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AI Enablement

AI training and coaching for your team

Turn AI tools into habits your team actually has.

Hands on AI enablement workshops and ongoing coaching to help your team adopt AI effectively.

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cohort · operations teamweek 3 of 6
72%weekly active
  • AMAisha M.
  • JTJonas T.
  • PRPriya R.
  • LKLuca K.
Up next, Workshop 4 · Automating reportsPrompt library · 38
Illustrative interface
Category
AI Enablement
Deliverables
04 core
Core stack
AI Literacy Workshops · Enterprise AI Playbooks · Executive Coaching
Typical first phase
Assess
The brief

Hands On AI Coaching, Corporate Workshops & Enablement Playbooks

Adoption isn't about buying tools. It's about people using them well. We assess where your team is, run hands on workshops tailored to your workflows, and stay on as coaches so the momentum holds.

Without it
  • You bought AI licences, and usage fizzled after a month.
  • A few enthusiasts use AI well; everyone else doesn’t know where to start.
  • Leadership worries about quality, data leaks and policy.
With Caudify
  • Teams using AI confidently inside their own workflows.
  • Playbooks and prompt libraries tailored to each role.
  • Clear, practical guidelines for safe and responsible use.
01

AI readiness assessment

Where your people, processes and policies stand today.

Capabilities
  1. 1Team skills survey
  2. 2Workflow opportunity scan
  3. 3Policy & risk review
02

Custom training programs

Role specific curricula built around your tools and your tasks.

Capabilities
  1. 1Role based learning paths
  2. 2Your data & examples
  3. 3Prompt libraries
03

Hands on workshop facilitation

Practical sessions where people build something they’ll use the next day.

Capabilities
  1. 1Live build sessions
  2. 2Use case sprints
  3. 3Leadership briefings
04

Ongoing AI coaching retainer

Continued support so adoption keeps growing after the workshop.

Capabilities
  1. 1Office hours
  2. 2Champion network
  3. 3Adoption reviews
How we deliver

AI is in the room from day one.

We don't bolt AI on at the end of a project. It's part of how we research, design, build, test and document every engagement, which is how a small senior team moves quickly without cutting corners. A person reviews everything the models produce.

On AI training

Your team learns on its own tools and data, with playbooks we built from using AI in our own delivery every day.

  • A person signs off. No model output reaches your users or your codebase without review.
  • Your data stays yours. Zero-retention API settings or private models when your data requires it.
  • Model-agnostic. We pick models by measured quality, cost and privacy, not by vendor.

  1. Discover

    Requirements mapped with LLMs

    With your permission, briefs, tickets, call notes and analytics exports go into a private workspace. LLMs map requirements, edge cases and open questions, so nothing said in a workshop gets lost. People run the workshops; the model keeps the record.

  2. Prototype

    Something real to react to, early

    First-pass screens, flows and copy variants are generated from the spec, then shaped by our designers. Decisions happen on a clickable prototype instead of a document, and the final design system stays human-made.

  3. Build

    AI pair programming, human review

    Engineers work with AI coding assistants for scaffolding, tests and refactors. Every change is read by a person and scanned by Caudilysis, our own static analysis platform, before it merges.

  4. Verify

    Evals and generated tests in CI

    AI features get an evaluation set built from real examples, and every model or prompt change is scored against it. Everything else gets generated test cases that an engineer reviews and keeps in the pipeline.

  5. Ship and learn

    Docs, runbooks and monitoring

    Documentation, runbooks and release notes are drafted with AI and checked by the team. After launch, monitoring and usage data feed straight into the next iteration.

Engagement

How a AI training engagement runs.

The indicative shape of a first engagement. Phases overlap on purpose, so we start building while discovery is still sharpening the plan.

  1. 01AssessSkills, workflows and policies.
    2 wks
  2. 02DesignCurriculum and playbooks.
    2 wks
  3. 03TrainWorkshops by role and team.
    4 wks
  4. 04CoachOffice hours and adoption reviews.
    6 wks

This is indicative only. Every engagement is scoped with you before it starts.

Fit

Is AI training right for you?

  1. 01

    You’ve rolled out AI tools and adoption is patchy.

  2. 02

    You want training built on your workflows, not generic demos.

  3. 03

    Leadership needs a responsible use framework.

Not sure it fits? A free assessment will tell you

caudify / services / ai_enablement.yml
# Service manifest
service:    AI Coaching & Training
category:   AI Enablement
deliverables:
  • AI readiness assessment
  • Custom training programs
  • Hands on workshop facilitation
  • Ongoing AI coaching retainer
stack:
  • AI Literacy Workshops
  • Enterprise AI Playbooks
  • Executive Coaching
  • Generative AI Workflows
  • Prompt Engineering Training
phases: 4
next_step:  free assessment
Questions

AI training questions, answered.

Which AI tools do you train on?

Whatever your team uses or plans to use, including ChatGPT, Claude, Gemini, Microsoft Copilot and the AI features inside your existing software. The focus is on workflows, so skills transfer between tools.

Is this for technical or nontechnical teams?

Both. Programmes are role specific, from sales, marketing and operations to engineering and leadership.

How do you measure success?

We agree adoption goals up front (usage, time saved on target tasks and confidence surveys) and review them throughout coaching.

Start with a free assessment.

Tell us what you're building, improving or automating. We'll come back with an honest view of where AI training fits and the fastest path to production.

Book a free assessmentTalk to the team
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