We dug into AI literacy for leaders last week. It's fair to say that it's a critical topic and NOT one to be delegated or outsourced. Actually, it's so important that I want to continue on that topic again this week. So, let's spend a little time talking about how real AI literacy is creating the new digital divide.

The New Digital Divide - Leaders Who Get AI vs. Those Who Don't

So here's the thing about the new digital divide, it's not really about technology access anymore. It's about whether you actually get it. And by "it," I mean AI.

Remember the early 2000s? The big question was who had internet access. Then in the 2010s, it shifted to who knew their way around digital marketing and social media. But now? In 2025, we're dealing with something way more personal. It's the gap between business leaders who understand AI and those still thinking, "Eh, someone on my team will handle it."

And if you read last week's article, then you know you can't just hand this off to someone else. Leaders who try? They're already eating dust. The gap's widening fast, and nobody's coming to save you.

The Leadership Gap Has Gone Cognitive

AI isn't just another tool in the toolbox. It's basically a whole new language. And if you can't speak it? You're stuck relying on translators who might not see things the way you do.

Five years ago, you could totally run a competitive company without knowing a neural net from a napkin. But today, that kind of ignorance will cost you. When you don't understand AI, even just the big-picture stuff, you can't make smart decisions, you can't push back when something doesn't make sense, and you miss opportunities that are right in front of you.

It's not about having the fanciest tech stack anymore. It's about being able to think with technology. That's the new leadership gap, and it's happening in your brain.

Remember those founders who wrote off digital transformation as "just a fad"? Yeah, their companies don't exist anymore. AI illiteracy is the same trap, just way faster.

The Comfort Zone Economy Is Collapsing

For decades, leaders could coast in what I call the comfort zone economy. You'd build up expertise in your niche, stick with what worked, and just keep optimizing. Nice and steady, right? Well, AI throws all that out the window.

Now your expertise has an expiration date. Business models built on years of experience are getting replaced by ones built on being able to pivot on a dime. In the old comfort zone economy, experience was pure gold. In the AI economy? It can quietly become dead weight if it stops you from asking, "Wait, could we do this way better with machines?"

The leaders who "get" AI aren't necessarily tech geeks. They're just curious. They're willing to tear down what used to work and rebuild it smarter. They know the future belongs to people who can re-learn faster than everyone else forgets.

Delegating AI Strategy Is the New Outsourcing Mistake

Remember the outsourcing boom in the 2000s? Everyone shipped their problems overseas to save money, only to realize, oops, they'd outsourced their actual core business too.

Well, history's doing its thing again. Leaders who don't understand AI are outsourcing their thinking. Not their coding, but their actual strategic thinking. They lean on consultants, vendors, or employees to decide what "AI transformation" should look like. The result? A strategy that serves everyone's agenda except the leader's.

The divide isn't between companies that have AI and those that don't. It's between leaders who can direct their AI and leaders who get pushed around by it.

The ones thriving right now? They're rolling up their sleeves and learning how AI systems actually make predictions. They're asking tougher questions. They don't need to code, but they need to know enough to spot when something's off.

The Myth of the "AI-Ready" Business

There's this myth floating around boardrooms that AI will magically make your business "smart" once you plug it in. That's not how any of this works. AI doesn't transform your business. You do.

Companies that are actually AI-ready already had leaders building cultures of experimentation and speed. They were rewarding curiosity and encouraging people to ask "why not?" long before AI showed up. Compare that to companies trying to jam AI into rigid hierarchies and outdated processes. It's like strapping a rocket engine to a tricycle...a recipe for disaster.

The new digital divide isn't about having access to AI tools. It's about having the mindset to use them right. The winners are already building that muscle memory of constantly reinventing themselves.

The ROI Divide - Why Some Companies Actually Get Results

Let's talk money. Every founder eventually has to answer the question, "Is this AI thing actually paying off?"

So, how are these projects shaking out? The companies seeing real ROI from AI aren't the ones with the fanciest models or the biggest budgets. They're the ones treating AI like a strategic teammate, not a shiny side project.

They're asking, "What's the smallest meaningful thing we can deploy this week?" They're measuring how fast they learn and iterate, rather that relying solely on traditional metrics. And they use what they learn to build momentum. That's how you get compounding returns...not with perfection, but with consistent progress.

The companies on the wrong side? They're still working on their "AI roadmap" PowerPoint decks while their competitors are already running live experiments. Analysis Paralysis has never been more expensive.

The Human Dividend

It's easy to think the AI revolution is all about automation and doing more with fewer people. But that's missing the point entirely. The companies winning with AI aren't replacing people. They're amplifying them. AI handles the grunt work so people can focus on the higher-level stuff involving creativity, judgment, strategy, relationships.

That's what I call the human dividend: using AI not as a cost-cutting tool, but as a capability multiplier. It separates businesses that grow by empowering people from those that shrink by just chasing efficiency.

Funny thing is, the more you learn about AI, the more you appreciate what makes humans irreplaceable. Things like empathy, ethics, and the ability to imagine something better.

Closing the Divide (Before It Becomes Permanent)

Here's the bad news first. The gap between AI-literate and AI-illiterate leaders is widening faster than anyone expected. Good news? You can still cross it, but you must start now. Here's how to catch up or stay ahead:

• Learn the language: Take one course. Read one whitepaper. Play around with one open-source model. Doesn't matter where, so just start.


• Ask better questions: When someone says "we'll use AI," come back with "to predict what, exactly? Based on which data?"


• Build internal literacy: Make AI training part of your culture, not a one-time thing.


• Reward curiosity: Celebrate experiments, even the ones that flop. Learning velocity is your competitive advantage now.


• Model the mindset: Your people are watching how you adapt. If you approach AI with confidence and humility, they'll follow your lead.

The leaders who make it through this transition will be the ones who realize AI isn't here to replace them...it's here to expose them. To show who's still learning and who checked out too early.

Final Thoughts - It's Not Too Late...Yet

Look, there's no shame in being late to the AI conversation. The only real risk is staying quiet. Every leader you admire once had no idea what they were doing either. They just learned faster than everyone else.

AI is the great equalizer, but only if you're willing to engage with it directly. Otherwise, it becomes the great divider by separating the people who lead the future from the people who just get led by it.

So ask yourself: when the next major AI shift happens, and it will, do you want to be scrambling to catch up, or setting the pace? That's not a tech question anymore. That's a leadership one.

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You probably understand the importance of fostering an AI-ready culture in your company by now. We talked about it last week, specifically for marketing. However, it applies to all areas of your company. We also know that culture starts at the top. So, how does a leader set the right culture and drive his or her company in the right direction? It all starts with AI Literacy for the leader.

Why Founders Must Learn AI Themselves...Not Just Delegate It

If there is one universal pattern emerging across founders right now, it’s that AI is no longer a specialist domain. It is a leadership literacy. And if you are a founder who believes you can “hire the AI person later” or “just source a contractor when it’s time”, then you are building your company on some really bad assumptions.

The same way the internet forced leaders to understand digital, or the same way SaaS forced leaders to understand subscription economics, or the same way mobile forced leaders to understand UX and distribution...AI is forcing leaders to understand how intelligence itself becomes a production input inside the business.

This is not optional. This is not outsourcing material. This is not “a skill below the founder pay grade.” AI is now foundational literacy to run a modern company, even if you never write a single line of code yourself.

Founders Who Don’t Understand AI Lose Strategic Positioning

Here’s the harsh reality most startup founders are still avoiding: Just about every industry right now is being rewritten by AI, but not by technical experts. It’s being rewritten by founders who deeply understand where AI creates leverage...and where it doesn’t. As a founder if you don’t know what AI is capable of, you cannot properly:

• Differentiate your business


• Understand the value lifecycle


• Build a realistic roadmap


• Resource correctly


• Defend against disruptions

Delegating AI planning is basically delegating market positioning. It can kill your company before it ever has a chance to thrive. In 2025, AI literacy leads to business competency.

The Founder Mindset Shift Most Are Not Making

Most founders still think of AI as a “tool category.” But here’s the truth:

AI is not a just another tool anymore. It is a capability layer inside every function: Finance, Ops, Recruiting, Marketing, Sales, Support, etc. They're all impacted by AI.

AI literacy is not knowing about the latest LLMs or how vector DBs work with AI. Rather, AI literacy is knowing:

• What kind of cognitive labor can be replaced, augmented, structured or systemized by using AI systems.


• Where leverage exists inside your business.


• How to model the business so machines can scale it.


• Which workflows to turn into systems, so human employees can focus on distinctly human work.

This is founder-level work. Not work to delegate to your new AI team.

Founders Don’t Need to Become Prompt Engineers — They Need to Understand Intelligence Architecture

There is a dangerous narrative right now online that AI literacy = “learn prompt engineering.” That's simply not the case. Prompting is a tactical skill, much like using Excel formulas. Founders need something significantly more strategic:

Intelligence Architecture — the design of how knowledge, reasoning, action, and autonomy get structured across the business so machines can do scalable execution.

That requires understanding:

• How decisions are made inside your business


• Where your business actually bottlenecks


• Where human judgment is uniquely valuable


• Where machines can safely make decisions


• How to control autonomy without losing all control

That cannot be outsourced to an agency. It also cannot be delegated to the junior employee or an intern.

If the founder cannot architect intelligent leverage, the company's growth is limited by human capacity.

Why AI Literacy Accelerates Go-To-Market the Most

Founders who deeply understand AI can get to revenue faster, because they leverage AI to do things like:

• Craft and adjust marketing narrative quickly


• Explain the value proposition in easy to understand terms


• Design pricing that maps to real business value


• Have an always on presence for customer sales and support


• Understand their target market more deeply

Customers are extremely fatigued right now from “AI fairy dust” startups. Buyers now want real operators who understand AI realistically, not theatrically. Gaining AI literacy is how you avoid becoming another founder who over promises and under delivers.

The 3 AI Literacies Founders MUST Master

You do not need to master deep ML research. You do not need to code LLM inference pipelines. But you do need these three in your toolkit:

1) Systems Thinking Literacy

Understanding how to break business workflows into modular, automatable, chainable components.

2) Applied AI Capability Literacy

Understanding what AI can realistically do, RIGHT NOW in 2025, in practical commercial contexts.

3) Autonomy & Control Literacy

Understanding how to design guardrails, override paths, and governance so autonomous agents don’t destroy trust, cripple operations, or tank your brand.

If you have these 3 competencies, you can build, scale, sell, and defend anything you bring to market. However, if you lack these, you are forced to always react from behind.

How Founders Can Build AI Literacy Fast — Without Overload

The best path isn't expensive courses. Not YouTube. Not TikTok. These are all great, but keep you grounded in the theoretical. The fastest path is to practice inside your own business. How?

1. Pick one real workflow that is currently slow and manual.


2. Re-design it as a machine-first workflow.


3. Give AI first-pass responsibility.


4. Only keep a human-first process step where nuance matters.


5. Run it for 2 weeks.


6. Observe the failures.


7. Refine and repeat.

This is where real literacy is built. One workflow at a time. Learning from your mistakes.

AI Literacy Will Become the New “Founder Filter”

Investors will start evaluating founders increasingly on this one dimension:

Do you actually understand how AI creates leverage inside THIS specific business, not conceptually, but in application?

If the answer is no, then founders will get filtered out. Not because the AI is hype, but because AI is leverage and founders who don’t know how to properly use leverage cannot scale modern companies.

This Is Founder Survival, Not Founder Hobby

The current AI environment is not about “who adopts AI fastest.” You can adopt AI quickly and still fail. Rather, it’s about who understands how to turn AI into strategic advantage, operational advantage and execution advantage. This is the new literacy of leadership. This is the new baseline for business and AI competency.

Founders who learn AI now will become founders who run companies the world can’t compete with by hand. However, founders who avoid AI literacy will end up reacting to everyone else’s moves in the marketplace, with no leverage left in their own business.

You do not need to become a data scientist. But you do need to become a leader who understands how machines can be leveraged to create business value. Think about this as the new power move in business. It cannot be outsourced or delegated to a junior employee. It must be developed at the top of the company.

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If you read last week's post, then you're probably well versed in the concerns of brand image in the age of AI. It's challenging to ensure you maintain a consistent brand image amongst the disparate AI technologies that can be leveraged to automate things. On top of that, there's a foundational element underlying everything that we discussed last week. What's that? Well, have you ever heard the quote, "Culture eats strategy for breakfast?" Yes, you must intentionally foster an AI-ready culture for your new AI-enabled marketing strategy to succeed. Let's learn how now.

Building an AI-Ready Culture - How to Prepare Your Team for Intelligent Marketing

Before your company's marketing team can AI-enabled, your people need to think intelligently about AI.

There’s a strange pattern emerging in the business world right now. Companies are investing in new marketing tools, launching data initiatives, and hiring “AI leads,” yet somehow, progress feels slow. Campaigns stall. Data gets siloed. Employees nod during AI meetings but go back to doing things the same old way.

The problem isn’t a lack of technology. We've never had a more abundance of technology accessible to the masses than now. No, it’s a lack of readiness. Specifically, cultural readiness.

Building an AI-ready culture isn’t about teaching everyone to code or turning your marketing team into data scientists. It's also not about mandating that you're now an AI-first company. Rather, it’s about shifting how your people think. How they think about data, creativity, and trust. This is what helps embed AI into your brand’s DNA rather than being some grotesque carnival side show.

1. Intelligent Marketing Starts with Genuine Curiosity

Let’s start with a foundational, and somewhat controversial, concept. Most marketers don’t need to “learn AI.” They need to learn to ask better questions of AI that's available to them.

When AI tools first enter the workplace, people often treat them like vending machines...type a prompt, get a result, and move on. But the teams that get real value out of AI don’t see it as a machine. Instead, they see it as a consulting partner. They challenge it, refine its output, and use it to uncover insights they never knew to look for. So, just like they don't need to learn how to fix or rebuild a vending, neither do they need to learn how to adjust weights or rebuild the LLM. They just need to learn how to work with it effectively.

That shift in mindset is what separates an AI-ready culture from a tool-happy one. Encourage your team to ask questions like:

• “What data would make this campaign smarter?”


• “How could AI help us personalize this without losing our brand voice?”


• “What assumptions are we making that AI might challenge?”


• "What patterns are in the data that we're currently not seeing?"

Curiosity fuels innovation. The more your team learns to think and collaborate with AI rather than about AI, the faster your marketing efforts evolve from traditional and reactive to industry leading.

2. Data Isn’t Scary — It’s the New Creative Medium

Ready for a truth that most marketers are still getting comfortable with in the age of AI? Data is no longer a technical asset to be managed by the IT team or data scientists. It’s now a creative raw material ready for the sculpting, much like clay is to the potter.

Every ad impression, click-through, and abandoned cart tells a story about your audience. The AI doesn’t make that story. Your team does, by deciding what data to feed it and how to interpret what comes out. That’s why an AI-ready culture treats data not as a compliance box to check but as the palette that paints the brand’s next move.

To help your team shift perspective, try running a simple workshop exercise. Give them access to anonymized customer data and ask, “What patterns do you see?” Don’t overexplain. Let the marketers, not the analysts, find meaning in the numbers. You’ll be amazed how quickly people start connecting insights to strategy once they stop fearing the spreadsheet and the data.

When your team starts finding the story in the data, you stop having to “sell” them on AI. They’ll start asking for it.

3. The New Collaboration - Humans + Machines + Meaning

Old-school marketing teams worked in silos. Creativity in marketing, data left to IT, leadership hovering over both. However, AI destroys that paradigm. It forces collaboration because the best outcomes in today's world come from blending human intuition with machine intelligence.

But collaboration only works when people trust each other...and the machine. That means setting clear expectations and establishing some guardrails. Things like:

• AI won’t replace creativity, but it will enhance it.


• AI won’t always be right, but it will be fast, adaptable, and willing to learn.


• AI may do the work, but the marketing team will approve it.

The key leadership task is to normalize co-creation with AI. Don’t just approve AI tools and walk away. Instead, demonstrate how to use them. Ask your marketing leads to show how they’re experimenting with campaign optimization, content personalization, or message testing. Celebrate the learning process, not just the final output.

When teams see AI as a teammate rather than a threat, the culture naturally adapts. Fear fades. Curiosity returns. Results follow.

4. Redefining Creativity - From Original Ideas to Adaptive Thinking

In an AI-driven world, creativity is no longer about originality...it’s about adaptability.

The days of building one perfect campaign and letting it run are gone. AI allows brands to test, tweak, and learn in real time. That means the most valuable creative skill isn’t artistic brilliance. It’s all about resilience. The ability to pivot based on what the data reveals.

In an AI-ready culture, creative directors and analysts speak the same language. They both ask, “What’s working, and what’s changing?” The designer isn’t afraid of metrics and the data scientist isn’t allergic to storytelling. Together, they build campaigns that evolve as fast as the customers they serve.

The organizations that thrive will be those that teach creative adaptability as a core skill...not a necessary evil of the environment we live in.

5. Leadership’s Role - Turning Fear into Empowerment

AI adoption always hits the same emotionally charged roadblocks: fear of replacement, fear of irrelevance, fear of making a mistake. These fears are very real and should be addressed. They’re leadership’s job to manage, not the team.

Leaders in AI-ready cultures focus on empowerment over enforcement. They don’t say “we’re implementing AI.” They say “we’re using AI to make your work more impactful.” That framing makes all the difference.

Great leaders also model transparency. When they use AI for strategic planning, performance analysis, or even drafting internal memos, they talk about it. They show what they’re learning, where it helps, and where it falls short. That kind of openness removes stigma and invites experimentation across the team. It also shows that they're not following the "Do as I say, not as I do" mentality.

AI doesn’t replace human leadership. Rather, it demands more of it. Because in a world where machines can execute, the human role is to inspire, interpret, and connect.

6. The Trust Equation - Ethics, Authenticity, and Brand Voice

AI doesn’t just automate marketing...it amplifies it. Which means that if your brand voice is unclear, you seem unauthentic, or your ethics are fuzzy, AI will magnify those cracks for the world to see.

An AI-ready culture prioritizes brand integrity from the start. It asks important questions like, “How do we maintain trust while scaling automation?” and “Where do we draw the line between personalization and privacy?”

Every company will answer these questions differently, but the key is consistency. If you say transparency matters, make your AI-driven campaigns transparent. If you claim empathy as a value, make sure your chatbots don’t sound like bureaucrats. In other words, align your AI behavior with your human values.

Trust isn’t something AI can build alone, but it’s certainly something it can destroy. Protect it like your brand depends on it...because it does!

7. The Payoff - When Culture and Capability Align

Once your team embraces AI as part of their mindset, something remarkable happens. The business gets faster. Campaigns become smarter. Decision-making becomes more confident. Creativity feels fun again.

That’s the power of cultural readiness. It transforms AI from a buzzword into something real and concrete. Your marketing stops reacting to trends and starts predicting them. Your people stop fearing change and start driving it.

In the end, intelligent marketing isn’t about the technology. It’s about the mindset. Build that first, and every tool you add will have a purpose and make an impact. Why? Because it's hardwired into the culture to embrace the right tools for the right jobs.

Final Thought

AI is changing marketing, as we saw last week. But culture determines whether it changes your organization for better or worse. The companies that win won’t be those with the biggest data sets or the fanciest algorithms. They’ll be the ones whose people think intelligently, act ethically, and stay curious long after the first AI campaign goes live.

Build that culture now. Your future brand will thank you for it.

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AI automation was the topic of the week last week. We learned about how to evolve beyond automation and to build a new competitive advantage. Automation is very important in today's hyper-competitive business market. We see AI being used to automate all aspects of business. With all that automation, how do you maintain or evolve your company's brand? Can AI hurt your brand image or can it help catapult your brand to a household name? Let's spend some time on that today.

Your Brand, Rewired - How AI Is Changing Marketing, Messaging, and Trust

We’ve officially entered the era where many company brands sound like it has a generic chatbot writing its emails and, sadly, many do. Yes, AI has become one of the loudest voices in marketing. It's generating posts, automating outreach, and personalizing everything from product recommendations to subject lines.

We all know it can be fast, scalable, and efficient. But if you’re not careful, it can also make your brand sound like everyone else’s. AI can either amplify your brand’s unique voice or erase it entirely. The difference comes down to how intentionally you use it.

What Is AI Really Doing to Brands?

Let’s be clear...AI isn’t just being used as a powerful new marketing tool. It’s rewiring the relationship between companies and audiences. How so? I'm glad you asked!

In traditional marketing, brands controlled the message. They told stories, shaped perception, and managed reputation from the top down. It was a tightly controlled process producing a finely curated experience. Today, AI-powered tools, whether it be recommendation algorithms or generative content, are reshaping that dynamic into a continuous, two-way conversation driven by data.

As you can probably imagine, that’s not a small shift. It’s a fundamental one that's not to be ignored. Customers no longer just consume your brand’s story. No, they co-author it through every click, comment, and conversation your AI systems respond to. Which means your “brand experience” is no longer what you say it is. It’s becoming what your algorithms say, do, and recommend every single day.

How AI Is Changing the Core of Marketing

AI’s dirty little fingerprints are all over modern marketing. That's probably obvious by now. Let’s break down the biggest transformations that are happening, and what they mean for your brand identity.

1. Campaigns to Conversations

AI has turned marketing into an ongoing dialogue rather than static touchpoints. Chatbots, email personalization engines, and real-time engagement systems now interact with customers continuously, not just during planned campaigns. This creates a new brand challenge...consistency. When dozens of often disparate AI systems are generating content simultaneously (i.e. posts, emails, support responses) brand consistency can break down rather quickly. It's important to ensure all the AI systems and interaction points are tuned to respond in a way consistent with your brand.

Pro Tip: Train your AI tools with brand-specific tone and persona guidelines. Treat them like new team members who need onboarding, not just prompts.

2. From Personas to Prediction

Marketing used to rely on static “personas” built from demographic data. Lots of hours were spent crafting the prefect personas and aligning them to interaction methods and types of content. Now, predictive models dynamically anticipate what each individual wants next and adjusts in real time. That’s very powerful, but also dangerous if not managed well. A perfectly personalized message that lacks human empathy can feel manipulative and inauthentic. Also, the brand that shows that it knows a little too much risks crossing into “creepy” territory.

Pro Tip: Pair predictive AI with ethical design. Make personalization feel helpful, not invasive. Transparency builds more trust than precision ever will.

3. From Brand Voice to Algorithmic Voice

In the past, brand voice was crafted through style guides and creative teams. Remember the hundred page Power Point templates full of brand fonts, colors and stock images? Now, algorithms generate much of your written and visual output. Over time, your AI model’s “voice” can start to define how people perceive your company. If it sounds generic, your brand will too. Training your models to have an appropriate voice is critical.

Pro Tip: Regularly audit AI-generated messaging. Reinforce distinct vocabulary, tone, and emotional cues that reflect your brand’s values. Human creativity still needs to set the rules of the system.

The Trust Factor - Why Authenticity Still Wins

Ironically, the more AI enters marketing, the more customers crave authenticity. In a world of flawless automation, something imperfect like a human story, an unscripted moment, or a genuine emotion tend to stand out.

People are observant and can tell when something “feels AI.” They may not know why, but their trust instinct kicks in. That’s why some of the most successful brands using AI do it quietly by blending automation with personality instead of replacing it. In other words, Don’t let your quest for efficiency kill your brand's humanity.

The AI Personalization Paradox

One retail brand learned this the hard way. After rolling out a hyper-personalized AI email campaign, they saw engagement plummet. Why? The content was accurate but soulless. It was “too perfect.” Customers felt like they were being analyzed, not spoken to.

The fix was simple, however. They reintroduced the art of human storytelling. Customer spotlights, behind-the-scenes updates, and handwritten-style notes were introduced and engagement rebounded 42% within two months.

Key Lesson: Authenticity can be at scale when it’s designed into the system, not when it’s left out for efficiency’s sake.

The Brand Trifecta - Consistency, Context, and Connection

To thrive in the AI marketing era, brands need a new playbook built around three principles:

1. Consistency

Ensure every AI-generated message aligns with your brand identity. Train models on your content archives. Use AI auditing tools to detect off-brand tone or language drift. Think of this as brand QA for the machine age.

2. Context

AI can process massive data, but humans provide meaning. Blend quantitative signals, such as behavioral data and engagement metrics with qualitative understanding like culture, emotion and empathy. Context keeps your brand grounded in human reality.

3. Connection

AI can engage customers, but it can’t build relationships. You still need people to do that. Use automation to free up your teams to focus on high-impact, personal interactions that deepen loyalty and trust.

What the Most Successful Brands Are Doing Differently

Coca-Cola - Creativity at Scale

Coca-Cola used generative AI to launch a co-creation campaign inviting fans to design artwork for limited-edition packaging. The result wasn’t just engagement, it was a global sense of ownership and community. AI wasn’t the storyteller. Rather, it was the stage for human fans to shine.

Shopify - Smart Personalization

Shopify’s merchants use AI-driven email systems that adapt to customer behavior, but always let the business owner approve final copy. That human review step preserves tone and prevents “AI drift.”

Duolingo - The Perfect Personality Blend

Duolingo’s AI voice and mascot work together in harmony to be quirky, supportive, and unmistakably on-brand. Their blend of humor and progress tracking feels human because it reflects human values like encouragement and consistency.

How to Build a Human-Centered AI Brand Strategy

Here are some steps that you can take to ensure your brand stays unique and trusted as AI continues to evolve:

1. Audit your current brand touchpoints. Identify where AI-generated content is already being used (i.e. emails, chatbots, content, ads, etc.) and evaluate tone and cohesion.


2. Create a “Brand Language Model.” Build a small internal dataset of brand-approved tone, vocabulary, and example copy. Use it to Fine-tune your tools to better embody your desired brand experience.


3. Reinforce human oversight. Use AI to draft and humans to refine. Encourage editors, marketers, and designers to be “AI curators,” not “AI operators.”


4. Be transparent about AI use. Customers appreciate honesty. Sharing a disclaimer like, “Powered by AI, reviewed by humans” can build more trust than trying to hide automation.


5. Train your team. Everyone who represents your brand, from sales to the support team, should understand how AI influences customer experience, what your ethical standards are and how to ensure both are complimentary.

The Future - AI as Brand Amplifier

AI isn’t going away...and it shouldn’t. When used well, it magnifies creativity, personalizes communication, and gives smaller companies superpowers once reserved for giants. But the defining skill of this new era isn’t prompt writing or data analytics. Rather, it’s brand orchestration. The ability to harmonize human creativity with machine intelligence in a way that feels effortless and authentic. In the future, the best brands won’t just sound human. They’ll feel human, because their AI will be guided by purpose, empathy, and trust.

Final Thought

AI can help you reach millions of people faster than ever before. But only your human story can make them care. As automation accelerates, your brand’s authenticity becomes its true competitive advantage. In this ever evolving age of AI, trust isn’t built by algorithms. Instead, it’s built by a healthy marriage between humans and AI. AI drives efficiency and volume while humans deliver that "human touch" that only they can provide.

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Well, we've been on a long journey of talking about AI startups. Most recently, we've hit on AI agent governance. Prior to that, we covered topics like how to scale your AI business and pitfalls to avoid. Let's get a little more focused on a related topic today. One that's on almost every business owner's mind when they think about how to use AI. What comes after automation?

Move from AI Automation to Competitive Advantage

By now, every business leader has heard the mantra, “Use AI to automate tasks and save time.” But the companies that truly thrive in the age of intelligent automation aren’t just saving time. Rather, they’re redefining how value itself is created.

Welcome to the era where automation becomes innovation, and efficiency becomes advantage. It’s not about the pure efficiency play of using machines to replace people. No, it’s about using them to reimagine your entire business.

The First Wave: Automation for Efficiency

The first wave of AI adoption was mostly tactical. Businesses focused on reducing costs, eliminating repetitive work, and streamlining operations. Chatbots handled basic inquiries. Predictive models optimized supply chains. Generative tools wrote marketing copy. It worked, but it also leveled the playing field since everyone had access to those capabilities.

Once every competitor has access to the same AI tools, efficiency stops being a differentiator. The question shifts from “How can we automate this?” to “What can we now do that was never possible before?”

That’s the transition we're in now. The transition where businesses go beyond productivity gains and start creating asymmetric advantage: unique capabilities, insights, and customer experiences powered by AI that competitors can’t easily replicate.

The Shift from Cost Reduction to Value Creation

A lot of AI strategies start with the CFO’s question, “How much can we save?” The companies that pull ahead ask a different question. They ask, “How much new value can we create?" Here’s what that might look like in practice:

• From automation to augmentation — Instead of replacing humans, AI amplifies their creativity, decision-making, and customer empathy.


• From process optimization to product innovation — Companies leverage AI insights to design new products, services, and pricing models.


• From reactive analytics to predictive strategy — Rather than explaining what happened, AI helps forecast what will happen and adapt in real-time.

This strategy requires leaders to reframe their mindset from “doing things better” to “doing better things.”

How Can AI Create Asymmetric Advantage?

Let’s unpack what makes AI a source of competitive advantage when used strategically.

1. Unique Data Assets

Your data, when cleaned, structured, and contextualized, becomes a moat. Two companies may use the same AI models, but the one with proprietary customer data, behavioral insights, or domain-specific context wins every time.

Lesson: Don’t just collect data. Curate it. Organize it. Teach your AI what makes your business different.

2. Proprietary AI Workflows

The next differentiator isn’t just what model you use, but how you integrate it into your business logic. The workflows, prompt chains, and feedback loops that connect your teams to AI systems become your intellectual property.

Think of it as “organizational prompting”, where the culture and process design of your company shape how AI behaves and performs for you.

3. Adaptive Decision Systems

Static reports are out. Continuous learning is in. When AI systems are allowed to monitor outcomes and refine themselves over time, your strategy becomes adaptive. You’re not just reacting to the market anymore. You’re learning and growing with it.

Example: A manufacturer that uses predictive maintenance data to adjust production schedules in real time. This isn’t just saving costs, it’s creating reliability customers will pay for.

From Projects to Platforms

Early adopters often fall into the “pilot project trap.” They build impressive prototypes that never scale beyond the innovation team. It's time to replace that fragmented approach with a platform mindset. In practical terms, that means:

• Creating shared AI infrastructure, not siloed systems.


• Establishing data governance standards across departments.


• Training every employee to be “AI fluent”, capable of collaborating with intelligent systems.


• Measuring success by business impact, not model accuracy.

When AI moves from a project to a platform, it stops being a novelty and becomes a growth engine.

The New Leadership Mandate: Shape, Don’t Chase

AI is moving too fast for any company to chase trends. The winning leaders set the narrative instead of following it. That means:

• Defining your AI ambition: What part of your business will AI transform most? Customer experience, operations, innovation, or perhaps all three?


• Prioritizing trust: Governance, transparency, and ethical design aren’t checkboxes, rather they’re the foundation of customer confidence.


• Building teams that blend human and technical intelligence: The next generation of leaders understands data science, design thinking, and business strategy equally well.

This era of AI Strategy is less about tools and more about orchestration by aligning technology, people, and purpose in a unified direction.

Case Study Snapshots: Who’s Getting It Right

Retail Reinvented

A fashion brand stopped using AI merely to forecast demand. Instead, it created a “co-design” experience where customers could generate and vote on new product concepts using AI tools. The result? New products customers felt they helped design followed by a 35% jump in preorders.

Healthcare at Scale

A medical group used AI not just to automate records but to analyze anonymized patient feedback for emotional tone. The insights helped doctors improve bedside communication, raising patient satisfaction scores by double digits.

Manufacturing Intelligence

A parts supplier built an AI-driven “decision cockpit” that combined logistics, weather, and order data to guide pricing and production dynamically. Instead of being reactive to demand shocks, they became predictive and far more profitable.

Practical Framework: Building Your AI Advantage Flywheel

Here’s a simple model to help leaders move from automation to competitive advantage:

1. Automate: Start with efficiency to free your people from repetitive work.


2. Augment: Give them AI tools that enhance creativity, insight, and speed.


3. Differentiate: Use data, workflows, and customer feedback to build unique AI-driven offerings.


4. Scale: Standardize success across the organization with a shared AI platform.


5. Adapt: Continuously learn, retrain models, and evolve processes as the market shifts.

Each step feeds the next, building momentum. Automation frees capacity for innovation, innovation drives differentiation, and differentiation justifies reinvestment in AI. That becomes your compounding advantage.

Measuring Success

Traditional metrics like ROI or cost savings miss the bigger picture. Instead, measure success across three dimensions:

• Velocity: How quickly can you test, learn, and deploy new AI initiatives?


• Adaptability: How well does your organization evolve as new tools and models emerge?


• Differentiation: Are your AI outcomes unique enough to strengthen your market position?

These are the new KPIs for the AI-powered enterprise...agility, learning speed, and strategic uniqueness.

The Human Multiplier

Ironically, the companies that gain the most from AI are those that invest the most in humans. Creativity, judgment, empathy, and ethics can’t be automated. But they can be amplified. The goal isn’t to make humans redundant, rather it’s to make them exponentially more effective. A well-designed AI ecosystem gives people superpowers: faster insights, broader reach, deeper understanding. That’s the real advantage.

Final Thoughts

The companies that will dominate the next decade aren’t necessarily the ones with the best models or biggest budgets. They’re the ones that treat AI as an evolutionary force, something that reshapes how they think, operate, and deliver value. Automation saves time. Competitive advantage builds empires. The best companies will be very intentional in making the transformation.

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What did you think about the AI Agent Governance topic we covered last week? Hopefully you found it useful. I think it sheds light on how important of a role that we humans play in the successful implementation and management of AI. It also sheds some light on the need to evolve how we lead and manage in the age of AI. Curious as to what I mean? Keep reading to learn more.

The Human Side of AI Leadership: How to Stay in Control When Machines Do the Work

For decades, leadership has been defined by clarity, control, and human judgment. But as artificial intelligence increasingly takes on cognitive tasks, ranging from analyzing data to making decisions, the traditional leadership paradigm will change. In this new era, where machines execute and humans orchestrate, the central question becomes: How do you lead when you’re no longer the smartest one in the room?

AI isn’t just another productivity tool. It’s a collaborator, an advisor, and sometimes even an autonomous decision-maker. That shift requires leaders to let go of old ways of doing things while focusing intently on the distinctly human skills like empathy, ethics, creativity, and trust-building. The leaders who survive and thrive won’t be the ones who know the most about technology. Rather, they’ll be the ones who know the most about people and purpose.

From Command to Coordination: A Leadership Paradigm Shift

In traditional management, authority is hierarchical, or vertical. Information flows up, decisions flow down, and leaders sit at the top of the hierarchy. In AI-enabled organizations, that model quickly breaks down. Decision-making becomes distributed, data-driven, and often instantaneous...much faster than any executive review cycle.

Instead of controlling every step, effective leaders must focus on designing systems and cultures that can operate intelligently on their own. In short, leadership shifts from command and control to coordination and calibration.

“AI leadership isn’t about giving better orders. It’s about asking better questions.”

AI-driven teams require clarity of mission more than micromanagement. Your role as a leader becomes defining what “good” looks like from an ethical, strategic, and operational perspective and then ensuring your systems understand those boundaries.

The Emotional Intelligence Edge

As AI takes on more analytical and tactical tasks, what’s left for humans? The answer is emotional intelligence (EQ), which is the one capability machines still struggle to replicate authentically. Leaders with high EQ excel at managing uncertainty, motivating teams, and resolving the subtle human tensions that automation often amplifies.

Think of it this way...AI can analyze patterns of behavior, but it can’t feel disappointment, anger, pride, or loyalty. Those emotions always drive human performance and culture. When AI becomes your team’s “silent partner,” it’s your emotional awareness that keeps humans engaged, connected, and aligned with purpose. Focus on:

• Empathy: Understanding how your team feels about working with AI, addressing fears of obsolescence, and reframing the narrative from “replacement” to “augmentation.”


• Transparency: Being open about how AI is used in decisions, so employees trust the system, and in turn, you.


• Psychological Safety: Creating an environment where people can challenge the output of AI without fear of retribution.

In an AI-first workplace, emotional intelligence is not “soft.” It’s strategic and a competitive advantage.

Accountability in the Age of Automation

One of the most dangerous pitfalls in AI-led organizations is the diffusion of responsibility. When decisions are automated, who’s accountable for the outcome? The human employee, the AI algorithm, or the company's leadership? If no one owns the result, trust erodes fast.

Leaders must stand up and hold themselves accountable, even when AI does the legwork. That means understanding the inputs and logic behind key models and being ready to justify outcomes in plain language. You don’t have to know every parameter in a neural net, but you do need to understand its decision boundaries and risk factors.

When things go wrong (and they will), the best AI leaders respond with resolve and transparency, not blame. They investigate the system’s failure the same way they would a human’s, by asking what conditions led to the error and how to improve the feedback loop.

Great leaders don’t hide behind the algorithm. They stand in front of it.

Reframing Trust: Humans, Data, and AI

AI systems are only as good as the trust they earn, and that trust depends on both data integrity and human integrity. Leaders must ensure that their teams understand why an AI recommendation is being made, not just what it says.

This requires fostering “explainability literacy.” Make explainability a team value, not just a technical feature or words in your marketing material. Encourage your staff to question, challenge, and verify AI outcomes. Over time, this builds mutual trust between humans and machines as well as between leaders and their teams.

In high-performing organizations, AI isn’t thought of as some mysterious fortune teller or mind reader. It’s a well-understood partner. That transparency is what turns AI from a black box into a trusted colleague.

The New Leadership Toolkit: Soft Skills, Hard Thinking

AI may automate intelligence, but it doesn’t automate wisdom. The next generation of leaders will need a different toolkit. This new toolkit is one that blends technical awareness with human-centered thinking.

1. Systems Thinking

No this doesn't mean thinking about a computer system. It means understanding how data, algorithms, and humans interact as part of one ecosystem. The ability to understand how small changes in data or policy can ripple through your organization in unexpected ways. The ability to see how everything must work in unison to accomplish the business objective.

2. Ethical Foresight

Be proactive about the indirect effects of automation. Just because an AI system can make a decision doesn’t mean it should. Ethical leadership is about foresight, not cleanup. You must be ready to intervene when the team is headed down an unethical path. Dealing with internal fallout is far better than making headlines in every business news journal across the world.

3. Adaptive Decision-Making

Move from rigid strategies, policies and processes to dynamic, data-informed learning loops. The faster your AI evolves, the faster your decision model must evolve with it. An antiquated and highly-bureaucratic decision framework can cause an AI initiative to fail just as quickly as a buggy algorithm.

4. Communication Mastery

Learn to translate complex AI insights into narratives that make sense to humans. The best leaders are storytellers who make data feel relevant, not robotic. Become great at helping people understand the "so what" for every AI insight. If they can't tie what you're explaining directly to business outcomes, then you've failed.

Case Study: When Leadership Fails to Adapt

In 2023, a mid-sized logistics firm implemented a powerful AI system to optimize delivery routes. Within months, efficiency improved, but employee morale plummeted. Drivers complained that the AI’s routes ignored real-world conditions like weather, fatigue, or local roadwork. Leadership, trusting the system’s “superior intelligence,” dismissed the employe feedback. Within six months, turnover spiked, and the system’s effectiveness declined as human expertise was lost.

The company eventually reversed course, integrating a hybrid decision model that combined AI routing with driver feedback. The result? Both performance and trust rebounded. The technology wasn’t the problem. Leadership was. They didn't value the human wisdom that was critical to success.

The lesson is simple: AI doesn’t replace human judgment or solid leadership skills. It amplifies the quality of leadership that's already in place. Good leaders become great leaders when they manage AI as tool for their employees. Bad leaders become terrible when they put AI on the pedestal above their own employees.

Be Curious, Not Controlling

AI leadership demands deep humility. Great leaders are able to confidently say “I don’t know” and to explore what the data might reveal. The most successful AI leaders adopt a stance of curiosity rather than control. They don’t fear being challenged by machines, rather they learn from them.

Ask your AI questions. Probe anomalies. Reward your team for discovering model blind spots or biases. Curiosity keeps you, and your team, in control because it keeps everyone engaged.

The opposite of rigid control isn’t chaos, it’s healthy curiosity.

As AI grows more capable, leaders who stay curious will see opportunities that rigid managers miss. They’ll spot ethical risks earlier, adapt faster, and build more resilient organizations. They'll also build a healthy company culture that drives employee loyalty, retaining that all-important human wisdom.

Redefining Leadership for the AI Era

So what does “staying in control” really mean in an AI-driven world? We've learned that it doesn’t mean micromanaging your employees. It also doesn't mean resisting every new AI breakthrough. Instead, it means leading from a place of principles rather than rigid processes. Setting the moral and strategic compass while allowing the systems to handle navigation. Essentially, it means installing strong safety guardrails and allowing your AI-augmented team to do their jobs.

Control in the AI age is about clarity, not dominance. It’s about knowing when to step in and when to step back. It’s about creating alignment between human goals and machine capabilities, so the system moves in unison.

And ultimately, it’s about remembering that leadership is a human act. Technology can make us faster, smarter, and more efficient. However, it can’t make us more compassionate, more ethical, or more visionary. That’s still on us, the human leaders.

Final Thought

The AI revolution won’t make human leadership obsolete. It will make it mission critical. The leaders who succeed in this new landscape won’t be the ones who know how to code or to dominate their employees. Rather, they’ll be the ones who know how to connect. How to make systems work in harmony. They’ll understand that AI isn’t a substitute for humanity, but it can be a mirror that reflects how well we lead ourselves.

Lead the humans. Control the machines. And never forget which one you are.

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I wrote about AI agents back in February 2025. If you're still not sure what agents are all about, go back and read that article now. Since then, agents have evolved and become a lot more prevalent. Actually, agentic AI is all thre rage.

Agents are very powerful, but as the saying goes, with great power comes great responsibility. If you're deploying an AI agent, you must ensure that you have proper controls in place. That means implementing strong governance. Let's dig into that today.

Governing AI Agents in Production: How to Monitor, Audit & Correct Autonomous Behavior

So, we know that AI agents can act, plan, and take multi-step actions on behalf of users and systems. It doesn't take much imagination to see the potential risk that poses. Let's break down some practical ways to monitor, audit, and safeguard the agents that you deploy.

Why agents require different governance than static models

Traditional models respond to prompts. Agents act on their own. They call APIs, send emails, create records, move money (sometimes), and take multi-step actions that can really streamline business operations. Because these actions can have irreversible consequences, governance must move from “model QA” to product-grade operational controls:

• Agents can compound errors over multiple steps.


• Agents may act with delegated permissions that require careful boundaries.


• Agent failures can create downstream business, legal, financial or safety incidents.

Core principles for agent governance

1. Design for observability. If you can't see what an agent did and why, you can't fix it.


2. Prefer constrained autonomy. Start with narrow, reversible actions and expand the agent's scope of control in a cautious, controlled manner.


3. Human-in-the-loop (HITL) by default for risky tasks. Humans should review important or irreversible actions until the agent proves itself. Then, humans should perform a random audit function.


4. Fail-safe first. Default to “do nothing” or “ask a human” when confidence is low in the agent's ability to complete the task successfully.


5. Auditability and explainability. Preserve decision trails that can be reconstructed later.

Monitoring: what to log and watch

Good monitoring is more than uptime. For agents, you need to monitor three key categories: actions, decisions, and effects. Below is a checklist of things to be able to monitor before a wide rollout

Essential logs

• Action log: Record every API call, external interaction, message sent, or resource changed (timestamp, actor, context, target).


• Decision trace: Save the reasoning or chain-of-thought summary used to choose the action (hashed or summarized for privacy where needed).


• Inputs & outputs: Retain the prompt/state before the action and the response after the action (store this securely).


• Confidence & provenance: Capture the confidence score, model version, data sources cited.


• Rollbacks/compensating actions: Record when and why a rollback occurred.

Here's a possible JSON log format to get you started

{"timestamp": "2025-09-29T14:32:10Z",

"agent_id": "invoice_agent_v1",

"session_id": "sess-abc123",

"action": "create_invoice",

"target": { "account_id": "acct-789", "invoice_id": "inv-20250929-01" },

"decision_summary": "Extracted line items -> grouped by client -> generated invoice draft",

"confidence": 0.87,

"model_version": "gpt-xyz-1.2",

"sources": ["document_123", "contract_456"],

"outcome": "success",

"rollback": false}

Key metrics to tack and report

• Task success rate (per-agent, per-task)


• Rollback frequency (how often actions were reverted)


• Escalation ratio (percent of actions flagged to humans)


• Latency & cost per action


• Anomaly rate (unexpected/unauthorized actions)

Auditing & explainability

How do you answer the question “why did it do that?” If you guessed audits, you'd be correct. Audits need to reconstruct the decision path. You won’t (and shouldn’t) log full chain-of-thought text in production for privacy and data volume reasons, but you must retain a compressed decision trail:

• Decision IDs: Comprised of hashable references linking inputs to intermediate steps to the final action.


• Source citations: Track the source for each claim or data point the agent used.


• Snapshot storage: Keep snapshots of state for high-risk actions (e.g., financial transfers) for a defined retention window.

Periodic audits

Schedule recurring audits: weekly for high-risk agents, monthly for medium-risk, quarterly for low-risk. Use a combination of automated checks (pattern detection) and human review (sampled cases) to verify that the agent is in compliance.

Corrective mechanisms & safe defaults

Below are some practical controls to limit potential harm from a malfunctioning AI agent.

• Global Kill Switch: Build a kill switch, or an immediate stop for all agent activity with a single command. Test it monthly.


• Scoped kill Switches: Build in the ability to disable a specific agent or a class of actions (e.g., “no outbound emails”).


• Permission Gates: Institute the requirement that the AI agent must request more privileged actions, which require human approval.


• Sandbox mode: Create and environment to allow agents to simulate actions and produce “what would happen” reports before doing the real thing.


• Compensating transactions: For reversible domains, create automated rollback flows, such as the ability to cancel an invoice, process a refund, or reverse updates.

Governance structures & organizational roles

Who's accountable for AI agent safety? Effective governance requires a defined governance model, named roles and responsibilities (ideally in the form of a RACI diagram), and an incident lifecycle.

Suggested roles

• Safety Owner: Product or engineering lead accountable for day-to-day safety and incident triage.


• Agent Review Board: Committee of cross-functional reviewers (product, engineering, legal, security) for major agent launches, permission upgrades and audit reviews/approvals.


• Compliance Liaison: Owns audit readiness, reporting to the Agent Review Board and any required external reporting.


• On-call Incident Responder: First responder responsible for handling immediate mitigation (activating kill switches, rollbacks, etc.).

Incident Resolution lifecycle (high-level)

1. Incident Detection (automated monitoring or customer reported)


2. Triage the incident to assess the incident and impact (Safety Owner + on-call first responder)


3. Mitigate this incident (activate kill switch, revoke permissions, execute a rollback, etc.)


4. Lessons Learned session to ensure it doesn't happen again (post-mortem and root cause analysis)


5. Remediate the root cause & document appropriately (bugs fixed, controls updated, etc.)


6. Communicate transparently about the issue, impact and resolution (customers, internal stakeholders, regulators as required)

Deployment strategies: How to roll agents out safely

Plan for a phased rollout to minimize collateral damage.

Parallel mode

Run an agent in parallel (observation-only) to compare proposed actions against business rules without actually executing them. This is critical for validating agent behavior under production-like conditions before going live.

Canary or pilot releases

Allow the agent to operate for a small group of users or accounts. Monitor metrics closely and expand only if the results are as expected and the agent is operating safely.

Phase in elevated permissions

Start with read-only access, then assign incremental permissions (write drafts, submit for approval, execute) as the agent proves itself. Each permission increase must be reviewed and approved by the Agent Review Board, monitored and periodically audited.

Examples of a rollout schedule

A hypothetical case study

A short fictitious example to illustrate governance in practice.

Imagine an invoice agent that drafts invoices from contracts and submits them to customers. In production it mistakenly billed a test account because a flag in the sandbox environment was unset. With governance in place the team:

1. Detected unusual billing via anomaly monitors (surge in invoices for test accounts).


2. Triggered the scoped kill switch to stop additional invoice generation.


3. Rolled back the erroneous invoices using automated compensating actions.


4. Ran a post-mortem and determined that the root cause was environment misconfiguration. Remediation called for an additional gate check and guardrails in the agent planner.


5. Published a customer-facing incident report and updated the risk register.

The end result: quick remediation, minimal customer impact, and improvements that made the agent safer.

Potential operational checklist for agent governance

Agent Governance Checklist

1. Observability

   - Action logs enabled

   - Decision traces linked to actions

   - Confidence & model-version metadata

2. Monitoring

   - Task success rate dashboard

   - Rollback & escalation metrics

   - Anomaly detection on actions

3. Safeguards

   - Global kill switch tested

   - Scoped kill switches available

   - Permission gates for privileged actions

4. Auditing

   - Weekly sample audits (high-risk agents)

   - Quarterly full audits

5. Roles & governance

   - Named Safety Owner

   - Agent Review Board charter

   - Incident runbook + post-mortem template

6. Rollout

   - Parallel -> Canary -> Pilot -> Full Production plan

7. Communication

   - Customer incident template ready

   - Internal escalation contacts documented

Abbreviated post-mortem template

Incident Post-Mortem

1. Title & date

2. Incident summary (1-2 sentences)

3. Timeline of events (concise)

4. Root cause

5. Impact Assessment (users, data, financial)

6. Immediate mitigation steps

7. Root cause fixes & owners (with deadlines)

8. Preventive measures & monitoring updates

9. Customer communications & compensation (if any)

10. Lessons learned

Final thoughts & next steps

Running AI agents in production raises the bar on the need for governance, but it’s also solvable with engineering discipline, thoughtful product design, and clear organizational ownership. Start small, expand in a controlled manner, and treat safety as a critical function, the same way you treat performance and reliability.

Immediate actions you can take today: enable action logging, define a Safety Owner, and add a “parallel mode” for your highest-risk agent(s). Those three moves drastically reduce collateral damage and buy you the time needed to build a robust goverenance model and implement associated controls.

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If you read last week's article, then you have a good idea on how to build trust with your AI startup. So, what comes next? Well, how do you actually get traction and grow your business. We don't want you stuck in the pilot phase for ever. So, let's explore some ways to build traction this week.

Moving From Pilot to Sustainable Profit: How AI Startups Can Win Their First Real Customers

We've talked extensively about how AI startups are popping up everywhere. We've also talked about how most never make it past the pilot stage. Here are some ways to avoid “pilot purgatory” and start building real customer traction.

Why So Many AI Startups Get Stuck in Pilot Purgatory

Pilots can be a double-edged sword. On one hand, they’re a great way to test a product in the real world with lower risk. On the other, they often stall for predictable reasons:

• No clear success metrics. Without defined outcomes, it’s hard to prove a pilot was worth paying for.


• Solving the wrong problem. Flashy AI tricks don’t matter if they don’t address a core pain point.


• AI curiosity, not commitment. Some companies just want to “check the AI box.”


• Integration headaches. A standalone pilot may break down in real workflows and systems.


• Too broad a focus. If your AI “does everything,” customers may not know what you actually solve.

Lessons from AI Startups That Escaped Pilot Purgatory

1. Hugging Face: Build a Community Before the Customers

We've talked about Hugging Face in past articles. It started as a chatbot app but pivoted when they saw demand for open-source AI tools. By fostering a developer-first community, they built credibility and adoption before monetizing.

Takeaway: Sometimes your first “customers” are users and developers who expand your reach.

2. Scale AI: Solve Painfully Specific Problems

Scale AI tackled a very specific problem, which was labeling training data. Their narrow focus won contracts with OpenAI, Cruise, and others.

Takeaway: Pick a specific problem that’s urgent and critical, and become the very best at solving that poblem. Launch a pilot with a clear plan to scale.

3. DataRobot: Sell ROI, Not Tech

DataRobot emphasized cost savings and faster predictions, not algorithms. Essentially, they focused on delivering clear business value and their ROI-driven messaging helped close deals.

Takeaway: Customers buy outcomes, not technology. Show the financial impact or some other way to deliver real business value to stand out from your competitors.

4. Gong: Build Insights Into the Workflow

Gong didn’t just analyze call transcripts, rather they delivered insights directly into sales managers’ workflows. This made adoption seamless, addressing a common barrier to new technology adoption.

Takeaway: Package insights so they fit naturally into the customer’s workflow, lowering the barrier for new technology adoption.

How do You Turn Pilots Into Paying Customers?

If you’re an AI founder worried about getting stuck in the pilot phase, then here are some steps to convert your experiment into recurring revenue:

Step 1: Choose Pilots Carefully

Ask: Does the company have budget authority? Is the problem urgent and tied to money or risk? Can impact be measured in 60–90 days?

Step 2: Define Success Metrics Upfront

Agree on adoption, accuracy, and ROI goals at the start and put them in the pilot agreement.

Step 3: Price for Commitment

Free pilots often go nowhere. Even small fees give customers skin in the game. Use tiered pricing to filter out “tire kickers.”

Step 4: Integrate Early

Don’t isolate your pilot. Integrate into workflows or systems from day one for higher adoption.

Step 5: Show Quick Wins

Design pilots to deliver visible results in 30–60 days to build momentum and executive support.

Step 6: Turn Champions Into Evangelists

Empower internal champions with dashboards, case studies, and wins they can brag about.

Step 7: Document and Scale

Each successful pilot should generate case studies, testimonials, and ROI data you can be used to drive future sales.

The Mindset Shift: From Producing Cool Tech to Becoming a Trusted Partner

The startups that thrive don’t just show off flashy AI toys. Rather, they solve real difficult problems, deliver measurable ROI, and fit into common workflows. They become partners, not vendors.

Remember, AI can be fun and exciting, but customers don’t buy excitement. They buy results.

Final Thought

Breaking out of pilot purgatory is the defining challenge for AI startups. But you can treat pilot as a springboard instead of the end result by choosing wisely, pricing smartly, and proving value. You’ll soon build traction that hype can’t deliver.

Because in the end, the AI startups that thrive aren’t the ones with the fanciest models. They’re the ones with the happiest customers.

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Well, the past few weeks have unpacked the reasons why so many AI startups fail, what you can do to beat the odds and have even put together a survival guide. What could be next?

We know AI startups are all the rage. We also know that for every success story like OpenAI or Anthropic, there are dozens of AI startups that quietly vanish. The number one factor that separates survivors from failures? Trust. So, that's what's next this week. Let's talk about building trust.

Building an AI Startup That Investors and Customers Actually Trust

In a world flooded with overhyped promises and half-baked AI products, winning (and keeping) the trust of investors, customers, and end-users isn't just a good idea. It's the secret sauce. Let’s dig into some practical steps, real-world examples, and some templates you can start using to build trust with your customers and investors.

Why Does Trust Matter?

AI startups often overpromise, underdeliver, or hide key details about how their technology actually works. Customers and investors don’t just want cutting-edge models...they want transparency, reliability, and accountability. Without those, even the coolest AI demo won’t last long in the real world.

Case in point: Babylon Health, once valued at $4B, collapsed after questions arose about the accuracy and safety of its AI-powered medical claims. The tech itself wasn’t the demise. It was the lack of trust that killed the company.

Compare that with Anthropic or Perplexity AI, who lead with transparency and safety. They not only push “smarter” AI, but they emphasize guardrails, explainability, and ethical use. That’s what builds credibility and trust.

How to Build Trust: A Playbook for AI Startups

Here are some hey ways to build lasting trust with your AI startup.

1. Publish Trust Artifacts

Don’t just say you’re transparent. Every startup can do that. Remember, actions speak louder than words. Publish documents that spell out how your AI works, what it can and cannot do, and how you handle data. Then, do exactly what you say you're doing in those documents.

• Model Card:
  

  Include model name & version, release date, training-data summary, intended use cases, evaluation metrics, known limitations, and a support contact. See below for an example:

  
  

  Model: Acme-Summarizer v1.0 (released 2025-08-01)
  Trained on: Mix of public web data + anonymized customer docs
  Intended use: Summarizing business text
  Not for: Medical, legal, or safety-critical advice
  Primary metrics: ROUGE-L 45, factuality 92% (sampled)
  Known limits: May omit key facts; verify critical outputs



• Datasheet for Datasets: Summarize sources, sampling, cleaning, and bias checks.


• "What We Can’t Do Yet" Page: Openly and honestly list the limits of your AI product.
  
  

  We do not provide medical diagnoses. Use our suggestions as drafts, not final decisions.



• Security & Compliance Summary: List encryption, audits, and compliance status.

2. Use Operational Checklists

Checklists keep you honest and prevent oversight. Start with these three:

Data Governance Checklist

• Inventory: what data you have, where it lives, who has access


• Retention & deletion policy


• Consent tracking for customer data


• Anonymization / minimization steps


• Immutable logs for dataset updates

Security Checklist

• TLS + encryption at rest


• Role-based access control (RBAC)


• Secrets management


• Automated backup + tested restore


• Incident response runbook

Compliance Checklist

• Data Protection Impact Assessment (DPIA) if handling personal data (GDPR)


• Map requirements for SOC 2, HIPAA, ISO27001 as needed

3. Run Pilots That Prove Value

Pilots build trust when they’re structured. Consider using this four-phase approach:

1. Discovery: Map data, define success metrics


2. MVP: Deliver a working feature for small user group


3. Pilot: Limited production use with metrics tracking


4. Evaluate & Scale: Decide go/no-go with customer

Create Clear Pilot Success Criteria

• Adoption: % of users using weekly


• Accuracy: % of outputs verified correct


• ROI: measurable savings or revenue lift


• Safety: zero critical incidents

4. Test and Monitor Relentlessly

Trust grows when customers know you’re always testing and looking for issues or vulnerabilities. Here’s one way to do that:

• Red Teaming: Stress-test your model quarterly


• Human Sampling: Audit 1–2% of outputs


• Monitors: Track uptime, cost, hallucination rate


• Rollback Criteria: Predefine thresholds for disabling features or rolling back to a previous version

5. Track Trust Metrics

You can't just assume that you're building trust. You also can't guess at how well you're doing. You must measure it.

• Quality: Accuracy, hallucination rate


• Usage: Retention, adoption, daily & weekly active users


• Business: Customer Churn, Net Revenue Retention (NRR), Lifetime Value (LTV) and Customer Acquisition Cost (CAC)


• Support: Customer Issue Escalations, resolution time


• Security: Incidents, audit findings

6. Communicate Transparently

Clear communication is half the battle.

Pre-Launch

Publish FAQs, model cards, and limitations upfront.

In-Product Disclaimers and Guidance

This content was generated by Acme AI. It may omit details. Click "Show Sources" to verify.

Incident Response Template

• Timeline: what happened & when


• Root cause


• Impact


• Mitigations


• Preventive actions

7. Build Trust Into Your UX

• Explain This Button: Show sources or reasoning


• Confidence Scores: Simple ranges, not magic numbers


• Feedback Loop: Easy reporting of bad outputs


• Data Controls: Clear opt-outs for training data

8. Formalize Governance

• Assign a Safety Owner


• Create an external Ethics Board (if working in a regulated domain)


• Conduct regular third-party audits


• Align contracts & SLAs with reality

Key Takeaways

Building an AI startup that people actually trust isn’t about showing off the smartest model. It's not about the wow factor. It’s about making your work transparent, reliable, and accountable from day one and never deviating from that philosophy.

• Publish trust artifacts


• Run disciplined pilots


• Track trust metrics


• Communicate openly (especially when things go wrong)


• Embed trust in product design and governance

Do this, and you won’t just avoid the AI startup graveyard, you’ll stand out from the crowd. Because in the long run, trust beats buzz every time.

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Last week, we unpacked why 90% of AI startups flame out. That’s enough to make any founder clutch their pitch deck a little tighter. But here’s the good news...failure isn’t inevitable. There’s a growing list of AI companies that not only survived but thrived by doing things differently. What exactly are they doing differently? And more importantly, how can you do the same? Let's dig a little deeper into that this week.

This isn’t about recycling the “why they fail” conversation that we had last week. You already know that story. If not, go back and read it now. This is about what comes next. If you’re thinking about building your own AI venture, how do you give yourself a real shot at being one of the survivors? Let's see if we can build an survivor guide for you to follow.

The AI Startup Survival Guide: How to Build for the Long Haul

Lesson 1: Solve Problems That Will Still Exist in Five Years

AI tech is moving faster than a toddler on a sugar rush. Today’s breakthrough can be next year’s open-source commodity. That’s why surviving startups pick problems that outlast the hype cycle.

Take Duolingo. They didn’t launch as an “AI company.” They launched as a language-learning platform. But they leaned into AI as it matured, first for adaptive learning and now for conversational bots. The problem of learning new languages never went away. However, the AI technology kept making their solution better.

Your survival lesson: Ask yourself, "If a better model drops tomorrow, will my core problem still matter?" If yes, you’re on stable ground. If no, you’re building on quicksand.

Lesson 2: Be Useful Before You’re Impressive

Some of the best survivors started small and almost boring. Grammarly wasn’t sexy at first. It just fixed typos better than Word. But it solved a daily irritation for millions of people, then layered on smarter AI as the tech matured.

Meanwhile, flashy launches like the Humane AI Pin promised the future of computing but delivered a clunky device people abandoned in a drawer. Impressive? It certainly sounded impressive. Useful? Not really.

Your survival lesson: Resist the urge to wow consumers when you're first starting out. Focus on being ridiculously useful. Start with features people can’t live without. The “wow” can come later once you've proven your product or service in the marketplace.

Lesson 3: Build a Moat Beyond the Model

Here’s the tough truth...most of you won't own the AI models. OpenAI, Anthropic, Meta, and Google do. And their stuff will always be cheaper and faster. Survivors know this. They don’t compete on the model. They compete on everything wrapped around it. That's how they differentiate themselves and make it difficult to copy what they're doing.

Look at Perplexity AI. It doesn’t matter that other startups can hook into OpenAI’s API. Perplexity’s moat is their experience by providing clear, cited answers in a search-like interface. That’s their differentiator.

Your survival lesson: Build moats in data, workflow integration, user trust, a desirable experience or brand. Don’t hinge survival on access to a single model. Your competitors have access to that model too!

Lesson 4: Grow with Customers, Not Just Investors

Raising $100 million feels good, but it doesn’t guarantee survival. Just ask Zume, the robot pizza startup that raised nearly half a billion before collapsing.

Now contrast that with Writesonic, which bootstrapped revenue early by selling affordable tools to creators. Customers funded their growth, not just investors. Today, they’re cash-flow positive and expanding sustainably.

Your survival lesson: Make your customer your first investor. If people pay you, you’ve got validation that there is demand for your product or service. If VCs pay you, you’ve only got runway, and eventually runways run out.

Lesson 5: Keep One Foot in Today, One in Tomorrow

AI startups die in two traps:

• Focusing only on today and they get leapfrogged by their competition, or


• Focusing only on tomorrow and they handicap themselves while their competition consistently delivers today.

The survivors straddle both. Jasper AI started with copywriting but quickly evolved to serve marketing teams with workflows, brand voice tools, and enterprise features. They nailed “today” while planting seeds for “tomorrow.”

Your survival lesson: Build for the customer in front of you, but keep the innovation pipeline full by exploring where the tech is headed and planning for how to leverage that tech to evolve. That’s your insurance policy.

Lesson 6: Transparency Builds Trust

AI is still the wild west, and trust is rare currency. Startups that treat users like guinea pigs erode consumer trust quickly. Those that are transparent about data use, model limits, and even failures stand out. Customers feel comfortable doing business with them and nobody likes to leave their comfort zone.

Perplexity AI wins points by showing where its answers come from. Compare that to Babylon Health, which overhyped its diagnostic AI and imploded when reality didn’t match promises.

Your survival lesson: Be honest. Show your work. Users and regulators will reward transparency more than perfection. Make them comfortable and they'll relish the thought of leaving.

Lesson 7: Hire for Grit, Not Just Brilliance

AI attracts brilliant people. But brilliance without grit builds a house of cards. Survivors know they need teams that can grind through uncertainty, not just dazzle with ideas that might be short-lived.

Anthropic is a great example. Their “constitutional AI” approach came from disciplined, principled work, not a rush to hype the market. Their culture emphasizes alignment, resilience, and thoughtful execution. That’s a team that can last.

Your survival lesson: Hire fewer “genius founders” and more builders, operators, and pragmatists. Brilliance fades without grit, so build a sustainable model with a solid, well-rounded team.

The New Survival Mindset

If last week’s post was about avoiding landmines, this one’s about building momentum. Here’s the distilled mindset of survivors:

1. Pick enduring problems and solve them.


2. Be useful before you’re impressive.


3. Build moats beyond the model.


4. Grow with customers, not just investors.


5. Keep one foot in today, one in tomorrow.


6. Lead with transparency.


7. Hire gritty teams, not just brilliant ones.

It’s not about being the “AI-first” startup. It’s about being the problem-first startup that uses AI wisely now while simultaneously building for the future.

Closing Thought

Yes, the odds are scary. But every AI unicorn you’ve heard of, whether it be Grammarly, Duolingo, Perplexity, Anthropic, started with the same odds. They survived because they remembered the golden rule: AI is the enabling tool, not the business.

So if you’re building right now, take a breath. Slow the hype train. Focus on solving something real, earning trust, and playing the long game. The rest? That’s survivorship in action.

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If you read last week's post, then now you know that you can leverage open source LLMs and do some powerful stuff. This greatly reduces the startup cost if you're looking to launch your own AI business. But is that a good idea? Are AI startups actually turning a profit or are they just hype? Here's a hint...things aren't looking so hot for most AI startups. Knowing that, can your startup idea actually succeed? Let's investigate the current state of AI startups today.

Why So Many AI Startups Fail And How Yours Can Beat The Odds

AI startups are all the rage right now. Everyone’s dreaming of launching the next ChatGPT. But here’s the harsh truth: AI startups have an alarmingly high failure rate. It’s not just people on social media spreading gloom and doom. According to recent studies, around 90–92% of AI startups fail (AIM Media House, AI4SP). And even beyond startups, an MIT report found that 95% of generative AI projects in businesses don’t produce any meaningful results (yahoo!finance, Tom’s Hardware).

So why? And more importantly, what can you do to avoid common pitfalls and beat the odds? Let’s dig in.

What’s Causing Such a High Failure Rate?

1. Chasing Technology

When a new AI model drops, it’s tempting to hop on the bandwagon. But launching with the flashiest tech doesn’t mean your idea will land. Aakash Gupta highlights in his article that founders get distracted by demos, chasing technology instead of users. The lesson? Focus fiercely on a clear use case...and nail it (Aakash Gupta).

2. Theory vs the Real World

Demos are great, but they are designed to sell a product and rarely function as well under real world scenarios. Humane’s AI Pin, for example, promised to replace smartphones, only to fail spectacularly. It left customers with dead devices and no compensation (cnet.com).

3. Failure to Solve a Real Problem

If nobody needs your product, it fails. Period. According to AIM Media House, lack of demand and poor product–market fit are leading causes of failure. RAND’s research confirms that many AI projects falter because teams misalign on what problem they're solving.

4. Data and Infrastructure Gaps

AI thrives on quality data and solid infrastructure. Too often, startups lack clean, accurate data, or don’t invest in pipelines and deployment frameworks, and that’s a recipe for disaster.

5. Lack of Product Alignment

Some AI startups seek funding before actually aligning their products to the market. With large sums of venture capital in hand, they launch their products and then learn that there is no clear target market.

6. High Compliance Burdens

AI startups face an evolving regulatory landscape. Compliance can eat up massive chunks of resources, putting them in a “compliance trap.”

7. Talent and Capital Constraints

AI needs deep expertise and costly compute. Starsky Robotics, a self-driving truck startup, shut down because talent and tech advances didn’t match investor expectations.

8. Hype vs. Reality

Nicknames like “AI bubble” are popping up. OpenAI’s Sam Altman warns that we’re seeing elevated excitement, and inflated valuations that don’t always match real-world impact.

Real AI Startup Failures and What They Teach Us

• Builder.ai: Claimed to offer AI-powered app development but mostly used humans. The company filed for bankruptcy amid misreporting concerns.


• Forward (CarePods): Raised $650M for AI-powered medical pods. Technical failures + poor adoption led to their collapse.


• Humane's AI Pin: The $700 wearable phone replacement. Launched, fizzled, and shut down in under a year.


• Babylon Health: Once valued at $4.2B, fell to regulatory woes and poor scaling in healthcare.


• Starsky Robotics: Autonomous trucking pioneer. Demos were impressive, but they couldn’t sustain funding or pace of innovation.


• Enterprise AI pilots: MIT found that 95% fail, largely due to poor integration and weak ROI.

How to Tilt the Odds in Your Favor

1. Start Small, Solve a Real Problem

Be ruthlessly specific. The most successful pilots focus on solving single well-defined problems, often in back-office automation, not grandiose promises.

2. Build Real Infrastructure

Cohere spent years building a stable serving platform. By the time they launched their API, they had $1M ARR and real traction.

3. Lean Startup + AI = Magic

Pair AI with Lean Startup principles: MVPs, rapid testing, feedback loops. Iterate smartly before scaling.

4. Fail Fast (But Learn Faster)

Test early, pivot quickly. Be disciplined and learn from failure. Failures should provide valuable lessons, not just burn resources.

5. Invest in Data & Compliance Early

Ensure clean data and proper governance upfront. In regulated sectors, compliance is not just protection, it’s a competitive advantage.

6. Bootstrap Your Idea

Test the market and prove demand before chasing big VC rounds. Every dollar should build additional value for an existing market, not just hype.

7. Build a Resilient Core

Writesonic thrives by staying lean and modular, balancing infrastructure across multiple models to reduce risk.

8. Build the Right Team & Culture

Focus on building a diverse team, establishing clear ownership, and building transparency to establish a solid foundation. Avoid founder syndrome, and build a culture of resilience.

AI Startups That Thrive

• Cohere: Takes an infrastructure-first approach and sought focused traction before scaling.


• Anthropic: Focuses on constitutional AI with clarity and discipline in innovation.


• Writesonic: Aims to be modular, cost-conscious, and relentlessly user-focused.


• MIT’s 5% Success Stories: The few firms that made AI pay off were pragmatic, adaptive, and grounded in ROI. Essentially, they focused on sound business principles

Final Thoughts

Look, I get why AI looks like a gold rush. But it's also a minefield. The winners? They're not blazing a new trail in the wild frontier. Rather, they’re picking their focus, identifying their market, building rock-solid foundations, and growing intentionally. If you're thinking of launching an AI startup, or advising one, here’s are some tips:

• Define one tangible problem.


• Build just enough to solve it reliably.


• Learn from every failure (fast).


• Secure your data, and don’t ignore compliance.


• Bootstrap market fit before chasing VC.


• Keep your team diverse and grounded.


• Choose users over hype and deliver consistent value.

Done right, AI may just be the biggest opportunity you’ll actually get. So, go build your future, but build it with intentionality, focus, and care.

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We took a little detour last week and focused on AI in healthcare. Admittedly, if you're not in healthcare, then it may not have been applicable to you. Today, let's get back to a topic that's more applicable to the AI masses. Have you heard of open source LLMs? Have you heard that they can be run locally on your own hardware? Let's unpack this topic today.

Use Open-Source LLMs to Power Your Business (Without Losing Your Soul)

Whether you're scrambling to keep your business afloat or closing in on the next level like a run away freight train, you've no doubt been studying how to best leverage AI. If you've heard people talking about open-source LLMs that they run locally, you're now tuned into one of the most exciting shifts in AI adoption. Let's walk you through why they matter, what's actually available, what they’re capable of, and how they stack up against the likes of ChatGPT.

1. Why Open-Source LLMs Could Be Beneficial To Your Business

• Privacy & data control. Open source LLMs run locally, so your data stays on your machine. No surprises. No “Did they train on my emails?” anxiety. That’s gold for lawyers, healthcare, or any business handling sensitive info.


• No vendor lock-in. Open-source = freedom. You can tweak the model, move it, or extend it. If the vendor used by our competitor changes pricing or terms, guess what? You're still in business because you're not affected.


• Offline resilience. Internet cut out? You're still in AI mode. For offline environments or reports to the boss when the network is dodgy, local AI can be lifesaving.


• Cost control. After the initial setup, you're not paying per token, per query, per trial. Just your hardware and electricity. Compare that to ChatGPT subscriptions and usage fees, that can add up. It definitely tips the scales in favor of an open source model.


• Learning by doing. Want to understand how the sausage is made? This is your chance. Tweak the prompts, inspect the internals, adapt the logic. You'll build deep AI expertise, and that's experience you can sell.

2. So, What's Actually Available? The Who's Who of Open-Source LLMs (Mid-2025 Edition)

OpenAI’s New Move: gpt-oss-20b and gpt-oss-120b

Yes, the same OpenAI that offers ChatGPT has an open source model. It delivered it's first open-weight models in years, available under Apache-2 on Hugging Face. Chain-of-thought capable, customizable, and runnable fully offline. The 20B version can run on consumer gear (~16 GB RAM), while the 120B model competes with proprietary options in benchmarks.

Mistral AI

Known for strong open-source performance, Mixtral series, now Mistral Small 3.1, Mistral Medium 3, and innovative reasoning models like Magistral Small and Magistral Medium (chain-of-thought capable).

Gemma (Google DeepMind)

Lightweight, powerful, and open. Gemma 3 released March 2025. It's ideal for running locally on smaller hardware and it's also multilingual and multimodal.

BLOOM

A massive 176B-parameter multilingual model from the BigScience open-science initiative. 46 human + 13 coding languages supported. Entirely open.

DBRX (Databricks / Mosaic)

A 132-billion-parameter mixture-of-experts model and only part of it activates per token. Delivered on benchmarks, dominating LLaMA 2, Mistral’s Mixtral, and xAI’s Grok in tests. Released under an open license.

IBM Granite

Coding-focused models (e.g., Granite 13B) outperform LLaMA 3 in code tasks. Fully open under Apache-2.

EleutherAI’s Legacy Models

GPT-Neo, GPT-J, GPT-NeoX, and Pythia are all open and foundational in the community. Great if you're a researcher or tinkerer.

TinyLlama

A 1.1B model based on LLaMA 2 architecture. It's tiny, efficient, expressive. It also handles downstream tasks impressively for its size.

Others (Emerging/TBD)

China’s DeepSeek R1, Qwen, Moonshot, MiniMax, Z.ai, etc., are starting to appear in open-weight form. Use cases are growing fast.

3. Which Models Shine Right Now and Why

• Broad Capability + Practicality: gpt-oss-20b hits a sweet spot with solid performance and lower resource needs.


• Raw Power: gpt-oss-120b and DBRX hold their own vs. proprietary models in benchmarks.


• Reasoning-First Models: Magistral Small & Medium are built for chain-of-thought logic.


• Small but Mighty: Gemma 3 and TinyLlama are super accessible with modest hardware.


• Multilingual & Large-Scale: BLOOM is the multilingual go-to.


• Domain-Specific: Granite 13B shines in code-heavy tasks.

4. Capabilities You Can Expect from Local, Open-Source LLMs

5. Open-Source vs. ChatGPT (or Other Hosted LLMs) — What’s the Trade-Off?

ChatGPT / Hosted LLMs (e.g., ChatGPT, Claude, DeepSeek Chat)

• Pros: Cutting-edge, polished, constantly improving UI, plug-and-play, with tools like browsing or plugins.


• Cons: Cost per use, data leaves your control, potential vendor policy shifts, black box / locked logic, API limits.

Open-Source Local LLMs

• Pros: Full ownership, privacy, customization, no per-token fees, offline usage, learning opportunities.


• Cons: Setup time, hardware requirements, model maintenance. Not as flashy out of the box...yet.

6. Who Should Pick What, and When?

Go Open-Source If:

• Data privacy is non-negotiable.


• You want full control and flexibility.


• You're tech-savvy, or at least up for learning.


• Budget-conscious with ongoing volumes.


• You want to build deep AI expertise.

Stick with ChatGPT-style If:

• You need results fast, with zero setup.


• You need tools like image, code, or browsing plugins.


• You're OK with recurring payments.


• You're fine with limited customization.

7. Making the Leap (Without Risking Everything)

1. Start small. Try TinyLlama or Gemma 3 on a test project. Spend some time to get comfortable with it.


2. Match the model to the task. For code, try Granite or DBRX. For logic, Magistral or gpt-oss.


3. Plan hardware. Consider Gemma/TinyLlama to run on minimal hardware requirements. Choose gpt-oss-120B if you have serious GPU power available to run it. Balance ability vs. access.


4. Run pilot experiments. Compare prompts, latency, accuracy vs. ChatGPT.


5. Evolve gradually. Once you're comfortable, layer in tuning, domain-specific enhancements, custom UI, or enterprise deployment.

Final Thoughts: Embrace the AI, Keep Your Voice

Here’s the deal: picking an LLM isn’t just about functionality, it’s about alignment. Do you want AI to feel like a glitzy black box or a tool that amplifies your voice, your values, your way? Open-source LLMs let you keep a human in the loop. They give you control. And they give you the chance to level up, not just your AI, but your ownership of it. So, pick the right LLM for you and let it do the heavy lifting for you.

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Last week was dedicated to the corporate employee. Particularly those who are interested in making the leap to entrepreneurship and want to leverage AI to do so. Let's dedicate this week to leaders. Not just any leaders, but healthcare leaders and investors. AI is showing up in healthcare in a big way. As a healthcare leader, it's good to have a solid understanding of the current state of AI in your field. How else can you make informed investment decision. Given that, let's lay out the current state now.

AI in Healthcare: What Healthcare Leaders and Investors Should Know Right Now (and the next 1–2 years)

If you run a hospital system, a clinic network, or a health-tech shop and someone on your team hasn’t already suggested “let’s do AI”, they probably will soon. AI isn't just some novelty technology that will fade away as fast as it blew up. No, it's rapidly moving into the healthcare space in big ways. It can already be found in areas like imaging, screening, workflow orchestration, and even clinical knowledge work. But, as always, hearing “it works” is not the same as “it’s ready for prime time in your org.” Let's take a realistic look at AI in the healthcare space, so you can make better decisions over the next 12–24 months on what's a fit and what isn't.

What AI capabilities already exist in healthcare?

AI’s presence in healthcare is no longer just about prototypes or academic studies. Today, it spans several mature and emerging categories:

• Diagnostic imaging & triage: Algorithms can detect conditions like intracranial hemorrhage, stroke, pulmonary embolus, and suspicious lesions on mammograms and CT scans. Some systems prioritize urgent cases, alerting radiologists in real time, while others integrate seamlessly with Picture Archiving and Communication Systems (PACS) for concurrent reading. In some regions, autonomous AI in mammography screening has already shown measurable improvements in detection rates.


• Autonomous point-of-care screening: FDA-cleared systems such as those for diabetic retinopathy screening allow primary care providers to offer specialized diagnostics without needing on-site specialists. These tools enable earlier detection and treatment while reducing patient referral delays.


• Clinical decision support & workflow automation: AI-enhanced systems now integrate with Electronic Health Records (EHRs) to recommend personalized care pathways, flag drug-to-drug interactions, and even suggest preventive interventions based on patient history and population health data.


• Large language models (LLMs): Beyond summarizing clinical notes and drafting patient instructions, LLMs are being embedded into secure medical knowledge assistants for clinicians, enabling human-language queries of guidelines, research papers, and patient charts.


• Remote monitoring & predictive analytics: AI-powered wearable integration can detect early signs of patient deterioration, predict hospital readmissions, and trigger early interventions. These systems are particularly effective in chronic disease management, ICU monitoring, and post-surgical recovery.


• Operational optimization: Predictive staffing models, supply chain demand forecasting, and operating room scheduling optimization are already in play, leading to reduced costs and more efficient use of resources.

Clearly, AI is already being used on a daily basis in meaningful ways. Many experts would say that this is the tip of the iceberg. With pressure to reduce cost, clinical staff shortages, and desire to improve outcomes, it's fair so say that AI will become more prevalent in the healthcare setting...not less. But, are they really accurate?

In specific, narrow tasks, accuracy can rival, and even sometimes exceed, that of human experts. Examples include AI-assisted mammography screening improving detection rates, and autonomous diabetic-retinopathy screening with robust sensitivity and acceptable specificity. But accuracy is task and data dependent. Prospective, local evaluations are still essential to validate acceptable accuracy in your specific situation.

What about ethics and legal implications? Are these systems even ethical and legal to use?

• Bias & fairness: AI can perpetuate biases from training data, so audits and subgroup performance reporting are essential to ensure the system is functioning fairly and without bias.


• Clinical validity vs. outcomes: Better detection isn’t always better care, so focus on outcomes. Prioritize investment in systems that actually improve outcome first.


• Privacy & data governance: HIPAA compliance and proper governance are critical. Both are feasible, but it requires proper controls, a strong understanding of the model algorithms, strong AI governance and frequent audits to truly protect patient privacy.


• Regulatory compliance: Verify claims against FDA resources and clearance pathways. Never trust marketing materials or the sales person. Ensuring the system will meet compliance requirements ahead of time will save a lot of time and money compared to finding out post-implementation.

So, what impact can I expect in the next 1–2 years?

AI’s short-term impact in healthcare is likely to be transformative in small, meaningful steps rather than massive overnight shifts. Here’s what leaders can realistically expect:

1. Workflow acceleration, not replacement: AI will continue to take over high-volume, low-complexity tasks. This may include things like triaging imaging studies, automating documentation, and routing critical alerts. This will free clinicians for more complex cases and decision-making, directly improving throughput and reducing burnout.


2. Point-of-care screening expansion: Autonomous AI systems will become more common in primary care and retail health settings, expanding access to diagnostics for underserved populations, particularly in ophthalmology, dermatology, and certain cardiovascular screenings.


3. LLMs in clinician tooling (cautiously): LLM-powered assistants will mature into reliable, context-aware helpers for physicians, nurses, and administrative staff. Expect integration into EHRs for natural language search, clinical summarization, and evidence synthesis, but always with human review safeguards. Some call this Augmented Intelligence to stress the importance of having a human in the loop.


4. Regulatory normalization: The FDA and equivalent bodies worldwide will publish more refined guidelines for adaptive AI, making compliance clearer and lowering the barrier for enterprise adoption.


5. Operational optimization at scale: Predictive AI for staffing, bed management, and supply chains will become mainstream, yielding cost savings and better patient flow, especially in high-volume hospitals.


6. Personalized treatment recommendations: Advances in AI-driven genomics interpretation and treatment response prediction will begin to influence oncology, rare diseases, and pharmacogenomics-based prescribing, albeit in controlled, evidence-driven environments.

How should a leader go about evaluating an AI solution before investing?

Here's a quick punch list to use as a framework for evaluating an AI solution. Obviously, each of these steps may require a lot of time and effort to complete, but you don't want to cut corners here. The time and effort invested now will pay dividends down the road.

1. Define the clinical question & expected outcomes.


2. Check regulatory status & existing evidence.


3. Plan a proof of concept for local validation.


4. Ensure data governance for the local validation.


5. Test for bias & fairness.


6. Assess integration & workflow complexity and potential impact.


7. Plan ongoing monitoring & maintenance needs and costs.


8. Pending a successful local validation, build the economic case for full scale implementation.

Additionally, consider the following:

• Create an AI governance board to govern both this investment and future AI investments.


• Demand vendor transparency to ensure you understand how the models work and what's changing when new versions are released.


• Invest in observability and re-validation once full scale implementation is complete.


• Train clinicians on how to use the model and, just as importantly, what the limits are.

Final thoughts

AI in healthcare is no longer theoretical. Narrow, validated models are delivering value today. Over the next 1–2 years, organizations that demand evidence, pilot locally, govern ethically, and measure outcomes will see the greatest benefits. If you invest the sweat equity in governance and validation now, AI will be an amplifier for safer, faster, more equitable care rather than some risky experiment sitting in a vendor slide deck.

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We learned last week about how AI can help with e-commerce. If you're in the corporate world, that may have gotten the wheels turning in your mind about leaving and starting your own business. Maybe it's your dream, but it feels too daunting. Afterall, you have responsibilities.

Well, I'd argue that it's not completely impossible to exit the corporate world and start your own business. Yes, there's risk involved, but there's risk in being a corporate employee these days too. Let's spend some time today examining one way to make the leap from employee to entrepreneur.

Escaping the Corporate World (Safely) and Launching Your Own AI Business

How to leave your job without losing your mind, your money, or your momentum.

Let’s start with the cold, hard truth

If you’re in a corporate job right now, you’re probably watching what little job security you may have had fly out the window. Layoffs are increasing. Budgets are shrinking. Meetings are multiplying. And maybe, just maybe, your soul is quietly shriveling.

But here’s the good news...AI is creating new types of businesses that never existed before. Jobs that don't necessarily require staying in Corporate America. And the best part? You don’t have to be a tech genius or burn your savings to participate.

You just need a solid strategy and plan to help you make the transition from the corporate world to running your own AI-based business. Let's see if we can help you with that today.

Part 1: Mindset Shift — From Employee to Entrepreneur

Leaving corporate life isn’t just a career move, it’s an identity shift. A huge change. You’re going from:

• Doer → Thinker


• Execution-focused → Value-focused


• Task-based → Outcome-based


• Stable paycheck → Self-driven income

This shift takes intentionality. Every hour you spend on your side business should be on laying a solid foundation and building assets (products, audience, systems) rather than just checking boxes. Every move should bring you one step close to exiting the corporate world.

Here are Some Mini-Mindset Exercises to Help Shift Your Thinking:

• Audit your time: track how much of your day is spent reacting to stuff in your corporate environment vs. being strategic and proactive.


• Write out your “anti-resume”: what you don’t want in your new AI-based business.


• Ask: What do I know that others would pay for if it was packaged and delivered clearly, with AI automation behind it?

Part 2: Pick the Right AI Business for You

Here’s are four possible beginner-friendly AI-powered business types to consider, and what it takes to launch them. Use this a launchpad to finding the right business for you.

1. Launch an AI-Augmented Consulting or Coaching Business

• Use your subject matter expertise, combined with relevant AI, to deliver high-value insights to clients


• Great for corporate veterans who want to pursue high-end B2B work

Examples: Fractional AI marketing advisor, AI onboarding consultant, leadership coach with AI-enhanced tools.

Tools to learn: ChatGPT, Notion, Loom

2. Start an AI Micro-Agency

• Offer done-for-you services powered by AI


• Scalable business model with repeatable client work

Examples: Create social media content, Set up AI chatbots, SEO content repurposing.

Tools to learn: Canva, ChatGPT, Zapier, Descript

3. Create and Sell AI-Enhanced Digital Products

• Create the product once and sell repeatedly


• Perfect for creators and introverts who shy away from face-to-face sales tactics

Example products: Prompt packs, workbooks, Notion templates.

Tools to learn: Gumroad, Canva, Beehiiv

4. Develop Low-Code AI SaaS (Software as a Service) or Other AI Tools

• Build niche tools driven by AI APIs for clients to interface with


• High risk business model with high reward potential

Example tools: AI resume analyzer, grant-writing chatbot, AI meeting summarizer.

Tools to explore: Bubble, OpenAI API, Stripe

Part 3: A Realistic 6-Phase Transition Plan

Phase 0 – Complete Your Pre-Flight Checklist

• Build an emergency fund: Save up 3–6 months of expenses to support you during your transition


• Establish your income goal: know your target income from your new business to safely exit the corporate world


• Get Health insurance: research options, understand costs and have a plan to sign up before your corporate insurance lapses


• Do a skills audit: what skills do you have that you can magnify and monetize with AI?

Phase 1 – Pilot Your Side Business (3–6 months)

• Pick one business model and validate it with a proof of concept (POC)


• Launch a simple offer with payment methods and other essentials, such as a scheduler, to launch the POC


• Get your first 3–5 test clients, which can be discouraging process...so, stick with it

Milestone: Work to achieve your first $1,000–$2,000 in side income...then celebrate

Phase 2 – Build Systems & Recurring Revenue

• Build repeatable systems and automate everything you can


• Document the client process


• Scale your business to 2–3 clients or deliver a second product

Milestone: $2,500–$4,000/month of recurring revenue

Phase 3 – Budget & Timeline Your Exit

• Track your 3-month income average


• Forecast a 6-month sales pipeline


• Secure 1–2 anchor clients

Milestone: 3 consistent months of side income that matches or exceeds your 3-month average + 6 months runway

Phase 4 – Give Your Notice & Shift Full-Time to your Business

• Exit the corporate world gracefully without burning any bridges


• Publicly launch your business


• Focus your time: 30% marketing, 40% delivery, 30% systems

Phase 5 – Strategically Expand Your Business

• Develop and promote upsell and client retainer packages


• Build a content engine (newsletter, social media, podcast, etc.)


• Hire a virtual assistant or support staff if needed

Milestone: $7K–$10K/month consistent revenue

Phase 6 – Long-Term Plan

• Build scalable assets: courses, community, IP


• Systemize growth and operations


• License or white-label your frameworks

Part 4: Tools to Stay on Track

• Project management: Notion, Trello


• Financial tracking: Wave, QuickBooks


• Learning: LinkedIn Learning, FutureTools.io


• Community: The AI Exchange, FailingCompany

Final Thought: This Doesn't Have to be a Leap, it can be a Smooth Staircase

Corporate escape doesn't have to a single gut wrenching decision. It can be an intentional transition. Each phase gives you more confidence, financial safety and personal clarity. You already have the hard-earned experience. Now pair it with leverage. Build slowly. Exit wisely. Grow confidently. And when you're doubting yourself? Remember:

“If AI can automate the routine, your job is to make what you do irreplaceably human.”

You’ve got this.

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