16+ years
in L&D, product marketing, and applied AI
AI strategy, training, and implementation for real workplace use
I help companies turn generative AI into practice: from selecting use cases to enablement, reusable assets, prototypes, and indicators that sustain adoption.
Experience that supports execution
The work combines expertise in learning, product, and applied AI, with consolidated indicators that preserve the context of each engagement.
50+ companies and contexts
served through projects, classes, and engagements
93% satisfaction
consolidated average across seven programs and workshops
Short answer
If your company wants to use AI more consistently, the work needs three fronts: clear use cases, practical enablement, and adoption follow-through. The goal is not just people understanding AI; it is people using AI better to gain time, quality, and autonomy.
How strategy, training, and implementation connect
AI adoption
Readiness assessment, use-case selection, follow-up rituals, and a plan to move AI from slides into team routines.
Training and workshops
Hands-on sessions for technical and non-technical audiences, with real work examples, guided exercises, and reusable materials.
Prototypes and automations
Demos, MVPs, and AI workflows to test value before scaling, using tools like ChatGPT, OpenAI API, n8n, TypeScript, and AWS.
Progress metrics
Indicators for usage, confidence, quality, time savings, rework reduction, and continuous improvement after training.
Services in detail
Compare the five workstreams by audience, outcome, and format. Each detail page explains the process, deliverables, and common questions.
AI Adoption Strategy
Upstream decisions to assess, prioritize, and govern an AI pilot before execution
- Leadership, HR/L&D, and transformation sponsors
- Priorities, a bounded pilot, accountable owners, and an adoption roadmap
- Strategic consulting
Learning Paths & Workshops
Synchronous, hands-on AI experiences designed around each audience's real work
- Technical and non-technical teams, leaders, and business units
- Guided practice, usage criteria, and continuity materials
- Executive session / workshop / path
AI Prototypes & Demos
Tools, workflows, and demonstrations to test value before scaling
- Product, innovation, operations, and technical leadership
- MVP or demo that proves value, limits, and effort before scale
- Prototype
Enablement Playbooks & Assets
Editable materials that enable teams to apply AI with criteria, autonomy, and internal maintenance
- HR/L&D, corporate universities, and team managers
- A validated, editable package ready for maintenance by its owners
- Playbooks / assets
Metrics & Continuous Improvement
Design of a measurement model from reaction to impact, subject to available data
- Sponsors, HR/L&D, analytics, and area managers
- Baseline, instruments, indicator dictionary, and a reading prototype
- Measurement design
Who I support
- HR, L&D, and corporate universities that need to structure AI literacy.
- Business teams that want to turn curiosity into clear use cases.
- Leaders who need to align risk, productivity, adoption, and behavior change.
- Product, innovation, and operations teams that need to test AI workflows before investing at scale.
Possible deliverables
- Map of adoption opportunities and barriers
- Learning paths by audience and maturity level
- In-person or remote workshops with practical exercises
- Playbooks, prompt templates, rubrics, and usage guides
- Prototypes, automations, and technical demos to prove value
- Metrics plan and post-training follow-up rhythm
Cases related to the offer
Two examples show how learning design and facilitation become assets people can apply at work.
AI Literacy for the Modern Workplace
A bilingual program combining self-paced learning about workplace AI with a live quiz for discussion and comprehension checks. The page shows a public demo, not a record of a real session.
Enterprise Training
An aggregated page covering corporate facilitation in people analytics, agile methods, design thinking, change management, and time management. This history demonstrates content adaptation and facilitation across different audiences; it does not attribute AI adoption programs to the named brands.
The Enterprise Training case demonstrates facilitation experience transferable to AI programs; the named brands are associated with the corporate topics documented in the case itself.
Frequently asked questions
How should a company start an AI adoption program?
The best start is choosing a few high-value use cases, understanding the audience's current skill level, defining safety criteria, and building a practical learning path. Adoption improves when training uses real team tasks and continues after the session.
Does generative AI training work for non-technical teams?
It works when the language is simple and the focus stays on work people already do: writing, summarizing, researching, analyzing, planning, supporting customers, preparing meetings, and automating repetitive steps.
What should you measure after an AI workshop?
Beyond satisfaction, measure usage frequency, confidence, autonomy, delivery quality, time savings, rework reduction, and how many use cases moved into real practice.
When does it make sense to build AI prototypes?
When the company still does not know whether an idea should become a product, process, or automation. A well-built prototype shows limits, risks, technical effort, and perceived value before a larger decision.
Direct channels
To share context about the challenge before our conversation, write to oi@dorly.com.br.