Enable AI at a strategic and tactical level across Eskwelabs by building the knowledge infrastructure that teams—and AI agents/workflows—use to do high-quality work. Fellows in this track translate messy institutional context into clear, usable artifacts: strategy memos, business plans, FAQs, operating docs, and “AI-ready” reference materials. They also develop core prompts and playbooks that make internal AI systems more reliable, aligned, and useful.

This is a generalist track: you’ll sit at the intersection of product thinking, operations, writing, and AI enablement.

Your work will contribute to the following outcomes:
1. Faster, higher-quality execution across teams because critical context is documented, structured, and easy to retrieve.
2. More reliable AI agents and workflows because prompts and reference materials are grounded in accurate organizational knowledge.
3. Reduced bottlenecks on leadership and specialists through self-serve documentation (FAQs, how-to guides, decision records, templates).
4. Better strategic clarity on programs and initiatives through concise memos, plans, and decision-ready briefs.
5. A scalable internal knowledge system where future fellows can build without re-learning everything from scratch.

A. Strategy Memos & Decision Briefs
Write crisp memos that clarify a problem, outline options, and recommend a path forward. These documents should be usable by humans and AI agents as reference.

‍Example: A 10 page memo proposing a new initiative: objective, target users, success metrics, risks, operating plan, resourcing needs, and phased rollout. This memo will then be referenced by AI agents and teams members when doing their work.

B. Business Plans & Initiative Playbooks
Develop practical business plans or initiative plans that teams can execute against. Focus on assumptions, timelines, dependencies, and decision points.

‍Example: A business plan for a new Eskwelabs offering including positioning, target audience, delivery model, unit economics assumptions, and go-to-market plan.

C. Program Documentation & FAQs
Document existing programs, processes, and policies into structured references (FAQs, runbooks, SOPs, one-pagers). These become the knowledge backbone for new team members and AI workflows.

‍Example: An onboarding FAQ for a specific team that answers common questions (submission formats, quality standards, turnaround expectations, escalation paths), plus a process map.

D. Core Prompts for AI Systems
Design and test foundational prompts for AI agents/workflows used internally—especially “management support” copilots that answer questions, guide work, and provide consistent standards.

‍Example: A prompt pack for an internal “EIF Helpdesk Chatbot” that can answer: how to complete common tasks, what “good” looks like, where to find templates, and when to escalate to a human.

E. Knowledge Infrastructure Design
Structure knowledge so it stays usable: naming conventions, doc templates, tagging, update cadence, and source-of-truth rules.

‍Example: A knowledge architecture for a team space (categories, doc types, ownership, update rules) with schemas and organizational diagrams.

This is a task-based, production-oriented strategy and enablement role. Work arrives as ambiguous questions (“What is our plan?”, “How does this work?”, “Can AI help with this?”). Fellows turn ambiguity into usable clarity through structured writing, good judgment, and strong information design. AI is used as a productivity multiplier (drafting, outlining, synthesis), but high performance requires careful verification, crisp logic, and an editorial bar that makes documents genuinely useful in real work.

This track is a strong fit if you like making complex things understandable and operational—and you have (or want to build) strong writing craft. Because the core outputs are memos, FAQs, and prompt packs, success here depends heavily on clear thinking expressed through clear writing.

You might come from backgrounds such as:
-Writing & Communication: journalism, creative writing, technical writing, copywriting, communications, English/literature, speech/forensics, debate, or editorial roles.
- Business / Management / Economics: strategy, operations thinking, structured decision writing.
- Policy / Social Sciences / Research: synthesis, clarity, documentation discipline.
- Product / UX / Education: user-centered knowledge design, playbooks, guidance, learning documentation.
- Tech-curious generalists: people who want to work close to AI enablement and systems without being primarily engineers.

What matters most is your ability to turn ambiguity into usable clarity: strong organization, good judgment, and writing that makes other people (and AI systems) more effective.

- Strategy memo writing and decision support
- Business planning and initiative design
- Knowledge architecture and documentation systems
- Prompt engineering for organizational use cases
- AI enablement and change management basics
- Turning messy context into reusable templates and references

Traits
- Generalist mindset: comfortable moving across topics and teams
- Strong writing and editing instincts; can be concise and unambiguous
- Logical thinking and structured reasoning
- Systems thinking: turns one-offs into reusable knowledge assets
- Practical creativity: can imagine use cases, questions, and failure modes for AI support tools

‍Skills
- Writing: memos, plans, FAQs, SOPs, and clear internal documentation
- Product thinking: user needs, success metrics, scope/constraints
- Prompt engineering: crafting reliable prompts, building test sets, iterating based on failures
- Research & synthesis: quickly learning a domain and producing accurate summaries
- Basic AI literacy: understanding what LLMs are good/bad at and how to design guardrails

- Product Manager / Product Ops: playbooks, enablement, scalable execution

- Strategy & Operations: planning, resourcing, operating models

- Chief of Staff (early-career path): decision support, cross-team alignment

- AI Program Manager / AI Enablement Lead: org-wide adoption, standards, and tooling

- Knowledge / Documentation / Enablement Lead: building institutional memory systems

Tasks are listed in a shared tracker with scope, owner, deadline, and expected deliverable format (memo, FAQ, prompt pack, playbook). Fellows claim tasks, produce artifacts, and submit links to final documents along with a short summary of what changed and why.

Fellows work independently but communicate proactively when context is missing or decisions are needed. Submissions should include:
- The artifact (doc / prompt pack)A short “how to use this” note
- Any assumptions, open questions, and suggested next steps
- Review outcomes are Approved or Revisions Needed, with clear editorial feedback.

You will report to the CEO’s office of Eskwelabs, who will meet with fellows twice a month to review outputs, align on priorities, and raise the bar on clarity, usability, and AI-readiness of the knowledge base.

You will be judged on whether your work makes the organization move faster with less confusion. Strong performance looks like:
- Documents people actually use (clear, scannable, decision-ready)
- Prompts that reliably help internal AI systems do better work
- Knowledge assets that stay maintainable (good structure + ownership + update logic)