Here's what caught my eye in the world of AI this week:

1. Absolutely packed with gold. Here's how we’ll turn this into a 2-minute “AI Quick Byte” for enterprise leaders looking to master AI product strategy—fast. One byte, eight bits. Let’s go.

   AI Quick Byte: Mastering AI Product Strategy

   Bit by Bit with Aakash Gupta + Miqdad Jaffer’s Strategic Framework

   This one was worth the paywall. I couldn’t resist subscribing just to finish it—and if you’re serious about AI Product Strategy, you’ll want to do the same.

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   Whether you’re leading an AI initiative or building your first feature, this piece delivers battle-tested insights you can apply today. The best part? The AI Product Decision Framework is free, easy to implement, and guaranteed to make you look like the smartest person in the room.

   👉 Pro tip: Grab the 7-day free trial and see for yourself why this article is a must-read for every strategic AI leader.

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   Bit 1: Why Your AI Strategy is Probably Wrong

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   Most companies treat AI like seasoning—sprinkle and hope it tastes good. But AI is not garnish. It’s the entrée.

   🔹 Tech-first ≠ user-first: Fancy demos fail without clear value.
   🔹 Copycat strategies crumble: What works for others might sink you.
   🔹 Cool ≠ Useful: Solve actual problems, not fantasy scenarios.

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   📌 Action: Start every AI initiative by ruthlessly clarifying the user problem—not the tech novelty.

   Bit 2: Learn from the Past to Build the Future

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   From narrow AI to AGI hype, we’ve learned one thing: focus beats flash.

   🔹 Narrow AI = massive ROI: Search, rec engines, and voice assistants dominate quietly.
   🔹 GenAI is here to augment: Use it to unblock creativity and speed.
   🔹 Future-proofing means layering: Build for Level 2, prepare for Level 3.

   ..

   📌 Action: Map AI opportunities to current pain points, not far-off futures.

   Bit 3: The 3 Laws of Effective AI Strategy

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   If you only remember three things:

   🔹 User > AI: Real-world impact beats clever AI.
   🔹 Data > Model: Your moat is proprietary data, not fancy models.
   🔹 Collaboration > Automation: Build WITH humans, not instead of them.

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   📌 Action: Audit current AI work—are these three principles baked in? If not, reboot.

   Bit 4: A Step-by-Step Framework That Works

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   AI product strategy isn’t mystical—it’s methodical.

   🔹 Start with metrics, not models
   🔹 Map trust-building user flows
   🔹 Prioritize based on impact + data readiness

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   📌 Action: Use Aakash & Miqdad’s 8-step framework to build your roadmap—beginning with friction points, not features.

   Bit 5: Pitfalls to Dodge Like a Pro

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   Even smart teams mess this up. Here’s how not to:

   🔹 Don’t chase hallucinations: Precision over pizzazz.
   🔹 Avoid the data poverty trap: No data? No feature.
   🔹 Current ≠ future limits: Design for change, not constraints.

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   📌 Action: Pressure-test your AI ideas against these four failure patterns.

   Bit 6: Learn from the Layers of Winners

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   From OpenAI to Runway to ServiceNow, each AI success story plays a different game.

   🔹 Foundation layer = business model innovation
   🔹 Vertical AI = domain obsession wins
   🔹 Enterprise AI = seamless integration > shiny features

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   📌 Action: Identify your layer. Then benchmark strategy to the winners in that space.

   Bit 7: Strategy Isn’t Static (Especially in AI)

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   AI evolves monthly. Annual strategy updates won’t cut it.

   🔹 Build-in adaptability: Design for rapid iteration and learning.
   🔹 Prototype fast: Use tools like Bolt or v0 to demo futures.
   🔹 Update constantly: Monitor shifts in AI capability and adjust accordingly.

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   📌 Action: Set up quarterly “AI retrospectives” to update priorities and align with capability jumps.

   Bit 8: Ethics Isn’t Optional

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   Forget “move fast and break things.” That’s how AI trust dies.

   🔹 Guardrails > Guidelines: Define what you WON’T build.
   🔹 Transparency builds trust: Clear UX and user controls win.
   🔹 Bias mitigation must be built-in: Not afterthought.

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   📌 Action: Add ethics and governance checkpoints to your product lifecycle. No exceptions.

   💡 Final Download

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   🎯 Strategic AI leaders don’t chase the next model—they build repeatable systems to unlock user value, powered by proprietary data and trust-first design.

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   🔧 Your Move:

   1. Run your current AI initiatives through this 8-bit lens.

   2. Reprioritize ruthlessly.

   3. Share this byte with your leadership team—and lead the AI wave, not just ride it.

Quote of the week

2. Want breakthrough AI solutions? Turn up the heat with a friendly, fast-paced challenge. Rally multiple Data Scientist teams to tackle your thorniest business problems—crown a winner, then merge the best ideas from all. You’ll unlock unexpected insights and creative angles no single team could reach alone. Just timebox it to two days to keep momentum high and rabbit holes at bay.

3. From Idea to Impact: The Strategic AI Roadmap for Today’s CEO To Article Post

🧠 Executive Summary for CEOs

AI isn’t just another tech trend—it’s a business transformation lever with real, measurable outcomes. The most advanced companies using AI are already seeing 38%+ EBIT growth, 2.6x revenue increases, and major productivity gains. But only ~10% of companies are successfully scaling AI. To lead the pack, CEOs must act strategically and decisively—right now.

✅ The 3 Strategic Plays You Need to Know

1. DEPLOY – Quick wins with off-the-shelf tools like Microsoft Copilot, ChatGPT Enterprise, and Adobe Firefly.

   • Goal: 10–15% productivity boost, happier teams, and faster workflows.

   • Examples: Meeting summaries, code generation, content drafts, invoice processing.

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2. RESHAPE – Redesign core workflows and reimagine how business is done.

   • Goal: 30–50% efficiency gains by transforming HR, Supply Chain, Customer Service, etc.

   • Examples: Faster hiring, accelerated R&D, automated underwriting, personalized marketing.

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3. INVENT – Build new AI-powered products, services, and even business models.

   • Goal: Create new revenue streams and prevent disruption.

   • Examples: AI-native offerings, hyper-personalized experiences, direct-to-consumer digital services.

🔧 Execution Plan: The CEO’s 6-Point Game Plan

1. Benchmark where you are
   ➤ Assess your AI maturity, identify high-value opportunities, and understand workforce impact.

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2. Prioritize for impact
   ➤ Select 3–5 areas to apply DEPLOY, RESHAPE, and INVENT plays. Aim for 2–3x ROI.

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3. Optimize spending
   ➤ Centralize AI investments, cut legacy waste, and refocus on value-driving use cases.

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4. Build foundations
   ➤ Invest in data quality, digital infrastructure, and Responsible AI (RAI) frameworks.

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5. Launch AI governance
   ➤ Create a structure that enables experimentation while maintaining control and alignment.

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6. Upskill & scale
   ➤ Treat this as a people-first transformation. Upskill leadership, inspire your teams, and lead by example.

🏁 Board-Ready Message

4. Analysis: The AI Train Is Leaving—Shopify Is Already On Board

Tobias Lütke’s memo is more than a company update—it’s a boarding call. It’s a bold, no-turning-back declaration that the era of AI-enabled work has arrived, and the cost of inaction is irrelevance.

🔍 What’s Really Going On?

Shopify’s CEO isn’t just encouraging employees to explore AI—he’s institutionalizing it. Reflexive AI use is now a baseline job expectation, not an R&D experiment. The message is crystal clear:

This letter serves as a strategic repositioning: AI is no longer optional, experimental, or "coming soon." It's embedded in performance reviews, product prototypes, and even headcount decisions. It’s cultural. It’s operational. It’s existential.

🧭 5 Key Signals from Shopify’s AI Mandate

1. AI fluency = modern literacy

Tobi calls AI a “tool of all trades” and a “multiplier.” In Shopify’s world, if you’re not engaging with AI, you’re not just standing still—you’re sliding. The implication: AI literacy will soon rival data literacy or digital literacy as a core competency.

2. Performance and promotions now hinge on AI usage

By linking AI usage directly to performance reviews and peer assessments, Shopify embeds AI adoption into career progression. This isn’t a suggestion—it’s now part of the employee value proposition.

3. AI-first thinking replaces headcount-first thinking

Before adding resources, teams must justify why AI can’t solve the problem. That’s a radical shift from traditional scaling strategies and shows Shopify is treating AI as a force multiplier, not just a tool.

4. Prototyping becomes a proving ground for AI adoption

All GSD (Get Shit Done) projects must explore AI in the prototype phase. This reorients innovation cycles around AI experimentation from day one. It’s about creating AI-native solutions, not retrofitting AI onto legacy ideas.

5. Culture change is the true unlock

This memo isn’t about tools—it’s about a team-wide mindset shift. Sharing AI wins and losses, prompting openly, learning by doing… Shopify is building collective AI muscle memory, fast.

⚠️ Warning to the AI-Curious but Passive

If you’re still dabbling with AI or waiting for a formal training course, the Shopify memo is your wake-up call. The early adopters are becoming exponentially more capable—and they’re building the future while others are still watching demos.

Lütke’s insight: AI acts as a multiplier of both talent and tools. If your peers are getting 10x results, and their tools are also 10x more powerful… you're facing a 100x productivity gap.

🧨 Bottom Line

Tobi’s memo is a modern-day “innovate or die” speech—with a warm smile and a GitHub Copilot window open behind it.

This isn’t about Shopify alone. It’s a preview of what’s coming across industries:

• AI will be expected.

• AI will be measured.

• AI will be rewarded.

The train is moving. Get on now—or risk trying to sprint after it later.

✅ Your Next Move

• Assess: Where are you today on AI fluency? Be honest.

• Act: Pick one high-value task and AI-enable it this week.

• Amplify: Share what you learn—lead by example, not perfection.

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5. Tamar Yehoshua on product leadership in the age of AI

Real talk from the front lines of AI product leadership. Tamar Yehoshua delivers the kind of hard-earned wisdom every product leader needs right now.

Noticing a pattern? Good. That means the core principles of AI strategy are starting to click—and this episode will take them to the next level.

Let’s dive in and learn from one of the best in the business.

💼 Executive Summary

In this episode of McKinsey on Building Products, Tamar Yehoshua, President of Product & Technology at Glean (formerly of Slack, Google, and Amazon), shares hard-earned insights on leading product innovation in the AI era. She outlines what it takes to build AI products that actually matter, scale enterprise adoption, and shape the future of work where roles blur, curiosity reigns, and AI transforms how we collaborate.

From building only what users need (not what’s trendy), to embedding AI tools in a way that drives actual behavior change, Tamar offers a practical, people-first lens for any leader aiming to make AI real—not theoretical—in their product portfolio.

🔑 Three Key Takeaways

1. Curiosity > Certainty

Top product leaders don’t have all the answers—they listen, learn, and adapt. The best PMs today are endlessly curious and unafraid to rethink strategies in the face of fast-moving AI advances.

💡 “Be data-informed, not data-driven. Your instincts still matter.”

2. Adoption Is a Human Problem, Not a Tech Problem

Leaders may be ready for AI, but teams often aren’t. Driving real AI impact means designing for ease, trust, and behavior change—not just building features.

💡 “An empty AI chat box doesn’t inspire action. Prompts, patterns, and shared usage do.”

3. Don’t Chase Features—Chase the Few That Matter

Most features don’t move the needle. Only a handful change the trajectory of your product. The challenge? Identifying those few through a mix of customer empathy and bold product vision.

💡 “There’s a graveyard of features no one used. Great PMs know when to say no.”

🎯 What to Do Next

• Lead by example: Use AI tools yourself and show your org how.

• Obsess over adoption: Design with real workflows in mind.

• Focus on impact: Prioritize features that drive real business outcomes.

• Stay curious: Try everything. Play. Learn. Iterate. That’s how you lead in AI.

Want to build AI products people actually use? Start with curiosity, scale with empathy, and never forget: the best AI product strategies are built human-first.

6. Sunday Funnies 🤣 .

7. Most Popular AI Agent Use-cases

What would a newsletter be without one bit on Agentic AI :-) ?

AI Agents are defining the enterprise playbook in 2025, and these six use cases are leading the charge—from Agentic RAG powering smarter search to Voice Agents reshaping customer conversations, and Coding Agents revolutionizing developer productivity. Whether you're building AI products or investing in the future of work, this post gives you a crystal-clear snapshot of where the market is heading—and who's already winning.

8. BUILD Framework – Your Ticket to Catching the AI Train

The BUILD Framework is a smart mental model for understanding the fast-moving AI landscape. Here's an analysis tailored for strategic AI leaders.

🚆 Analysis:

If the AI train is leaving the station, the BUILD Framework is a first-class boarding pass. In a time where new tools and models drop daily, BUILD helps cut through the noise and answer three critical questions:

Let’s break it down:

🧱 Base – The Raw Talent

Think of foundational models as brilliant interns: unlimited potential, zero context. They generate, predict, and reason—but don’t know what your business does or how it operates.

• Key Insight: LLMs are non-deterministic. You must treat them like flexible tools, not software functions.

• Strategy: Don’t ship anything built solely on Base-level AI. It's your starting point, not your product.

🛠️ Upgrade – Giving AI the Tools & Context

This is where AI becomes useful. You plug in tools, knowledge, reasoning frameworks, and memory systems.

• Key Insight: “Upgrades” = functionality + domain knowledge + tool use.

• Strategy: Every AI product should include thoughtful upgrades. Think RAG, tools like Slack or Figma APIs, and specialized prompts for your vertical.

🧠 Improve – AI That Gets Better Over Time

If you're not improving, you're decaying. This layer introduces feedback loops: evals, fine-tuning, reinforcement learning.

• Key Insight: AI can’t stay static—it needs a growth path, like any employee.

• Strategy: Build in evaluations and human feedback from day one. Train your product like you’d train your team.

🎯 Lead – From Tasks to Goals

Agents live here. Instead of telling AI how to do every task, you tell it what to accomplish.

• Key Insight: Agents shift AI from a tool to a teammate.

• Challenge: Compounding errors. A 5% fail rate per step = 54% task success over 12 steps. That’s product poison.

• Strategy: Use specialist agents and smaller, scoped tasks until reliability improves. This is still early days for autonomous systems.

🤝 Delegate – Teaming Up with Specialist AIs

This is the frontier. Coordinating a “team” of agents across functions, each with narrow expertise.

• Key Insight: This is true AI orchestration—think agent hierarchies, manager/worker setups, or even multi-agent games.

• Strategy: Treat it like team management. Who does what? How do they pass the baton? Add rules, roles, and oversight.

🧠 Why BUILD Matters

The BUILD Framework is a mental map that scales with complexity:

• It helps leaders place new developments quickly—no more guessing if “this tool matters.”

• It avoids shiny object syndrome by asking, “Does this solve a real problem in our stack?”

• It reveals gaps in your product strategy: Are you stuck at “Upgrade” and skipping “Improve”? Trying to “Lead” without solving “Delegate”?

📣 Bottom Line: You Need a Framework or You’ll Fall Behind

AI is evolving faster than most teams can structure. The BUILD framework gives tech leaders a structured compass to:

✅ Classify
✅ Prioritize
✅ Deploy
✅ Scale

And most importantly—avoid wasting time.

🚀 Executive Action Steps:

1. Print the BUILD model and stick it on your strategy wall.

2. For any new AI tool or update, ask: Which layer does this serve?

3. Assess your AI roadmap—are you over-indexing on Base? What’s your Improve/Lead/Delegate strategy?

Until next time, take it one bit at a time!

Rob

Thank you for scrolling all the way to the end! As a bonus check out The Art and Science of Good Decisions: Balancing Instinct and Data Ben Yoskovitz.

Data can guide you—but it’s your instincts that help you see around corners. In a world awash with metrics, dashboards, and AI-generated insights, it's tempting to trust the numbers blindly. But Ben Yoskovitz reminds us that data without context can mislead, and instincts—honed through hard-earned experience and domain expertise—are often the quiet voice pointing to what truly matters. The real magic happens when you blend structured analysis with sharp intuition, knowing when to trust the data, when to question it, and when to just ship it.