Glossary
The language of Lucy, in plain words.
The named ideas we use most, each defined once, with a link to where it is explained visually.
- Agency model
- You decide, design and direct; Lucy coaches; what you build does the work. Lucy never presents herself as the one doing the work and never acts on your data or systems. Learn more
- AI coach
- An AI that helps a person build their own capability and solutions, rather than doing tasks for them. Lucy is a proactive AI coach: today she coaches when you ask, and proactive coaching, where she offers help first, is planned. Learn more
- Approved path
- Using your company’s approved AI tools. Lucy coaches inside that boundary, and the aim is that using the approved tools becomes the easy way to work, so fewer people reach for unapproved ones. Learn more
- Capability stays with you
- What you learn with Lucy stays with you across jobs, tools and model generations. What you build for work stays with your organization. Both sides know it up front. Learn more
- Coaching Loop
- Lucy’s five-step cycle: observe, spot, plan, coach, celebrate and share. Learn more
- Cost collapse
- The cost and effort of turning an idea into a working solution has dropped by roughly 10 to 100 times for people who know how to direct AI (Lucy Labs estimate), so the person with the problem can now build the fix. Learn more
- Earned permission
- The right to observe real work and guide change from the inside, granted person by person because the architecture and the value make granting it rational. No software update can grant it; people have to choose to. Learn more
- Evolutionary Framework
- The order that makes AI pay off: earn trust, show value, drive adoption, build capability, scale. An order, not a menu. Learn more
- Executive Dashboard
- A leader’s view of value created on named workflows, capability trends, approved-tool adoption and unmet tool demand, built from team-level, aggregated observation. Never private prompts or individual rankings. Learn more
- First-session interview
- Lucy’s first conversation with a new person: it maps their work in their own words so coaching is personal from day one. Learn more
- Four locks
- Why AI value stays stuck in most companies: leaders can’t see how work really happens; the people who know the work best aren’t allowed to build; they aren’t able to; and they aren’t rewarded for it. The locks only open together. Learn more
- Pattern
- A reusable recipe from a successful solution: the shape of the work, tools, process, governance and lessons learned, attributed to its builder. Never the builder’s private prompts or content. Learn more
- Personal Coaching Plan
- A plan built from the first-session interview, observed workflows and a skill baseline: priority workflows, milestones and the smallest useful first thing to build. Yours to see and change. Learn more
- Shadow AI
- AI tools people bring to work without approval. Microsoft’s 2024 Work Trend Index found 78% of AI users bring their own AI tools to work. It is what unmet demand looks like. Learn more
- Signal + Content → Context
- How Lucy turns observation into understanding: signal (events, app and window context) and content (what is on screen) become context, the understanding coaching runs on. Learn more
- Solution Builder
- Someone who has moved from consuming AI to designing and building AI-supported solutions for their own work. Builders think in solutions (always be prepared for meetings) rather than tasks (prepare for this meeting). Learn more
- Stage honesty
- Being clear about what is available today and what is still coming. We keep that list in one place. Learn more
- Team-size gating
- A privacy rule for what leaders see: team-level patterns appear only for teams large enough that no one can be singled out. No admin can switch it off. Learn more
- Three flywheels
- Why each win makes the next one cheaper: adoption (wins create social proof, which creates more adoption), investment (visible results become budget, and budget funds the next thing people build) and learning (with each win, Lucy learns how to help people build better solutions, adapt to different preferences and understand different industries, so she coaches better). Not the same as the three reinforcing loops. Learn more
- Three reinforcing loops
- How customer progress strengthens Lucy Labs: customer value, product learning and company growth, each with its own evidence and owner. These are different from the three flywheels. Learn more
- Tool Gap Intelligence
- The loop that turns unmet tool needs into decisions: gaps are logged, requests are pooled across the organization, a threshold triggers a business case, and leaders decide on evidence. Learn more
Questions
Questions about the terms
Is Lucy a leadership or sales coaching tool?
No. Lucy is an AI coach for building AI capability: she helps people learn to use AI on their own work and build their own AI solutions. She isn’t an executive or leadership coaching chatbot, a sales-call analyzer or a course. What people build with her keeps working after the coaching ends.
What’s the difference between the Coaching Loop, the Evolutionary Framework and how Lucy learns?
They work at different scales. The Coaching Loop is the five steps Lucy repeats for each win: observe, spot, plan, coach, celebrate and share. The Evolutionary Framework is the five stages of a rollout, in order: earn trust, show value, drive adoption, build capability, scale. How Lucy learns has five steps of its own: observe, interpret, confirm or correct, measure and reuse carefully.
What’s the difference between the three flywheels and the three reinforcing loops?
The three flywheels explain why each win makes the next one cheaper inside a customer: adoption (wins create social proof), investment (visible results become budget) and learning (each win makes Lucy a better coach). The three reinforcing loops explain how customer progress strengthens Lucy Labs as a company: customer value, product learning and company growth, each with its own evidence and owner.
Is a Solution Builder the same as a citizen developer?
They overlap, but they aren’t the same. A citizen developer usually builds apps on a low-code platform. A Solution Builder is anyone who has moved from consuming AI to designing and building AI-supported solutions for their own work, in whatever shape fits: a prompt, a workflow, a script or an agent with clear limits. The defining habit is thinking in solutions (“always be prepared for meetings”) rather than tasks (“prepare for this meeting”).
What’s here today vs. what’s coming
Some features described on this site, like proactive coaching, are still being built.
