From scattered AI use to shared operating standards
A structured engagement that moves your organization from informal AI experiments to safe, repeatable adoption — starting with an AI Adoption Review and ending with a working AI Playbook.
The AI Sprint Method: From Review to Playbook
A structured path for companies that want practical AI adoption, not scattered experiments. We start with diagnosis, collect team-level evidence, train people progressively, align leadership, set up working environments, and leave behind a usable AI Playbook.
- Step 1
AI Adoption Review
Clarify the current state of AI adoption, visible risks, blocked workflows, and the highest-value opportunities.
- Step 2
Systemic Data Collection
Collect team-level signals: current tools, internal champions, success and failure stories, automation targets, and data-sensitivity concerns.
- Step 3
Workshop 1 — Foundations and Safe Usage
Build a shared baseline across technical and non-technical teams: terminology, principles, tools, data classification, approved environments, and live examples.
- Step 4
Workshop 2 — Leadership Alignment
Agree on tools, licenses, data-cleaning procedures, access rules, ownership, and practical governance standards.
- Step 5
Workshop 3 — Environment Setup and Practical Skills
Set up environments, connect tools, use out-of-the-box skills, and adjust skills live around real team workflows.
- Step 6
Workshop 4 — Advanced Usage and Managed Automation
Introduce cron jobs, managed agents, repeatable automations, and real-time workflows for more complex operational tasks.
- Step 7
Post-Training AI Playbook
Provide a practical internal playbook with approved tools, data rules, workflow templates, automation opportunities, responsibilities, and next steps.
What companies leave with
The sprint produces usable operating capability, not just education. Every engagement ends with the same concrete outcomes in place.
A usable AI working environment
Configured around the team's real workflows and ready for daily use — not a sandbox or a demo.
Multiple out-of-the-box automations
Practical automations the team can use immediately across research, reporting, document processing, internal coordination, customer support, operations, and sales support.
The ability to create and adjust skills
Team members can build reusable AI instructions, workflows, and skills — and update them as processes change.
A clear approach to sensitive data
Explicit rules for confidential, customer, employee, financial, and strategic data — including access control, anonymization, and human review.
A team-level AI adoption leader
At least one internal person who understands the system well enough to guide teammates, spot new automation opportunities, and keep the workflow improving after the sprint.
An AI Playbook
A practical internal guide with working examples, rules, templates, prompts, automations, and operating principles — written for the company's actual team and workflows.
Go deeper when the Sprint reveals a bigger need
Some teams need additional support after the Sprint — for private infrastructure decisions, AI cost control, or company knowledge systems. These are separately scoped, only when justified.
See advisory servicesPrivate AI Infrastructure Advisory
Assess private workspace, model-serving, and hosting options before committing budget.
Token Cost Optimization
Reduce avoidable spend across models, prompts, retrieval, and routing.
Knowledge Base Consulting
Design a company-wide knowledge strategy your AI tools can actually rely on.
Start with an AI Adoption Review
Map the current state of AI adoption in your company and identify the safest, highest-value next steps.
Book an AI Adoption Review