Duration
2–6 weeks
AI Adoption Strategy
Assessment, use-case selection, and program design to move AI from slides into team routines — with change management, metrics, and a follow-up rhythm to course-correct.
Service at a glance
Audience
Leadership, HR/L&D, and transformation sponsors
Outcome
Use-case map, adoption plan, and follow-up rituals
Format
Consulting
Who it's for
- Leaders and transformation sponsors who need to align risk, productivity, and adoption.
- HR, L&D, and corporate universities structuring AI literacy.
- Business teams that want to turn curiosity into clear use cases.
What's included
- Maturity and workflow assessment
- Mapping resistance and skill gaps
- Adoption plan grounded in ADKAR and change management
- Metrics for usage, confidence, and time savings
- Ongoing check-ins to course-correct
How it works
- Maturity and workflow assessment of how teams actually work.
- Mapping resistance and skill gaps.
- Choosing a few high-value use cases to start with.
- Adoption plan grounded in ADKAR and change management.
- Follow-up rituals tracking usage, confidence, and time savings.
What you get
- Map of adoption opportunities and barriers
- Prioritized use-case map
- Adoption plan with follow-up rituals
- Metrics for usage, confidence, and time savings
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.
How do you know adoption is working?
Beyond satisfaction, track usage frequency, confidence, autonomy, delivery quality, time savings, and how many use cases moved into real practice.
Contact
To talk about this service, write to oi@dorly.com.br.
Other services
Learning Paths & Workshops
Hands-on sessions for technical and non-technical audiences, with real work examples, guided exercises, and reusable materials — the goal is leaving the session using AI on concrete tasks.
AI Prototypes & Demos
MVPs, automations, and demos to test an idea's value before scaling — using tools like ChatGPT, OpenAI API, n8n, TypeScript, and AWS to prove what works with a tight scope.
Enablement Assets
Playbooks, guides, prompt templates, rubrics, and knowledge bases built from your company's real context, so teams keep using AI autonomously after the workshop.
Metrics & Continuous Improvement
Metrics for usage, confidence, quality, and time savings to know whether AI adoption is working — plus a follow-up rhythm to update content and course-correct.