Team AI Bootcamp
AI Workshop for Teams: A Hands-On AI Bootcamp Built on Your Stack
A two- to four-week cohort for 8–25 people, run on the tools you already pay for. Everyone ships working AI assets on real tasks in your own accounts, and the team keeps the prompt library and playbook.
Why a bootcamp beats a one-off AI workshop
A single AI workshop is a good start and a poor finish. People learn a technique, go back to a full inbox and lose it. BCG's AI at Work 2025 study found that employees with five or more hours of training were more likely to become regular AI users, 79% versus 67%, and only 36% said their training was enough.
The Team AI Bootcamp is built around that finding. It spreads practice over two to four weeks so habits form between sessions, gives each participant well over five hours of hands-on work and ties every exercise to a real task from their job. Section's AI Proficiency Report 2026 found 69% of knowledge workers at the experimenter stage and only 2.7% at practitioner level. The bootcamp is designed to move people from trying AI to relying on it for specific work.
AI workshop for teams: bootcamp format at a glance
| Element | Detail |
|---|---|
| Cohort size | 8–25 people from one company |
| Length | 2–4 weeks, depending on modules and role tracks |
| Sessions | Two live sessions a week plus guided lab time on real tasks |
| Delivery | In person, virtual or hybrid |
| Tools | Your stack: Claude, ChatGPT, Gemini or Copilot, on business-tier accounts |
| Every participant ships | 2–3 working AI assets on real tasks |
| The team keeps | A shared prompt library and a team AI operating playbook |
| Measurement | Baseline before kickoff, then post-bootcamp and 30/60/90-day checks |
The hands-on AI workshop syllabus, mapped to the 4Ds
Modules follow the 4D AI Fluency Framework, developed by Anthropic with Rick Dakan and Joseph Feller. Bring your stack: every exercise runs in the tools and accounts your team already uses.
Kickoff and baseline
A stack review, a 4D self-check and a baseline of time spent on target tasks. Each participant picks two or three real tasks to build for.
Delegation
Mapping each role's week to decide what to hand to AI, what to keep and which mode fits: automation, augmentation or agency.
Description
Giving AI context, examples, constraints and a clear definition of done. Participants build their first Project or reusable skill for ChatGPT, Claude or Gemini around a real task. This is where prompt engineering lives, taught as context design rather than tricks.
Discernment
Evaluating output, spotting confident errors and building review checklists. Anthropic's AI Fluency Index (February 2026) found only 8.7% of analyzed conversations showed users checking facts, so we drill it.
Diligence
Data rules, disclosure and accountability for AI-assisted work, tied to your AI acceptable use policy.
Capstone
Each participant demos their assets on live work, the team reviews them, and the strongest prompts and instructions go into the shared library.
Role tracks for sales, marketing, operations and customer service
Mixed cohorts share the core modules, then split into role tracks for build time.
Sales
Account research, call prep, CRM notes and proposal drafts. Typical assets: a deal-desk Project and a follow-up email assistant, with rules for customer data.
Marketing
Brand-voice drafting, content repurposing, campaign briefs and reporting summaries. Typical assets: a brand-voice Project and a reusable content brief skill.
Operations
SOP assistants, vendor follow-ups, report packs and process documentation. Workflows that should run on their own are handed to our AI automation team.
Learn moreCustomer service
Reply drafting with tone rules, knowledge-base answers, ticket summaries and escalation notes. Typical assets: a reply assistant and a knowledge-base Project.
What the team keeps
- 2–3 working AI assets per participant, in your own accounts
- A shared team prompt library, organized by role and task
- A team AI operating playbook: approved tools, review rules, where assets live and who maintains them
- A manager briefing pack for the leads who run the team day to day
- Before-and-after 4D self-checks for every participant
- A 30/60/90-day adoption plan
The manager briefing
Teams copy what their managers do. Before kickoff, Operator Intelligence briefs the managers of every participant on what their people are building, how the 4Ds work and how to review AI-assisted work. At the end, managers see the capstone demos and the adoption plan, with clear asks: protect practice time, review assets in one-on-ones and add new use cases to the playbook. For senior leaders who set AI priorities, the Executive AI Workshop is the better fit.
What we need from you
- A sponsor who owns the outcome and attends the capstone
- 8–25 participants with business-tier seats on your chosen tool
- Protected time: about three hours a week per participant
- Two or three real tasks per person, chosen before kickoff
- Sample documents for those tasks, with sensitive data removed or approved
- Managers available for the opening and closing briefings
- An admin or IT contact for accounts, sharing and connectors
How we measure bootcamp results
Baseline
Before kickoff: time on each target task, current AI use and a 4D self-check for every participant.
Post-bootcamp
At the capstone: assets shipped and tested, the repeat 4D self-check and time on the same tasks using the new assets.
30, 60 and 90 days
Active use of each asset, prompt library contributions, new use cases and time on target tasks against the baseline, reported to the sponsor.
Frequently asked questions
How much of my team's time does the bootcamp take?
Plan on about three hours a week per participant for two to four weeks, split between live sessions and lab time. Because participants build on their own tasks, much of that time produces real work.
Is this a prompt engineering workshop?
Partly. Prompt engineering is covered in the Description module as context design: giving AI the background, examples and constraints a task needs. The bootcamp goes further, into evaluating output, data rules and building assets the team keeps. Teams with custom GPTs or Gems can use build time to migrate them, following our migration guide for custom GPTs and Gems.
Which tools do participants need?
Business-tier seats on the tool your team uses, whether that's Claude, ChatGPT, Gemini or Copilot. Teams on Claude can go deeper into Projects, Skills and connectors; see Claude training for teams. Teams on ChatGPT or Gemini get the same depth through ChatGPT and Gemini training.
Can one cohort mix roles?
Yes. Core modules are shared, and participants split into sales, marketing, operations or customer service tracks for build time. Mixed cohorts often find cross-team use cases a single team would miss.
What happens after the bootcamp?
Teams usually take one of two paths: an AI Workspace Setup to turn the strongest assets into maintained, shared instances, or an automation pilot through our AI enablement practice for workflows that should run on their own.
What does a team AI bootcamp cost?
Cost depends on cohort size, length, the number of role tracks, the tools involved and in-person versus virtual delivery. We don't publish fixed prices; see how we price and request a scoped proposal.
Plan a hands-on AI bootcamp for your team
Tell us who's in the cohort, which tools you use and which tasks matter most. We'll send a scoped syllabus and proposal.
Plan a team bootcampRelated
AI Training & Fluency
All programs, from executive workshops to workspace setup.
Executive AI Workshop
AI fluency for leadership teams in a half or full day.
AI Workspace Setup
Turn bootcamp assets into maintained team instances.
Claude Training for Business Teams
Projects, Skills, connectors, Cowork and Claude Code.
AI Acceptable Use Policy Template
The data rules behind the Diligence module.
AI upskilling program
A 60–90 day program with AI champions.
Claude Code training
For participants ready to automate files, data and reports.