnostic conversation makes sense. - ID: contact-write - Area: Before contacting Artik Lab - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: none - Risk or limit: A first exchange without context produces generic, less useful answers. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### Is a project document already required? No, an honest description of the problem and context is enough. A structured document helps but is not essential. It is more important to clarify which process creates cost, delay or risk and who can validate a possible result. - ID: contact-before - Area: Before contacting Artik Lab - Audience: managers and function leads - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab - Risk or limit: A first exchange without context produces generic, less useful answers. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### Can the first conversation be in multiple languages? Yes, the site and public material cover Italian, English, Spanish, French and Brazilian Portuguese. The operating language is agreed according to the people involved. Consistency across versions helps international teams read the same positioning without market-specific promises. - ID: contact-international - Area: Before contacting Artik Lab - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI management consulting - Related courses: none - Risk or limit: A first exchange without context produces generic, less useful answers. - Next step: Bring the case to the first conversation with process, goal, available data and constraints. ### What if the company is not ready to contact Artik Lab? It can start from the Atlas, this FAQ and the course catalog. If the need is still unclear, collect internal examples, note recurring questions and identify one process with visible cost. This makes the later conversation more concrete. - ID: contact-not-ready - Area: Before contacting Artik Lab - Audience: leaders and owners - Search intent: initial orientation - Need signals: interest in AI with no defined project - Related services: AI applications atlas - Related courses: Role-Based AI Literacy & Responsible Use - Risk or limit: A first exchange without context produces generic, less useful answers. - Next step: Use the Atlas to recognise similar patterns before shaping the project.