Knowledge Management & Curation - The Thinking Lens (Page 2)

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Knowledge Management & Curation

A collection of 38 posts

Harnessing the IKEA Effect for AI-First Products

Enterprise Software & UX

Harnessing the IKEA Effect for AI-First Products

We love what we build, even when imperfect. That’s the IKEA Effect: effort creates value. In SaaS, let users build: templates, safe drafts, sharing, audit trails, so they feel ownership. Co-create with AI as copilot or constructor. Earn loyalty with portability, not lock-in. Show ownership clearly.

The “One Prompt” Fallacy: Why Voice, Not Prompts, Wins

Prompt Engineering & AI Instructions

The “One Prompt” Fallacy: Why Voice, Not Prompts, Wins

Stop chasing magic prompts. The fix is not a template. Define your problem, then train AI on your own writing to build a voice profile. Feed real samples, extract patterns, and revise in your style. Protect your data. Do not copy tone. Engineer sharper thinking, not generic outputs. Your voice wins.

Stop Hiring PM “Flavors.” Hire Keystones.

Cross-functional Collaboration

Stop Hiring PM “Flavors.” Hire Keystones.

Stop hiring PM “flavors” like “AI PM” or “Growth PM.” Hire Keystone PMs who integrate design, engineering, data and GTM, frame real problems, learn fast, and influence without authority. Durable capabilities compound; trends fade. Hire for outcomes, not buzzwords. Build missions, not titles.

LLMs, Longform, and Low-Code: What Worked (and Broke) in Real-World Tests

Prompt Engineering & AI Instructions

LLMs, Longform, and Low-Code: What Worked (and Broke) in Real-World Tests

LLMs aid with tight, scoped tasks: cut redundancy, multipass feedback, summary maps, brainstorming. But they struggle with long inputs, continuity and consistent voice. Useful for scaffolding and agents, not autopilot. Keep humans on structure, logic and style. Use models to spot patterns.

The Invisible Decline of Reasoning

Data-Driven Decision Making

The Invisible Decline of Reasoning

Roadmaps update and backlogs move, yet when asked "Why are we building this?"—silence. That’s reasoning decay: motion without logic. Framing is shallow, strategy drifts, learning unused, priorities wobble, judgment fades, process over purpose. The backlog turns into noise.

Where Did the Thinking Go?

Data-Driven Decision Making

Where Did the Thinking Go?

Rituals are in place, delivery is steady, yet no one can answer why we're building. The logic layer has faded: discovery doesn't shape backlogs, strategy doesn't guide tradeoffs, output eclipses outcomes. Reconnect intent to action by making reasoning visible in every decision. Think with intention.

The Hidden Cost of Framework Thinking

Human-AI Collaboration

The Hidden Cost of Framework Thinking

We live in a world of playbooks and quick frameworks. They help, but when they replace thinking, contribution dies. Stop blind execution. Ask: Is this valid for my context? Original thought is the value. Don't run someone else's logic. Slow down, think, and make it yours. Choose thinking over hacks.

It’s a Great Metaphor, But It’s Still Not an MVP

Data-Driven Decision Making

It’s a Great Metaphor, But It’s Still Not an MVP

That skateboard-to-car graphic isn't an MVP. It's a delivery roadmap. MVPs are for learning: the smallest test to validate riskiest assumptions. In complex orgs, call things correctly: MVP for learning, POC for feasibility, V1 for shipping. Clarity prevents waste and misaligned bets. Validate first.

Backlog as Mirror: Why Product Teams Get Stuck

Data-Driven Decision Making

Backlog as Mirror: Why Product Teams Get Stuck

Shipping continues but outcomes stall. The backlog records motion without meaning. When strategic reasoning fades, dysfunction shows up: contextless items, reactive prioritization, discovery without decisions. In healthy teams, the backlog is a reasoning space that links work, trade-offs, and goals.

Ghosts in the System: Why Naming Dysfunction Isn’t Fluff. It’s Infrastructure

Organizational Structure & Governance

Ghosts in the System: Why Naming Dysfunction Isn’t Fluff. It’s Infrastructure

Naming a dysfunction doesn’t solve it, but it makes it visible. Without shared language, teams treat symptoms, not systems: feature soup, strategy drift, prioritization theater. Name it to align, track patterns, and move from vague complaints to structural diagnosis and action.

The Hidden Skill That Unlocks Generative AI

Prompt Engineering & AI Instructions

The Hidden Skill That Unlocks Generative AI

Generative AI’s power is tied to our ability to communicate with clarity; its output reflects the input. I saw this with my 8-year-old daughter: her vague prompt for a unicorn yielded generic results, but a detailed one created what she imagined. AI is a mirror.

Discovery Debt – When your product forgets faster than it learns.

Data-Driven Decision Making

Discovery Debt – When your product forgets faster than it learns.

You talked to users and did research, but the system forgets. Discovery Debt is rotting context: insights learned, then lost. Work ships, learning vanishes. Fix it by wiring memory into decisions and backlogs so every ticket ties to evidence, assumptions, and outcomes. Make learning visible Always

We’re Delivering Faster. So Why Are We Falling Behind?

Data-Driven Decision Making

We’re Delivering Faster. So Why Are We Falling Behind?

Teams ship faster yet impact is unclear. Backlogs balloon into strategy placeholders, idea dumps, and priority battlegrounds. Velocity masks drift. Treat the backlog as a decision system tied to strategy, discovery, and outcomes, not a queue.