These annual reports provide the best view into how people are using Large Language Models (LLMs), drawing on usage data from the two largest platforms, Claude and ChatGPT.

While both underscore the unprecedented speed of AI adoption and its utility in knowledge work, this year we’re starting to see a glimpse into the next wave of AI startups—and also where the largest bottleneck will be to truly operate at scale.

How 700 million monthly active users used ChatGPT last year

ChatGPT’s consumer data shows that about 73% of its usage is now for non-work tasks, with 23% for works tasks. But perhaps most interesting, people are using AI primarily for advice. Practical Guidance - from fitness routines to career advice - now dominates at 28.3% of all usage.

In addition, the “Asking” category - seeking information to inform decisions - represents 49% of all messages and growing faster than doing tasks on your behalf.

One reason for this shift is that people naturally provide rich context when asking ChatGPT for advice. They share the background, the people involved, what they’ve tried before, and what they’re hoping for. This context-rich approach works—users rate these advice interactions significantly higher than simple task completion.

This trend will only accelerate. Over the next few months, I expect the AI user experience will continue to make this even better, with easier ways to upload critical information to improve the advice.

The Flip Side -Where Businesses Hit a Wall

Anthropic’s data provides a different perspective, given its heavy user-base for coding and engineering tasks. Seventy-seven percent of enterprise API usage is simple automation. Interestingly, companies want to move to complex advisory systems - the kind consumers are using above - but are struggling to do so.

The reason is Context. It is the largest constraint to more sophisticated use. As Anthropic notes, “Curating the right context for models will be important for high-impact deployments of AI in complex domains.”

Enterprise AI without proper context is like GPS without knowing your starting location—technically impressive but practically useless.

If you want AI to develop a sales strategy? It needs your CRM data, deal histories, competitive intel, market conditions, and all that tribal knowledge in people’s heads. The more sophisticated the advice you want, the more context you need to provide.

The information exists, but it’s scattered across a dozen systems, trapped in silos, buried in email threads, locked in legacy databases, and worst of all - sitting undocumented in employees’ minds. This isn’t a small problem. Anthropic warns:

“This could represent a barrier to broader enterprise deployment for some important tasks that rely on dispersed context that is not already centralized or digitized, and for some firms, costly data modernization and organizational investments to get this contextual information may be the largest bottleneck for AI adoption.“

Before AI can transform your business, you need to transform how you manage information. This isn’t about having all your data in one place. It’s about making your organization’s knowledge - both documented, recorded, captured and tribal - accessible to AI systems.

Your Next Move

Pick a decision your team makes regularly. Document all the context someone would need to make that decision well. Map out every piece of information needed. Where does that information live today? What’s digital? What’s in people’s heads? That gap is your roadmap.

The question isn’t whether AI can give good advice and be as good as human experts. The question is whether your business can give AI the context it needs to advise you.

Those are my Thoughts From the DataFront

Max

My first job was all 5 AM wake-ups and market research. Every morning, I'd read through dozens of reports and news articles, then send out a summary email before the opening bell. The rest of my day was spent building comparison tables—endless spreadsheets filled with production numbers, debt ratios, and commodity prices.

At the time, it felt like grunt work. But those tedious tasks were actually building something invaluable: my apprenticeship in thinking. Learning to spot patterns in data. Understanding what questions mattered. Developing judgment about what information was signal versus noise.

Today, ChatGPT could knock out my entire first-year workload—and that's a massive problem.

The Learning Pathway is Disappearing

Recent research from Stanford and Harvard shows that this typical early career experience is at risk of being destroyed. AI isn't just changing work randomly—it's systematically targeting entry-level positions that have historically served as professional training grounds.

The studies show that:

• Younger workers (aged 22-25) experienced a 13% decline in job opportunities in AI-heavy fields like customer service and software development.

• Meanwhile, senior workers in the same companies and occupations saw stable or even growing employment.

Why AI Targets Entry-Level Jobs

The simple answer is that AI excels at routine tasks, while it struggles with complex judgment.

Entry-level roles are often very predictable, rule-based tasks such as basic customer service, initial code reviews, data entry, and first-draft content creation. These roles follow clear rules and procedures - exactly AI’s sweet spot.

Senior roles are different. They demand nuanced decision-making, strategic thinking, managing human dynamics, and building trust—skills AI cannot replicate and often augments.

Essentially, AI is replacing the "training ground" where new professionals traditionally learned to work, while making experienced workers more powerful

Why Should You Care?

This creates a problem that goes far beyond individual career paths. While companies see obvious cost savings from AI adoption, the long-term consequences could be devastating.

If AI handles the practice-level work, how will the next generation develop expertise?

When I started my career, it was not the tasks itself that taught me to think—it was wrestling with getting the tasks done correctly. The learning happened in the struggle, not the summary. Knowledge comes from trying new approaches, eliminating what doesn't work, and evolving fast.

We can't bring back grunt work—AI owns that now. But we can redesign how we work with AI to keep the learning alive through faster feedback loops.

The Solution: Active Dialogue with AI

Think of AI as your thinking partner, not your replacement. Instead of Artificial Intelligence think Assistance Intelligence- AI that doesn't just give answers, but teaches you how it got there.

The secret is active dialogue. When working with AI, provide the context and interrogate it:”Walk me through your analysis step-by-step”. “What patterns do you see?”, "What assumptions are baked in?" ,"How does this change if we flip this variable?".

This creates accelerated learning:

• Critical thinking develops through questioning AI outputs

• Pattern recognition sharpens by analyzing more scenarios

• Decision-making skills compound through higher-volume practice

Your Next Move

We're at a crossroads. We can let AI hollow out the apprenticeship system and deal with the talent crisis in a decade. Or we can redesign how people develop expertise alongside AI.

Look at your entry-level roles. Are you using AI to eliminate learning opportunities or amplify them?

Those 5 AM market reports taught me to think not because of what I read, but because of how I wrestled with making sense of it all. That struggle—the process of developing judgment through practice—can't be automated away.

The companies that get this right will have the only advantage that matters: people who can think. The future belongs to companies that make their people more capable, not companies that make them unnecessary.

Those are my Thoughts From the DataFront
Max

This week when Canadian banks released their quarterly results, I faced exactly this challenge. I wanted to understand the main themes across all the major Canadian Banks - but I had maybe 30 minutes, not 3 hours. So I decided to test something: Could NotebookLM, Google's AI-powered research assistant, transform a pile of banking investor presentations into an interactive learning experience?

Watch the 4-minute demo above to see exactly how this played out - and why this changes how you should think about research.

What Makes NotebookLM Different

Think of NotebookLM as your personal research analyst that only knows what you tell it. Unlike ChatGPT, which draws from the entire internet (and sometimes makes things up), NotebookLM is "source-grounded." It only uses the documents you upload.

Here's how it works:

1. You upload your documents

2. NotebookLM becomes an expert on that specific content

3. Chat with it to ask questions, brainstorm ideas, summarize key points, and identify connections across documents.

4. Generate new content with the sources in your notebook such as: study guides, briefing docs, FAQs, mind-maps, and audio/video overviews

How This Changes How You Work

Traditional research is linear: read document A, then document B, then try to remember what document A said while analyzing document C. NotebookLM turns all your documents into one interactive conversation.

Unlike general AI tools, NotebookLM becomes an expert specifically on your documents, making it more accurate than general AI tools for deep, specialized analysis. In practice, this means:

1. Students can upload course materials and generate study guides.

2. Researchers can synthesize dozens of papers with exact citations.

3. Professionals can transform articles and meeting notes into searchable knowledge bases.

Now it’s your turn. What complex research challenge is sitting on your desk right now? Try uploading those documents to NotebookLM and see what insights emerge. The tool is free, the only investment is your curiosity.

Those are my Thoughts From the DataFront

Max

Watch the demo above.

What I Built

A six-agent AI system that reads my daily newsletters, writes analysis, and publishes to social media. Each agent handles one specific job, working together like a small team:

• Lead coordinates everything

• Email Summarizer digests 10+ newsletters

• Researcher monitors web and academic sources

• Reviewer scores quality

• Optimizer enhances content

• Publisher posts to X

The whole thing runs autonomously. Fact-checks everything. Maintains my writing voice. Handles errors when things break. Professional-grade output, every single day.

This Changes Everything

I'm not a developer - just someone passionate about understanding AI. By studying documentation from Anthropic, Microsoft Autogen, and Google's A2A protocol , I could articulate what I wanted clearly. Claude Code wrote the implementation.

We've hit an inflection point. The barrier between idea and working prototype has essentially disappeared. You can now test concepts, validate workflows, and prove value without writing code - you just need to be a clear thinker who understands the technology.

This shift matters because prototypes prove value. They turn abstract ideas into something tangible that gets buy-in, budget, and eventually that professional team to scale it properly.

The barrier isn't technical skill anymore. It's imagination, research skills, and clarity of thought.

See It In Action: Follow @TheDatafront and watch it evolve as I continue building.

Your Turn: What's that one task you do every week that makes you think "there has to be a better way"? If you can describe the process clearly, you can now build the solution.

Those are my Thoughts From the DataFront

Max

The digital revolution turned passive consumers into content creators, powering Google, Meta, and TikTok through user-generated content. Now AI is driving the next leap: turning consumers into software producers.

At Thoughts From the DataFront, we've been tracking this shift—how AI is democratizing technology and putting sophisticated tools within anyone's reach.

This week, instead of just talking about it, I'm showing it.

Every month, my wife Jess and I dread reviewing our credit card statements, trying to figure out where our money went. So I turned this monthly headache into a perfect test case for everything we discuss here on this blog.

What I Built

Using Claude with some additional custom tools - I built an AI-powered system that transforms any credit card statement into financial analysis with savings recommendations— delivered to my email in under 2 minutes.

How It Works

Step 1: Read & Categorize - AI reads the PDF, extracts 100+ transactions, and categorizes each purchase into logical groups.

Step 2: Analyze & Visualize - It creates a Google Sheets database and an interactive chart

Step 3: Recommend & Deliver to Email - It generates money-saving recommendations for each category sent to my email.

The entire process that would typically take hours of spreadsheet work happened automatically.

Why This Matters

This isn't just about one credit card statement. It's proof that the AI democratization we've been discussing is real and accessible to everyone right now.

Using Claude AI integrated with Zapier, Google Sheets, and Gmail, anyone can build this through conversation—no coding expertise required.

Remember, the future belongs to people who can think clearly about problems and communicate those needs to AI. The technology is ready. What will you build first?

Your Next Move: Watch the demo in this post, then message me with problems you're trying to solve. Let's figure out how AI can tackle them together.

Those are my Thoughts From the DataFront

Max

Most businesses hear "AI agents" and imagine some complex, autonomous system that requires a PhD to understand. The reality is much simpler.

Research from Anthropic, based on analyzing dozens of successful AI implementations across industries, reveals that the most effective AI Agent systems follow surprisingly simple patterns.

Let's explore the two building blocks required to get started: (1) the augmented LLM and (2) four proven workflow patterns.

The Augmented LLM

Before we dive into the four workflow patterns, let’s discuss what makes an AI agent different from regular ChatGPT.

AI agent systems are AI applications that can take actions, use tools, and work through multi-step processes rather than just answering questions. Instead of a simple back-and-forth chat, these systems can actually do things: search databases, send emails, analyze data, or update your CRM systems.

Successful AI agent system starts with an "augmented LLM” - a regular AI model enhanced with three key capabilities:

1. Retrieval: can search and find information from databases, documents, or the internet

2. Tools: Can take actions like sending emails, making calculations, or updating systems

3. Memory: Can remember important information from previous interactions

Think of it like ChatGPT with hands, eyes, and a notebook.

The good news is that you don't have to build these connections from scratch. Anthropic's Model Context Protocol (MCP) makes connecting to tools easy by allowing developers to plug into a growing ecosystem of third-party capabilities.

Four Workflow Patterns to Get Started

Having an augmented LLM isn't enough. The next step is to define your workflow—structured paths that tells your AI how to handle complex tasks. Think of these as the step-by-step processes you'd give to a human employee, but designed for AI to follow.

Here are four proven patterns to follow:

Workflow Pattern 1: Prompt chaining (The Assembly Line)

What it does: Breaks complex tasks into sequential steps, where each LLM call handles one piece and passes results to the next. You can add human checks (i.e. "gate” in the diagram below) on any intermediate steps to ensure that the process is still on track.

Best for: Tasks you can break into clear, predictable steps

Example: Writing an outline of a document, checking that the outline meets certain criteria, then writing the document based on the outline.

Workflow Pattern 2: Routing (The Smart Dispatcher)

What it does: Analyzes incoming requests and automatically routes them to the right specialized process.

Best for: Situations where different types of inputs need different handling

Example: Directing different types of customer service queries (general questions, refund requests, technical support) into different downstream processes, prompts, and tools.

Workflow Pattern 3: Orchestrator-workers (The Dynamic Project Manager)

What it does: Like a project manager, a central LLM analyzes complex tasks, breaks them down and assigns pieces to specialized workers, then combines the results.

Best for: Complex tasks where you can't predict all the steps beforehand

Example: Search tasks that involve gathering and analyzing information from multiple sources for possible relevant information.

Workflow Pattern 4: Evaluator-optimizer (The Quality Control)

What it does: Like having a built-in editor, one LLM generates a response while another evaluates and provides feedback in a continuous improvement loop.

Best for: Tasks where iterative refinement adds clear value and you have clear evaluation criteria

Example: Literary translation where there are nuances that the translator LLM might not capture initially, but where an evaluator LLM can provide useful critiques.

From Patterns to Practice: A Simple Decision Framework

Now that we understand the four workflow patterns, the question becomes: how do you actually choose and implement the right one for your business?

Look at a process in your business that involves multiple steps or decisions. Ask yourself:

• Can this be broken into clear sequential steps? Start with Prompt Chaining

• Do different inputs need different handling? Try Routing

• Is this too complex to predict all steps? Use Orchestrator-Workers

• Does the output need iterative improvement? Build Evaluator-Optimizer

Your Next Move: This week, pick one repetitive process in your business and map it to these four patterns—the technology is ready, so which problem will you solve first?

Those are my Thoughts From the DataFront

Max

Source: Anthropic's Guide to Building Effective Agents

The real question is: How much control should humans keep when AI gets involved?

The Human Agency Scale (HAS): Five Levels of Control

Stanford's researchers created something called the Human Agency Scale. Think of it like a sliding scale of control—from AI doing everything to humans staying completely in charge.

Here's how it works:

H1: AI handles everything (full automation)

AI does the entire job with almost no human help.
Example: AI transcribing meeting notes or generating monthly reports

H2: AI leads, Humans Guide

AI does most of the work but asks for your approval at key moments.
Example: AI drafts your emails but waits for you to hit "send"

H3: Equal partnership between human and AI

You and AI work together as equals, each doing what they do best.
Example: AI analyzes market data while you interpret what it means for strategy

The study found that H3 (equal partnership) is the dominant worker-desired level for 45.2% of occupations.

H4: Human leads, AI assists

You're in control, but AI helps make your work faster and better.
Example: AI helps you write a presentation, but you control the story

H5: Human drives , AI support

You make all the decisions. AI just provides information when you ask.
Example: AI gives you research data, but you handle all client conversations and domain expertise

How This Changes Which Skills Get Valued

As organizations start mapping their tasks across these five levels of control- Stanford's research revealed three fundamental shifts that are reshaping the job market.

1. Information Processing Is Becoming Commoditized

Skills like analyzing data or keeping up with knowledge, which pay well today, will likely need less human input as AI gets better at these tasks. Think of AI handling the heavy lifting of sorting and understanding information while humans focus on interpretation and decision-making. The value shifts from processing information to understanding what it means and what to do about it.

2. Human Connection Skills Are Appreciating

Skills involving interpersonal communication, team leadership, and resource management are becoming more valuable as AI handles routine tasks. When AI agents can draft emails and schedule meetings, humans can focus on building relationships, managing complex team dynamics, and inspiring others toward shared goals. As our world becomes more digital, purely human skills become more precious.

3. Complex Judgment Stays Human

While AI excels at processing data instantly, it can't read a room, build trust under pressure, or navigate ethical gray areas. This creates expanding opportunities for humans for nuanced decision-making that requires understanding context, consequences, and human nature.

Key Insight: As AI gets better at handling data and information, the edge comes from the things only humans can do—understanding what the numbers actually mean, building real relationships, and making tough calls when there's no clear answer. The winning companies will deliberately focus their people on these human strengths.

Your Next Step

Instead of asking "What should we automate?" start asking "What level of partnership works best for each task?"

Pick one workflow in your organization. Map each task against the Human Agency Scale:

• H1-H2 tasks: Good candidates for automation

• H3 tasks: Design collaborative processes

• H4-H5 tasks: Build AI assistance tools

Look specifically for tasks where workers express frustration with repetitive work, but the outcome still requires human judgment or relationship management.

The future belongs to companies that make their people more capable, not companies that make them unnecessary.

Those are my Thoughts From the DataFront

Max

Stanford researchers surveyed 1,500 workers and 52 AI experts across 844 tasks. Their findings suggest a surprising disconnect between current AI investments, where workers want help, and where the technology currently is.

In today’s post, we will summarize these findings and provide some insight into: Where AI should be deployed? and where investments are currently missing the mark?

What Workers Truly Want (and Resist)

Nearly half of all workplace tasks (46.1%) got positive ratings for AI automation from workers themselves.

Why do they want AI help?

• 69.4% want to free up time for high-value work

• 46.6% find tasks repetitive or tedious

• 46.6% see opportunities for quality improvement

Workers' biggest concerns:

• 45.0% lack trust in AI accuracy and reliability

• 23.0% fear job replacement

• 16.3% believe AI lacks essential human qualities

  

The Four Investment Zones: Are You Focusing on the Right Opportunities?

So how do we use workers’ desire for automation to guide AI investments? Stanford's framework maps AI opportunities using two simple questions: Do workers want this automated? Can AI experts confirm the technology is ready? This creates four distinct zones that should guide every AI dollar you spend.

Zone 1. “Green Light” = High worker desire + High AI capability

These are your immediate wins. Workers want these tasks automated, and AI experts confirm the technology can handle them today.

Examples:

• Tax preparers want to automate "scheduling appointments with clients" - it's routine work that AI handles well, freeing them for higher-value tax strategy.

• Public safety tele-communicators want to automate "maintaining files of emergency call information" because AI efficiently organizes such data.

Zone 2. R&D Opportunity = High worker desire + Low AI capability

This is where smart money should go for future competitive advantage. Workers desperately want help here, but AI experts say the technology isn't ready yet. These represent promising directions for AI research and development.

Examples:

• Computer scientists want to automate "approving and adjusting operational budgets" - complex decision-making, strategic oversight, and nuanced judgment regarding financial implications current AI struggles with.

• Video game designers wish they could automate "creating production schedules and prototyping goals" - human creativity, judgment, and adaptation to uncertainty that challenge today's AI.

Zone 3. “Red Light” = Low worker desire + High AI capability

Proceed with extreme caution here. AI experts say the technology can handle these tasks, but workers resist automation here. Deploying AI in these areas may lead to worker resistance or negative societal implications.

Example:

• Logistics Analysts resist automating "contacting potential vendors to determine material availability", despite AI's high technological capability for this type of information gathering and communication.This resistance is likely influenced by concerns losing creative control, diminishing the "human touch" in their work

Zone 4. Low Priority = Low worker desire + Low AI capability

These tasks are less urgent for AI development. Neither workers nor AI experts see immediate potential here.

Example:

• Ticket agents show little desire to automate "tracing lost, delayed, or misdirected baggage for customers" - and current AI finds this difficult due to complex, real-world problem-solving requirements.

The Investment Mismatch

One of the most salient insights from the report is that 41% of Y Combinator startup funding flows into Zone 3 and 4 (“Red Light” and Low Priority zones).

Meanwhile, Zone 1 (“Green Light” zone - immediate wins) and Zone 2 (R&D Opportunity zone - future competitive advantages) remain under-funded.

Your Next Move

The Stanford research makes the path forward clear: Smart AI deployment follows a worker-centric and technology readiness approach. The biggest opportunities exist where these factors align—not where technologists think they should.

This week, ask your team two questions:

1. What repetitive tasks do you wish AI could handle?

2. What work do you never want to give up?

Map their answers to these four zones. You'll quickly map your real opportunities. And you might discover your next competitive advantage hiding in that Zone 2—where workers want help but no good solutions exist yet.

Remember, the best way to predict the future is to help create it—preferably by solving problems people actually have.

Those are my Thoughts From the DataFront

Max

But not all tasks and jobs are created equal. While AI can now code at near-human levels and handle many routine functions, Apple just published research that reveals some limitations in AI reasoning that every business leader needs to understand.

Before you start redesigning roles and restructuring teams, you need to know where AI will help you and where it breaks down.

Hint: Company complexity plays a critical role in your near-term AI plans.

Where AI Excels

AI is great at repetitive, predictable work like processing documents, answering basic customer questions, writing standard reports, and gathering information. These jobs are genuniely at risk.

But most real business work is messy and unpredictable.

Apple's Reality Check: Five Key Findings

Apple's latest research, "The Illusion of Thinking," tested current reasoning models—the latest evolution of AI specifically designed to "think through" problems with detailed chains of reasoning—on logical reasoning tasks. They found five limitations in today's systems:

1. AI doesn't gradually get worse as problems get harder—it hits a wall and accuracy drops to zero. One moment it's solving problems correctly, the next it's completely failing.

2. When problems get really complex, AI models actually reduce their thinking effort. They generate fewer "reasoning tokens" right when they should be working harder. It's like watching someone give up on a difficult math problem instead of buckling down.

3. Even when researchers gave AI the exact step-by-step solution, models still failed on complex problems. It seems to have trouble following a recipe.

4. AI might excel at one type of logical puzzle but completely fail at another puzzle requiring the same number of logical steps. This suggests they're relying on memorized patterns from training data, not genuine reasoning.

5. On simple problems, AI often "overthinks"—finding the right answer early but then exploring wrong paths anyway.

Key Insight: Today’s AI aren't reasoning through problems; they're performing sophisticated pattern matching while creating an "illusion of thinking."

What This Mean For Your AI Projects?

Real company jobs and processes are messy, they involve:

• Multiple stakeholders with competing priorities

• Undocumented tribal knowledge and informal processes

• Many different types of documents and systems to get an answer

• Judgment calls across ambiguous trade-offs

• Problems that don't fit standard frameworks

The larger your organization, the more this complexity multiplies. What looks like a simple task on paper often requires navigating unwritten rules, understanding political dynamics, and drawing on institutional memory that don’t exist anywhere in the company’s documentation and data.

Apple's key insight applied: When business problems require true reasoning rather than pattern matching, current AI will hit the same performance wall they found in controlled tests.

What Stays Human (For Now)

Based on Apple's findings about AI's reasoning limitations, certain categories of work remain firmly with human minds:

• Situations requiring reasoning through novel scenarios rather than pattern matching. Think strategic pivots, crisis management, or designing solutions for unprecedented challenges.

• Work requiring understanding of how different parts of an organization interact, especially when those relationships aren't formally documented.

• Decisions where stakes are high and precedents are limited. AI struggles when it can't lean on familiar patterns.

• Roles requiring reading between the lines, managing personalities, or building trust over time. This can be from changing approach based on subtle environmental cues or stakeholder feedback.

The Bottom Line

Amodei's warning deserves attention, but Apple's research suggests the transition will be more gradual and uneven than a simple "job apocalypse." AI will continue advancing, but fundamental reasoning limitations mean human judgment remains critical for complex organizational work (for now).

Understanding AI's limitations isn't about slowing down adoption—it's about implementing it where it actually works. The opportunity isn't in mass job replacement—it's in smart deployment.

The companies that thrive won't be those that replace humans fastest—they'll be those that best understand where AI can replace tedious tasks and where human reasoning remains irreplaceable.

Your Next Move: Pick one role in your organization you're considering for AI automation. Ask yourself: Does this role require genuine reasoning through novel scenarios, or is it primarily pattern matching on familiar problems?

The answer will tell you whether you're looking at a quick win or a longer-term challenge.

Those are my Thoughts From the DataFront

Max

Research Paper:

"The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity"

Context on Apple's Research

Apple's findings align with independent research from Meta's Yann LeCun and other leading AI researchers on reasoning limitations. The research is led by Samy Bengio, Apple's Director of AI Research.

Worth noting: Apple released this research while trailing in AI development, just before major OpenAI announcements. Regardless of timing or motivation, the core findings match broader research trends.

These limitations reflect late 2024 models and may not apply to future architectures.

Excitement about AI's potential → disappointment → Back to business as usual

The teams seeing value from AI aren't necessarily using better tools—they're asking better questions. The problem isn't the AI. It's that most people don't know how to ask good questions.

Why This Matters

Most people treat AI like Google with a personality- they make vague requests, expect detailed responses, then get disappointed when answers are generic.

I like to think of AI like hiring a brilliant intern who knows everything but has zero context about your business. Give them vague instructions, get useless work. Give them clear direction, and they'll amaze you.

The difference isn't the AI—it's the quality of the question.

Compare these two approaches:

Bad: "How do I increase sales?"

Result: Generic tips about customer service and pricing

---

Good: "I run a local restaurant. Our lunch rush is slow because office workers don't know we exist. What are three low-cost ways to attract nearby office workers during lunch?"

Result: Actionable ideas—lunch specials, office delivery partnerships, targeted local ads

---

A Framework for Better Questions

Instead of throwing vague “google-like” search requests at AI and hoping for the best, here's a simple framework that consistently delivers better results:

1. Start with What You Already Know

The best AI interactions happen when you already understand the domain. Share your existing knowledge, your hypothesis, or what you think the answer should look like.

When you know what good looks like, you can guide the AI toward better answers and immediately spot when it's heading in the wrong direction.

Instead of: "How do we improve customer retention?"

Try this: "I think our customers cancel in month 2 because they never complete our setup process. Help me design a 90-day plan to fix this specific problem."

2. Be Ruthlessly Specific

AI takes your questions literally - it responds to what you ask, not what you meant to ask. Include key details like audience, goals, constraints, and context.

Vague: "Write a marketing email"

Specific: "Write an email to customers who bought our software last year. Tell them about the new mobile app feature and include a 20% upgrade discount."

3. Create Context Before Content

Begin by explaining relevant background information. What does the AI need to know to give you a useful answer?

Basic: "Fix this email"

With context: "I'm responding to a 5-year customer who spends $2,000 annually. They're upset about a shipping delay. Help me write an apologetic response that offers a 10% discount."

4. Break Down Complex Questions

AI works best with clear, focused questions. When you have extremely long content or complex reasoning across multiple ideas, break it down. Give AI smaller, more manageable chunks instead.

For complicated tasks, guide the AI through step-by-step reasoning. Ask it to "think out loud" or create a plan before giving you an answer. The logic behind a recommendation is often more valuable than the recommendation itself.

Too broad: "What's our growth strategy?"

Step-by-step: "Let's build a growth strategy. First, help me identify our top 3 competitive advantages. Then, which customer segments would value these most?"

5. Iterate and Refine

Don't expect perfection on the first try. If the AI's response isn't quite what you needed, clarify or adjust your question. Often a single sentence of clarification can completely transform the quality of the response.

First attempt: "Analyze our customer churn"

Refining: "Looking at that churn analysis again, I need you to focus specifically on customers who left after less than 3 months and identify patterns in their usage before cancellation"

The Unexpected Leadership Benefit

In "Generative AI: Working Faster, But Are We Thinking Less?" , we discussed the worry that AI might make us lazy thinkers. But I believe the opposite is happening. Learning to ask AI better questions makes you better at managing people.

The same skills that get better AI results, also make you a better leader:

• Explaining exactly what you want

• Providing clear context upfront

• Defining success before starting

• Breaking complex problems into steps

If you can get an AI to deliver useful work, you can do the same with your team.

The discipline of good questioning trains your brain to think more systematically about problems and solutions- making you a better manager, strategist, and communicator with your human colleagues.

Your Next Move

Pick one task you normally give to AI. Rewrite your prompt using this framework. Compare the results.

In a world where everyone has AI tools, the most valuable skill isn't knowing the answers—it's knowing what to ask.

Every company will have ChatGPT. The winners will be those whose people know how to think with it.

Those are my Thoughts From the DataFront

Max

From Microsoft and Google to IBM and PwC, companies aren't just cutting teams to save money—they're restructuring to make room for AI. Microsoft alone let go of 6,000+ employees, while Satya Nadella reports that 30% of their code is now AI-written.

These job cuts are scary, but before we panic, let's zoom out a bit. Throughout history, technological revolutions have consistently created more jobs than they've eliminated, though not always in obvious ways or immediate timeframes.

To understand where the real opportunities lie in this AI transformation, we need to distinguish between first and second-order effects.

First-Order vs. Second-Order Effects

First-order effects are the immediate, obvious impacts of AI:

• AI writing emails and reports

• Chatbots answering customer questions

• AI helping programmers write code faster

• Virtual assistants scheduling meetings

These are valuable but predictable. However, history suggests the real transformation comes from second-order effects - the ripple effects that happen as society adapts:

• New jobs and industries emerging to support AI

• Changes in how we train workers

• Shifts in what skills become valuable

• Entirely new business models

Think about smartphones: The first-order effect was communication and internet access. The second-order effects—which were harder to predict—included entirely new sectors like ride-sharing, mobile payments, and on-demand delivery.

The Evolution of Work: From Deskilling to Non-Skilling

To better understand where we are in this transformation, Stanford's Erik Brynjolfsson offers a useful framework for understanding how we've historically adapted to technological change in the workplace:

1. Deskilling: Where technology simplifies complex jobs (like assembly lines replacing craftsmen)

2. Upskilling: Where technology requires more advanced skills (like Excel transforming accounting)

3. Non-Skilling: Where AI might eliminate the need for human workers in some roles entirely

We're currently navigating between waves 2 and 3—and the businesses that thrive will be those that understand this transition. Let's look at a concrete example of a second-order opportunity that's emerging directly from this workforce transition.

Displacement Agencies: A Second-Order Opportunity

With all this change come both challenges and opportunities. A McKinsey study suggests that by 2030, at least 14% of employees globally will need to change careers due to technological disruption. This creates a significant opportunity for a new type of organization—what I call "displacement agencies."

These specialized organizations would help workers navigate career transitions by:

1. Identifying transferable skills workers already have

2. Providing targeted training for new skills

3. Offering career guidance through transitions

4. Connecting retrained workers with new opportunities

These agencies help both sides: workers who need new jobs and companies that need people with the right skills for AI. They solve the big problem that we are seeing when technology changes faster than people can keep up, potentially leaving many behind.

To learn more read my post "Displacement Agencies: Retooling and Reskilling to Keep Up With the Pace of Change", where I detailed how these organizations could help bridge the gap between technology's acceleration and our ability to adapt. I believe these agencies will become increasingly crucial for both individuals and the broader economy.

Your Next Move

While first-order effects like job displacement are immediate and visible, the second-order effects create entirely new industries, roles, and opportunities.

As we navigate between the upskilling and non-skilling phases of this transformation, the organizations that thrive won't be those merely replacing workers with AI, but those identifying the new human needs this shift creates. Whether it's displacement agencies helping workers transition, or entirely new business models we haven't yet imagined, the biggest opportunities often emerge from technology's second-order effects.

The key question isn't "Which jobs will AI replace?" but "What new human needs will emerge as AI transforms how we work?"—and that's where the next wave of innovation awaits.

Those are my Thoughts From the DataFront

Max

Microsoft's latest research on 31,000 workers provides compelling validation of this thesis.

They've identified what they call "Frontier Firms"—companies that completely rebuilt their operations around AI. These firms share three distinct traits:

1. Intelligence on Tap: AI assistance available everywhere, not just in specific departments

2. Digital Colleagues: AI treated as team members, not tools—taking on real responsibilities and collaborating on complex work

3. Amplified Performance: Human creativity + AI processing power = results neither could achieve alone

Frontier firms are seeing strong results - they are achieving 35% faster task completion and 28% better decision-making accuracy. With 71% of frontier firm workers report their company is thriving.

The Three-Phase Evolution

What's particularly interesting is how these companies evolve. Microsoft discovered that companies don't transform overnight. They evolve through three distinct phases:

Phase 1: Assistant Phase (AI as helper)

Where most organizations are today

AI handles routine tasks and boosts efficiency. Think email drafting, meeting scheduling, basic data analysis. Many companies are stuck here, treating AI as a fancy productivity tool.

Phase 2: Colleague Phase (AI as Team Member)

The breakthrough moment for most organizations

AI agents work alongside humans on complex projects (called ‘digital colleagues’). They take on specific responsibilities, contribute to projects, and help employees scale their impact. This is where real collaboration begins.

Phase 3: Manager Phase (Human-Led, AI-Operated)

The Frontier Firm advantage

Humans set strategy while AI orchestrates entire business processes. Leaders become "agent bosses," managing AI systems that handle complex workflows end-to-end.

Quick Self-Assessment: Which phase are you in?

□ Still figuring out how to use AI tools

□ AI helps with specific tasks but not integrated

□ AI is becoming a team member

□ AI handles workflows while we focus on strategy

---

The Rise of the Agent Boss

As organizations evolve through these phases, a new type of leader emerges: the "agent boss."

Microsoft identified this as the key differentiator between Frontier Firms and everyone else. These aren't technical roles—they're leaders who orchestrate AI systems like they would human teams. Some call them "agent whisperers," reflecting their ability to get the best performance from AI colleagues.

What Agent Bosses Actually Do:

• Design AI-powered workflows from scratch

• Delegate complex tasks to the right AI agents

• Think in systems, not individual tools

• Bridge human strategy with AI execution

Leaders increasingly see managing AI agents as core to their team's responsibilities, with 44% of Canadian managers expecting this to be part of their role within five years.

Why This Matters Now

Microsoft's research carries a stark warning: "The pilot phase is over."

The gap between Frontier Firms and everyone else isn't just widening—it's accelerating. While 46% of executives list AI expansion as their top priority, only 35% of organizations actually use AI agents for workflow automation. This disconnect reveals the real challenge: knowing AI matters is easy; transforming your organization around it is hard.

The companies succeeding aren't just better at using AI—they've fundamentally reimagined what work looks like. They've moved from asking "How can AI help us?" to "How should we rebuild now that AI exists?"

The question isn't whether AI will transform your industry—it's whether you'll be leading that transformation or scrambling to catch up.

Your Next Move

1. Identify which phase you're in

2. Pick one process to completely rebuild with AI

3. Start developing your "agent boss" skills now

Those are my Thoughts From the DataFront

Max

Reference - 2025: The Year the Frontier Firm Arrives in Canada

As these agents take on more responsibility in your business, how do you ensure they represent your brand values and don't become expensive liabilities?

Why Every AI Needs Guardrails

AI agents without proper guardrails are like cars without brakes - powerful but dangerous. Without guardrails, your AI tools could steer you into dangerous territory:

• Sharing sensitive company information

• Giving inaccurate advice that damages customer trust

• Producing content that doesn't match your brand voice

• Violating industry regulations specific to your business

Any of these missteps can lead to damages to your organization that can quickly erase all the productivity gains you were expecting from your AI initiative.

The Five Essential Guardrails Every Business Needs

Based on recent McKinsey research, successful AI implementations require five types of guardrails:

1. Appropriateness Guardrails - Filter out harmful or offensive content that could damage your brand.

2. Hallucination Guardrails - Prevent AI from "making things up" by checking its answers against trusted information sources.

3. Regulatory Compliance Guardrails - Ensure outputs follow relevant laws for your industry.

4. Alignment Guardrails - Make sure content maintains your brand voice and doesn't drift from its main purpose.

5. Validation Guardrails - Verify AI outputs meet specific criteria before taking action, especially for sensitive tasks. This would typically include human review for high-risk decisions or recommendations.

How Guardrails Actually Work

The most effective guardrail systems consist of four key components working together:

• Checkers - Your first line of defense, scanning AI-generated content to detect errors and flag issues

• Correctors - Once a checker identifies an issue, correctors refine or improve the flagged content (fixing inaccuracies, removing inappropriate content, etc.)

• Rails - Manage the interaction between checkers and correctors, triggering corrections when content fails to meet standards

• Guards - Coordinate the overall system to ensure smooth operation

While no guardrail system guarantees complete safety, implementing these components creates competitive advantages by building trust and avoiding legal issues.

Real-World Examples

ING developed an AI chatbot with guardrails to ensure accurate and safe customer interactions. The guardrails filtered out sensitive information and potentially risky financial advice while ensuring regulatory compliance. Since this was a customer-facing tool, designing these guardrails from the beginning was essential to protect both customers and the bank's reputation.

Your Next Move

Identify which interactions and processes carry the highest potential for harm if AI makes a mistake, then design your guardrails around these priority areas.

Remember, effective guardrails aren't just safety measures—they're competitive advantages that build trust with your customers and partners as you scale AI across your business.

Those are my Thoughts From the DataFront

Max

Notable Links:

• What are AI guardrails? - McKinsey

• AI needs guardrails, but they won't be easy to build - The Globe and Mail

While everyone's talking about AI agents, few business leaders have a clear framework for identifying which types could deliver immediate value. That's about to change.

Google analyzed over 600 real world AI implementations across industries, and identified six key ways businesses are using AI agents to deliver immediate value.

Understanding these six agent types gives you a practical lens to cut through AI hype and identify real opportunities for your business. Let's break down these six agent categories, what they do, and how leading companies are using them to transform their operations.

You can also check out the comprehensive list of over 600 real-world use cases for more inspiration and concrete examples.

The Six AI Agents

1. Customer Agents: Making Customer Interactions Easier

These are AI systems that directly interact with customers to improve their experience and provide support.

Key benefits:

• 24/7 customer support without staffing costs

• Personalized interactions at scale

• Faster resolution times

Examples

• Mercedes-Benz: They have smart AI in their new cars that you can talk to for things like searching for places and getting directions. They also have AI on their website to help you shop online.

• Discover Financial: They have a virtual helper that uses AI to answer customer questions to help you better.

2. Employee Agents: Boosting Productivity Within Your Organization

These are internal AI tools that help employees work more efficiently by taking care of routine tasks and giving quick access to company information.

Key benefits:

• Automate repetitive administrative tasks

• Centralize knowledge and make it instantly accessible

• Reduce operational costs

Examples

• Uber: They use AI to automatically summarize what customers say, which helps their support staff save time.

• Cintas: They built a smart internal search tool with AI so their customer service and sales teams can quickly find the information they need.

3. Creative Agents: Enhancing Marketing and Content Creation

These are AI tools that help create different kinds of content to improve marketing and creative work, like social media posts and images.

Key benefits:

• Scale content creation without expanding headcount

• Test multiple creative approaches simultaneously

• Personalize marketing at the individual level

Examples

• PODS: They created the "World's Smartest Billboard" using AI to adapt messages to different neighbourhoods in real-time, creating over 6,000 unique headlines.

• Radisson Hotel Group: Personalized advertising at scale using AI, achieving a 50% rise in ad team productivity and over 20% revenue increase

4. Code Agents: Empowering Software Development

These are AI tools that help software developers write code and find mistakes to speed up the coding process and improve the quality of software.

Key benefits:

• Reduce development time for routine functions

• Catch bugs earlier in the development cycle

• Lower the barrier to entry for technical roles

Examples

• Regnology: They built a Ticket-to-Code Writer tool with AI to automatically convert bug tickets into actionable code.

• Capgemini: They used Code Assist to improve software engineering productivity, quality, security, and overall developer experience.

5. Data Agents: Unlocking Insights from Information

These are AI systems that turn raw data into useful insights to help organizations make better decisions and improve their operations

Key benefits:

• Extract patterns from complex datasets

• Make predictions based on historical trends

• Surface insights that would otherwise remain hidden

Examples

• BMW Group: They use AI to make their factory planning and supply chains better, which makes their manufacturing more efficient.

• Geotab: They use AI to look at tons of data from vehicles to help companies manage their fleets better and save on fuel.

6. Security Agents: Protecting Your Organization from Threats

These are AI systems that help protect organizations by automatically finding, analyzing, and responding to security threats.

Key benefits:

• Continuous threat monitoring

• Rapid identification of unusual patterns

• Automated response to common attacks

Examples

• Airwallex: They use AI to detect and stop fraud in real-time when people are making payments.

• BBVA: They use AI to find and deal with security threats across their systems more quickly and accurately.

Your Next Move

The companies seeing the greatest ROI from AI aren't implementing all six agent types at once. They're starting with a single, high-impact opportunity.

Look at your current business challenges and ask: "Which one of these agent types could solve your most pressing problem?" Begin there, with clear metrics to measure success.

Start small, learn fast, and expand methodically. The key to success with AI lies not just in the technology itself, but in how thoughtfully you put it into practice.

Those are my Thoughts From the DataFront

Max

EY has unveiled their EY.ai agentic platform, built on NVIDIA’s AI stack, to fundamentally change how 80,000 tax professionals work by deploying 150 specialized AI agents focused on tax, risk, and finance operations.

What EY Is Actually Building

The EY.ai Agentic Platform combines EY's domain expertise with NVIDIA's AI stack to create AI agents that tackle the most time-consuming parts of tax work:

• Data collection and preparation

• Document analysis and review, and

• Income and indirect tax compliance

These agents don't work in isolation. They operate within an orchestration framework that coordinates their activities, using domain-specific models fine-tuned for tax and risk management. The system incorporates guardrails for responsible AI use and can be deployed across cloud, on-premises, and edge environments.

The scale is also pretty impressive. These agents will help handle more than 3 million tax compliance outcomes and redefine over 30 million tax processes annually.

“AI agents give businesses the power to navigate complexity with unprecedented intelligence. By combining EY’s deep domain expertise with NVIDIA AI, we’re shaping a new era of enterprise services—where AI-driven reasoning transforms financial decision-making, risk management and regulatory compliance at global scale.”

-Jensen Huang, NVIDIA founder and CEO

Theory Becomes Reality

If you're an avid reader of Thoughts From the DataFront, you'll recall our discussion about those 19th century factory owners who gained little by simply replacing steam engines with electric motors. The breakthrough came when they rebuilt their entire factory layouts around electricity's capabilities.

EY is doing the same with AI agents—not just automating existing processes, but redesigning workflows around what AI does best. Their implementation also checks every box from our four criterias for agent ready processes:

• Information-intensive: Tax compliance involves massive document processing

• Repetitive: These are standardized processes performed millions of times

• Reversible: Human experts still guide and verify the work

• Bottleneck: These tasks typically slow down high-value professional work

Key Learnings For Business Leaders

1. Reimagine, don't retrofit: EY isn't just adding AI to existing workflows—they're rebuilding processes around AI's capabilities

2. Start with document-heavy tasks: Focus on areas with clear workflows and high information processing demands

3. Think in systems, not tools: The power comes from specialized agents working together, not isolated AI applications

4. Combine domain expertise with AI: Effective agents blend technical capabilities with deep understanding of specific business domains

The technical pieces are now in place for businesses to reimagine their operations around AI agents. The organizations that move diligently to identify the right processes and rebuild them will gain significant advantages in productivity.

The Great Rewiring isn't coming—it's here. And the organizations that embrace it fastest will define what work looks like in the age of AI.

Those are my Thoughts From the DataFront

Max

"AI usage is now a baseline expectation at Shopify," Lutke wrote, directly challenging his entire organization to adapt or risk "stagnation, slow-motion failure."

Lutke’s New AI Rules

Lutke isn't just encouraging AI adoption – he's mandating it:

• Teams must prove why they can't use AI to meet goals before requesting more staff

• AI exploration must dominate the prototype phase of any project

• AI usage will be evaluated in performance reviews

• Everyone must comply – including executives (CEO included)

Why This Matters

I believe this memo signals a turning point for how businesses approach AI. Shopify has recognized that AI isn't just a competitive advantage but a fundamental necessity.

“Our job is to figure out what entrepreneurship looks like in a world where AI is universally available.The call to tinker with it was the right one, but it was too much of a suggestion. This is what I want to change here today.”

Lutke describes AI as a "10X" tool, allowing his best employees to tackle "implausible tasks" they "wouldn't even have attempted before" and get "100X the work done."

Five Actions for Business Leaders

Here are five concrete steps to incorporate AI in your business, inspired by Lutke:

1. Make AI usage mandatory. "I don't think it's feasible to opt out of learning the skill of applying AI in your craft."

2. Use AI for faster prototyping. AI "dramatically accelerates" creating something teammates can "look at, use, and reason about in a fraction of the time it used to take."

3. Include AI in performance reviews. Make it clear that using AI effectively is part of everyone's job.

4. Create knowledge-sharing mechanisms. Shopify uses dedicated Slack channels like #revenue-ai-use-cases and #ai-centaurs where employees share successful prompts and approaches.

5. Reframe resource requests. Before approving new headcount, ask: "What would this look like if AI agents were already part of the team?"

What resonates with me most is Lutke's message about continuous growth: "If you're not climbing, you're sliding." At Thoughts from the DataFront, we've discussed at length that the businesses that thrive won't be those with the most advanced AI – they'll be those who make AI usage a fundamental part of their culture and operations.

As the saying goes, the best time to plant a tree was 40 years ago, but the second best time is today. Your window to gain competitive advantage from AI is rapidly closing. Soon, it won't be a differentiator – it will be the baseline expectation for simply staying in the game.

Those are my Thoughts From the DataFront

Max

Read Lutke's full memo here

Now it's time to get tactical: how exactly do you execute this rewiring in practice?

Many organizations understand AI's potential but struggle to translate that understanding into structured implementation that delivers measurable returns.

The challenge isn't just making the internal business case based on initial 10-15% productivity gains. According to McKinsey's research, it's developing the systematic approach in redesigning workflows around AI that enables companies to outperform competitors by 2-6x on shareholder returns.

The Multiplier Effect: The difference comes from moving beyond point solutions to thoughtfully integrating AI into your business processes. When done right, these benefits compound—developing competitive advantage that drives significant value.

The Six Implementation Areas

Let's break down the six areas that create this advantage—whether you're running a 10-person operation or a 10,000-person company.

1. Strategic Roadmap - Start With Value, Not Tools

Businesses often jump straight to tools (like ChatGPT) without clarity on why they're implementing AI.

• Align top management around specific business domains to transform with AI

• Identify customer journeys or business processes that could generate measurable value

• Set clear metrics for success before implementation begins

2. Talent - Ensure Teams Can Execute and Innovate

You don't need a team of Stanford PhDs. What you need is the right mix of skills to both implement and innovate with AI.

• Build a core team with the right technical skills to lead implementation and understanding of your business model and data

• Create training plans to help existing employees (not just IT staff) learn new skills

3. Operating Model - Redesign Workflows, Don't Just Add AI

This directly connects to our "Don't Just Add AI" discussion. Success comes from reimagining processes, not merely adding technology to existing workflows.

• Integrate business and technology teams deeply—eliminate the handoff mentality

• Identify bottlenecks and rebuild processes with clear handoffs between AI and human judgment

• Focus on both customer-facing products and the platforms that support your staff

4. Technology - Build Technology Infrastructure That Enables Speed

Create technology environments that enable teams to access the data, applications, and tools they need to rapidly innovate.

• Set up cloud systems that can be easily updated as technology changes

• Choose AI tools that complement processes and work well with what you already have

• Find the right balance between building your own solutions and buying ready-made ones

5. Data - Quality Data Drives Quality Results

AI is only as good as the data it runs on. Build architectures centered around reusable data products.

• Create curated, packaged data elements that teams can easily access

• Ensure these data products can be used across different applications

• Continuously enrich your data as your business and interactions with clients evolve

6. Adoption and Scaling - Capture Value and Manage Risks

The final piece is about actually realizing returns on your AI investments.

• Start with one high-impact workflow with clear metrics for success

• Document both wins and challenges to inform your next project

• Create ways to expand successful projects across your organization

The Bottom Line

The McKinsey framework confirms what we've been discussing here at Thoughts From the DataFront - successful AI transformations are not primarily a technology challenge. It's about connecting strategy, people, processes, and data around a clear business goal.

The businesses seeing real returns aren't necessarily using more advanced AI. They're implementing it more thoughtfully, with clear roadmaps, measured outcomes, and reimagined workflows that leverage what AI does best.

Your next move: Look at these six areas in your own business. Where are you strongest? Where are you weakest? Starting with just one area that needs improvement will put you ahead of most competitors.

Those are my Thoughts From the DataFront

Max

Reference Link: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/rewired-and-running-ahead-digital-and-ai-leaders-are-leaving-the-rest-behind

It's a fair question. While tools like Cursor let anyone create working software through natural language, there's a world of difference between a functional prototype and a secure enterprise application.

Anyone can sketch a building, but you still need architects and engineers to ensure it won't collapse. Similarly, AI-powered development works best as a prototyping tool for business leaders - creating the blueprint that professionals can transform into secure, production-ready systems.

Before diving into code, you need structure. Here's how to build your first AI-powered prototype safely:

Framework for Non-Developers

1. Start with Purpose

Before you ask AI to generate a single line of code, answer and document the following:

• What specific problem are you solving?

• Who will use your solution and what will they do with it?

• What data in what formats will be input, processed, or transformed?

• Whether you'll handle personal user data (payment info, credit data, health records)

• What data sources you'll access, both online and locally?

• How will you know if it's successful?

The clearer your purpose, the better your result. Just like building a house, you start with blueprints not hammers.

2. Think Like a Developer

Software engineers anticipate problems before they happen:

• Who needs access and what kind of permissions should they have?

• How will your app verify users and check their inputs?*

• How will your system respond to unexpected data or errors?

• What will alert you when something breaks?

• How will you secure stored data and protect passwords?

Identify potential risks early—you don't need to solve every problem, but you should know where the vulnerabilities might be.

3. Narrow Focus

Don't try to build the whole house at once:

• Create the simplest version that solves the core problem

• Focus on one workflow before expanding

• Use existing templates (from Github) when possible

• Document your decisions as you go

Start with a minimum viable product rather than building every feature at once.

4. Test Aggressively

You don't need to be a developer to spot problematic code:

• Build a visual interface to see your tool working in real-time

• Ask AI to explain its code—if it struggles to explain or you struggle to understand, that's a warning sign

• Try unexpected inputs (like special characters or extreme values) to test how your tool handles surprises

• Use free security testing tools from OWSAP** (top 10, api security, mobile)

• Ask a professional review any major issues

Build in your process the ability to test your prototype.

5. Know When To Call the Pros

Bring in professional developers when:

• Your tool handles sensitive data (such as user identifiers, locations, payment data, health information, etc.)

• It's integrated into business processes

• You need to connect with other systems, internally or externally

• You're considering external access

Professional developers transform your prototype into production-ready software, addressing security, scale, and maintainability.

Now…Some Practical Tips

• Use Cursor to write and organize your code—it handles much of the technical complexity

• Try Perplexity to research technical details and find example code and get specific guidance to provide to Cursor—ask questions like "How do I connect to Gmail?" or "Show me login system examples"

• Run everything locally first before deploying anywhere

• Build a simple interface so you can see what your software is doing

• Look for GitHub templates as starting points rather than building from scratch

• Apply zero-trust principles to verify everything, trust nothing

• OWASP.org to test the resulting output code for security vulnerabilities

• Keep your projects internal until they've been professionally reviewed

Key Takeaway

The power of AI in coding isn't eliminating developers— it's reducing the translation gap between business needs and working software. Instead of lengthy requirements documents, you can show developers exactly what you need through a working prototype.

This collaboration model—business experts create prototypes that developers build into secure production solutions—represents the future of software development. The gap between idea and implementation has never been smaller, and the need for responsible development practices has never been greater.

Your Next Move: Start by taking a small, non-critical business process and prototyping it with Cursor this week. Even if you've never written code before, you'll be surprised how quickly you can create something functional - and more importantly, you'll learn exactly where you need professional help to take it further.

Special thanks to Paul from Essential Risk Management whose thoughtful comments on last week's post inspired this collaboration. Together, we've developed a practical framework that empowers business leaders to safely explore AI-powered development while respecting professional software engineering principles.

Those are my Thoughts From the DataFront

Max

Definitions

*Zero Trust is a security concept meaning "verify everything, trust nothing." Even if something looks legitimate, your system should always authenticate and validate it.

**OWASP (Open Web Application Security Project) offers free resources to test web applications, APIs, and mobile apps for common security vulnerabilities.

That's exactly what's happening with 'vibe coding,' a breakthrough approach where you build software by having a conversation with AI instead of programming it manually. As Andrej Karpathy (founding member of OpenAI and Tesla's former AI director) put it, 'The hottest new programming language is English.'

This shift doesn't just change how we build software—it changes who gets to build it. And that might be the biggest business opportunity of all.

What Is Vibe Coding?

Vibe coding is creating software by talking to AI instead of writing traditional code:

• Describe what you want in plain English

• Review what the AI creates

• Refine with conversational feedback

You focus on what you need, not how to build it.

Key Takeaway: Vibe coding democratizes software development by allowing those without coding expertise to turn their ideas into digital products.

Why This Changes Everything

Traditional software development involved complex handoffs between business experts and technical teams. Vibe coding flips this model completely:

1. Domain experts create solutions directly - No more waiting for developers

2. Prototype in days not weeks - See working prototypes immediately and refine in real-time

3. Focus on outcomes, not code - "What problem are we solving?" vs "How do we code this?"

You don't need coding expertise—just the ability to describe what you want, recognize when it works, and provide feedback.

Cautionary Note: While perfect for prototypes and internal tools, production apps still need professional developers for security and scale. Think of vibe coding as creating the blueprint that professionals can perfect.

Finding Your First Vibe Coding Project

To find your first vibe coding project, look for tasks in your daily life that are:

• Information-intensive? Does the process involve gathering or analyzing information?

• Repetitive? Do you perform this task regularly?

• Reversible? Can mistakes be easily fixed without major consequences?

• Bottleneck? Does this process slow down more valuable work?

  

See It In Action: My AI News Aggregator

My daily AI news research is a perfect candidate - it's information-intensive, repetitive, low-risk, and is one area where I spend a lot of time.

Here's the simple prompt I used to get started in Cursor:

Create an AI news aggregator that: 
- Fetches recent AI-related articles from multiple sources 
- Scores articles (0-100) based on keywords, company significance, tech relevance, credibility, and freshness 
- Displays the top 10 highest-scoring articles with title, source, score, and description 
- Updates in real-time when requested 
- Filters for articles from the past 3 days only

After about 2-3 hours of debugging and refinement, here's the working result:

From idea to working software in one afternoon. What would have traditionally required technical expertise, significant development time, and considerable resources now happens over a weekend for just a few dollars of compute cost.

Ready to try vibe coding?

Here are two tools that make it incredibly easy:

🛠️ Wispr Flow - Speak to your computer through this interface that connects directly with your applications

🛠️ Cursor - AI-powered code editor that turns conversation into working software

This weekend: Identify a repetitive information task that consumes your time. Use Cursor to describe an agent that automates this process. You might be surprised how quickly you can turn a daily bottleneck into an automated workflow.

I'd love to hear about your experiences - share them in the comments or send me a DM!

Key Takeaway: The real power of vibe coding isn't just faster software development—it's democratizing who gets to create. By removing coding as a barrier, domain experts can now build their own solutions.

Those are my Thoughts From the DataFront

Max

Well, the latest "State of AI" survey from McKinsey confirms our thesis: fundamentally redesigning workflows is the single biggest factor in whether companies see financial returns from their AI investments. Yet despite this, only 21% of organizations have started to do this.

Let's break down the key findings and what they mean for your business:

1. The AI Implementation Gap

McKinsey identified 12 key adoption practices (identified in the chart) that correlate with positive financial impact from AI.

Among the 12 best practices, tracking well-defined KPIs for gen AI solutions shows the strongest correlation with bottom-line impact. For larger organizations, establishing a clearly defined roadmap for AI adoption is also particularly impactful.

Most organizations are still in early stages - less than one-third of companies follow most of these best practices, and fewer than one in five track specific KPIs for their AI initiatives.

The gap between large and small companies is substantial:

• 52% of large companies have dedicated teams driving AI adoption (vs. 23% of smaller firms)

• 42% have regular internal communications about AI value (vs. 30%)

• 37% have senior leaders actively engaged (vs. 34%)

• 31% have established role-based capability training (vs. 17%)

• 25% have created clearly defined roadmaps (vs. 12%)

2. The Coming Reskilling Wave

Organizations have started reskilling their workforce to adapt to AI, but the real wave is yet to come:

Today: 44% of companies have reskilled just a tiny fraction (5% or fewer) of their employees.

Within three years: 19% of employees are expected to be reskilled

This massive shift aligns perfectly with what we've been saying about the need to reimagine work and training, but also about the huge opportunity for more targeted training programs for companies and employees.

3. Finding Balance: How Leading Companies Structure AI Efforts

Just as we found in our Buy vs Build post , successful companies are striking a careful balance between centralized strategy and decentralized execution:

• Risk and compliance (57%) and data governance (46%) are most frequently fully centralized, reflecting organizations' need for control over AI risks and data integrity.

• AI adoption (23%) and tech talent (29%) are least often fully centralized, with most organizations preferring hybrid approaches that balance central oversight with local implementation.

This pattern makes sense—organizations centralize functions that manage risk and data integrity while distributing implementation to where the work actually happens.

What This Means For You

Despite the progress, most organizations still report no company-wide financial impact from generative AI. We're clearly in the early stages, but the path to success is becoming clearer:

1. Reimagine, Don't Retrofit: Don't just add AI to existing processes – rebuild workflows around what AI does best.

2. Make AI a Leadership Priority: When CEOs get directly involved, AI initiatives succeed. This isn't an IT project – it's a business transformation.

3. Measure What Matters: Define clear KPIs for your AI initiatives. Without measurement, you can't improve or demonstrate value.

4. Start Reskilling: Your AI strategy needs a parallel talent and training strategy.

The biggest winners aren't those who adopt AI first, but those who reorganize their operations to fully leverage the technology's capabilities.

Those are my Thoughts from the DataFront

Max