Amodiovalerio Verde

VP of Product–Strategic Operator–Systems Thinker with experience spanning startups, scale-ups, and enterprise SaaS. I specialize in bridging the gap between product, business and technology, leading teams to build scalable systems. Views are my own.

Prompt Engineering & AI Instructions

From “Don’t Do” to “Do Well”: Designing Instructions That Make AI Useful

Instruction design turns a chaotic GPT into a reliable tool. Replace a 'don't do' list with a clear operating model: retrieve verbatim from the knowledge base, format consistently, handle exceptions, and test like software. Positive, explicit rules cut variance and improve UX.

Prompt Engineering & AI Instructions

Your Copy is Correct. It’s Also Forgettable.

Generative AI boosts efficiency but flattens voice. When tools push language toward the average, brands and leaders lose distinctiveness. Use AI for polish, not personality. Document tone, keep signature quirks, and build private style models to protect authenticity at scale. Keep your edge.

Process Automation & Efficiency

The Verde Archive bot

LinkedIn buries my old posts, so I built a lightweight AI lookup. Titles and links sit in JSON with ChatGPT-made tags. A custom GPT does prompt-based retrieval. Not full RAG, just fast recall. Next: automate updates via API. Guardrails keep it a helper, not a clone.

AI Governance & Risk Management

When Words Lie: The Invisible Risk in Your Product

Words shape trust. Product language isn’t neutral: it trains mental models. “Friend” now means barely acquainted; “I’m thinking...” makes users over-trust AI. Audit copy, cut unearned metaphors, and test for over-trust. Tech runs on tokens; products run on words. Clarity is a trust contract.

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.

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.

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.

Data-Driven Decision Making

Stop Doing Discovery Karaoke

Match discovery to risk, not habit. Most teams default to interviews, tests, and betas, which miss blind spots. Treat Compliance & Ethics as a first-class risk. Build a simple map: risk → stage → methods. Use AI to speed clustering, summarization, and signal detection. Focus on uncertainty reduction

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.

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.

Prompt Engineering & AI Instructions

"Ahahaha! Busted! You used an em dash—must be ChatGPT!"

Ahahaha! Busted. You used an em dash, must be ChatGPT. I learned about hyphen, en dash, and em dash only recently. I asked ChatGPT, checked Treccani and Merriam Webster. I use AI a lot, but the voice is mine. Tools help me write clearer. The thoughts and the words are still mine. Fully human. True.

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.

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.

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.

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.

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.

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.

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

Data-Driven Decision Making

The Prioritization Theater

When urgency replaces logic, roadmaps become theater. Frameworks and scores look right, but escalation wins, decisions shift, and PMs broker backlogs. Real prioritization means framing problems, making trade-offs explicit, linking to strategy, and empowering 'not now' with a clear why. Make it real.

Data-Driven Decision Making

The Illusion of Progress

Shipping fast isn't progress. Clean backlogs and on-time releases can mask weak impact. When strategy is a slide deck, and success means 'shipped', teams sprint in circles. Progress is learning, behavior change, and clarity. Ask: what are we trying to learn, change, and prove? Then adjust. Not ship.

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.

Cross-functional Collaboration

Is your Agile process truly agile, or just Agile theater?

Agile isn’t one-size-fits-all. Too many teams perform Agile theater: rituals without impact. In Enterprise SaaS, complexity demands both alignment and autonomy. A shared backlog as a single source of truth adds clarity and coordination without killing ownership or adaptability.