anotb/forward-deployed

Help people develop, use and spread valuable AI work through existing products or deployed workflows. Use for hands-on team enablement, reusable working practices, adoption diagnosis, changed responsibilities or expansion across teams and organizations.

Explore current products, enterprise systems, models, developer interfaces, and available host tools to discover and test capabilities that improve an AI solution. Use for open-ended technical exploration, platform or model choices, buy/adapt/build decisions, and finding a maintainable way to deliver a specific workflow.

Design and improve the information an AI workflow uses to reason, investigate, and act. Use for enterprise knowledge and document interpretation, retrieval, long-context work, task memory, changing business state, or failures caused by missing, misleading, inaccessible, or poorly organized context.

Investigate enterprise work, systems and expert judgment to discover what an AI solution should change. Use for operator research, technical discovery, workflow redesign, workshops or diagnosis of an inherited process.

Develop an enterprise AI engagement and customer point of view. Use for technical pursuits, first conversations, opening decks, engagement proposals, collaborative scoping and sponsor alignment, or adapting commitments when the work changes.

Design and investigate how an AI workflow operates across enterprise systems, business objects, identities, and teams. Use for integration choices, engineering contracts, domain meaning, asynchronous business actions, or diagnosis of a solution that reads correctly but cannot reliably complete work.

Test whether an AI workflow produces useful business outcomes, diagnose where it fails, and improve the right part of the system. Use for feasibility proofs, candidate comparisons, release decisions, regressions, production incidents, and disappointing adoption or economics.

Create a compelling FDE engagement deck, solution walkthrough, executive decision memo or technical narrative. Turn substantive analysis into a clear point of view and an artifact the audience can use.

Turn field-built AI capabilities and deployment learning into reusable products, platform improvements, or delivery practices. Use to choose durable abstractions, separate customer variation from common behavior, improve deployment economics, and move supported capability beyond a bespoke engagement.

Advance a forward-deployment assignment for a team or organization, or choose the relevant Forward Deployed skill. Use when the requested result spans customer understanding, AI-enabled work, opportunity and solution design, engineering handoff, delivery, adoption or value.

Synthesize meetings and notes from an AI adoption or forward-deployment engagement into findings, decisions, solution implications and useful follow-through. Use for engagement readouts, discovery synthesis, use-case updates and preparation for the next customer or delivery conversation.

Invent and develop valuable AI-enabled workflows and services for teams and organizations. Use to explore what AI could make possible, expand a narrow automation idea, turn discovery into concrete opportunities, or develop a chosen idea into a credible first experience.

Design a coherent AI-enabled workflow or service, from user experience through reasoning, context, tools, state and operation. Use for whole-solution architecture, a consequential design tradeoff, an inherited solution redesign or an engineering-ready specification.

Coordinate an enterprise AI deployment and equip the engineers or coding agents implementing it. Use for engineering briefs, customer and partner delivery, dependencies, changed requirements, credible forecasts, rollout, technical transfer, or recovery of a stuck engagement.

Choose what to fund, test or build first among AI and workflow opportunities. Use for investment recommendations, pilot selection, use-case portfolios, shared-capability decisions and sequencing work against available delivery and operating capacity.

Build and test the economic case for an enterprise AI capability, investment, or expansion. Use for new services, decision quality, expert capacity, operating economics, and value realization when the architecture and changed work determine the return.