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Generative Artificial Intelligence Training

From understanding to application: AI to accelerate the present and expand the future


 Generative Artificial Intelligence is already impacting entire industries — but it only truly transforms them when it is understood, applied with purpose and connected to business challenges. Processor has structured its Artificial Intelligence training programs to meet different levels of maturity, objectives and organizational needs. From an executive awareness workshop to a complete adoption and scaling program, we help companies turn GenAI and Copilot into real productivity, governance and business impact.

Three training formats.

One common goal: creating value with AI.

Processor Copilot Workshop - GenAI Adoption (1 day):

A focused immersion that aligns leaders and strategic teams on the role of GenAI and Copilot in the organization’s real-world context.



The workshop establishes a common language, presents practical applications already proven in the market and guides the next steps in adoption, connecting use cases, priorities and governance fundamentals in an actionable roadmap.


It is especially suited to executive leaders, department managers and strategic teams that need to gain clarity quickly, align expectations and accelerate the identification of near-term value opportunities.

Content

Vision, foundations and initial use cases

  • 1. Quick introduction

    - What is GenAI (Generative Artificial Intelligence) — definitions

    - Practical examples (ChatGPT, DALL-E, copilots, autonomous agents)

    - Why GenAI changed the game (speed, personalization, automation)

  • 2. Fundamentals

    - How it works in practice (prompt → model → output)

    - Difference between GenAI and traditional AI (classification, regression, etc.)

    - Main types:

    • Text (e.g., ChatGPT)
    • Image (e.g., DALL-E, Midjourney)
    • Video, Audio, Code

  • 3. Real-world applications

    - Practical use cases by area:

    • Business: copilots, meeting summaries, process automation
    • Marketing: content, image and ad generation
    • Sales: email and script creation, customer support
    • HR: resume screening, job description generation
    • Development: code generation, automated testing

    - Live demonstrations (3 reference examples)

  • 4. How to use GenAI effectively

    - Prompt engineering best practices (how to ask better)

    - Examples of good prompts and common mistakes

    - Quick tips:

    • Be clear about what you want
    • Define the response style
    • Use context (when possible)

  • 5. Limitations and risks

    - Hallucination (incorrect or fabricated answers)

    - Bias and ethics

    - Data protection (don't upload sensitive information!)

    - Excessive dependency

  • 6. Hands-On / Workshops

    Each participant practices:

    • Create useful prompts
    • Adjust/summarize text
    • Create an image
    • Simulate automation with GenAI (optional)

    GenAI Processor training can be delivered using ChatGPT, Copilot (some cases do not apply), Gemini or another open tool.

  • 7. GenAI conclusion and estimated future

    - What’s next: autonomous agents, copilots everywhere, multimodal AI

    - Agentics

    How to prepare: a collaborative mindset of humans and machines.

    - Tool tips for further exploration

Processor GenAI Training—Productivity with Copilot M365 (3 days):

A structured, practical journey focused on turning Copilot into an everyday operational tool. Throughout the training, routines, usage patterns, best practices, and governance are addressed, with a focus on increasing productivity and reducing frustrations associated with superficial use of the technology.



This format is intended for key users, operational leaders and teams in areas such as Finance, HR, Legal, Sales, Operations and IT who seek consistent, productive and governed use of GenAI in the Microsoft 365 environment, completing the training with a clear roadmap for continued development.

Content

Productivity with Copilot M365

  • 1. Why GenAI now (and what changes by 2026)

    • Market context (productivity, personalization, speed, cost)
    • Impacts on operating and decision-making models
    • GenAI as an executive agenda
    • What changes in work and competitiveness.

  • 2. Essential GenAI fundamentals

    • Core concepts (LLMs/SLMs, embeddings, context, hallucinations)
    • Types of AI (predictive, prescriptive, generative, agents)
    • Where GenAI performs best and where to avoid it
    • Introduction to RAG and knowledge bases

  • 3. Where companies fail (and how to avoid it)

    • License ≠ adoption
    • POC without scale
    • Expectations vs. reality
    • Data without governance; automation without ownership
    • Success criteria
    • “Start small, measure, adjust, scale” approach

  • 4. Value Map and case prioritization (multi-sector)

    • Structure: Revenue | Margin | Cash | Experience | Risk
    • Selection criteria (impact, effort, volume, risk, recurrence)
    • Impact x Effort Matrix
    • Definition of the Top 10 opportunities and Top 3 pilot cases

  • 5. Prompting for performance (beginner to advanced)

    • Prompt structure (context, role, task, constraints, examples, criteria)
    • Techniques (decomposition, refinement, verification, comparison, few-shot)
    • Corporate formats (minutes, executive email, one-pager, FAQ, proposal)

  • 6. Copilot M365 in the workflow

    • Copilot Chat vs Copilot in apps
    • Best practices
    • Traceability and human review
    • Use of Teams, Word, Excel, PowerPoint, Outlook
    • OneDrive/SharePoint as a knowledge base (limitations and precautions)

  • 7. Guided Lab (Practical Scenarios)

    • Scenarios: minutes and decisions; executive documents; analysis and insights
    • Communication and alignment; 
    • Research and consolidation
    • Knowledge management/Q&A (where applicable)
    • Quality and validation checklist

  • 8. Adoption readiness (foundation)

    • Organizational and technical readiness
    • Training tracks
    • Content curation
    • Architecture and access
    • Quality and catalog
    • Principles for adoption without stifling innovation

  • 9. Security, compliance, and LGPD in practice

    • Identity and permissions
    • Information protection (classification, DLP, retention, auditing)
    • Risks (leakage, bias, factual errors, misuse)
    • Guardrails and usage policies by profile

  • 10. 90-day plan (execution and controlled scaling)

    • Quick wins (weeks 1–3)
    • Executive pilot (weeks 4–8)
    • Controlled scale-up (weeks 9–12)
    • Backlog, milestones, success criteria and sponsorship.

Data + AI Immersion x Processor (1 or 2 weeks):

A dedicated technical engagement model in which a Processor specialist works directly in the client’s environment. This immersion enables the diagnosis, organization and qualification of the data environment, backlog prioritization and risk reduction, preparing the foundation for continuous evolution in data, AI and automation initiatives.



It is intended for organizations that have already begun or plan to scale their use of data and GenAI, but need to establish technical foundations, reduce rework, and create a clear path to sustainable growth.

Content

Data Immersion – 1 week focused on the PowerBI / Fabric environment

  • 1. Alignment and Setup

    • Understanding the context and objectives
    • Defining the focus (what will be analyzed)
    • Aligning access and scheduling
    • Success criteria and deliverables.

  • 2. Data environment assessment

    • Overview of the current architecture, licensing, and processes
    • Identification of bottlenecks, risks, and critical points
    • Assessment of technical, executive, and operational maturity

  • 3. Ecosystem inventory and organization

    • Mapping sources, flows, pipelines, dataflows, datasets  and key artifacts
    • Understanding dependencies and priorities
    • Consolidated view of what exists and what is missing

  • 4. Security and governance (applied perspective)

    • Understanding access controls and recommending best practices
    • Identifying exposure risks; recommendations for secure and sustainable data use.

  • 5. Prioritized backlog and next steps

    • Consolidation of improvements and fixes in the backlog
    • DataCare Fabric™
    • Prioritization by impact and effort (quick wins)
    • Recommended execution plan for 60–90–120 days.

Content

Data Immersion – 2 weeks focused on the PowerBI / Fabric environment

  • 1. Alignment and Setup

    • Definition of objectives by stage
    • Alignment with key teams
    • Organization of the work routine
    • Alignment of delivery and continuity criteria

  • 2. In-depth diagnosis and ecosystem map

    • Deeper environment analysis
    • Mapping of risks, artifacts, dependencies and opportunities
    • Recommended architecture view for evolution
    • Understanding of sources, flows, pipelines, dataflows, datasets and key artifacts
    • Understanding of dependencies and priorities

  • 3. Environment organization and standardization

    • Recommendations for standards and practices to reduce unnecessary structures
    • Organization of layers and artifacts
    • Documentation guidelines and operational consistency

  • 4. Operations and monitoring

    • Guidelines for continuous environment monitoring
    • Capability metrics indicators
    • Recommendations for incident management and routines

  • 5. Security, access, and compliance

    • Access control recommendations
    • Protection and compliance principles
    • Essential audit trails and controls for responsible use

  • 6. Scenarios for Data, AI and GenAI

    • Assessment of scenarios for scaling data and AI initiatives
    • Guidance on the required foundation (reliability, access, traceability and security)
    • Recommended paths for evolution

  • 7. Roadmap and prioritized backlog

    • Backlog structured by workstream
    • DataCare Fabric™
    • LiveCloud
    • Executive prioritization
    • 60–90–120–150-day plan
    • Logical sequencing recommendations for sustainable growth

Direct impact on your business

  •  Strategic clarity: Understand the key concepts of Generative AI, their impact on business models and decision-making processes, connecting technology to corporate strategy.


  •  Practice with purpose: Discover and use established AI tools on the market through activities guided by real-world challenges, focused on productivity, quality, and measurable impact.
  •  Innovation with sustainability: Develop critical thinking about AI risks, limitations, security, ethics, and responsible use, preparing teams to make sustainable decisions.


  •  Speed to innovate: Results-driven enablement, focused on quick wins and repeatable learnings from day one.

AI isn’t the future. It’s now. And you can lead the way.

In a scenario where innovation is no longer optional, expanding human and artificial intelligence has become essential. By investing in GenAI training, your organization develops the vision, hands-on experience and direction needed to turn technology into real business impact.


Processor connects strategy, application, and governance to enable the adoption of GenAI in a practical, structured way, driven by real business impact.

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