Ruby documentation

Start with the work

Build useful AI habits around the work your team already does. These guides explain how to choose a starting point, teach your people, and turn an experiment into a repeatable workflow.

Software access cards beside connected workflow blocks

Licenses are not a strategy

Turn access to AI into a clear plan for how your firm works.

Start here

Choose a task, practice, review, and improve

Find your starting point

Choose one recurring task with an owner and a measurable result.

Practical guide

Find your starting point

Choose a recurring task where employees spend time gathering information, drafting a first version, checking an output, or repeating an established process. Describe what a good result looks like before choosing a tool.

Name the task and the person who owns it.
Record the time, quality, and review effort it takes today.
Practice on an approved example.
Review the output and improve the instructions.
Decide whether the workflow is useful enough to repeat.

Next steps

Read Licenses are not a strategy, or talk with Ruby about the work your team wants to improve.

Browse by topic

Get startedFind a useful first workflow and a practical starting point for your firm.ConceptsUnderstand the tools, skills, workflows, and decisions behind useful AI adoption.AI TrainingHelp your team build useful AI skills around the work they already do.AI Strategy & ConsultingChoose where AI belongs, what to prioritize, and how to measure progress.AI EngineeringPlan agents, integrations, and systems with clear permissions and human review.Organizing AI in your firmMake ownership, shared instructions, and ongoing AI support easier to manage.By industryFind guidance for applying AI to your industry’s information and decisions.ComparisonsCompare ways to bring AI capability into your firm before committing.PlaybooksFollow practical steps for testing AI workflows and repeating what works.Field notesExplore documented lessons from practical AI work.

All guides

AI training vs AI consulting vs AI engineering: which does your firm need?
A plain decision guide for leaders choosing between AI training, AI strategy consulting and AI engineering, with the symptoms that point to each and the order that usually works.
Build vs buy: how to decide on AI tools for your firm
A five-rung ladder for AI build-vs-buy decisions, from using existing tools better to building custom systems, plus the ownership questions to answer before you build anything.
Custom AI agents for your business: what they are and who should build them
A plain-English explanation of AI agents for operators, how to tell whether a workflow is a good first agent, and what to look for in whoever builds it.
Hiring an AI lead vs an outside consultant vs a consulting team
A fair comparison of four ways firms get AI help, an in-house AI lead, a solo consultant, a specialist consulting team or a large consultancy, with an honest decision guide.
How to keep client data safe when using AI
A practical approach to AI data safety for firms. Classify your data, map it to approved tools, ask vendors the right questions, and put guardrails around agents.
How to run an AI pilot that doesn't stall: a six-week playbook
A week-by-week playbook for running an AI pilot at a firm, with a one-page pilot charter, clear success criteria and a scale, adjust or stop decision at the end.
How to train non-technical employees to use AI
A practical approach to AI training for non-technical teams, built on small role-based groups, real tasks, four durable habits, and follow-up that turns a session into everyday practice.
Where should your firm start with AI?
Start with the work, not the tools. A simple workflow inventory and scoring method to find the two or three places AI is most likely to help your firm first.
Why giving your team ChatGPT licenses isn't an AI strategy
Licenses give people access to AI. A strategy decides which work changes, who learns what, and what gets built. Here is the difference and a simple way to start.
Why your team isn't using the AI tools you paid for
AI adoption usually stalls for five fixable reasons (relevance, skill, permission, time and proof). A diagnostic for each, plus a 30-day plan to restart usage.