AI adoption
Readiness assessment, use-case selection, follow-up rituals, and a plan to move AI from slides into team routines.
AI consulting and training for companies
I help companies turn generative AI into real workplace use: adoption, workshops, learning paths, playbooks, prototypes, automations, and progress metrics.
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 I help
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.
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
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.
Contact
To talk about an AI adoption program, workshop, training, or prototype, write to oi@dorly.com.br.