Update to Forward Deployed

{
  "description": "Help an enterprise put AI to work through broad fluency, role-specific practice, workflow redesign, and reusable methods. Use for enterprise chat, coding and workspace adoption, AI training, frontier-user programs, stalled use, and expansion across teams.",
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  "name": "adoption-and-scale",
  "skill_md_contents": "---\nname: adoption-and-scale\ndescription: Help an enterprise put AI to work through broad fluency, role-specific practice, workflow redesign, and reusable methods. Use for enterprise chat, coding and workspace adoption, AI training, frontier-user programs, stalled use, and expansion across teams.\n---\n\n# Adoption and scale\n\nHelp people work with AI and develop better methods. Combine broad access and practical fluency with focused workflow redesign. Enterprise chat, coding tools, shared workspaces, and integrated agents can each serve the goal.\n\nInspect the available products, context access, sharing, and execution surfaces. Try demanding tasks with employees and produce working material they can use. A training plan alone is insufficient when the assignment is to help a team adopt the capability.\n\n## Build fluency through consequential work\n\nTeach people to commission an outcome, assemble useful context, delegate substantial work, inspect the result, and improve the approach. Practice with their unfinished tasks: an analyst investigates competing explanations, a product manager builds a prototype, an engineer delegates a migration, or a team turns scattered evidence into a maintained work product.\n\nDemonstrate a complete task, including a correction or new constraint, then have participants apply the method independently. Show what deeper delegation makes possible: more alternatives, new analyses, executable artifacts, and work that previously needed several specialist handoffs. Teach the domain judgment needed to use the result alongside the product mechanics.\n\nMatch support to role and experience. Broad introductory practice gets people started; focused sessions develop demanding methods. Read [workforce practice](references/workforce-practice.md) when designing hands-on enablement or spreading an employee's invention.\n\n## Find and spread frontier practice\n\nLook for employees already doing useful, surprising work with AI. Study their task framing, context, tool use, corrections, and finished artifacts. Pair them with domain experts and colleagues who can test the method on different work. Give them time and practical support to develop it.\n\nMake the working method reproducible: its trigger, outcome, maintained context, tool access, task state, and how people inspect and complete the work. Package these in a skill, workspace, tool, or reusable artifact. Have someone outside the original team complete a task without its author's constant help, then improve the method from that attempt.\n\nMake discovery easy through working demonstrations, role communities, and a small maintained collection of useful assets. Give promising ideas a route to engineering when repeated context assembly, durable work, or system actions justify integration. Keep local experimentation open and retire methods that no longer help.\n\n## Redesign the work around the capability\n\nFollow a task from demand to accepted outcome. Identify what AI can now complete, which handoffs can disappear, and where people should spend more judgment. A team able to investigate, analyze, and prototype in one workspace may change how it scopes projects and involves specialists. Develop that operating possibility with the people who do the work.\n\nResolve the redistributed effort. Faster preparation can leave reviewers doing more reconstruction; automatic analysis can create more findings than a team can pursue. Adjust the experience, responsibilities, work admission, and management expectations together. Remove redundant work once the new path supports its purpose.\n\nWhen use stalls, observe a willing skilled user, a newcomer, and a bypassed case. Distinguish capability defects, inaccessible context, awkward handoffs, missing competence, and conflicting incentives. Choose the intervention at the cause. Read [workflow adoption](references/workflow-adoption.md) for a worked product-research redesign.\n\n## Learn from outcomes and new capability\n\nUse samples of completed work alongside aggregate usage to see what is improving. Examine task completion, quality, elapsed time, total labor, new coverage, and whether people can use and adapt the method independently. Usage describes reach and behavior; its value depends on the task.\n\nInclude the experiments people can now attempt and the work they could not previously do. Test promising claims against comparable work, accounting for task mix and concurrent changes. A useful change may improve depth or coverage before it reduces cost.\n\nChoose the next team or task for the value it can gain and what it will teach about reuse. Read [expansion](references/expansion.md) when moving a practice or deployed capability into a different population.\n\n## Leave the organization able to improve it\n\nGive domain experts and technical owners a practical way to improve the method. Put corrections into the maintained context, skill, tool, or workflow that caused the problem. Have the team onboard a colleague, make such a change, and use it on the next task. Keep current product discoveries flowing into practice.\n\nDeliver the working session, shared asset, changed workflow, or next deployment. Leave people able to do more and develop the next method themselves.\n"
}