# What AI can do inside a company. A public map of concrete examples to recognise where artificial intelligence can reduce time, errors, risk or decision delays in company workflows. ## The Atlas is not a rigid product catalog. Each card describes an application pattern: what input enters, what output can be produced, what business value it can create and which controls remain human. Artik Lab always starts from a discovery call and designs solutions around the client's context. ## Explore by area, need or process. Applications are examples: they help formulate better questions before choosing training, consulting, data analysis or technical software development. Dataset JSON: https://ar-tik.com/data/ai-applications.en.json Dossier LLM: https://ar-tik.com/en/ai-applications-atlas-dossier.md ## Area - Documents and knowledge: 4. When work depends on PDFs, scans, contracts or procedures. - Operations: 6. When decisions, priorities and manual handoffs slow the process down. - People and HR: 3. When skills, onboarding or feedback remain scattered across functions. - Customer, marketing and sales: 4. When customers, content and sales generate signals nobody is reading. - Technical and software: 4. When rules, code, drawings or technical systems need to become verifiable. - Governance, compliance and risk: 3. When AI use, privacy, risk and responsibilities still lack clear boundaries. - Production, quality and maintenance: 3. When production, quality or maintenance data arrives too late to guide action. - Training and internal memory: 2. When internal knowledge and training material need to remain accessible. - Data science and decisions: 5. When histories, KPIs or signals need validation before anything is built. - Cross-functional tools: 2. When AI is needed to explore, synthesise or prepare cross-functional decisions. ## What AI can do inside a company. ### Extract data from documents and scans PDFs, images and forms become text, tables and structured fields reusable in company systems. - Operating example: When a process shows a similar need, pdfs and attachments are used to produce structured database and support time reduction, with human review recommended. - Area: Documents and knowledge - Input: PDFs and attachments, scans and images, completed forms - Output: structured database, operational report - Value: time reduction, fewer errors, traceability - Need signals: scattered documents that are hard to consult, manual copying between emails, spreadsheets and systems - Human review: recommended - Risk: medium ### Check consistency across documents Reports, contracts, specifications and procedures are compared to find discrepancies, divergent versions and inconsistent definitions. - Operating example: When a process shows a similar need, pdfs and attachments are used to produce operational report and support fewer errors, with human review required. - Area: Documents and knowledge - Input: PDFs and attachments, internal documentation, contracts and policies, tenders and specifications - Output: operational report, risk map - Value: fewer errors, risk reduction, traceability - Need signals: recurring errors in documents, procedures or controls, scattered documents that are hard to consult - Human review: required - Risk: medium ### Make company knowledge searchable by meaning Manuals, procedures and knowledge bases become semantic search with answers grounded in citable sources. - Operating example: When a process shows a similar need, internal documentation are used to produce semantic search and support transferable knowledge, with human review recommended. - Area: Documents and knowledge - Input: internal documentation, PDFs and attachments, manuals and training material - Output: semantic search, FAQs and answers - Value: transferable knowledge, faster decisions, more consistent service - Need signals: scattered documents that are hard to consult, critical knowledge concentrated in a few people - Human review: recommended - Risk: medium ### Turn meetings, emails and tickets into operating memory Transcripts and threads are cleaned, summarised and converted into traceable decisions, tasks, deadlines and risks. - Operating example: When a process shows a similar need, emails and tickets are used to produce actionable digest and support traceability, with human review recommended. - Area: Operations - Input: emails and tickets, transcripts and notes, tickets and requests - Output: actionable digest, roadmap and priorities - Value: traceability, faster decisions, transferable knowledge - Need signals: recurring decisions that are slow or based on incomplete information, critical knowledge concentrated in a few people - Human review: recommended - Risk: low ### Generate controlled documents from templates Reports, letters, contracts, FAQs and communications are produced from data and templates, with formal consistency and human review. - Operating example: When a process shows a similar need, structured database are used to produce controlled drafts and support time reduction, with human review required. - Area: Documents and knowledge - Input: structured database, internal documentation, contracts and policies - Output: controlled drafts, FAQs and answers - Value: time reduction, fewer errors, more governable compliance - Need signals: manual copying between emails, spreadsheets and systems, recurring errors in documents, procedures or controls - Human review: required - Risk: medium ### Map processes and redesign workflows Real work is reconstructed as-is, read for bottlenecks and transformed into a to-be scenario with priorities and controls. - Operating example: When a process shows a similar need, transcripts and notes are used to produce roadmap and priorities and support clearer priorities, with human review recommended. - Area: Operations - Input: transcripts and notes, logs and process states, emails and tickets, spreadsheets - Output: roadmap and priorities, business case - Value: clearer priorities, faster decisions, avoided costs - Need signals: recurring decisions that are slow or based on incomplete information, manual copying between emails, spreadsheets and systems, AI already used without shared rules - Human review: recommended - Risk: medium ### Triage emails, tickets and requests Incoming communications are classified by urgency, topic, responsibility and required action, with controlled response drafts. - Operating example: When a process shows a similar need, emails and tickets are used to produce actionable digest and support time reduction, with human review recommended. - Area: Operations - Input: emails and tickets, tickets and requests, internal documentation - Output: actionable digest, controlled drafts, priority ranking - Value: time reduction, more consistent service, clearer priorities - Need signals: manual copying between emails, spreadsheets and systems, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium ### Plan shifts, resources and priorities Availability, constraints, skills, leave and demand are combined to propose feasible and explainable plans. - Operating example: When a process shows a similar need, spreadsheets are used to produce plan and assignments and support production efficiency, with human review required. - Area: Operations - Input: spreadsheets, ERP and business systems, KPIs and time series - Output: plan and assignments, dashboards and filtered views - Value: production efficiency, faster decisions, avoided costs - Need signals: planning that is still highly manual, historical data available but not turned into signals - Human review: required - Risk: medium ### Forecast demand and workload Historical orders, revenue, tickets or production become operating forecasts for purchasing, shifts and capacity. - Operating example: When a process shows a similar need, transactions and purchases are used to produce verifiable forecast and support faster decisions, with human review recommended. - Area: Operations - Input: transactions and purchases, KPIs and time series, production data - Output: verifiable forecast, dashboards and filtered views - Value: faster decisions, avoided costs, production efficiency - Need signals: historical data available but not turned into signals, planning that is still highly manual - Human review: recommended - Risk: medium ### Keep requirements, decisions and stakeholders alive Project meetings and documents feed an evolving dossier with requirements, latent conflicts, decisions and issues. - Operating example: When a process shows a similar need, transcripts and notes are used to produce roadmap and priorities and support traceability, with human review required. - Area: Operations - Input: transcripts and notes, requirements and specifications, internal documentation - Output: roadmap and priorities, risk map - Value: traceability, fewer errors, transferable knowledge - Need signals: recurring decisions that are slow or based on incomplete information, critical knowledge concentrated in a few people - Human review: required - Risk: medium ### Read customer feedback, reviews and tickets Unstructured texts are aggregated by theme, sentiment, recurring needs and priority actions. - Operating example: When a process shows a similar need, text feedback are used to produce operational report and support more consistent service, with human review recommended. - Area: Customer, marketing and sales - Input: text feedback, tickets and requests, public sources - Output: operational report, priority ranking - Value: more consistent service, recovered commercial value, clearer priorities - Need signals: abundant feedback that is not analysed, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium ### Discover market and target needs Public sources and provided material are synthesised into maps of pain points, language, segments, partners and opportunities. - Operating example: When a process shows a similar need, public sources are used to produce operational report and support recovered commercial value, with human review recommended. - Area: Customer, marketing and sales - Input: public sources, text feedback, internal documentation - Output: operational report, business case - Value: recovered commercial value, clearer priorities, faster decisions - Need signals: abundant feedback that is not analysed, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium ### Codify brand voice and content Interviews, approved examples and commercial material become operating guidelines and coherent multi-channel drafts. - Operating example: When a process shows a similar need, internal documentation are used to produce policies and guardrails and support time reduction, with human review required. - Area: Customer, marketing and sales - Input: internal documentation, text feedback, public sources - Output: policies and guardrails, controlled drafts - Value: time reduction, recovered commercial value, traceability - Need signals: recurring errors in documents, procedures or controls, manual copying between emails, spreadsheets and systems - Human review: required - Risk: low ### Support sales, pricing and recommendations Purchase history, catalogs and competitive information help build pitches, bundles, commercial priorities and price scenarios. - Operating example: When a process shows a similar need, transactions and purchases are used to produce operational recommendations and support recovered commercial value, with human review required. - Area: Customer, marketing and sales - Input: transactions and purchases, internal documentation, public sources - Output: operational recommendations, business case - Value: recovered commercial value, faster decisions, clearer priorities - Need signals: historical data available but not turned into signals, recurring decisions that are slow or based on incomplete information - Human review: required - Risk: medium ### Map skills and capability needs Skills, roles, future goals and trends are connected to define development, upskilling and reskilling priorities. - Operating example: When a process shows a similar need, aggregated hr data are used to produce roadmap and priorities and support transferable knowledge, with human review required. - Area: People and HR - Input: aggregated HR data, internal documentation, public sources - Output: roadmap and priorities, operational report - Value: transferable knowledge, clearer priorities, faster training - Need signals: critical knowledge concentrated in a few people, AI already used without shared rules - Human review: required - Risk: medium ### Support recruiting and onboarding Job descriptions, applications and onboarding material are structured to prepare evaluations, communications and initial paths. - Operating example: When a process shows a similar need, cvs and applications are used to produce operational report and support time reduction, with human review required. - Area: People and HR - Input: CVs and applications, aggregated HR data, manuals and training material - Output: operational report, controlled drafts - Value: time reduction, fewer errors, faster training - Need signals: manual copying between emails, spreadsheets and systems, critical knowledge concentrated in a few people - Human review: required - Risk: high ### Simplify recurring HR policies and requests Policies, benefits, procedures and recurring requests become FAQs, drafts and guided paths under HR control. - Operating example: When a process shows a similar need, aggregated hr data are used to produce faqs and answers and support more consistent service, with human review required. - Area: People and HR - Input: aggregated HR data, internal documentation, contracts and policies - Output: FAQs and answers, controlled drafts - Value: more consistent service, time reduction, more governable compliance - Need signals: manual copying between emails, spreadsheets and systems, scattered documents that are hard to consult - Human review: required - Risk: high ### Define requirements, MVP and acceptance criteria A technical need becomes requirements, user stories, non-functional constraints, estimates and first-release boundaries. - Operating example: When a process shows a similar need, requirements and specifications are used to produce roadmap and priorities and support fewer errors, with human review required. - Area: Technical and software - Input: requirements and specifications, transcripts and notes, internal documentation - Output: roadmap and priorities, tests and checklists - Value: fewer errors, traceability, avoided costs - Need signals: recurring decisions that are slow or based on incomplete information, recurring errors in documents, procedures or controls - Human review: required - Risk: medium ### Accelerate development, refactoring and tests Existing code and specifications guide controlled code generation, unit tests, refactoring and quality audits. - Operating example: When a process shows a similar need, code and repositories are used to produce tests and checklists and support time reduction, with human review required. - Area: Technical and software - Input: code and repositories, requirements and specifications - Output: tests and checklists, operational report - Value: time reduction, fewer errors, traceability - Need signals: recurring errors in documents, procedures or controls, manual copying between emails, spreadsheets and systems - Human review: required - Risk: high ### Read specifications and produce technical documentation Tenders, specifications, reports and technical sheets are analysed for critical requirements, risks and documentation drafts. - Operating example: When a process shows a similar need, tenders and specifications are used to produce operational report and support risk reduction, with human review required. - Area: Technical and software - Input: tenders and specifications, internal documentation, technical drawings - Output: operational report, controlled drafts, risk map - Value: risk reduction, fewer errors, traceability - Need signals: scattered documents that are hard to consult, recurring errors in documents, procedures or controls - Human review: required - Risk: high ### Interpret images, drawings and technical material Photos, drawings and renders become descriptive sheets, component analyses, dimensions and verifiable technical narratives. - Operating example: When a process shows a similar need, operational photos are used to produce operational report and support transferable knowledge, with human review required. - Area: Technical and software - Input: operational photos, technical drawings, internal documentation - Output: operational report, controlled drafts - Value: transferable knowledge, faster decisions, fewer errors - Need signals: critical knowledge concentrated in a few people, scattered documents that are hard to consult - Human review: required - Risk: medium ### Build AI governance, policies and risk matrix Activities, data and decisions are classified into autonomy, supervision or exclusion zones with clear operating rules. - Operating example: When a process shows a similar need, internal documentation are used to produce policies and guardrails and support risk reduction, with human review required. - Area: Governance, compliance and risk - Input: internal documentation, policies and guidelines, transcripts and notes - Output: policies and guardrails, risk map, roadmap and priorities - Value: risk reduction, more governable compliance, clearer priorities - Need signals: AI already used without shared rules, recurring decisions that are slow or based on incomplete information - Human review: required - Risk: high ### Prepare compliance, legal and privacy documents Contracts, notices, registers, procedures and letters are prepared as preliminary support to be reviewed by specialists. - Operating example: When a process shows a similar need, contracts and policies are used to produce controlled drafts and support time reduction, with human review required. - Area: Governance, compliance and risk - Input: contracts and policies, internal documentation, completed forms - Output: controlled drafts, risk map - Value: time reduction, more governable compliance, risk reduction - Need signals: manual copying between emails, spreadsheets and systems, recurring errors in documents, procedures or controls - Human review: required - Risk: high ### Test AI assistants against misuse Chatbots and assistants are stressed with manipulation, data leakage and conflicting instruction scenarios, then hardened with guardrails. - Operating example: When a process shows a similar need, internal documentation are used to produce tests and checklists and support risk reduction, with human review required. - Area: Governance, compliance and risk - Input: internal documentation, requirements and specifications, policies and guidelines - Output: tests and checklists, policies and guardrails, operational report - Value: risk reduction, more governable compliance, more consistent service - Need signals: AI already used without shared rules, recurring errors in documents, procedures or controls - Human review: required - Risk: high ### Analyse HSE anomalies from operational images Site or department photos are read to identify non-compliance, risks and preventive measures to verify. - Operating example: When a process shows a similar need, operational photos are used to produce operational report and support risk reduction, with human review required. - Area: Production, quality and maintenance - Input: operational photos, internal documentation - Output: operational report, risk map - Value: risk reduction, faster decisions, more governable compliance - Need signals: recurring errors in documents, procedures or controls, manual copying between emails, spreadsheets and systems - Human review: required - Risk: high ### Optimise production, orders and quality Customer schedules, ERP, cycles, non-conformities and historical costs support priorities, quotes and corrective actions. - Operating example: When a process shows a similar need, erp and business systems are used to produce plan and assignments and support production efficiency, with human review required. - Area: Production, quality and maintenance - Input: ERP and business systems, production data, spreadsheets - Output: plan and assignments, operational report, operational recommendations - Value: production efficiency, fewer errors, avoided costs - Need signals: planning that is still highly manual, recurring errors in documents, procedures or controls - Human review: required - Risk: medium ### Manage maintenance, assets and spare parts Failure history, sensors and interventions become control priorities, maintenance windows and operating alerts. - Operating example: When a process shows a similar need, sensors and telemetry are used to produce alerts and thresholds and support production efficiency, with human review required. - Area: Production, quality and maintenance - Input: sensors and telemetry, production data, logs and process states - Output: alerts and thresholds, priority ranking, dashboards and filtered views - Value: production efficiency, avoided costs, risk reduction - Need signals: historical data available but not turned into signals, planning that is still highly manual - Human review: required - Risk: medium ### Create training, quizzes and slides from internal material Manuals, slides and scattered documents become syllabi, quizzes, case studies and role-based learning material. - Operating example: When a process shows a similar need, manuals and training material are used to produce faqs and answers and support faster training, with human review recommended. - Area: Training and internal memory - Input: manuals and training material, internal documentation, transcripts and notes - Output: FAQs and answers, controlled drafts - Value: faster training, transferable knowledge, more consistent service - Need signals: critical knowledge concentrated in a few people, scattered documents that are hard to consult - Human review: recommended - Risk: low ### Build assistants for company memory Internal documentation feeds Q&A assistants, including voice interfaces, that answer with sources and clear usage boundaries. - Operating example: When a process shows a similar need, internal documentation are used to produce semantic search and support transferable knowledge, with human review required. - Area: Training and internal memory - Input: internal documentation, manuals and training material, policies and guidelines - Output: semantic search, FAQs and answers, policies and guardrails - Value: transferable knowledge, more consistent service, time reduction - Need signals: critical knowledge concentrated in a few people, scattered documents that are hard to consult - Human review: required - Risk: medium ### Produce executive reports and visual assets Data, KPIs and heterogeneous material become narrative reports, infographics, presentations and coherent visual content. - Operating example: When a process shows a similar need, kpis and time series are used to produce operational report and support faster decisions, with human review recommended. - Area: Cross-functional tools - Input: KPIs and time series, spreadsheets, internal documentation - Output: operational report, dashboards and filtered views, controlled drafts - Value: faster decisions, traceability, recovered commercial value - Need signals: historical data available but not turned into signals, manual copying between emails, spreadsheets and systems - Human review: recommended - Risk: low ### Detect anomalies and degradation in machinery Time series and industrial sensors are used for alerts, degradation analysis and predictive maintenance with verifiable thresholds. - Operating example: When a process shows a similar need, sensors and telemetry are used to produce alerts and thresholds and support production efficiency, with human review required. - Area: Data science and decisions - Input: sensors and telemetry, production data, KPIs and time series - Output: alerts and thresholds, verifiable forecast, dashboards and filtered views - Value: production efficiency, avoided costs, risk reduction - Need signals: historical data available but not turned into signals, planning that is still highly manual - Human review: required - Risk: medium ### Segment customers, churn and cross-selling Transactional and behavioural histories become segments, risk rankings, bundles and differentiated commercial actions. - Operating example: When a process shows a similar need, transactions and purchases are used to produce priority ranking and support recovered commercial value, with human review required. - Area: Data science and decisions - Input: transactions and purchases, text feedback, KPIs and time series - Output: priority ranking, operational recommendations, business case - Value: recovered commercial value, clearer priorities, more consistent service - Need signals: historical data available but not turned into signals, abundant feedback that is not analysed - Human review: required - Risk: medium ### Optimise energy, quality and line performance Telemetry, consumption, quality and machine parameters reveal efficient profiles, waste and operating recommendations. - Operating example: When a process shows a similar need, sensors and telemetry are used to produce dashboards and filtered views and support production efficiency, with human review required. - Area: Data science and decisions - Input: sensors and telemetry, production data, KPIs and time series - Output: dashboards and filtered views, operational recommendations, business case - Value: production efficiency, avoided costs, faster decisions - Need signals: historical data available but not turned into signals, recurring errors in documents, procedures or controls - Human review: required - Risk: medium ### Analyse territories, profitability and trends Aggregated fiscal, territorial or commercial data become maps, clusters, profitability drivers and decision roadmaps. - Operating example: When a process shows a similar need, transactions and purchases are used to produce dashboards and filtered views and support faster decisions, with human review recommended. - Area: Data science and decisions - Input: transactions and purchases, public sources, KPIs and time series - Output: dashboards and filtered views, operational report, business case - Value: faster decisions, clearer priorities, recovered commercial value - Need signals: historical data available but not turned into signals, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium ### Know when not to build a model The first value can be a negative verdict: available data does not yet contain the useful signal and collection must improve. - Operating example: When a process shows a similar need, kpis and time series are used to produce operational report and support avoided costs, with human review recommended. - Area: Data science and decisions - Input: KPIs and time series, transactions and purchases, logs and process states - Output: operational report, business case, roadmap and priorities - Value: avoided costs, clearer priorities, traceability - Need signals: historical data available but not turned into signals, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: low ### Use AI as a discovery lab Cases, material and constraints are explored to generate hypotheses, scenarios, concepts, role simulations and opportunities to verify. - Operating example: When a process shows a similar need, internal documentation are used to produce operational report and support recovered commercial value, with human review recommended. - Area: Cross-functional tools - Input: internal documentation, text feedback, public sources - Output: operational report, operational recommendations, controlled drafts - Value: recovered commercial value, clearer priorities, faster decisions - Need signals: recurring decisions that are slow or based on incomplete information, abundant feedback that is not analysed - Human review: recommended - Risk: low ## From map to real process: start with a call. This page helps orientation. The solution is designed only after reviewing sector, constraints, available data, responsibilities and the decision to improve. 1. **Initial context**: Before the meeting Artik Lab prepares a first reading of public context and any material shared by the company. 2. **Structured conversation**: During the call two or three high-potential workflows are identified, together with constraints, risks and urgencies. 3. **Targeted proposal**: The output is a calibrated path: training, consulting, data analysis or technical prototype, with expected results and control criteria. ## FAQ ### Is the Atlas a catalog of ready-made products? No. It is a map of concrete examples. Artik Lab starts from a discovery call and designs the path around the client's real process. ### Are all applications automations? No. Some are training, some analysis, some technical software or governance. AI can assist, suggest, find signals or draft, while sensitive decisions remain governed. ### How are recognisable cases avoided? Cards aggregate patterns and sectors, removing names, clients, natural persons, proprietary data and details that could identify a project.