AI Adoption Strategy
Upstream decisions to assess, prioritize, and govern an AI pilot before execution
- 2-6 weeks
- Leadership, HR/L&D, and transformation sponsors
- Priorities, a bounded pilot, accountable owners, and an adoption roadmap
Adoption, learning, and hands-on work
My work connects training, product, and implementation. I turn AI into clear use cases and create the materials, routines, and prototypes that help teams move from interest to adoption.
Change Readiness
Readiness assessment, use-case design, and alignment across technical teams, business stakeholders, and leadership.
AI Enablement
Needs assessment, programs, learning paths, and workshops for audiences at different levels of expertise.
AI Prototypes & Demos
MVPs, automations, and demos that test ideas before scaling.
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Companies served
Key outcomes of my work
Learning Paths & Workshops
Synchronous, hands-on AI experiences designed around each audience's real work
- 2-8 hours per session
- Technical and non-technical teams, leaders, and business units
- Guided practice, usage criteria, and continuity materials
AI Prototypes & Demos
Tools, workflows, and demonstrations to test value before scaling
- Short cycles, with an early first version
- Product, innovation, operations, and technical leadership
- MVP or demo that proves value, limits, and effort before scale
Enablement Playbooks & Assets
Editable materials that enable teams to apply AI with criteria, autonomy, and internal maintenance
- 1-3 weeks
- HR/L&D, corporate universities, and team managers
- A validated, editable package ready for maintenance by its owners
Metrics & Continuous Improvement
Design of a measurement model from reaction to impact, subject to available data
- Implementation and cadence agreed
- Sponsors, HR/L&D, analytics, and area managers
- Baseline, instruments, indicator dictionary, and a reading prototype
Selected work
Articles about AI adoption at work
Practical guides from my blog (written in Portuguese): adoption, prompting, metrics, security, and AI trends at work, always focused on real usage.
Chatbot or AI agent: what's the difference (and what do you need)?
A chatbot talks; an agent acts. Who decides the next step determines cost, risk, and supervision.
What is AI automation (and when is it better than an agent)?
Automation follows a fixed path: trigger, ordered steps, same outcome. In most cases, it solves the problem better and more cheaply than an agent.
What is an AI harness: the rig that makes a model work
A harness is the layer that chooses tools, context, permissions, and limits, which helps explain why the same model can deliver different results.