AI Productivity for Teams
We help companies safely adopt AI by educating employees, setting up shared workspaces, designing practical use cases, and creating company-specific AI playbooks.
The reality
AI is already inside your company — but probably without structure.
Shadow AI and unclear tool usage
Employees use whatever AI tool they can find, with no shared standards or visibility.
Sensitive data in unmanaged workflows
Customer, financial, and strategic data ends up in public chatbots with no policy in place.
Low adoption outside power users
A handful of enthusiasts get real value; most of the team stays hesitant or blocked.
Scattered knowledge and duplicated work
Answers live across five tools, so teams keep rewriting what already exists.
Unclear cost, value, and ownership
Multiple subscriptions, no measurement, and nobody accountable for outcomes.
The 15-day journey
Learn to build AI into the way your company works
A structured education and enablement program that helps employees and leaders move from isolated AI use to a shared, systemic way of working.
- 01Days 1–3
Review and collect
We begin with a focused review and collect evidence from across the team: how people currently use AI, where they get stuck, which practices already work, and where sensitive data or unclear ownership creates risk.
- Current AI usage and team experience
- Successful, failed, and blocked workflows
- Internal champions and adoption barriers
BenefitThe Sprint starts from the company's real experience without turning into a large-scale audit.
- 02Days 4–6
Build a shared foundation
Employees develop a common understanding of AI terminology, capabilities, limitations, data sensitivity, approved environments, and responsible human oversight.
- Practical AI foundations
- Safe data-handling principles
- Shared terminology and working standards
BenefitTechnical and non-technical employees can make better AI decisions using the same principles.
- 03Days 7–9
Align leadership
Leadership agrees on how AI should be introduced and governed: which environments are appropriate, how access and ownership should work, and where human review remains necessary.
- Tool and environment principles
- Access, ownership, and responsibility
- Practical governance decisions
BenefitThe team gains clear direction and leadership alignment.
- 04Days 10–12
Learn systemic AI work
Through practical workshops, employees learn how to move beyond isolated prompts and organize AI work into reusable context, skills, workflows, and appropriately managed automations.
- Reusable context and instructions
- Skills and repeatable workflows
- Managed agents and automation principles
BenefitEmployees learn how to create and improve a systemic AI workspace themselves instead of depending on externally maintained solutions.
- 05Days 13–15
Establish the operating model
The lessons, decisions, examples, responsibilities, and next steps are consolidated into a company-specific AI Playbook.
- Approved principles and data rules
- Working patterns and practical examples
- Ownership and continued learning
BenefitThe company leaves with a shared operating model and the internal capability to keep developing it after the Sprint.
Use cases
Eight high-leverage packs, one per team
Each pack ships with curated skills mapped to the real workflows of that function.
Marketing
Brand voice, positioning, ICP and angle-finding skills so content stops being generic AI slop.
Sales
Qualification, call summaries and win-loss extraction — reps reclaim selling time, CRM stays clean.
HR & People Ops
Onboarding, compliance and people analytics so HR scales without absorbing all the operational load.
Product Management
Problem clarity, outcome definition and experiment design — roadmaps become testable bets.
Finance & Operations
Reconciliation, SaaS metrics and data-quality skills wired to your sources for faster reporting cycles.
Customer Support
Triage, de-escalation and handoff detection so response quality stops depending on who's on shift.
Knowledge & Org Memory
SOPs, meeting processing and enterprise search — institutional knowledge survives team changes.
Data & Business Intelligence
EDA, metric reconciliation and exec summaries so analysts explain change instead of cleaning data.
AI Playbook
Every company needs an AI Playbook
A single source of truth for how your company uses AI — what's allowed, what isn't, and exactly how to do it. Tailored to your tools, data, and culture.
- Terms and taxonomy
- Data classification and sanitization
- Rules of engagement
- Approved AI tools
- Tailored examples
- Prompt templates
- Anti-patterns
- Incident response
Optional advisory services
Optional services after the foundation is clear
Some teams need deeper support after the Sprint: how to stay sovereign, reduce costs, or manage company context better.
Private AI Infrastructure Advisory
Guidance on private workspaces, model-serving, and self-hosted or managed architectures for teams with strict privacy or control requirements.
Token Cost Optimization
A review of model choices, prompts, retrieval, and routing to cut avoidable AI spend without losing output quality.
Knowledge Base Consulting
Structure, ownership, and source-of-truth rules for company knowledge so AI tools can actually produce reliable answers.
Ready to take AI seriously inside your company?
In 30 minutes, we'll show you where AI fits in your workflows and what your team needs to adopt it safely.
Book an AI Adoption Review