1. The Two-Day Workweek and the Human Speed Bump

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   Bill Gates recently mused that the two-day workweek may soon be our reality. A tantalizing vision, no doubt — and yet, it echoes the same techno-utopian promises whispered during the dawn of the PC era: “Work less, live more.” We’ve heard this song before.

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   But let us be clear — the brakes on this future won’t be found in silicon. It’s not the technology that lags; it’s us. As I’ve long contended, and as we see unfolding in real time, the true bottleneck isn’t in processors or data pipelines, but in the slow churn of human adaptation. Our institutions, our education systems, our corporate structures — they were designed for a slower age. And while the velocity of innovation hurtles forward, our social systems groan, trying to reorganize for a future already at the doorstep.

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   Can we keep up? Perhaps. But adaptability is not evenly distributed. Smaller, more agile organizations — frontier firms — will maneuver this transition with greater ease. Bureaucracies, on the other hand, may drown in the very wave they try to ride.

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   The illusion of a shorter workweek may persist, but paradoxically, our mental bandwidth is stretched thinner than ever. The pace of knowledge expansion is now so fast, it’s no longer about keeping up — it’s about choosing your arena. Go broad, explore widely — then drill deep into what truly excites your curiosity. That’s the beauty and the burden of our age: a buffet of infinite learning, but only one plate.

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   My challenge — perhaps yours too — is not in finding what to learn, but in choosing among the many things I want to learn.

2. The biggest skill of the AI era? Learning how to learn.

The leadership mandate has shifted. You’re not just leading teams—you’re leading learners. And your company’s relevance hinges on how well you build environments for curiosity, creativity, and continuous skill-building.

The best part? When learning is fun, it scales.

• Upskilling is now a strategic function.

• Fun learning = sticky learning.

• Culture of growth beats culture of “goals.”

ACTION BYTE: Design learning like a product. And market it like one, too.

Deep Dive

3. Prompting is the UX layer of AI — and it’s evolving from art to architecture.

Executive Summary: Most execs treat prompting like wizardry: something you “just try” until it works. But The Prompt Report shifts the frame — it’s not about tricking models; it’s about systematically shaping behavior. This is the Rosetta Stone of prompt engineering, decoding 58 techniques, mapping core categories (text, multimodal, agents), and aligning strategy with structure.asas

It’s the kind of taxonomy that turns individual hacks into organizational playbooks.

• Prompting isn't monolithic — it's a modular system of techniques across 7 domains.

• In-Context Learning (ICL) isn't just a parlor trick — it’s a primary scaffold for reasoning.

• “Answer engineering” — often neglected — is as critical as prompt design for reliable output.

BIT 1: In-Context Learning (ICL) — The Workhorse of Prompting Summary: ICL is foundational: you teach the model a new task within the prompt — no retraining needed. By feeding it a few examples (“shots”), the model mirrors the pattern.

• Use when fine-tuning is overkill but task clarity is critical • Few-shot examples > zero-shot for tasks needing structure • Boost results with thoughtful exemplar ordering, label balance, and formatting

Example: To classify sentiment:

Tweet: I love this place! → Positive
Tweet: This is awful. → Negative
Tweet: I’m not sure what to think... → ?

ACTION BYTE: Treat ICL like scaffolding — show the model your logic before asking for its own.

BIT 2: Chain-of-Thought (CoT) — Thinking in Public Summary: CoT encourages the model to “think out loud.” Add a reasoning nudge (“Let’s think step by step”) and watch logic unfold. It’s the highest-leverage shift for reasoning tasks.

• Use for math, logic, planning, decision flows • Works in zero-shot but excels with few-shot templates • Pair with Self-Consistency for robust multi-path validation

Example: Q: If a toy costs $12 and you pay with a $20 bill, how much change do you get? A: Let's think step by step. $20 - $12 = $8. Final answer: $8.

ACTION BYTE: Inject reasoning phrases. Output clarity follows input transparency.

BIT 3: Prompt Paraphrasing — Say It Five Ways Summary: Change the words, not the intent. By rewriting prompts in multiple styles, you generate diverse perspectives and surface the most robust phrasing.

• Use when model output is brittle or inconsistent • Great for brainstorming, creativity, or sensitive requests • Can be automated for high-volume prompt testing

Example: Original: “Summarize the report.” Paraphrases: “Give me the key takeaways.” / “What are the main points?” / “Explain this like I’m a board member.”

ACTION BYTE: Paraphrasing is debugging for language. Run multiple versions and pick the strongest.

BIT 4: Role Prompting — Assign the Right Persona Summary: You’re not just prompting a model — you’re casting it. Give it a role, and it will adopt tone, knowledge, and style to match.

• Use when tone, audience fit, or expertise are key • Especially effective in customer service, coaching, and writing • Pair with style constraints for high-fidelity outputs

Example: “Act as a CTO advising a Fortune 500 board. Should we adopt open-source LLMs?”

ACTION BYTE: Think like a casting director. The role you assign shapes the script.

BIT 5: Least-to-Most Prompting — Deconstruct to Dominate Summary: Break big problems into bite-size parts. This technique prompts the model to decompose before solving.

• Use for complex or layered tasks • Ideal for strategy, analysis, planning, or compliance reviews • Reduces hallucinations by simplifying reasoning pathways

Example: “Step 1: Identify key stakeholders. Step 2: List their incentives. Step 3: Recommend a win-win strategy.”

ACTION BYTE: Complexity isn’t the enemy — unmanaged complexity is. Decompose first.

BIT 6: Meta-Prompting — Prompts that Write Prompts Summary: Let the model design the prompt. Meta-prompting unlocks adaptive, self-improving workflows.

• Use when exploring new task spaces • Crucial for building internal tools or “prompt ops” interfaces • Empowers non-experts to scaffold expert-level prompts

Example: “Improve this prompt: ‘Summarize the article.’ → Suggested: ‘Summarize in 3 bullet points, focusing on insights for B2B marketers.’”

ACTION BYTE: Ask the model: “How would you prompt this?” Let it teach itself.

BIT 7: Prompt Ensembles — Ask Many, Choose Wisely Summary: Ensemble prompting runs multiple versions, gathers answers, and selects the best. Like voting, but for language.

• Use for critical outputs where reliability > speed • Combine with CoT or role prompting for diversity • Balances creativity with confidence

Example: Run 5 CoT versions → pick consensus or use a second LLM to arbitrate.

ACTION BYTE: In high-stakes tasks, one prompt is risky. Use a panel.

BIT 8: Retrieval-Augmented Prompting — Bring Your Own Facts

Summary:
Prompting can tap external data too. Retrieval-augmented prompting fetches documents or facts on-the-fly, keeping outputs grounded in real context.

• Use for knowledge-intensive tasks like QA, summarization, or analytics

• Reduces hallucination risk by grounding answers

• Works best when paired with search or vector DB tools

Example:
“Using the document above, summarize our quarterly revenue trends by product line.”

ACTION BYTE: Don’t just prompt the model. Feed it the world. Retrieval is the new context..

BYTE SUMMARY: Prompting is no longer just a skill — it’s an infrastructure layer. This isn’t about better phrasing. It’s about better systems. From ICL to meta-prompts, each technique adds a layer of intelligence to your org’s interaction with AI. Treat your prompts like products: design, test, iterate, deploy.

4. Unpacking AI’s building blocks—because LLM ≠ Agentic AI.

Executive Summary:

We often toss around acronyms like LLMs, RAG, and “Agents” as if they’re synonyms. They’re not. Brij Kishore Pandey’s visual breakdown clarifies the evolutionary ladder of AI—from foundational language models to autonomous, multi-agent ecosystems.

Understanding these four distinct layers—LLMs, RAG, AI Agents, and Agentic AI—helps leaders make smarter bets. LLMs generate; RAG fetches; Agents act; Agentic systems collaborate. It’s not just taxonomy—it’s your tech stack’s future.

• LLMs generate based on training data but can lack grounding.

• RAG injects fresh, relevant info via retrieval techniques.

• AI Agents execute, integrate memory, and loop through tasks.

• Agentic AI = multi-agent teamwork and shared autonomy.

ACTION BYTE: Learn the stack, speak the language. Knowing these layers helps you staff and scale wisely.

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5. Data is new oil—but only if you refine, secure, and route it.

In the AI era, your data strategy is your company strategy. Shreekant Agrawal’s blueprint shows how unified governance transforms data from a liability into a strategic enabler.

At the heart is the enterprise catalog—no longer just a directory, but a nerve center for compliance, collaboration, and trust. With smart orchestration, AI enrichment, and real-time observability, data becomes not just accessible—but alive.

• Unified governance reduces cost, boosts trust, and accelerates insights.

• AI automation enables scale and smarter rule enforcement.

• Catalogs evolve into data marketplaces + rule engines.

ACTION BYTE: Rethink governance as a service—not a silo.

6. Friday Funnies 🤣 .

7. AI eats software. Philosophy eats AI. Here’s what that means for you.

Executive Summary:
Now we are going one more level deeper so hold on to your data propellers, this is cool wonky stuff.

AI without meaning is just syntax. That’s the crux of Tony Seale’s argument: if you want systems that align with your business, you need more than training data—you need philosophy baked into your data architecture.

With a shared vocabulary and a machine-readable ontology, AI doesn’t just follow instructions—it starts to think like your business.

• Most AI fails because it learns from misaligned signals.

• Structured meaning is the secret ingredient.

• Ontological cores make your models smarter and safer.

ACTION BYTE: Before scaling AI, scale shared understanding. Your ontology is your operating system.

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If data is the oil, Governance, Risk and Compliance (GRC) is the refinery—and most companies are still figuring out the controls.

Executive Summary:

McKinsey’s latest global survey reveals a common truth: GRC is still mostly aspiration. Despite recognizing its value, many enterprises operate with fragmented tools, underfunded teams, and boards that delegate too much, too far down the org chart.

But a few leaders are breaking away. They’re elevating risk and compliance functions, integrating AI, embedding GRC into incentives, and treating it as a core strategy—not just a checkbox.

• Most GRC teams are under-resourced and disconnected from strategy.

• Mature companies integrate GRC with decision-making and technology.

• AI and aligned incentives are game-changers for GRC maturity.

ACTION BYTE: If your GRC head doesn’t have board access, your risk management might be mostly paperwork.

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

Rob

YouTube | SV AI Think Tank Meetup | LinkedIn | Your Free AI PRD

Thank you for scrolling all the way to the end! As a bonus check out this white paper from IBM - Agentic AI in Financial Services :

This isn’t automation. It’s autonomy—and it’s time to get serious about control.

Executive Summary: Agentic AI shifts the paradigm from task automation to dynamic orchestration. IBM’s paper outlines how agents with memory, reasoning, and API autonomy are rewriting the rules of customer service, fraud detection, IT ops, and software development.

But these agents aren’t just smarter—they’re harder to govern. Memory drift, emergent goals, multi-agent collusion—these risks are real, and growing.

• Agentic systems require new controls at every layer—tools, logic, goals.

• Risk and compliance leaders need a seat in the build process, not the review stage.

• Cross-jurisdiction regulation is coming fast (EU, AU)—prepare now.

ACTION BYTE: Treat Agentic AI like a new division—govern it accordingly.

P.S.

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