My 17 Minute AI Workflow To Stand Out At Work

Main technological idea / workflow

The video proposes an “AI workflow to upgrade knowledge work” in 2025 by treating AI primarily as an input-improvement system, not a “write-for-me” system.

The core model is a knowledge-work loop:

1. Input (data/knowledge/information you gather)
2. Processing (turns input into output)
3. Feedback (use output to improve future input)

Over time, this spirals upward in quality.

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Key critique of common AI use

• Simply asking generic questions to tools like ChatGPT tends to produce average/broad results because LLMs are limited by average internet-level training data.
• The speaker argues this leads to “garbage in, garbage out”—even good prompting can’t fully fix weak or low-quality source inputs.

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Proposed solution: use academic research as “high-quality input”

Instead of relying on SEO blogs or generic advice, the workflow is to:

• Pull peer-reviewed, well-cited academic papers
• Convert them into actionable business frameworks for teams (positioned as being like becoming “HBR for your team”)

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3-tool workflow (explicit tools and roles)

1) Elicit — academic paper discovery

• Used to search for papers matching a question.
• The free tier provides:
  • a summary of top papers
  • lists of relevant papers
• The speaker sorts results by most cited.
• Output emphasis: find peer-reviewed, well-cited academic sources (often via links to Semantic Scholar).
• If access is locked, the speaker searches for an available PDF elsewhere (e.g., publisher sites or Google).

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2) NotebookLM (Google) — grounded synthesis over uploaded sources

The speaker emphasizes a key feature: unlike ChatGPT/Claude, NotebookLM doesn’t rely on internet knowledge; it uses only uploaded sources.

Workflow steps:

• Upload one PDF (and later multiple PDFs)
• Generate an auto table of contents (used as a “map” first)
• Ask suggested questions to get a gist without reading everything
• Use click-through citations to locate where claims appear in the paper
• Combine multiple papers so the assistant answers using both sources, reducing dilution from generic web search results

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3) Claude — turn research summaries into deliverables

After extracting conclusions via NotebookLM, the speaker uses Claude to produce:

• A more detailed business plan (example: a 12-month, three-phase, four-module plan)
• An 80/20 leadership-focused action list (example: task design optimization, authority distribution, contextual support)
• A practical design matrix/framework (example: goal clarity vs. interdependence levels)

Limitation noted: if you ask Claude too broadly without strong upstream inputs, outputs become buzzword-level advice (e.g., SMART, communicating feedback, “having clear goals”) rather than usable structure.

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Example scenario: improving underperforming teams

The video provides a concrete example:

• Problem: teams are underperforming but staffed with bright people who seem unmotivated/complaining.

Comparison:

• Generic search (“How to improve team performance at work?”) yields empty broad recommendations (goals, open communication).
• The academic workflow yields more specific actionable levers, such as:
  • task meaningfulness
  • autonomy
  • team coordination
  • effective leadership

Then:

• Claude translates the synthesized conclusions into a structured plan and a matrix to assess task-design choices.

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Emphasized product capabilities / “why this works”

• Elicit: improves inputs by finding credible academic sources.
• NotebookLM: improves reliability by grounding answers in only your uploaded PDFs, with citation navigation.
• Claude: improves outputs by converting research-derived points into business-ready plans and frameworks.

Overall claim: higher-quality underlying inputs produce non-generic outputs that are harder for others to match.

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Main speakers / sources (as referenced in the video)

Speaker (main): The unnamed creator/host describing the workflow.

AI tools used:

• Elicit
• Google NotebookLM
• Claude

Human sources / publishers mentioned:

• Harvard Business Review (HBR)
• McKinsey Quarterly
• MIT Sloan Review
• Academic work referenced via “meta-analytic review” attributed to Greg and War (exact titles not provided in subtitles).