This newsletter has always been about one thing. Giving you something you can use on Monday morning. Not theory. Not hype. Not another “state of AI” recap you skim and forget. You’re here because you want to move faster, spend less, and become the person in your company who actually gets AI working.

That’s what AI Adopters Club is. A place for people who build, test, and implement. And this week I’m putting that promise to work with two things I made specifically for you.

Thanks for reading AI Adopters Club! This post is public so feel free to share it.

Last week’s article on the data lake landscape hit a nerve. Dozens of replies. People forwarding it to their CTOs. One reader sent it to their entire leadership team with the note “this is exactly what I’ve been trying to explain.” The message was loud: you’re stuck between enterprise platforms you can’t afford and spreadsheets held together with hope. Your data lives across 15 disconnected SaaS tools and nobody can get a straight answer about the business.

So instead of writing another article about it, I built you something.

Find out what your data gaps are actually costing you

The assessment costs $99. If you’re a subscriber, you have it for free. Search your inbox for “AIAC Assessment Code” and you’ll find your access code.

19 questions. About 7 minutes. You answer questions about your company, your data landscape, and your tools, and you get back:

* A readiness score across five dimensions: fragmentation, manual burden, data trust, AI readiness, and decision velocity

* A dollar estimate of what your data gaps are costing you annually, based on real salary benchmarks and infrastructure data from the research

* A consolidation roadmap customised to your industry and tool stack, with a concrete first step

* Industry-specific insights drawn from diagnostic work across 20+ organisations in healthcare, hospitality, education, financial services, and technology

This is not a generic quiz that tells you “you should adopt AI.” It’s grounded in the same research I shared last week. The cost estimates reference verified data: 633 enterprise purchases, analyst salary benchmarks, and infrastructure pricing across the major platforms.

If you’ve ever walked into a budget meeting and struggled to explain why your data infrastructure needs investment, this gives you the numbers to make your case.

What your results will look like

* Estimated annual cost of data gaps: $100K–$250K

* Biggest gap: data fragmentation (1.5/4.0)

* Relative strength: AI readiness (2.5/4.0)

* Phase: Audit and Connect, with your specific tools listed and a concrete next step

Seven minutes and you’ll know whether to fix foundations before investing in AI tools, or whether you’re closer to ready than you thought.

Get your own AI chief of staff running this Saturday

I’ve been building Claudia, my open-source AI chief of staff, for a while now. She’s on GitHub. Anyone can install her. And most people who try get stuck somewhere in the setup.

Introducing Claudia, Your Personal AI Chief of Staff. 🤖 - Watch Video

This workshop fixes that. Saturday, April 11th. 10 spots. $75.

By the end of the session, you’ll have:

* Claudia installed and running on your machine

* Your email and calendar connected

* Your first morning brief delivered before you’ve finished your coffee

* Access to a WhatsApp group of everyone who’s building with her

If you’re a consultant, founder, executive, or solopreneur juggling relationships, commitments, and context across dozens of conversations, Claudia takes the overhead off your plate. The last workshop sold out, so if you’re interested, don’t wait.

Reserve your spot: https://claudia.aiadopters.club/workshop

There’s a short video walkthrough showing what she does and what you’ll get out of the session.

Two things this week. One free for subscribers, one paid. Both built because this club is about giving you tools that work, not just words that sound good.

Adapt & Create, Kamil

Thanks for reading AI Adopters Club! This post is public so feel free to share it.

Hey Adopter,

Your support team already knows how to resolve most tickets. The problem is that knowledge lives inside three or four people’s heads, and when they’re off sick or swamped, your queue turns into a guessing game.

Most companies try to fix this by buying an AI chatbot, plugging it into their help desk, and hoping it figures things out. It won’t. An AI with no decision logic, no knowledge base, and no guardrails is just a very expensive way to annoy your customers faster.

The real fix works in the opposite direction. You document the escalation logic your best agents already follow, map the knowledge they rely on, feed the AI real conversations so it learns how messy inputs become clean decisions, then define exactly where it must stop and hand off to a human.

Six steps. Three hours of focused work. The output is a complete AI support agent with decision trees, a structured knowledge base, trained conversation patterns, and hard guardrails that prevent it from touching anything dangerous.

But here’s the part that matters even if you never build the AI agent: Steps 1 to 3 produce a documented escalation playbook your human team can use immediately. New hires follow it on day one. Senior agents stop getting pulled into tickets they shouldn’t be handling. The AI layer is a bonus, not a prerequisite.

The difference between support teams that automate well and those that waste money on chatbots comes down to one thing: whether they documented the logic before they deployed the technology.

Let me show you the exact workflow.

Your coworkers are using the same free prompts as everyone else. Premium members get the workflows that create separation.

Part one builds the human playbook

Before any AI touches a ticket, you need three things: a map of your support operation, decision trees for your highest-impact issue types, and an operational kit with handoff protocols and scripts.

Map first, build second

Step 1 asks you to describe your support operation in plain language. No forms, no placeholder brackets, just a few honest sentences about your team, your tools, and what breaks most often. The AI reads your description and produces a proposed tier structure, a ranked list of your top issue types by escalation frequency and customer impact, and its best guess at where tickets get stuck.

Then it asks you 3 to 5 targeted questions to fill gaps. Yes/no or one-sentence answers. You correct anything it got wrong, confirm the rest, and walk away with a validated support map.

This map alone is worth the exercise. Most support managers have never seen their operation laid out this cleanly.

I keep running into the same problem when I advise businesses on AI. The AI isn’t the bottleneck. The data is.

Most companies I work with have their information scattered across 10 to 20 different tools. Salesforce has the customer data. QuickBooks has the financials. HubSpot has marketing. Slack has conversations. None of them talk to each other. When someone asks “what did we spend on customer acquisition last quarter?”, three people pull three different numbers from three different systems. That’s the real reason AI projects stall. You can’t build anything intelligent on top of data that’s fragmented, duplicated, and inconsistent.

So I went and investigated what it actually costs to fix this. Not the vendor pitch version. The real version, with verified numbers, public filings, and the pricing pages they hope you never read. What I found was worse than I expected.

This article breaks down the true cost of a “proper” data infrastructure for a mid-size company, why you keep getting sold tools you don’t need, what you actually need instead (it fits on a napkin), and where the market is heading. It’s a long one. Worth it.

Let’s start with a number that should scare you.

If this hits close to home, forward it to whoever owns the data conversation at your company. And if you want to talk through your situation, reply to this email. I advise businesses on this and I’m happy to get on a call.

Someone forgot to check the meter

A 200-person company got a $60,000 monthly Snowflake bill. Not because they were doing anything ambitious. Queries nobody knew were running had been racking up compute for weeks. No alerts. No guardrails. No one watching.

That’s $720,000 a year. On accident.

You’d think this was a freak event. It’s not. It’s consumption-based pricing doing what consumption-based pricing does. The meter runs whether you’re looking or not. And Snowflake’s billing minimum charges you for 60 seconds of compute even when your query takes three. Run ten dashboard refreshes at three seconds each, you pay for ten full minutes. For BI-heavy workloads, that means paying up to 20x the compute you use.

This isn’t a bug. It’s the business model. And it’s working perfectly, for them.

The real math nobody puts on the first slide

Your CTO says you need a “proper data stack.” Your board agrees. A consultant nods along. A vendor shows up with a deck full of logos and a quote that looks reasonable. Here’s what reasonable turns into for a 200-person company once you follow the money all the way to the end.

The platform. Snowflake’s median annual contract is $100,000, based on 633 real purchases tracked by Vendr. But the sticker price is a fraction of the real cost. A MotherDuck analysis found the true total cost of ownership runs 2.4x higher once you account for compute overhead, the 60-second billing minimum, and egress fees. Your $100K platform is closer to $240K.

The connectors. Your data lives in Salesforce, HubSpot, QuickBooks, Slack, and a dozen other tools. You need something to pull it all together. Fivetran will do it for $15,000 to $24,000 a year. Unless you need more than a few connectors, in which case their March 2025 pricing restructure hit multi-source setups with 40-70% cost increases. 35% of G2 reviewers now cite cost as their top complaint.

The dashboards. Looker or Tableau with enough seats to be useful: $24,000 to $60,000 a year. Less than that and you’ve built an expensive system only three people can access.

The people. This is where it gets ugly. You need a minimum of three data engineers to keep the lights on. At $125,000 to $136,000 base salary, fully loaded with benefits and overhead, that’s $160,000 to $200,000 per head. Three of them: $480,000 to $600,000 a year.

Add it up.

Component Annual cost Snowflake (true TCO) $240,000 Fivetran connectors $15,000 - $24,000 BI tool $24,000 - $60,000 3 data engineers (fully loaded) $480,000 - $600,000 Total $760,000 - $924,000

For a 200-person company. Before you’ve produced a single report anyone trusts.

And here’s the part that should make you angry: at one company, 3 of 8 data engineers spent 80% of their time keeping the stack running. Not building anything. Not analysing anything. Maintenance. That’s IT support with a fancier job title and a $180K salary.

Why you keep buying it anyway

Three forces keep this cycle spinning.

The vendor’s incentives point away from yours. Snowflake’s revenue grows when your queries are inefficient. That’s not a conspiracy theory. It’s their business model, filed with the SEC. Instacart’s Snowflake bill went from $13 million to $51 million in two years before they brought in a dedicated team to claw it back through optimisation. They got it down to a projected $15 million, which means roughly $36 million of that spend was waste. Instacart has hundreds of engineers. You have maybe two. The vendor has no reason to tell you you’re overspending. You are the margin.

Fear of being wrong. “Nobody got fired for buying Snowflake” is the 2026 version of “nobody got fired for buying IBM.” Your CTO recommends it because it’s the safe choice, not the right one. The name carries the meeting. The invoice carries the budget. And when the board asks “are we doing data right?”, pointing at a Snowflake logo feels safer than explaining why you chose something they’ve never heard of.

The buzzword tax. “AI-ready data” is the phrase doing the most damage right now. Every vendor pitch in 2026 includes it. Only 6% of enterprise AI managers say their data infrastructure is actually ready for AI. Six percent. Because “AI-ready” means clean, unified, queryable data. That’s it. Every warehouse does that. The label is marketing, not a capability. You’re paying a premium for a sticker on the same box.

What you need fits on a napkin

Strip the jargon and a 200-person company needs four things.

Your SaaS tools connected to one place. Salesforce, HubSpot, QuickBooks, your HRIS, all feeding into a single source. That’s integration.

Duplicates resolved, names consistent. “John Smith” in Salesforce and “J. Smith” in QuickBooks are the same person. Your system should know that. That’s entity resolution and cleaning.

Someone can ask questions without writing SQL. Whether that’s a BI dashboard or an AI chat interface, the point is the same. A non-technical person asks a question, gets a trustworthy answer. Not “let me file a ticket with the data team and wait three days.”

Someone knows what data exists and who can see it. Governance. Not glamorous. Absolutely necessary. When your sales VP can see payroll data because nobody set permissions, that’s not a feature.

That’s the list. Four items. Yes, somewhere under the hood there’s a data lake or a warehouse. You don’t need to know which one, how it works, or what Apache Iceberg is. You have gigabytes of data, maybe low terabytes. The industry is selling you a fire truck when you need a garden hose.

And yet 67% of organisations don’t trust their own data enough to make decisions with it. They spent the money. Built the stack. Hired the engineers. Still can’t get a straight answer about last quarter’s revenue without three people arguing over spreadsheets. The problem was never the size of the platform. It was whether anyone could use it.

The cracks are showing

The big vendors won’t frame it this way, but the numbers tell the story.

Snowflake’s net revenue retention dropped from 131% to 126% in 12 months. Existing customers are spending less. They added 1,735 new customers in FY2025, but those customers are smaller. The growth engine is sputtering where it used to roar.

Fivetran’s pricing overhaul drove engineers to Reddit looking for alternatives. “Fivetran has a reputation for being eye-wateringly expensive” is a direct quote from r/dataengineering, and it’s one of the polite ones.

Only 23% of data projects finish on time and on budget. The rest blow past deadlines, burn through cash, or get quietly abandoned. And 21% of companies replaced their data platform entirely in 2024. One in five. That’s not normal churn. That’s buyer’s remorse at scale.

Meanwhile, the ground is shifting underneath. DuckDB grew 136% year-over-year in the Stack Overflow developer survey, hitting 25 million monthly PyPI downloads. It’s open-source, runs on a laptop, and handles the analytical workloads that 90% of mid-market companies will ever throw at it. MotherDuck, the cloud version, raised $133 million in total funding. A company called Definite offers a complete data stack for $250 a month.

The market is voting with its wallet. And it’s voting against complexity.

Startups are spending $100,000 a year on modern data stacks before generating a single meaningful business result. That number used to be the cost of doing business. Now it’s the cost of doing it wrong.

A new category is emerging. One vendor, one contract. Connectors, cleaning, modelling, dashboards. No engineers required. This category barely existed two years ago. It’s real now.

The gap nobody’s filled yet

Here’s where you sit today.

The enterprise stack costs $760,000 to $924,000 a year and requires a team to operate. You know this because you’re either paying it or you’ve been quoted something close.

The lean DIY approach, DuckDB plus Airbyte plus a BI tool plus duct tape, costs $80,000 to $140,000 a year. But it requires someone technical to stitch together four or five tools and keep them running. When a connector breaks at 2am, that’s your problem. When Fivetran changes its pricing, that’s your problem. When two tools update their APIs on the same week and nothing syncs, that’s a Tuesday. Reddit’s honest assessment of enterprise data tools for mid-market companies: “overkill unless you have 50 people in your data team.”

There should be something in the middle. Not a stack you assemble from parts. An end-to-end data platform, one vendor that handles everything. You pay more than DIY because you’re paying for “I never think about data infrastructure again.” You pay dramatically less than enterprise because you’re not funding three engineers and a Snowflake habit. You get clean, unified data without learning what a DAG is.

That option barely existed two years ago. It’s emerging now. The companies that find it first will spend a fraction of what the enterprise stack costs, ship faster, and never post a job listing for a data engineer.

The question for 2026 isn’t which data stack to build. It’s whether you need to build one at all.

Adapt & Create, Kamil

Halfway through, I showed them a single research finding. The room went quiet.

Your expertise doesn’t predict your AI performance.

I watched faces shift. Some looked uncomfortable. Others looked relieved. Because if the problem isn’t your knowledge or your experience, then you actually have something specific to fix.

That’s what I want to talk about today.

OpenAI finally said it out loud

Last week, OpenAI released a report called Ending the Capability Overhang. The capability overhang is their term for the gap between what AI can do and what most people are actually getting out of it.

The tools are ready. The humans aren’t.

Their data shows power users extract roughly six to eight times more value from the same AI tools as typical users. Same subscription. Same model. Wildly different results.

This isn’t about using AI more. It’s about using it differently.

The research that changes how you think about AI skills

Researchers at Northeastern University and UCL tested 667 people. They measured performance alone, then performance with AI assistance.

The finding that should make you uncomfortable: being good at your job didn’t make people good at working with AI.

Some average performers saw massive gains when AI joined them. Some top experts barely improved at all. Years of experience, advanced degrees, deep domain knowledge, none of it predicted who would benefit most.

The people who got results weren’t smarter. They weren’t more senior. They were doing something different.

What the winners did differently

The researchers found one habit that separated high-gainers from everyone else. They called it Theory of Mind. The ability to step into another perspective.

In practice, it looked like three things.

First, they set the scene. Before asking anything, they gave background. Who they are, what they’re working on, who the output is for.

Second, they filled in gaps. They asked themselves: what do I know that the AI doesn’t? Then they included it. Company context, internal jargon, what they’d already tried.

Third, they treated bad answers as information. When the AI missed the mark, they didn’t just rephrase and retry. They figured out why it missed. Did it misunderstand the goal? Lack a constraint? Then they adjusted.

This is what separates someone who uses AI from someone who collaborates with it.

Build your Human API

I call this skill your Human API.

An API is an interface. It’s how one system talks to another. Your Human API is how well you translate what’s in your head, your expertise, your context, your judgment, into something AI can actually work with.

Your expertise is table stakes. The multiplier now is how clearly you communicate with the machine.

And unlike your IQ, unlike your years of experience, this is a skill you can build.

I recorded a short video walkthrough of this concept. It covers the research, the three-question protocol, and what this means for your work. Watch it here if you want the full breakdown.

The 10-second protocol

Before any important AI request, run this checklist. Takes ten seconds.

Context. What am I holding that the AI doesn’t have? Your role. The situation. What you’ve already tried. Who the output is for.

Needs. What does the AI need to know to give me something useful? Your constraints. Format preferences. What “good” actually looks like.

Verification. If this answer misses the mark, what will I check first? Did I explain the goal clearly? Did I leave out something obvious to me but invisible to the machine?

Three questions. Ten seconds. Changes every interaction.

Two prompts to build your Human API

I want you to try something before you close this email. Here are two prompts you can use today.

Prompt 1: Diagnose your Human API

Copy and paste this into ChatGPT or Claude:

I want to evaluate how effectively I communicate with AI tools, what I call my "Human API." If you have memory of our past conversations, analyze the patterns you've observed: - How clearly do I typically provide context about my role, situation, and goals? - Do I explain constraints and success criteria, or leave you guessing? - When outputs miss the mark, do I diagnose what went wrong or just rephrase the same question? - What context do I consistently forget to share that would help you help me? If you don't have memory of our past interactions, interview me instead: - Ask me 5-6 questions about how I typically use AI tools - Probe for specific examples of recent interactions - Then diagnose where my Human API breaks down End with three specific recommendations for improving how I communicate with AI, ranked by impact.

Prompt 2: Build your Human API profile

After you run the diagnostic, use this to create something you can reuse:

Help me build my "Human API," a profile that captures how I work so AI tools can serve me better. Interview me with 5-6 focused questions covering: - My role and what I'm accountable for - The domain expertise I bring, what I know that most people don't - My most common tasks where AI could help - How I prefer to receive information, format, length, tone - What "good work" looks like in my context After the interview, produce a one-page "Human API Profile" I can paste into future AI conversations or save as custom instructions. Keep it concrete and usable, not generic. Start with your first question.

Why this matters now

The winners of the Intelligence Age won’t just be the smartest people in the room. They won’t be the ones with the most credentials or the longest resumes.

They’ll be the ones who communicate best with the machine.

OpenAI knows this. The research confirms it. And the companies asking the right questions are already acting on it.

Build your Human API. Start now.

Adapt & Create, Kamil

Word count: ~1,050

Want me to adjust tone, length, add/remove sections, or punch up any specific part?

I shared a stage at Limitless Live 2025 with Harper Carroll (Stanford AI researcher, former Meta engineer, now at Nvidia), Ari Meisel (productivity expert and author of The Art of Less Doing), and John Lee. Jim Kwik’s event brought together people hungry for practical ways to level up, and we got to spend time unpacking what AI means for ambitious professionals.

The video is below. Here are the moments worth watching twice.

Stop treating AI like R2D2

One of my favourite lines from the panel: most people use AI like they’re talking to a little can robot. They ask it for answers. That’s backwards.

Treat it like Yoda instead. Ask for questions. Ask for help thinking through your day, your decisions, your blind spots. The shift from “give me the answer” to “help me think” changes everything about how useful these tools become.

Harper put it well: AI is a probability machine generating words based on distributions. It can go down low-probability paths and just keep going. You need to stay in the driver’s seat.

The DJ analogy that stuck

Here’s another frame I shared: AI is a great DJ. You decide which vinyls to play.

I wrote 48 children’s stories based on the 48 Laws of Power. Not because AI told me to, but because I saw a creative mashup worth exploring. AI handled the cross-domain synthesis. I handled the vision and the weird idea.

That’s the split. You bring direction. AI brings speed.

The word that should trigger you

Ari Meisel dropped something I’ve been saying for years about automation, and it applies perfectly to AI: listen for the word “every.”

Every time I post on social media. Every time I onboard a client. Every time I send a weekly report.

That word signals repetition. Repetition signals opportunity. Before AI, you needed no-code tools to automate these things. Now you can build workflows in minutes. Small hinges swing big doors.

ChatGPT projects, and why almost nobody uses them

I asked the audience: how many of you use ChatGPT projects?

Barely any hands went up. That’s a miss.

Projects let you create separate spaces with custom instructions and documents. I have one for my business, one for school board work, one for family stuff, one for biohacking. Inside each project, I run individual chats like team members. A CMO chat. A CRO chat. A coaching partner.

It’s not just organisation. It helps your brain context-switch properly. When you’re in your business project, you think like a business owner. When you switch to family, you shift gears. The tool forces the compartmentalisation your brain needs anyway.

Tonight’s challenge from John

John Lee threw down a challenge for the audience. Build an app tonight using three steps.

First, tell ChatGPT what app you want to create. Then use this prompt: “Ask me 10 questions one by one about building this app that gives you 95% certainty that it will work.” Answer the questions.

Second, ask it to turn everything into JSON code.

Third, paste that code into Base44.com and hit generate. Your first MVP will be ready in about thirty minutes.

I added a second recommendation: take the same JSON to Lovable.dev. Let both platforms build at the same time. Compare the outputs. Play them off each other.

This is vibe coding. It’s not theory. It’s a Tuesday night project that gets you building.

The director’s chair

The first article I published on my newsletter was called “Welcome to the director’s chair.”

We all got a promotion we never asked for. If you were a graphic designer, you’re no longer a pixel pusher. You’re directing the work. You’re setting the vision. AI handles the execution you used to grind through.

That promotion comes with new skills to learn. Critical thinking. Storytelling. Knowing when to push back on AI outputs and when to trust them.

Harper reminded us: AI hallucinates. A Stanford professor faced perjury charges because he used a ChatGPT-generated source that didn’t exist. Validation is your job now.

The real skill that matters

We talked about what separates people thriving with AI from those feeling lost.

Growth mindset came up. Adaptability. Willingness to learn.

But Ari nailed something deeper: in the past, physical labour kept us fit. Now fitness is a choice. The same will happen with thinking. You can choose not to think critically if AI handles everything. Staying mentally fit will become a decision you make on purpose.

The winners will be the ones who keep their brains sweating.

Watch the full panel

The video captures more than I could fit here. Conversations about AI replacing jobs (spoiler: it won’t, but jobs will evolve). The future of tokenomics and personal AI agents. Why storytelling becomes more important as AI handles the mechanical work.

Press play. See what resonates.

Adapt & Create, Kamil

Hey Adopter,

The greeting card industry should be dead. WhatsApp is free. iMessage takes three seconds. Yet Hallmark still moves 6 billion cards a year.

Generative AI should have been the final nail. Tools that write poems in milliseconds obliterate the “effort premium” that makes a $6 card worth buying. But Hallmark looked at the same tools everyone else was racing to deploy and made a different bet entirely.

They decided AI should be invisible.

While competitors like Moonpig now use AI to write your messages for you, Hallmark pointed their algorithms at everything except the words on the card. Supply chains. Inventory. Workflow. The operational scaffolding that gets human sentiment from your hand to someone’s mailbox.

The result is a masterclass in knowing what AI should touch, and what it should leave alone

.

The strategy that flips common wisdom on its head

Most AI adoption stories follow a predictable arc. Company launches chatbot. Company launches AI writing assistant. Company announces they are “reimagining” their product with generative tools.

Hallmark’s playbook is the opposite. Their leadership calls it “Preservationist Innovation”, a framework that uses machine learning to protect the human core of the product rather than replace it.

Think about that for a moment. A century-old card company has built a more coherent AI strategy than most tech firms.

The full case study breaks down exactly how they did it, and why smaller operators can steal the same approach without Hallmark’s budget.

Download the full report to get:

* The “Recipient Graph” architecture that outperforms standard recommendation engines by tracking relationship history, not purchase history

* The exact infrastructure stack that cut total cost of ownership by 60% while funding AI experimentation

* A side-by-side analysis of Hallmark vs Moonpig vs Shutterfly, showing three distinct AI philosophies in the same market

* The “Sign & Send” product design that succeeded by making AI invisible, next to the “Video Greetings” failure that proved adding friction kills adoption

* A framework for “Human-in-the-Loop” creative workflows that protect brand quality without slowing output

The economics of “invisible” over “impressive”

Hallmark’s private ownership gives them something public companies cannot afford: patience. While listed firms scrambled to announce GenAI features to satisfy Wall Street, Hallmark could quietly kill failed experiments without tanking their stock.

Their Video Greetings product, a high-profile initiative that let recipients scan QR codes to watch personalized video montages, was discontinued by 2025. The friction was too high. Scanning a code interrupts the emotional moment. Users already have better, free tools for sending videos.

The lesson Hallmark took from this failure is worth more than most AI strategy decks: AI should remove friction, not add it.

Sign & Send, by contrast, works precisely because users do not feel like they are using AI. You photograph your handwritten message. The app uses computer vision to extract it. Hallmark prints it on a physical card and mails it for you. The customer experiences convenience. The AI stays in the background.

For SMB operators watching larger competitors announce flashy AI features, this is the counterargument. The question is not “how do we use AI that customers notice?” It is “how do we use AI that customers benefit from without ever thinking about?

”

Why recommendation engines failed them, and what they built instead

E-commerce recommendation logic breaks in the gifting industry. Standard collaborative filtering says “users who bought X also bought Y.” Works fine for camping gear.

It fails catastrophically for greeting cards.

A single buyer acts as multiple personas. The same 35-year-old woman is a daughter buying for her mother, a wife buying for her husband, and a manager buying for an employee. Recommending a romantic Valentine’s card because she previously bought a romantic anniversary card is a disaster if her current search is for a sympathy card.

Hallmark’s data team, led by executives like Chai Pallapothula, built a custom “Recipient Graph” that creates shadow profiles for the people you buy cards for, not for you. When you log in, the system asks “who is this person buying for today?” not “what does this person buy?”

If the system detects you purchase a card for “Mom” every May and every October, it builds a profile for your mother. The next Mother’s Day, it recommends cards that match the tone of the birthday card you bought in October.

This longitudinal relationship tracking is their moat. Amazon sells cards as commodities. Hallmark sells them as artifacts of tracked relationship history.

For any business selling products that are purchased for others rather than the buyer, this distinction matters enormously.

What you'll learn:

Why the engineer-to-PM ratio is collapsing to 1:1 at top AI companies

The three pillars hiring managers actually screen for now

How to avoid the "vibe coding" trap that creates debt you can't manage

If you're serious about staying ahead of the curve, subscribe and hit the bell. Drop a comment: what skill are you doubling down on for 2026?

#AIcareers #TechJobs #AndrewNg

A lot of you have asked how I create the visuals for this newsletter. Fair question. Most AI content looks the same. Generic stock imagery. Bland diagrams that could belong to anyone. The kind of visuals that scream “I spent 30 seconds on Canva.”

I spend about 15 minutes per newsletter on visuals. Sometimes less. The images you see here, the diagrams, the animated GIFs, they all come from a repeatable workflow I’ve built over months of testing.

Today I’m showing you the entire process. I recorded a video walking through every step in real time. No editing out the mistakes. No pretending it works perfectly every time.

But if you prefer to read, here’s the breakdown.

The problem with most newsletter visuals

Generic visuals kill credibility. Your readers scroll past them. They add nothing. Worse, they signal that you grabbed whatever was convenient rather than creating something that actually reinforces your message.

Custom visuals do the opposite. They make your content memorable. They create a recognisable brand. They show you give a damn.

The old way of solving this meant hiring a designer or spending hours in Photoshop. Neither works when you’re publishing weekly.

Share this post with your HR department so that they cover your premium subscription to AI Adopters Club ;)

The problem with most newsletter visuals

My entire visual workflow runs on five tools. Each one handles a specific job.

Claude generates the initial image concepts. I paste my newsletter content and ask it to identify the core visual idea. It returns three text prompts I can use for image generation.

Google Gemini creates the actual images. I’ve set up a custom Gem with my brand guidelines, colour palette, and reference images. When I paste a prompt, it produces visuals that match my newsletter’s look.

Napkin.ai handles diagrams. Paste any text, highlight a section, and it generates infographic options automatically. The iceberg diagram, the flowcharts, the comparison visuals, they all come from here.

Grok (Imagine) animates static images. Drag and drop an image, and it automatically generates motion. No prompting required, though you can add instructions for more control.

EasyGIF converts animations to lightweight GIFs. I keep them under one megabyte so they load fast and don’t bloat your inbox.

Step one: extract the visual concept

I copy my newsletter draft into Claude. Then I run a prompt I’ve saved in my RightClickPrompt extension. The prompt reads the content and identifies which visual elements would clarify the central idea at a glance.

It returns three options. Something like:

* A crumbled piece of paper with handwritten resolutions lying abandoned next to an open planner

* Three flat stepping stones crossing a shallow stream, each labelled with a month

* A simple quarterly calendar with red accent marks

I pick whichever resonates most with the article’s core message.

Step two: generate the image

I’ve built a custom Gem in Google Gemini called “AIAC Images.” Think of it like a custom GPT but for image generation.

The Gem includes:

* My brand’s visual identity guidelines

* Colour palette specifications

* Dimension and texture preferences

* Reference images uploaded to the knowledge base

* The red mug rule, because brand consistency matters

When I paste a prompt from Claude, Gemini produces images that already look like they belong to my newsletter. No more fighting with generic outputs that need heavy editing.

ChatGPT’s new image model works too. I tested both in the video. Gemini currently handles brand consistency better for my use case.

Step three: create the diagrams

Napkin.ai changed how I think about explanatory visuals.

Paste your newsletter text. Select a paragraph. Click generate. It automatically suggests infographic formats based on the content structure.

Writing about hidden factors behind a problem? It might suggest an iceberg diagram. Comparing options? A side-by-side chart. Showing a process? A flowchart.

You pick the format, adjust the styling to match your brand colours, and export directly to your clipboard. Paste into your newsletter editor. Done.

Step four: animate when it adds value

Animation catches attention in crowded inboxes. But heavy video files kill load times.

Grok makes this stupidly simple. Drag and drop your image, and it starts animating automatically. No prompt needed. If you want more control, you can add instructions, but the default output usually works.

Then I run the video through EasyGIF to convert it to a compressed GIF. Scale it down, keep it under one megabyte, save.

The animated header you see at the top of some newsletters? That’s the result.

Why this workflow works

Speed matters. If creating visuals takes an hour, you’ll skip them when deadlines hit. Fifteen minutes is sustainable.

Consistency matters more. Every visual reinforces brand recognition. Readers start to recognise your content before reading a single word.

The tools handle different jobs well. Claude thinks conceptually. Gemini executes visually. Napkin structures information. Grok adds motion. EasyGIF compresses for delivery. Each tool plays to its strength.

The prompts that make it work

The quality of your outputs depends entirely on your prompts. Here’s what I feed Claude:

1. Read the article in full. 2. Identify the core concept or key theme of the article (e.g., a single idea, a central topic, a main point). 3. Propose a single object, or up to three related objects/characters, arranged in a simple scene, that symbolically represents the main idea. Make them very clear and easily understandable. • The objects should clarify the central meaning at a glance. • Avoid unnecessary details or sub-themes—keep it minimal and direct. 4. Do not include any stylistic instructions (e.g., no mentions of realism, watercolor, futuristic, etc.). 5. Output Format: Prefix to each of the 3 prompts: “Using the project instructions and uploaded files, please create the following image:”

Simple. Specific. It forces Claude to distill the article down to its visual essence rather than throwing everything at the wall.

What I’d change if starting over

I’d build the Gemini Gem earlier. I spent months manually adding brand instructions to every prompt. The Gem saves that repetition entirely.

I’d also collect reference images from day one. The more examples Gemini has of your visual style, the better it matches your brand. Start building that library now.

Your turn

Watch the full video. See the workflow in action with all the awkward moments included. Then try it yourself.

You don’t need design skills. You need a system. This is mine.

Adapt & Create, Kamil

Markup AI plugs into your tools and workflows (LLMs, CMS, Zapier, etc.) to automatically check every piece of content against your custom guidelines, flag issues, and provide on-brand rewrites at scale.

Hey Adopter,

Nine out of ten companies claim they trust AI to generate content. Then they manually review every single piece before publishing.

Something isn’t adding up.

I sat down with Matt Blumberg, CEO of Markup AI and a veteran of four technology companies over 30 years, to dig into this contradiction. You can watch our full conversation in the video above.

A recent survey from Markup AI of 266 C-suite and marketing leaders found that 92% of organisations use significantly more AI for content than a year ago. Half of all enterprise content now involves generative AI in some capacity. The engines are running.

The problem? Human review has become the new choke point. Eighty percent of organisations still rely on manual checks or spot reviews to verify AI output. Content piles up. Review cycles stretch. Teams wait.

Matt put it bluntly during our conversation:

“It’s a Ferrari with bicycle brakes.”

The trust gap nobody talks about

Here’s the uncomfortable truth. Ninety-seven percent of leaders believe AI models can check their own work. But when it’s time to hit publish, they don’t act like it.

Matt explained the core issue in our conversation. Large language models are predictive machines. They don’t verify facts against reality, they generate probable text based on patterns. “Ask ChatGPT to correct its own hallucination,” he said, “and it says ‘you’re absolutely right’ and absorbs your correction. It can’t check its own output.”

One system can’t create content and audit that content at the same time. The inputs that shaped the output are the same inputs that would evaluate it. You need a separate system with different inputs and a different architecture.

This isn’t a minor technical point. It’s the reason your content teams are stuck.

Who owns this problem, exactly

The survey revealed fragmented ownership. Forty percent say IT is primarily responsible for AI content oversight. Thirty percent say marketing. Twelve percent point to an AI committee. Nine percent name compliance. Two percent say legal.

Only eight percent view it as a shared responsibility.

When I asked Matt what breaks when nobody owns the oversight, his answer was direct. “If nobody owns it, it doesn’t get done. If everybody owns it, nothing gets done. Everyone thinks someone else is handling it.”

He made another point that stuck with me. IT typically steps in because they understand the technology. But they don’t live in the business the way content creators do. They don’t know the brand voice nuances or regulatory requirements that a compliance officer would flag in two seconds.

Meanwhile, employees aren’t waiting for permission. Seventy-nine percent of organisations admit their teams use multiple LLMs or unapproved AI tools. Shadow AI is already fragmenting whatever governance you thought you had.

What leaders actually worry about

When asked their biggest concerns about AI-generated content, leaders ranked them: regulatory violations at 51%, intellectual property issues at 47%, inaccurate information at 46%, and brand misalignment at 41%.

Fifty-seven percent report their organisation faces moderate to high risk from unsafe AI content today.

One hallucinated statistic in a press release. One compliance violation in a product description. One off-brand message in a customer email. The speed that AI delivers can become the speed at which your reputation unravels.

A different kind of AI for a different job

Gartner’s head of research coined a term that’s gaining traction: Guardian Agents. The idea is simple. The only thing fast enough to monitor AI at scale is AI. But not the same AI.

A Guardian Agent is a separate system, purpose-built to check content against brand standards, compliance rules, and accuracy requirements. It doesn’t generate content. It evaluates it.

Matt walked me through how his company built Content Guardian Agents. Upload your brand guidelines and terminology dictionary. Connect the system through APIs or browser plugins. When content flows through, the agent scores it, flags risks, and either rewrites or routes to human review, but only for exceptions.

“You can configure it in about five minutes,” he told me. “Anytime a new document shows up in a repository, whether that’s GitHub, SharePoint, Google Drive, whatever your content system is, it checks the document and shoots the author comments in Slack flagging high-risk problems.”

That last part matters. Human in the loop was always meant for exceptions, not every piece of content. Otherwise, why bother with AI at all?

Want to see Content Guardian Agents in action? Markup AI is currently in early access mode. You can grab an API key and start testing for free at markup.ai.

Governance as Competitive Advantage

I asked Matt what his experience across four companies taught him about AI governance. His answer surprised me.

“Companies that build quality in upfront win in the long run. It’s always harder to retrofit trust later. Once the genie is out of the bottle, you’re scrambling.”

He compared it to the early days of email marketing. Companies that did permission marketing instead of spam, that prioritised quality from the start, were the ones still standing a decade later. The burn-and-churn operators flamed out.

AI content will follow the same pattern. The organisations that figure out governance now, while everyone else is still manually reviewing everything, will move faster and safer than their competitors.

Gartner predicts that forty percent of CIOs will demand Guardian Agents within two years. The question isn’t whether this becomes standard. It’s whether you’re ahead of the curve or playing catch-up.

Your AI can write content. The question is who’s making sure it’s right.

Watch the full conversation with Matt in the video above to hear more about the technology adoption curve, why security will be the next frontier for Guardian Agents, and what enterprises need to codify before any of this works.

Adapt & Create, Kamil

Would you like to talk about your company?

Hey Adopter,

By the end of this newsletter, you’ll have a clear playbook to position yourself as your team’s go-to AI expert, even without a technical background.

Your company has an AI adoption problem you can solve

87% of organizations believe AI will give them a competitive advantage. The same research shows 87% of machine learning projects never make it to production.

That gap between belief and execution is your career opportunity.

Across most companies right now, employees are quietly using ChatGPT, Gemini, and other tools without guidance. Leadership surveys suggest this “shadow AI” usage is far higher than executives realize. The result: fragmented experimentation, potential data leaks, and an “illusion of productivity” where high-volume output masks hidden rework costs.

Someone needs to bring order to this chaos. That person builds credibility, visibility, and career leverage. The role doesn’t require seniority or a technical degree. It requires curiosity and initiative.

Which raises the question: what does it take to become that person?

AI Adopters Club is a reader-supported publication. Consider becoming a premium member and get to these benefits.

The AI champion profile

The AI champion role isn’t reserved for senior leadership or engineers. Many successful champions are mid-level employees: team leads, managers, executive assistants, SMB owners. What defines them is a specific combination of mindset and action.

Intellectual curiosity over credentials

Champions commit to learning the material themselves. One effective approach: use AI tools to accelerate your own AI education. ChatGPT with web plugins can summarize dense industry articles from sources like McKinsey or TechCrunch, letting you ask clarifying questions and internalize concepts faster than traditional reading allows.

Communication over technical depth

Strong champions translate complex concepts into clear, relatable terms. Your value isn’t in understanding every technical detail. It’s in helping colleagues see how AI applies to their specific work without jargon or hype.

Action over permission

Champions don’t wait for a mandate. They identify their team’s pain points, experiment with solutions, and share results openly. This proactive stance separates advocates who talk about AI from champions who demonstrate its value.

Good, but knowing the profile isn’t enough. You need to build the foundation that makes action effective.

Build AI literacy in three layers

AI literacy is the baseline capability for using AI effectively, ethically, and safely. It breaks into three components.

Technical understanding. You don’t need to code. You need a foundational grasp of how AI systems work: what machine learning is, why data quality matters, why AI produces confident-sounding errors called “hallucinations.” This understanding helps you explain to colleagues why certain outputs need verification.

Practical application. Learn prompt engineering, the skill of formulating requests that get useful results. Know what AI can and cannot do within your specific role. Understand when human judgment is non-negotiable. This practical knowledge separates smart AI users from those who copy and paste without thinking.

Ethical awareness. At Samsung, employees accidentally leaked sensitive source code and confidential meeting notes by pasting them into ChatGPT. AI champions know the risks: bias in algorithms, privacy concerns, the danger of feeding confidential data into public tools. This awareness protects both you and your organization.

With literacy established, the next question becomes: how do you prove value in a way that builds momentum?

The pilot playbook for career leverage

Action without measurement is invisible. Here’s how to make your AI work visible and valuable.

You walk into a meeting unprepared. Again.

Ten minutes with your VP to pitch the new process. Fifteen minutes with the client to discuss scope changes. Twenty minutes with your boss for a quarterly check-in. You know these conversations matter. You’re winging it because you had seventeen other things to handle first.

Steve Brown had a worse version of this problem. Ten minutes per month with his oncologist to make treatment decisions. For cancer. No do-overs.

His solution: spending two hours with AI before each appointment, rehearsing the conversation until he knew exactly which questions mattered. That preparation pattern caught a misdiagnosis multiple specialists missed and surfaced a treatment alternative that put him in complete remission.

CureWise turned that into a system now used by other cancer patients. But the preparation pattern works for any meeting where the stakes are high and the time is short.

Why ten minutes breaks most conversations

Brown’s oncologist isn’t lazy. Shortage of oncologists, rising cancer rates. Same reason your VP is triple-booked. Everyone has more decisions than time to think through them.

Default mode is shallow. Quick check-in, defer hard questions to “next time.” Nothing gets resolved.

Brown couldn’t afford that. Cancer grows exponentially. Delaying the right decision by three months changes survival odds.

The pattern: don’t show up asking vague questions. Show up testing specific hypotheses.

“Do you think this treatment will work?” wastes time. “My genomic report shows these three mutations. The literature suggests these drugs target them better than standard protocol. What am I missing?” changes the conversation.

His doctors didn’t have time to parse his 15-page genomic report. He did that work beforehand with AI. Then used the appointment to validate his conclusions and catch blind spots.

One appointment, his AI prep surfaced a drug alternative based on his specific tumor mutations. Mayo Clinic agreed. Protocol switch followed. Complete remission.

That doesn’t happen if you show up asking “what should we do next?”

Which raises the question: how do you prepare at that level without a PhD in AI?

The preparation pattern anyone can use

You don’t need to code multi-agent systems. You need to stop winging important conversations.

Here’s how you could adapt Brown’s pattern:

Before your next high-stakes meeting

Step 1: Dump context into AI Open ChatGPT. Paste everything relevant. Project background, past decisions, constraints, what failed, what’s at stake. Give it the full picture.

Step 2: Ask for three conflicting recommendations Prompt: “Give me three different approaches to this problem. Make them genuinely different. Then argue why each one could be the right choice.”

This shows you the decision space instead of jumping to one answer.

Step 3: Flip the perspective Prompt: “I’m leaning toward option two. Now argue against it. What am I not seeing? What could go wrong?”

This catches your blind spots. Brown used multiple AI agents arguing. You do a simpler version by prompting different positions.

Step 4: Identify your knowledge gaps Prompt: “What information am I missing that would change the decision? What should I ask in the meeting?”

Now you have specific questions instead of vague concerns.

Step 5: Rehearse the conversation Prompt: “I have ten minutes with the decision-maker. Here’s what I know, what I’m recommending, my questions. How should I structure this?”

Brown spent two hours on this before each oncologist appointment. You can do it in thirty minutes before your next project review.

The difference: you walk in knowing what matters, what’s uncertain, and which questions unlock the decision.

Theory is one thing. Here’s what happens when people actually do this.

Where this actually changes outcomes

Lisa Booth is using CureWise for metastatic breast cancer treatment. She’s not a programmer. She dumps context, gets multiple perspectives, identifies gaps, prepares specific questions. Then shows up to specialist appointments with focus. Her doctors appreciate it. They discuss actual treatment trade-offs instead of explaining basics.

Same pattern in business:

Project approval: “Here are three approaches, cost-benefit on each, what I’m missing from engineering. Which constraint should drive the decision?”

Vendor evaluation: “I’ve mapped our workflow to these three options, where each breaks down. What am I not seeing about implementation risk?”

Performance review: “Here’s what I accomplished, where I struggled, three skills I could develop. Which creates most value for the team?”

Every one changes the meeting. Your boss doesn’t do your thinking. They validate your reasoning and fill your gaps. Better decisions faster.

Which compounds over time. Better questions lead to better answers. Better answers lead to better decisions. Better decisions change who gets asked to the next meeting.

Why this makes you indispensable

The person who asks the right questions becomes the one people want in the room.

Not because you have all the answers. Because you’ve done the work to frame the decision clearly. You’ve thought through alternatives. You’ve identified what’s uncertain.

Brown’s oncologist has limited time. Patients who show up prepared get more value from those ten minutes. Same applies to your VP, your client, your boss.

This is how AI makes you better at your job. Not by replacing you. By giving you leverage to prepare at a level that used to take a team of analysts.

Six hours manually researching vendors, reading case studies, building spreadsheets. Or forty minutes with AI getting to the same synthesis, then validating with your network.

You still need judgment. But you show up sharper.

Brown caught his cancer because his AI preparation surfaced one test his doctors hadn’t ordered. You won’t save your life. But you might save your project, your client relationship, or your promotion by asking the question nobody else prepared.

Companies that scale AI aren’t buying expensive enterprise tools. They’re the ones where individuals figured out how to prepare better, move faster, ask smarter questions. Then that behavior spreads.

Start tomorrow. Take your next important meeting. Spend thirty minutes preparing with AI instead of walking in cold.

Adapt & Create,Kamil

Hey Adopter,

You're stuck approving every quote, scheduling every job, chasing every invoice. Your business can't grow because everything still needs your personal attention.

These four AI prompts will change that. They extract what's in your head, map it into systems someone else can run, and create a hiring plan that gets you out of daily operations in the next 90 days. (if you choose to accept this mission ;)

Most people react to AI, but paid subscribers profit from it by getting the tools they need to win the AI game.

You can't delegate what's in your head

Customer calls at 4pm on Friday. You scramble through old emails looking for that pricing sheet. Find three different versions. Pick the middle one, hoping it's current. Send the quote on Monday morning, then wonder why they went silent. Meanwhile, jobs finish but invoices sit in your mental queue for weeks. When the new hire asks about the procedure, you say "just watch me do it once." Revenue climbs while profit remains flat because every single transaction still needs your personal stamp of approval.

The problem isn't that you're bad at delegation. It's that your operational knowledge lives in your head, not in systems that someone else can follow. Four AI prompts fix this by extracting what you know, mapping it into step-by-step workflows, and creating a hiring brief for the right person to run them. Each conversation builds on the last one, giving you a concrete 90-day handover plan that finally gets you out of daily firefighting.

The Obituary: How AI Killed the Em Dash

The em dash died last Tuesday. Not officially. But you know it when you see it now, that telltale rhythm of artificial intelligence trying to sound human and failing spectacularly.

Here's What Happened

Picture this: you're reading a LinkedIn post.

"Here's what I learned about success — it requires patience — persistence — and the ability to pivot when necessary."

Three em dashes. Perfect spacing. Zero soul.

That's not writing. That's a robot wearing a human costume to a dinner party.

I used to love that punctuation mark. It gave my sentences breathing room. More dramatic than a comma, less final than a period. It made text sound like conversation, like someone actually talking instead of reciting from a manual.

English is my third language. I never mastered the formal rules for em dashes. Half the time I copied them from Google because I couldn't remember the keyboard shortcut.

* Alt + 0151 on Windows, if you're curious.

* Option + Shift + Hyphen on Mac.

I looked it up again just now.

But rules didn't matter. The mark worked. It made my writing feel alive.

The Golden Age: When Punctuation Had Purpose

The Origin Story

Back in the 1700s, printers needed something between a hyphen and a full stop. They measured it against the width of the letter M in their typeface. Hence "em dash." Smart solution for a real problem.

Writers fell in love fast. Laurence Sterne scattered them through Tristram Shandy like confetti. Emily Dickinson practically built poetry around them. They weren't following style guides. They were following the rhythm.

The Survival Story

The mark survived typewriters (two hyphens became the standard substitute), survived early computers, survived the transition to digital publishing. It earned its place by being useful.

Then something changed.

When Patterns Become Predictable

How Machines Learned Wrong

Machine learning models studied millions of articles, blog posts, and social media updates. They identified patterns. Em dashes appeared frequently in engaging, conversational content. So the algorithms learned to use them.

But they learned wrong.

The Human vs. AI Difference

* Humans use em dashes when they naturally pause in speech

* AI uses them on schedule. Every third sentence. Every emotional beat. Every transition between ideas.

The pattern became predictable, then annoying, then a dead giveaway.

The Evidence

You know the posts I'm talking about:

"Success isn't about perfection — it's about progress — and progress happens one small step at a time — which is why I believe in celebrating small wins."

Four em dashes. Zero variation. Metronome writing that sounds like it came from a content farm in 2024.

The Casualties: What We Lost

The Collateral Damage

Real writers started avoiding em dashes to dodge the AI label. We lost a perfectly good punctuation mark because machines couldn't use it with restraint.

That's genuinely sad. The em dash committed no crimes. It just got conscripted by bad algorithms.

Fighting Back Against Robot Writing

What Now: Human Alternatives

I'm switching to alternatives that still feel human:

The New Arsenal

* Colons work great for reveals

* Semicolons connect related thoughts; they feel elegant when used sparingly

* Parentheses create quiet asides (whisper, don't shout)

* Full stops create emphasis through simplicity

Period.

See what I did there?

The Real Lesson: Pattern Recognition

This isn't really about punctuation. It's about pattern recognition. AI writing has tells:

* Repeated structures

* Predictable rhythms

* Overused transitions

The em dash became a casualty because it got overused by machines that don't understand restraint. But the principle applies to everything. Overuse any technique and it becomes a signature. Use someone else's signature too often and it stops being authentic.

Staying Human in an AI World

If You're Using AI to Draft Content

* Edit the output ruthlessly

* Cut every second dash

* Vary your sentence length

* Replace some transitions with line breaks

* Make it sound like you, not like everyone else using the same prompts

If You're Writing Manually

Self-Assessment Questions

* Do you always structure paragraphs the same way?

* Do you rely on the same transitions?

* Break the habit before it becomes a tell

The Complete AI Humanization System

While avoiding em dash overuse is a good start, there's a bigger picture here. If you're using AI to help with your writing, you need a systematic approach to make it sound authentically human.

I've developed a 5-minute prompt technique that transforms robotic AI drafts into conversational, engaging content. It goes beyond punctuation fixes to tackle the core issues: corporate jargon, predictable rhythm, and those telltale AI phrases that make readers' eyes glaze over.

Want the complete system? Check out my guide on making ChatGPT sound more human – it includes the exact prompt I use and a step-by-step process for humanizing any AI output.

The Memorial

The em dash will be missed. But it won't be forgotten.

Final thought: In a world where AI can mimic human writing patterns, the most rebellious act might be learning to write like ourselves again. Not following the patterns that algorithms expect, but creating our own rhythm, our own voice, our own authentic way of connecting with readers.

The future belongs to writers who sound like humans talking to humans. Make sure you're one of them.

Adapt & Create,Kamil

What's your take on AI writing patterns? Hit reply and share your observations about the tells that make content feel machine-generated. Your insights might make it into next week's newsletter.

TLDR: AI thought leaders are solving tomorrow's problems while you need to win today's battles. Their grand visions contain useful insights, but only if you know how to extract the practical tactics from the theoretical noise.

What You'll Get From Reading (and watching👆)This breakdown shows you how to separate Silicon Valley speculation from workplace strategy. You'll learn which expert insights actually apply to your career and which ones are just intellectual entertainment. More importantly, you'll discover the specific positioning moves that help you stand out while everyone else chases AI hype.

AI thought leaders love to make grand pronouncements about the future of work. But here's what I've noticed after reading through their latest insights: most of them are solving problems you don't have while ignoring the ones you do.

Take Geoffrey Hinton's warning about a 10-20% chance that AI will "take control from humans." That's fascinating cocktail party conversation, but it doesn't help you explain to your boss why your team needs better AI tools. Or consider Demis Hassabis suggesting everyone should study mathematics and physics to understand "how these systems are put together." Great advice if you're 22 and choosing a major. Not so helpful if you're 35 with a mortgage and need to stay relevant in your current role.

Good friends don’t let friends drown in AI noise. Share this & help them get real, actionable value.

The disconnect isn't malicious. It's structural. These leaders are building the future, but you're living in the present. They're thinking in decades while you're thinking in quarters. They have the luxury of theory while you need tactics that work tomorrow morning.

But buried in their grand visions are some genuinely useful insights for those of us operating in the real world. Let me translate the signal from the noise.

The Business Value Obsession You Should Steal

Lee Bogner from Mars Inc. gets one thing exactly right: start with the business problem, not the technology. His framework for agentic AI sounds futuristic, but the principle is immediate and practical.

Before you pitch your next AI initiative or recommend a tool to a client, ask these three questions Bogner's approach implies:

* What specific business outcome are we trying to achieve?

* What human processes are we trying to improve, not replace?

* How will we measure success in terms people care about?

This isn't just good advice for AI projects. It's how you position yourself as someone who thinks strategically rather than tactically. Whether you're an ambitious professional trying to lead AI adoption internally or a consultant selling AI strategy, this business-first framing separates you from the tool-obsessed crowd.

The Skills Gap Everyone's Missing

Here's where the thought leaders reveal something important through what they don't say. Demis Hassabis talks about studying STEM. Fei-Fei Li advocates for scientific thinking over "science fiction." Jeremy Howard pushes for AI accessibility.

What none of them address directly is the messy middle where most of us live: you understand enough about AI to see its potential, but not enough to implement it confidently. You can write a decent prompt, but you can't evaluate whether the output is actually better than what your team was producing before.

This gap is your opportunity. The market doesn't need more AI engineers. It needs more AI translators, people who can bridge the technical capability with business reality. That means developing what I call "applied AI literacy":

* Learning to critique AI outputs rather than just accepting them

* Understanding when AI recommendations align with your business logic and when they don't

* Becoming the person who can explain AI capabilities and limitations to stakeholders who matter

The Junior Crisis That's Already Here

The Workera platform highlights something that should worry every leader: AI is automating the entry-level tasks that historically taught people how to work. Junior employees used to learn by doing repetitive analysis, drafting basic reports, and handling routine client communication.

If AI handles these tasks, how do we develop the next generation of talent? More importantly for you, how do you demonstrate value when basic competency becomes automated?

The answer isn't to compete with AI on efficiency. It's to own the judgment layer above it. Become the person who knows when AI analysis is sufficient and when it needs human refinement. Learn to spot the patterns AI misses and the context it can't understand.

For consultants, this represents a massive opportunity. Your clients are facing this junior crisis too. They need frameworks for developing talent in an AI-augmented workplace. They need strategies for maintaining institutional knowledge when basic tasks are automated. This isn't theoretical future planning. This is happening now.

Quick Poll: Where Are You Really Stuck?

I want to understand what's actually blocking your AI progress, not what you think should be blocking it. Choose the option that best describes your current situation:

* I understand AI's potential but struggle to identify which specific use cases would actually move the needle in my role or business

* I can implement AI tools successfully but have trouble measuring and communicating the business value to stakeholders who matter

* I know how to use AI tactically but can't figure out how to position myself strategically as the "AI person" without seeming like I'm chasing trends

* I'm confident in my AI skills but struggle to translate that expertise into concrete business opportunities or career advancement

Reply and let me know which resonates most. Your answer helps me understand what practical content to prioritize in future issues.

The Practical Takeaway Everyone Needs

The thought leaders are right about one thing: the shift to skills-based work is accelerating. But they're wrong about what skills matter most.

It's not about learning to code or studying neural network architectures. It's about learning to work with AI as a thinking partner rather than a magic black box. It's about developing the judgment to know when to trust AI recommendations and when to override them.

Most importantly, it's about positioning yourself as someone who can translate AI potential into business results. Not the person who can build AI systems, but the person who can make them useful.

Whether you're trying to become indispensable in your current role or building an AI advisory practice, that translation capability is your competitive advantage. The thought leaders will keep building the future. Your job is to make it work in the present.

Adapt & Create, Kamil

Most companies treat AI like a buffet. Sample everything, master nothing, wonder why they're still hungry.

Your competition isn't stuck here. While you're debating vendor demos and drowning in AI anxiety, smart operators are building unfair advantages with stuff they already own.

This week, I sat down with John Stix, founder of Nucleus AI and the guy who built Fibernetics into one of Canada's largest private telecom companies. Our conversation cuts through the AI noise with surgical precision.

What you'll get from the full interview:

* The exact mental shift that transformed a 20-year legacy company from AI-paralyzed to market-dominant

* A specific vetting framework to separate real AI providers from basement operations

* The asset stack methodology for turning existing infrastructure into a competitive advantage

* Why voice-first implementation delivers faster ROI than complex data integrations

* Real ROI metrics that prove AI impact beyond vanity metrics

* John's contrarian business philosophy that prioritizes partnership over product

Here's how he flipped the script from fear to advantage.

The Paralysis Trap

Picture this. You've got legacy systems, deep employee relationships, processes that work. Then AI shows up like a wrecking ball. Everyone's screaming "adopt or die."

John's team felt it. Fibernetics handles 600 million minutes of data monthly. That's serious scale with serious stakes.

"If I deploy AI wrong, how does that impact my relationships? How does it impact my business? What about my IP?"

Most leaders freeze right here. Caught between doing nothing and risking everything.

But John's team made a mental shift. Instead of asking "How do we protect ourselves from AI?" they asked "What do we already own that AI could multiply?"

In the full interview, John breaks down exactly how this reframe happened and gives you the step-by-step process for auditing your own asset stack.

Your Hidden Gold Mine

Here's where most companies screw up. They think AI adoption means starting from scratch. New tools, new hires, armies of data scientists.

Wrong.

John's breakthrough came from auditing what they already had. Two decades of voice infrastructure where calls cost them nothing. Millions of real phone numbers. Global partnerships. In-house software team.

"We started thinking about our core assets differently. What if we could develop an AI that leverages our assets and gives us an unfair advantage?"

That shift changed everything. AI went from threat to key. Instead of replacing their business, it unlocked value they'd been sitting on for years.

The full conversation reveals John's complete asset stack methodology and the specific questions you need to ask about your own infrastructure.

AI Adopters Club is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Start Small, Win Fast

Most AI projects fail because companies try to boil the ocean. They want everything integrated into their data lake before proving basic value.

John cuts through this mess with one rule. Start with voice.

"The most efficient way to experience quick ROI is to start with voice. Communication with vendors, customers, your staff. It doesn't need to be deeply integrated into your data lake to get going."

This isn't just smart tactics. It's strategic genius. Voice systems solve real problems now. They need minimal tech integration. Results show up fast.

In our interview, John walks through the specific ROI metrics that matter and how to measure them in your first 90 days.

Spotting Real Players

AI vendors multiply like rabbits. Most sound convincing until you dig deeper. John shared the questions that separate real providers from garage operations.

"Take time to understand their infrastructure. How do they integrate infrastructure? What's their voice experience? Whose AI are they using? Where is it located? Are they securing your data?"

This isn't about credentials or marketing speak. It's operational depth. Can they handle your scale? Do they get your industry's constraints? Have they actually built what they're selling?

The complete interview includes John's full vetting checklist and the red flags that should make you walk away immediately.

The Partnership Secret

When entrepreneurs ask John what business to start, he skips the market analysis. His answer throws everyone off.

"Don't find a business. Find a friend."

John's been in business with Jody Shnarr since 1994. Through failures, pivots, massive success. Relationship first, opportunities followed.

"It's really life and passion. You're sharing that."

John's full philosophy on business partnerships and why the relationship matters more than the business plan is something every entrepreneur needs to hear.

The Speed Revolution

Here's what's coming that'll separate winners from watchers. John sees leaders calling their data directly. Conversational access to company info. Decisions at light speed.

"When the AI and your data lake becomes an interaction, this will enable leaders to make decisions at a speed that we've never seen before."

Companies building toward this now get an insurmountable advantage. Everyone else gets quarterly dashboards and committee meetings.

The interview dives deep into what this transformation looks like practically and how to start building toward it today.

Your Next Move

Stop treating AI like a threat to manage. Start seeing it as a multiplier for what you own.

The full interview with John delivers a complete playbook for this transformation. No theory, no evangelism. Just the proven framework a real company used to turn AI paralysis into market dominance.

Listen to the complete conversation to get:

* The exact questions for auditing your asset stack

* Step-by-step voice-first implementation strategy

* Complete vendor vetting framework with red flags

* Real ROI metrics that prove business impact

* John's partnership philosophy for sustainable success

* The future of conversational data access and how to prepare

Connect With the Source

John Stix shares practical AI insights on LinkedIn and helps businesses break through their paralysis. Want to see real AI infrastructure in action? Check out Nucleus.com.

Connect with John: LinkedIn - John Stix

Explore Nucleus AI: nucleus.com/

Fair warning: John's approach isn't for dabblers. It's for leaders ready to transform their communication infrastructure and leverage AI for genuine competitive advantage.

Adapt & Create,Kamil

Hey Adopter,

Most teams treat standard operating procedures like that gym membership they never use. They know they need them, they pay for expensive consultants to create them, then watch as those 47-page documents collect digital dust while everyone still asks "how do I do this again?"

The problem isn't that SOPs are worthless. It's that creating them traditionally is painful, time-consuming, and produces documents that feel more like legal contracts than actual help guides.

Here's what you're dealing with: Your best performer leaves, and suddenly nobody knows how to handle the monthly reporting process. Your new hire spends three weeks figuring out what should take three hours. Your team keeps reinventing the wheel because the "official process" exists somewhere in a shared drive that nobody checks.

Sound familiar? You're not alone. But there's a better way.

The AI gold rush is on; free tips won’t get you rich; paid subscribers get the actual map & shovel.

What You'll Walk Away With Today

By the end of this edition, you'll have a system that transforms any process demonstration into a professional SOP in under 10 minutes. No more struggling with blank documents or wondering what steps you forgot to include.

You'll get a proven prompt template that extracts clear instructions from messy video transcripts, plus a framework that ensures your SOPs actually get used instead of ignored.

The AI-Powered SOP Method

The secret isn't writing better documentation. It's capturing knowledge differently.

Instead of staring at a blank document, trying to remember every detail, you demonstrate the process once while speaking through it. Record yourself doing the actual work, explaining each decision as you make it.

This method works because it captures not just the steps, but the reasoning behind them. The context that makes the difference between a rigid checklist and an actually helpful guide.

Your New SOP Creation Workflow

Step 1: Record Your Process

Use Loom or any screen recording tool. The key is speaking through everything you're doing. Don't just click silently through the interface. Explain why you're choosing that option, what you're looking for, and what could go wrong.

Most people skip this narration step. That's a mistake. The spoken explanation becomes the foundation for your SOP's clarity.

Step 2: Extract the Transcript

Loom automatically generates transcripts. If you're using another tool, most platforms offer this feature, or you can upload the audio to a transcription service.

Step 3: Deploy the AI

Use the prompt template below with your transcript. ChatGPT will structure your rambling explanation into clear, actionable steps.

Let me tell you what's actually happening with AI right now.

Most companies are completely screwing it up. They're either doing nothing (paralyzed by the hype), doing everything (throwing money at consultants with no strategy), or doing just enough to put "AI" in their LinkedIn profile (implementing a chatbot nobody uses).

Meanwhile, a small group of professionals is quietly lapping everyone else. These people aren't smarter or better funded. They've just figured out what nobody's talking about: the power isn't in choosing between small, safe AI moves or big, risky ones. It's in deliberately doing both.

This isn't theoretical. Urban Company boosted satisfaction 5% with basic chatbots while Waymo is betting billions on autonomous driving. Netflix used recommendation algorithms to hook viewers while it prepared to reshape an entire industry. Your career follows the same pattern: learning prompt engineering today builds toward the machine learning specialization tomorrow.

The winners have discovered that AI isn't an either/or game. It's a now-and-later strategy that creates compound advantages while everyone else debates which single path to take.

By the time you finish this email, you'll know exactly how to implement this two-track approach while your colleagues are still stuck in PowerPoint purgatory.

Good friends don’t let friends drown in AI noise. Share this & help them get real, actionable value.

The Two-Track AI Strategy Smart Players Use

After analyzing hundreds of AI implementations, we've identified two distinct approaches that drive real results:

Foundation Plays: Low-risk moves that deliver immediate value by optimizing what already exists. These boost productivity, cut costs, and build your reputation as someone who gets things done while everyone else is still planning.

Breakthrough Bets: Higher-risk, higher-reward initiatives that could fundamentally transform your business model or career trajectory, but might fail if technology or markets shift unexpectedly.

The tension isn't theoretical. It's the difference between becoming 20% more efficient or potentially changing the game entirely. Between keeping pace or breaking away from the pack.

Foundation Plays: Small Moves That Actually Work

Foundation Plays aren't flashy, but they deliver consistent wins by optimizing existing processes rather than reinventing them:

* Customer service automation delivers measurable wins. Urban Company implemented chatbots using Azure OpenAI Service that resolved up to 90% of customer queries, boosting satisfaction by 5%. Motel Rocks saw a 43% ticket deflection rate with AI handling and a 9.44% jump in customer satisfaction.

* Meeting and workflow enhancement drives immediate productivity. ClickUp's ChatGPT-based tool increased agent solves per hour by 25% within just one week of implementation. Telstra used AI to summarize customer histories, reducing follow-up calls by 20%, and 90% of agents reported being more effective.

* HR and finance automation cuts administrative waste. Unilever slashed recruitment time by 75% with AI-powered assessments while increasing diversity among hires by 16%. IBM's internal "AskHR" chatbot saved 12,000 hours over 18 months in their HR department.

For individuals, foundational AI upskilling represents the same low-risk, high-value approach. Taking online courses like Google's AI Essentials (under 10 hours), learning basic prompt engineering, and experimenting with tools like ChatGPT all build relevant skills without requiring major career shifts.

These moves share key traits: they're low-risk, deliver fast returns, and work regardless of which AI future materializes. They're the pocket change that actually adds up while everyone else is chasing imaginary millions.

Breakthrough Bets: When Transformation Beats Optimization

Breakthrough Bets aren't for everyone or every situation. But when they hit, they don't just improve the game, they flip the table entirely:

* Proprietary AI models create uncopyable advantages. Market research firm Kantar developed custom AI solutions trained on their unique historical datasets. Their analysis shows 82% of successful AI applications rely on internal, proprietary data that competitors can't replicate.

* Business model transformation can redefine industries. Netflix fundamentally changed how it delivers value by placing AI at its core, using algorithms for content recommendations and production decisions. Waymo invested billions over a decade in autonomous vehicle technology, aiming to revolutionize transportation entirely.

* AI-powered market intelligence creates competitive edges. Iceland Foods partnered with Kantar to use ConceptEvaluate AI for rapid testing of a new wellness meal range, accelerating innovation cycles. Google utilized Kantar's LINK AI to evaluate 11,000 ads in under a month, a scale impossible with traditional methods.

For professionals, Breakthrough career bets include pursuing advanced AI specialization (like becoming an AI Researcher requiring advanced mathematics and PhD-level knowledge), pivoting careers into dedicated AI roles, or launching AI-focused startups like Cursor (founded by Michael Truell) to help engineers write code using natural language.

These moves involve substantial risk, longer timelines, and significant investment. But they create the potential for market leadership, career transformation, or breakthrough innovation that incremental improvements never will.

How to Play Both Sides While Everyone Else Picks One

The most effective approach isn't choosing one path. It's building a balanced portfolio:

* Start with Foundation, aim for Breakthrough. Use Foundation Plays to build capabilities, gain experience, and generate savings that can fund more ambitious projects.

* Turn quick wins into strategic options. Rather than jumping straight into massive commitments, use intermediate steps like pilot programs to test hypotheses before scaling.

* Recognize the synergy. The productivity gains and skills built through Foundation Plays often create the necessary capacity to execute more transformative opportunities.

* Stay flexible. The optimal balance isn't static. Market shifts, competitor actions, and technological developments require continuous reassessment of your AI strategy.

The companies winning with AI right now aren't making a single bet. They're making a series of intelligent moves, each building on the last. Small optimizations that fund bigger experiments. Safer bets that create the option to make bolder ones later.

This isn't just theory. It's playing the game at multiple levels simultaneously while most teams are still stuck in planning mode.

Ready to Implement Your First Foundation Play?

DOWNLOAD NOW: The Foundation Play Finder

Stop theorizing and start executing. I've created a step-by-step implementation guide that walks you through exactly how to identify, evaluate, and launch your first AI Foundation Play in the next 30 days.

This free 6-page toolkit includes:

* Detailed worksheets for identifying your highest-impact opportunities

* Scoring templates to evaluate which plays will deliver fastest

* Measurement frameworks to prove your success

* Implementation checklists to make it happen while others are still planning

The Balance That Drives Real Results

The AI winners won't be the ones who bet everything on transformation. Or the ones who only make safe, incremental changes. They'll be the smart players who do both, starting with the foundation and systematically creating options for bigger moves.

In a world obsessed with either playing it completely safe or making wild bets, the advantage goes to those who can do both while everyone else is stuck in PowerPoint purgatory.

Adapt & Create,

Kamil

Hey Adopter,

While everyone's busy debating which AI hallucination is more accurate, Bloomberg spent 15 years quietly building an AI powerhouse that actually delivers business value, not with manifestos or moonshots, but through the unglamorous work of marrying domain expertise with deliberate execution.

The AI Trophy Case Problem

Most companies treat AI like a trophy—shiny in the cabinet, useless in practice. Bloomberg took a different path. As early as 2009, they were developing practical applications like a Federal Reserve sentiment model trained on Bloomberg News headlines. Not to win awards or headlines, but to solve a specific business problem: quantifying market sentiment for trading decisions.

While your competitors were drafting their third AI vision statement, Bloomberg was already automating data extraction from financial documents, achieving over 99% accuracy and cutting ingestion time from 24 hours to under a minute.

The difference? They started with the problem, not the technology.

Your AI Strategy Is Probably Backward

Most AI strategies follow the same doomed pattern: chase the shiny new model, then desperately hunt for a problem it might solve. All while your data sits in silos, unusable and untrustworthy.

Bloomberg flipped this approach. They didn't start by asking "how do we use AI?" They asked "what information do financial professionals desperately need that's currently impossible to get?" Then they built from there.

Their path to BloombergGPT—a 50-billion parameter LLM trained on 700 billion tokens of financial data—wasn't an overnight decision. It was the culmination of years spent solving real problems, building domain expertise, and collecting the right data.

What Bloomberg doesn’t put in their press releases, now in your hands.

The rest of this article sheds light on Bloomberg’s real AI strategy: the unfiltered, behind-the-scenes blueprint that powered BloombergGPT and gave them a market edge.

Inside, you’ll get:

🔑 The semi-agentic AI architecture Bloomberg quietly built🔑 How Fact Capital gained an edge using Bloomberg’s AI ecosystem🔑 A proven framework to decide what to automate, what to augment🔑 The internal org shifts that made their AI rollout unstoppable

This isn’t theory. This is how one of the world’s sharpest firms operationalised AI while others were still “experimenting.”

Join our premium subscribers and get access to the full case study, so you can steal their playbook and deploy it in your business.

While we usually dissect how corporations butcher AI implementation, today we're exploring something equally fascinating: how the same tech that's generating your quarterly reports might help you live long enough to see a hundred of them.

Cut the Hype: What's Really Happening

Let's get this straight: AI isn't magically granting immortality. But unlike most corporate AI initiatives, the longevity sector is actually building something useful.

The research community has shifted from treating individual diseases to targeting the root causes of aging itself. They're not just managing decline—they're trying to rewrite the rules.

Some AI models are boldly predicting human lifespans of 150 years. That's probably optimistic—like your company's AI transformation timeline. But unlike your digital transformation that transformed nothing, longevity research is delivering tangible results.

Three Areas That Sound Interesting To Me

1. Aging Clocks: Your Biological TPS Report

Forget your birthday—it's just a number on HR forms. Companies like Deep Longevity have built AI systems that measure your "biological age"—how old your body actually is versus what it says on your driver's license.

These systems analyze everything from blood markers to DNA methylation patterns to your gut microbiome—even voice patterns and facial features. Think of it as a performance review for your cells, but one that actually tells you something useful.

The gap between your biological age and chronological age (what researchers call "MileAge delta")—reveals whether you're aging faster or slower than average. It's like knowing if your project is ahead of schedule or doomed.

Some companies are even developing organ-specific aging clocks. Because yes, your liver might be working overtime while your brain is coasting—not unlike your actual team.

2. AI Drug Discovery: Skipping the 10-Year Meeting

Remember the last time your company tried to launch a new product? Five years of meetings, PowerPoints, and "strategic alignments" before anything happened?

Insilico Medicine isn't playing that game. They created Rentosertib, the first AI-designed drug for idiopathic pulmonary fibrosis (try saying that in a meeting) to reach human trials. Their AI identified both the target and designed the molecule in 18 months—not the decade it typically takes.

By 2025, they had 30+ drug candidates with 10 cleared for human trials. Their edge? Generative AI that designs molecules human scientists wouldn't dream up—like if ChatGPT actually generated useful code instead of apologizing for its limitations.

3. Digital Twins: Testing on Your Virtual Clone, Not Your Career

Imagine running experiments on a digital copy of yourself instead of risking your actual health. No more one-size-fits-all solutions that fit nobody.

Human Longevity, Inc. offers comprehensive assessments combining genome sequencing, advanced imaging, and biomarker analysis. Their AI flags health issues years before symptoms appear—like catching project failures before they tank your quarterly numbers.

This approach flips healthcare from "fix it when it breaks" to "prevent it from breaking"—a concept your IT department still hasn't grasped with your work laptop.

The Players: Who's Actually Building vs. Who's Just Talking

The longevity space is like your company's tech stack—a mix of innovative solutions and flashy vaporware. Here's who's doing what:

* Insilico Medicine: Beyond creating actual drugs in trials, they've built a humanoid lab robot named "Supervisor" that wears a lab coat and learns by watching scientists. It's the lab assistant that doesn't steal your lunch from the fridge.

* Deep Longevity: Creates AI aging clocks and partners with insurance companies to offer psychological age assessments via smartphone apps. They're building tools people actually use—a concept foreign to most enterprise software.

* GERO.AI: Analyzes human data to identify aging targets and has achieved "systemic rejuvenation" in elderly mice with AI-predicted interventions. That's like promising your stakeholders results and actually delivering them.

* Human Longevity, Inc.: Offers "Precision 100+" program targeting health beyond a century. Basically, the executive health plan that actually works.

This isn't some small side project. The longevity market is projected to grow from $2.7 billion in 2023 to $4.1 billion by 2030. Venture funding has exceeded $1 billion, with tech titans like Sam Altman, Jeff Bezos, and Peter Thiel writing serious checks.

The Future: Half Fascinating, Half Terrifying

The longevity field is moving toward sci-fi territory. Insilico's humanoid robot creates a closed loop where AI designs experiments, robots execute them, and results feed back into the AI—eliminating the human bottleneck that slows your company projects.

Some researchers envision AI-guided nanorobots patrolling our bloodstream like microscopic maintenance crews, clearing cellular debris and fixing tissues. It's still theoretical, but so was your company's digital transformation until it became unavoidable.

The Hard Parts Nobody Talks About

Like that AI implementation your CEO announced prematurely, longevity tech faces serious obstacles:

* Biological Complexity: Aging involves countless interconnected biological pathways we don't fully understand—like your company's legacy systems that nobody documented.

* Data Quality: AI models trained on biased or incomplete data will give you garbage results. Just like that predictive model your analytics team built using only successful sales data.

* Ethical Questions: Who gets access to these technologies? What happens to retirement plans, healthcare systems, and social structures when people routinely live past 100? These are the societal implications your company's AI ethics statement glosses over.

* Regulatory Hurdles: Convincing regulators to approve "anti-aging" treatments is tough since aging isn't classified as a disease—like trying to get budget approval for a project that doesn't fit existing categories.

The Bottom Line

Whether we'll live to 150 remains uncertain, but one thing's clear: AI is accelerating longevity research faster than your last software integration project.

For business leaders, this isn't just interesting—it's disruptive. Insurance models, retirement planning, healthcare delivery, and workforce dynamics will all need to adapt. The companies that anticipate these shifts will be positioned to thrive; the rest will end up like Blockbuster wondering what happened.

So while your enterprise struggles to use AI for basic process automation, other sectors are using it to fundamentally extend human life. Maybe that's worth thinking about during your next pointless status meeting.

Until next week, when we return to our regular programming of corporate AI follies.

P.S. If you found this interesting, please share it with a colleague whose AI strategy could use some life support.

This newsletter is part of the AI Adopters series, where we typically focus on practical AI integration for business. Today's special edition explores the frontier of AI in longevity research.

Although Google’s AI products don’t always get the same buzz as other mainstream AI platforms, they offer powerful and practical solutions worth exploring. Google continues to quietly innovate, providing useful resources and experiments to support your ongoing AI learning journey.

Join me for my next live video in the app

In this exclusive case study, I dissect how a century-old law firm—traditionally about as innovative as a fax machine—transformed itself into "the world's leading AI advisory firm" while its competitors were still debating whether to allow associates to use ChatGPT.

Share this article with your favorite lawyer, if something like that even exists.

The Bold Move

In late 2022, while your IT department was probably still sending "think before you print" email signatures, Allen & Overy (now A&O Shearman) made a decision that would make most corporate committees break out in hives: they would "disrupt the legal market before someone disrupts us."

With its billable-hour addiction and partnership politics, the traditional legal industry was the perfect target for AI-driven transformation. But unlike the typical corporate approach of forming a committee to study the committee that would eventually explore AI, A&O actually did something.

Let's be honest—this is like watching your grandfather master TikTok while your tech-savvy cousin is still figuring out email. Impressive, unexpected, and slightly embarrassing for everyone else.

Download the Full Report

Unlock the complete insights from our exclusive 40-page analysis: "AI-Powered Business Transformation: Strategies, Competitor Insights, and Lessons from the Legal Frontier." This comprehensive resource includes:

* Industry-Wide AI Implementation Analysis: Current applications across Marketing, Operations, HR, Finance, and Legal sectors with real-world metrics and case studies

* A&O Shearman's AI Revolution: Detailed breakdown of their sandbox approach, governance framework, and strategic vision

* Implementation Roadmap: Step-by-step guide for leveraging AI in your organization with practical governance templates

* Failure Prevention Guide: Analysis of high-profile AI implementation failures and how to avoid common pitfalls

* Future Trends & Technologies: Strategic forecasting of AI developments for the next 5-10 years to keep your organization ahead of the curve

That shiny new AI project your company just launched? There's an 85% chance it's headed for the graveyard of failed initiatives, according to recent research from Tom's Hardware.

It's not because AI doesn't work. It's because most companies implement it about as strategically as assembling IKEA furniture blindfolded.

TLDR: ▪️ Data shows 85% of AI projects fail despite billions in investment ▪️ The SCALED Framework addresses the exact problems research identified ▪️ Successful "AI champions" get promoted 76% faster than their peers ▪️ Only 13% of companies are AI-ready (will yours be one of them?)

The Real Reasons AI Projects Crash and Burn

Research from RAND Corporation highlights why so many AI initiatives fail:

* Misaligned goals: Business and tech teams speaking different languages

* Poor data quality: 99% of projects struggle with this, per Vanson Bourne

* Overwhelming complexity: Most teams try to boil the ocean instead of starting small

Meanwhile, your competitor launched their AI initiative six months ago. Already cutting costs by 22% while your team is still "exploring options."

The Career-Defining Opportunity You Can't Miss

While everyone else is floundering, this is your moment to stand out:

* 76% of "AI champions" received promotions within 18 months

* Average budget increase of 34% for teams with successful AI implementation

* Enhanced job security as AI skills become essential (not optional)

But according to Cisco's AI Readiness Index, only 13% of organizations are fully prepared for AI implementation in 2024.

Introducing the SCALED Framework: Your Blueprint for AI Success

After analyzing dozens of successful (and failed) AI implementations, I've developed a framework that addresses the exact failure points research has identified:

The SCALED Framework Revealed

Welcome to the insider view! Let's break down the research-backed framework that's helping ambitious professionals succeed where 85% fail.

S - Simplify

Why it matters: Research from Way We Do shows complexity kills efficiency—most people can only hold 4-7 items in mind at once.

Action steps:

* Break complex processes into manageable pieces

* Create clear decision trees for AI-enhancement priorities

* Translate technical jargon into business language

Real-world success: A retail marketing director focused solely on email automation (instead of their entire customer journey) and achieved 67% faster response times in just 6 weeks.

C - Confident

Why it matters: Wharton Executive Education identifies confidence as the foundation of leadership, especially critical in navigating AI's technical complexities.

Action steps:

* Build your "minimum viable knowledge" to ask the right questions

* Create small early wins that build momentum

* Develop a framework for cutting through vendor hype

Quick win: Start bi-weekly "AI Office Hours" where you facilitate solutions without needing all the technical answers yourself.

A - Automate

Why it matters: Gartner predicts conversational AI alone will save $80 billion in contact center costs by 2026.

Action steps:

* Map your team's current workload and identify the 20% of tasks consuming 80% of time

* Use the "worth automating" calculator to prioritize (time saved × frequency)

* Build automation in layers, starting with simple components

Case study: Siemens implemented Azure AI for real-time issue reporting, significantly enhancing team collaboration and efficiency, as detailed by VKTR.

Download: Task Automation Prioritizer template [Link for premium subscribers]

L - Lead

Why it matters: 65% of project failures stem from poor leadership, according to SPR research.

Action steps:

* Position yourself as a translator between technical and business teams

* Create psychological safety for experimentation

* Develop stakeholder management strategies that address resistance

Pro tip: Research confirms that framing AI as "augmentation" rather than "automation" dramatically reduces team resistance.

E - Evaluate

Why it matters: Microsoft Learn emphasizes evaluation metrics for model quality, safety, and performance as critical success factors.

Action steps:

* Move beyond vanity metrics to business outcomes

* Build a simple dashboard connecting AI initiatives to results

* Create feedback loops for continuous improvement

Research insight: Studies in PMC show limited AI adoption in healthcare due to incomplete evaluation frameworks.

Template included: One-page AI Impact Scorecard [Download for premium subscribers]

D - Digital-Ready

Why it matters: 92.7% of executives identify data quality as a significant barrier to AI success, according to NewVantage Partners research cited by Ataccama.

Action steps:

* Assess current digital infrastructure through the AI readiness lens

* Identify and prioritize data accessibility and quality gaps

* Build a roadmap balancing quick wins with long-term capabilities

Success story: IBM implemented predictive maintenance AI, reducing equipment downtime by 20% through robust digital infrastructure, as noted by Neuroject.

AI Adopters is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

From Framework to Results: Your 30-Day Action Plan

The SCALED Framework isn't theoretical—it's designed for immediate application:

* Week 1: Use the AI Process Simplification Worksheet to identify one process ripe for AI enhancement

* Week 2: Apply the Automation Opportunity Calculator to quantify time savings

* Week 3: Create your leadership communication plan with the Stakeholder Mapping Template

* Week 4: Set up your measurement framework using the AI Impact Scorecard

Why This Works When Other Approaches Fail

While other frameworks exist, SCALED uniquely addresses all critical failure points identified in research:

The window for becoming your organization's AI champion is closing fast. The question isn't whether AI will transform your industry—it's whether you'll lead or try to catch up.

Adapt & Create,Kamil

AI Adopters is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

So I sat down with Ahmed Mekallach recently, and let me tell you – this guy is redefining how construction companies are using AI in ways that will make your corporate digital transformation look like it's still figuring out how to use Excel.

Ahmed's a civil engineer with a decade of experience who's now leading Myte Group, where they're building custom reasoning systems that take AI way beyond the "let me ask ChatGPT" approach most companies are still stuck on. His work spans from construction to healthcare, but what caught my attention was how he's solving problems in one of the most paper-heavy, archaic industries out there.

Construction's Trillion-Dollar Problem

Let's be honest – construction has been about as quick to adopt new tech as your uncle is to give up his flip phone. The stats are painful:

* 90% of large construction projects run over budget (by a whopping 80% on average)

* They finish about 20 months behind schedule

* The productivity gap represents a $1 TRILLION opportunity

Most construction companies are essentially "data-handling" entities outside of physical labor – yet they're drowning in paperwork, manual estimating, and field management processes that haven't changed since the 90s.

Beyond Generic AI

Here's where things get interesting (and where you might want to take notes for your industry). Ahmed's approach isn't about slapping ChatGPT onto existing problems. Instead, they're building custom reasoning systems that:

* Embed domain expertise into AI workflows – Instead of generic responses, these systems understand construction-specific constraints, codes, and practices.

* Compress planning timelines dramatically – What used to take weeks (creating roadmaps, estimating costs) now takes hours or even minutes.

* Create full visibility and control – Unlike black-box AI, these systems provide detailed outputs (flowcharts, estimates, timelines) that can be audited and adjusted.

One example Ahmed shared was their solution that helps construction teams program their experience and optimize everything from estimating to fieldwork. The results? Some companies have cut delays by 30% and reduced costs by 20% on major projects.

Lessons for Every Industry

While you might not be in construction, there are some brilliant takeaways from Ahmed's approach that could revolutionize how you implement AI in your own field:

1. Focus on Workflow-Specific AI, Not Generic Tools

The companies seeing real ROI aren't just using off-the-shelf AI. They're creating custom reasoning systems that understand their specific domain challenges.

Quick Win: Identify your most data-heavy workflow and map out all the domain expertise that isn't captured in your current tools. That's your AI goldmine.

2. Domain Experts Are Your New Programmers

Ahmed predicts that future programmers won't be coders – they'll be domain experts who can "program their experience" into AI systems.

Action Step: Partner your most experienced team members with your tech people. The magic happens when domain knowledge meets technical implementation.

3. Start Small, With Measurable Results

The most successful implementations Ahmed's seen start with a targeted use case where AI can make a noticeable impact.

Try This: Choose one workflow where you're currently losing time or money, establish baseline metrics, and implement a focused AI solution. Measure the difference in 60 days.

Hidden ROI

What struck me most from our conversation was how the real value isn't just in automating tasks – it's in capturing institutional knowledge that would otherwise walk out the door when experienced staff leave.

By building custom reasoning systems, companies are essentially creating digital versions of their best practices that can be scaled, improved, and passed on. That's a competitive advantage that generic AI simply can't deliver.

AI isn't just another tech tool – it's quickly becoming what Ahmed calls a "source of intelligence" that can be piped into any industry, just like electricity.

The question isn't whether you'll adopt AI. It's whether you'll be an early winner or playing catch-up for the next decade.

Adapt & Create,

Kamil

Ever caught yourself introducing yourself with your job title at a party?

"Hi, I'm [name], I'm a [insert impressive-sounding role here]."

Yeah, we've all been there. In our world of back-to-back meetings and endless Slack notifications, it's easy to let our work become our entire identity.

But here's the thing that's keeping me up at night: As AI reshapes our workplace faster than a Gen Z-er can say "okay boomer," millions of us are facing an identity crisis. What happens to our sense of purpose when ChatGPT can write our reports, or when algorithms can manage our projects?

Purpose 2.0?

I recently had a fascinating conversation with Craig Filek, the creator of Purpose Mapping and co-founder of Purpose.ai, about this very challenge. And let me tell you, it was an eye-opener.

As Craig put it during our conversation:

"Purpose is existential. From your first breath till your last, your purpose is the same. It's why you exist... Mission is strategic. If purpose is the why, mission is the what? So what mountain am I going to climb?"

The Real Transformation

Here's what's really going on: We're not just facing a technological transformation – we're experiencing a fundamental shift in how we define ourselves and our worth. Craig shared something that hit home: Purpose isn't what you do, it's why you exist. Your job title? That's just your current mission.

Think about it this way: When you learned to drive, did you become a "car operator," or did you gain freedom to explore new places? When you mastered Excel, did you become a "spreadsheet manager," or did you gain the power to analyze and make better decisions?

The same principle applies to AI. It's not about what tasks AI might take over – it's about how we can use AI to amplify who we really are and what we're truly meant to do.

Key Insights for the AI Age

Here are some key insights from my conversation with Craig that might just change how you think about your relationship with AI:

* Purpose vs. Mission Your purpose remains constant throughout your life – it's your unique pattern, like a fingerprint. Your job or "mission" is just one way you express that purpose at a given time. When AI disrupts your current mission, your underlying purpose doesn't vanish – it's an opportunity to express it in new ways.

* The Identity Trap Many successful professionals (yes, I'm looking at you, fellow manager) fall into what Craig calls the "first mountain trap." They achieve success in one area and, when faced with change, try to replicate the same pattern instead of exploring new heights. Sound familiar?

* The Real Opportunity Instead of asking "Will AI take my job?" try asking "How can AI free me to focus on what truly matters?" Craig suggests focusing on your "zone of genius" – those unique abilities that light you up and put you in a flow state.

What This Means for You

Start mapping your own purpose beyond your current role. What patterns have followed you throughout your life? What activities put you in a flow state? These aren't just feel-good questions – they're your competitive advantage in an AI-driven world.

For example, if you're a project manager who loves bringing order to chaos, that skill isn't limited to managing Gantt charts (which AI might handle soon). It could evolve into orchestrating complex AI implementations or helping teams navigate technological change.

The truth is, AI isn't here to replace your purpose – it's here to help you express it more powerfully. While AI handles the routine tasks, you can focus on the uniquely human elements of your work: building relationships, showing empathy, making complex judgment calls, and innovating in ways that machines simply can't.

Take Action Now

Want to start discovering your own purpose? Craig has generously offered AI Adopters Club readers special access to try the beta version of Purpose.ai. You can access it here: purpose.ai/adaptandcreate

I challenge you to reflect: What aspects of your work light you up that no AI could replicate?

In the world of AI, the most valuable skill isn't coding or prompt engineering – it's knowing who you are and what unique value you bring to the table.

Adapt & Create, Kamil

Think your desk organization system is complex? Wait until you see how AI organizes its "brain."

Today, we're breaking down the AI learning pyramid – a framework that explains how modern AI systems acquire and apply knowledge.

Like human education progressing from general knowledge to professional specialization, AI systems use a layered approach to become both broadly capable and specifically skilled.

Share this with Gavin :)

In this issue, you'll learn:

* The 5 layers of AI learning

* How each layer contributes to AI's capabilities

* What this means for your business implementation

Watch the video for a more detailed explanation. 👆

Why Understanding This Matters

Just as businesses need both general-purpose tools and specialized solutions, AI systems require different layers of learning to be truly effective. Understanding this structure helps you make strategic decisions about AI implementation while managing costs and expectations.

The AI Learning Pyramid: Breaking It Down

Level 1: Foundation Model Training (The Base)

Think of this as AI's university education – broad, comprehensive, and expensive. This phase equips AI with pattern recognition and basic reasoning capabilities. It's like that colleague who somehow knows a little bit about everything (but actually useful).

You probably won't be training foundation models yourself unless you have significant computational resources. However, you can leverage pre-trained models to reduce implementation costs significantly.

Level 2: Fine-Tuning

This is where AI gets its specialization – like getting a master's degree in your specific industry. Picture teaching a general-knowledge expert the specific terminology and procedures of your field.

This stage allows you to adapt AI to your industry's unique needs, but be cautious - quality data is crucial to avoid "hallucinations" or biased outputs.

Level 3: Retrieval-Augmented Generation (RAG)

Think of this as AI's ability to "look things up" in real-time. Instead of memorizing everything, it can pull information from databases when needed. It's like having an assistant who knows exactly which file to pull from the cabinet during a meeting.

This capability means your AI can stay current with your latest documents, policies, and data without constant retraining. It's particularly effective for customer support or compliance tasks.

Level 4: Context Window Management

This is AI's working memory during your conversation – like having a whiteboard that gets erased after each meeting. While larger memory allows for more nuanced conversations, it also increases operational costs.

The temporary nature of this layer provides built-in privacy protection while allowing you to balance the depth of conversation against computational expenses.

Level 5: Persistent User Memory (The Top)

Like your coffee order at your favorite cafe, some AI systems can remember your preferences across conversations. However, this raises important questions about data ownership and privacy.

While personalization can boost efficiency, implementing clear opt-in policies and data anonymization practices is crucial for protecting user privacy.

Bonus Level: Projects

Some LLMs like Anthropic’s Claude or ChatGPT have something called “Projects” the way these work in practice is like a small RAG in form of a file upload to said project folder and Persistent User Memory in form of an instructional prompt.I use these every time I work on a book or a client project where I want to retain a certain knowledgebase in the same place and always accessible by the mode. Try it out it’s great!

Practical Implementation Guide

Here's how to actually put this pyramid knowledge to work in your organization:

Match Your Needs to the Right Layer

* For Quick Answers & General Tasks: Use foundation models through tools like ChatGPT or Claude. Perfect for drafting emails, basic research, or general writing tasks.

* For Industry-Specific Work: Invest in fine-tuned models if you're handling specialized content like medical terminology or legal documents. Start with a small test set of data before full deployment.

* For Real-Time Data Needs: Implement RAG when accuracy on current information is crucial. For example, customer service teams can use RAG to pull from your latest product documentation or policy updates.

Smart Resource Allocation

* Start Small: Begin with the foundation layer through existing tools before investing in custom solutions.

* Cost-Effective Scaling: Use cloud-based solutions for sporadic needs, consider on-premise deployment only when usage justifies the investment.

* Data Strategy: Create a clear process for what data goes where:

* Public/non-sensitive content → Foundation models

* Proprietary data → RAG systems

* Sensitive information → Fine-tuned private models

Security Checklist

* Set up access controls for each AI tool based on data sensitivity

* Create clear guidelines for what information can be input into each system

* Implement monitoring for unusual usage patterns

* Regular training for team members on safe AI usage

Like that office coffee machine that no one knows how to program properly, once you understand the basics, everything becomes much clearer.

Adapt & Create,Kamil

P.S. Want to dive deeper into any of these layers? Let me know in the comments, and I’ll cover it in our next issue!

Hey Adopter,

Had a fascinating chat with Doug Hohulin this week about AI in healthcare, and it sent me down a rabbit hole of research that I need to share with you. Forget the usual AI hype – let's talk about what's actually happening in the trenches of healthcare transformation.

Connect with Doug Hohulin: https://www.linkedin.com/in/doughohulin/

His Books:

* 2030: A Blueprint for Humanity's Exponential Leap https://amzn.to/4gGgjMW

* Tech Powered Healing: The Future of Medicine in the AI Age https://amzn.to/4aYVoTM

The Big Picture No One's Talking About

You've probably heard the ambitious claim: "AI will save a billion lives." Sounds like typical Silicon Valley hyperbole, right? But here's the thing – the numbers actually add up, just not in the way most people think.

Take this mind-bending stat: AI-powered early detection systems are already catching breast cancer 20% more accurately than human radiologists alone. Not in some lab experiment, but in a real trial with 80,000 women. And here's the kicker – it actually reduced radiologists' workload by 44%.

But that's just the tip of the iceberg.

The Silent Revolution

While everyone's obsessing over chatbots, some genuinely wild stuff is happening:

Remember the COVID vaccine sprint? Moderna had their mRNA vaccine ready for trials in 42 days. Not because of some breakthrough in biology, but because they'd been using AI to optimize their mRNA platform for years. That's the difference between a pandemic lasting months versus years.

Or consider this: Johns Hopkins deployed an AI system that reduced sepsis deaths by 20%. Not through some fancy robot surgery, but by catching warning signs hours before humans typically notice them. In healthcare, hours don't just matter – they're often the difference between life and death.

The Plot Twist

But here's where it gets interesting. BlueDot's AI flagged the COVID-19 outbreak in Wuhan on December 31, 2019 – over a week before the WHO's official alert. An AI system, scrolling through local news and airline data, essentially saw a pandemic coming before the world's premier health organization did.

The Dark Side We Need to Talk About

Let's get real for a minute. In 2019, researchers uncovered something disturbing: a widely used hospital algorithm was systematically discriminating against Black patients. Not because anyone programmed it to be racist, but because it used healthcare spending as a proxy for medical need. The result? Black patients had to be significantly sicker than white patients to receive the same level of care.

This isn't just a technical glitch – it's a stark reminder that AI can amplify existing inequalities if we're not careful. But here's the silver lining: once exposed, this bias could be corrected. Try doing that with human bias, which often goes undetected for decades.

The Real Revolution Isn't Robots

The most profound change happening isn't what you'd expect. AI is handling the grunt work – the endless data entry, the routine scans, the repetitive tasks that burn out healthcare workers. European studies suggest AI could free up 1.8 billion work hours annually in healthcare. That's equivalent to adding 500,000 full-time healthcare professionals to the workforce.

Think about that. The real promise isn't AI replacing doctors – it's AI giving them back the time to actually be doctors. As Dr. Eric Topol puts it, "The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time-honored connection and trust – the human touch – between patients and doctors."

Looking Ahead

We're at a fascinating inflection point. By 2030, experts project AI could prevent about $150 billion in healthcare costs annually. But more importantly, it could help bridge the healthcare access gap affecting 4.5 billion people worldwide.

The next decade won't be about robots performing surgery (though that's happening too). It'll be about AI democratizing expertise, catching diseases earlier, and making healthcare more human by handling the inhuman parts of the job.

After diving into this research and chatting with Doug, I'm convinced we're looking at healthcare's transformation all wrong. It's not about AI doing the impossible – it's about AI making the possible accessible to everyone.

Your weekend reading: Check out this MIT study about how AI discovered a new antibiotic by screening molecules in ways humans never could. It's a perfect example of AI not replacing human intelligence but expanding what we can achieve.

Adapt & Create, Kamil

P.S. Yes, I genuinely believe we'll save those billion lives. Not through any single breakthrough, but through thousands of small improvements adding up to something revolutionary. Just like how AI actually works – less magic, more methodical progress.

AI Adopters is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Hey there, AI Adopters!

"We have thousands of recorded sales calls and scripts. Can AI help us understand what works and generate better scripts that actually convert?"

This question 👆 from one of my premium subscribers perfectly captures a challenge I'm hearing more and more: how to transform that goldmine of call center data into actual sales results. You know that one script your team's been using since 2019 – the one that makes your agents sound like they're reading IKEA furniture instructions? Today, I'm showing you exactly how to use AI to analyze your existing call recordings, QA data, and scripts to generate sales conversations that actually convert.

This isn't about robots replacing your team – it's about using AI to clone your top performers' winning moments across every call. Let's dive in.

What AI Sees That We Miss

Remember when you last listened to a "perfect" sales call and thought, "If only we could clone this conversation"? Well, that’s exactly what AI is doing now, just more systematically and at scale. By analyzing thousands of call recordings, AI tools can identify patterns that our human brains might miss – like the fact that saying "investment" instead of "cost" in the first 30 seconds might increase your conversion rate by 12%.

Here’s what’s fascinating: According to my research, most call centers are sitting on a goldmine of data without realizing it. Your call recordings, QA sheets, and existing scripts aren’t just compliance requirements – they’re the building blocks for AI-driven optimization.

The Science Behind Better Scripts

The magic happens when AI analyzes successful vs. unsuccessful calls. It’s not just about what words are used – it’s about the entire conversation flow. Modern AI platforms can identify:

* Sentiment shifts during calls (when did the customer go from frustrated to interested?)

* Key moments that led to successful conversions

* Patterns in objection handling that actually worked

* Common points where deals are lost

But here’s what makes this really powerful: AI doesn’t just tell you what worked – it helps create dynamic scripts that adapt in real-time based on customer responses.

The Implementation Blueprint

Based on extensive analysis and real-world implementations, here’s how to approach this transformation:

First Phase: Data Preparation

Start by digitizing and organizing your existing call data. This means getting your recordings, scripts, and QA data into a format that AI can analyze. Modern platforms like Convin.ai or NICE CXone can help automate this process.

Second Phase: Analysis & Insights

This is where AI really shines. It will analyze your data to identify:

* High-converting conversation patterns

* Optimal response flows for different customer types

* Effective objection-handling techniques that are already working in your team

Third Phase: Script Enhancement

Using these insights, you can create dynamic scripts that:

* Adapt to customer responses in real-time

* Provide agents with proven objection handlers

* Suggest optimal next steps based on the conversation flow

Real World Impact

Let me share something concrete: A recent implementation with a financial services provider showed a 17.2% increase in first-call resolution after implementing AI-optimized scripts. The key wasn’t just better scripts – it was better understanding of when to use different approaches.

The Technology Stack

Based on my analysis, several platforms stand out for different needs:

* Convin.ai shines in automated quality management, analyzing 100% of customer interactions to identify what works. It’s particularly strong for teams looking to start with AI script optimization.

* Balto.ai offers dynamic scripting that adapts in real-time, making it excellent for complex sales environments where flexibility is crucial.

* NICE CXone provides comprehensive features including intelligent scripting and performance management, ideal for larger operations needing enterprise-grade solutions.

The Price of Progress: 2025 Platform Comparison

Let’s talk about the elephant in the room – cost. I know budget discussions can make even the most enthusiastic AI champion nervous, so I’ve broken down the latest pricing to help you navigate these waters.

Entry-Level Solutions

If you’re just starting your AI journey, Convin.ai offers the gentlest entry at $50/month per agent, with a free trial to test the waters. It’s like getting the keys to a Tesla Model 3 when you’re used to driving a Civic – powerful enough to impress but won’t break the bank.

Mid-Range Options

Balto.ai starts at $100/month and, while they don’t offer a free trial, their optional setup fee structure means you can customize your initial investment. Think of it as buying a car with exactly the features you need, no more, no less.

Enterprise-Grade Platforms

NICE CXone offers a fascinating tiered approach:

* Digital Agent starts at $71/user/month

* Voice Agent at $94/user/month

* Omnichannel Agent at $110/user/monthAnd their Ultimate Suite tops out at $249/user/month.It’s like choosing between different Tesla models – each tier adds more horsepower to your operation.

Pay-As-You-Go Innovation

Google Cloud Contact Center AI takes a different approach with usage-based pricing:

* $0.007 per virtual agent request

* $0.024 per minute for speech-to-text

* $0.06 per chat session for Agent AssistPerfect for those who want to pay for exactly what they use, no more, no less.

Comprehensive Solutions

Five9 rounds out the options with plans ranging from $175 to $325 monthly, with their digital and core plans starting at the lower end. Think of it as an all-inclusive resort package – everything you need in one predictable payment.

The Bottom Line

Remember, these prices are just the cover charge. The real value comes from:

* Implementation support (some include it, others charge extra)

* Training packages (especially crucial for complex systems)

* Contract flexibility (longer commitments often mean better rates)

* Support levels (because 3 AM emergencies happen)

* Add-on features (those nice-to-haves that become must-haves)

The key isn’t finding the cheapest option – it’s finding the one that’ll generate the best ROI for your specific situation. Just like any good investment, the initial price tag is only part of the story.

Making It Work

Here’s what separates successful implementations from failures:

* Start Small: Begin with a pilot group to test and refine your approach. This allows you to demonstrate ROI before scaling.

* Focus on Integration: Ensure your chosen solution works seamlessly with your existing CRM and call center infrastructure.

* Maintain Human Oversight: Use AI as a guide, not a replacement for human judgment. Your best agents’ insights should inform how you implement AI suggestions.

Next Steps

Ready to transform your call scripts? Start by:

* Auditing your current call data and recording systems

* Identifying your key conversion metrics

* Selecting a platform that matches your scale and needs

Remember, the goal isn’t to create robotic conversations – it’s to empower your agents with insights that make every call more effective.

Adapt & Create,Kamil

P.S. Want to dive deeper? Premium subscribers can access our detailed platform comparison guide and ROI calculator in the resource center.

References and Further Reading

Primary Sources

* Top 10 Companies Using Generative AI to Enhance Customer Service - Convin

* The Impact of AI and Machine Learning on Call Center Operations

* How Call Center AI Powers Your Contact Center & Its Benefits

* Leveraging AI in Call Centers [Use Cases + Tips] | Sprinklr

* Impact of AI on Call Centers: 7 Key Impacts in 2025 - Invensis

Platform Documentation

* Balto.ai Product Documentation

* Google Cloud Contact Center AI Documentation

* Real-Time Guidance Platform - Call Center Software - Balto.ai

* Convin Complete Guide on Call Center Software

* NICE CXone Pricing and Features Guide

Case Studies and Implementation Guides

* Top AI-Powered Call Center Software Tools in 2025 - Convin

* Call Center Scripting Software: AI for Dynamic Customer Service - Balto

* Transforming Customer Service with Google Contact Center AI (CCAI)

* Top AI Tools for Call Center: 2025 Guide by Nectar Desk

* 20 Best Call Center Scripting Software Of 2025 - The CX Lead

Note: All links and resources are current as of January 2025. Access to some resources may require subscription or registration.

Hey Adopter,

OpenAI wants you to spend $200 monthly on their new research tool. But most teams get better results by combining two $20 tools. I know that sounds too good to be true, but stick with me for the next few minutes.

I analyzed every major AI research tool on the market. What I found will save you money and boost your research efficiency. You'll learn how to build a better research stack for $40, pick tools that actually fit your workflow, and avoid the costly mistakes most teams make when choosing AI tools.

My analysis includes clear comparisons backed by performance data, specific recommendations for your budget, and real examples of teams saving over $1,800 monthly. Whether you're planning to upgrade your tools or just want to cut costs, you'll find actionable insights here.

The New Research Arsenal

1. ChatGPT Deep Research ($200/month)

✨ The Premium Player

* 100 monthly queries ($2 per deep dive)

* 26.6% accuracy on expert-level tasks (verified benchmark)

* Process time: 5-30 minutes per query

* Full file support: PDFs, spreadsheets, images

* Python integration for data analysis

* Comprehensive citations with methodology summaries

The Reality Check: Yes, it's pricey, and those 30-minute processing times aren't for the impatient. But for deep technical analysis? It might just be worth every penny.

2. Google's Gemini Deep Research ($20/month)

🔄 The Workspace Wonder

* Parallel research through AI "mini clones"

* Seamless Google Workspace integration

* Interactive document editing

* Specialized market analysis capabilities

* Real-time collaboration features

The Fine Print: No file uploads and struggles with paywalled content. Perfect if you live in Google's ecosystem, limiting if you don't.

3. STORM (Free)

📚 The Academic Ace

* LaTeX/Markdown export

* Academic-style citations

* Wikipedia-style report generation

* Cost: Absolutely free

Worth Noting: Earlier reports about direct academic database integration were overstated – it's more about academic-style outputs than special database access.

4. Perplexity Pro ($20/month)

⚡ The Speed Demon

* 300 daily searches

* Real-time web updates

* Multi-model support (Claude/GPT-4/Sonar)

* 67% faster than standard ChatGPT

* End-to-end encryption

The Trade-off: Jack of all trades, master of none. Great for quick research, less suited for deep analysis.

Performance Showdown

Who Should Buy What?

Enterprise Teams ($1M+ Annual Research Budget)

✓ ChatGPT Deep Research is your play if:

* You're doing technical due diligence

* Market analysis is your bread and butter

* Accuracy matters more than speed

Mid-Size Teams ($100K-$1M Research Budget)

The Optimal Stack:

* Gemini Deep Research for collaborative projects

* Perplexity Pro for daily research Total cost: $40/month (that's $160 less than Deep Research)

Academic Users

Best Combo:

* STORM for academic writing

* Perplexity Pro for fact-checking Total cost: Just $20/month

The ROI Reality Check

Before jumping on the $200 Deep Research bandwagon, do this quick math:

* Track weekly research hours

* Multiply by your hourly rate

* Factor in accuracy requirements

* Consider collaboration needs

Example:

* 10 hours/week research at $100/hour = $4,000 monthly

* If Deep Research cuts that by 50% = $2,000 saved

* Minus $200 subscription = $1,800 monthly benefit

The Bottom Line

ChatGPT Deep Research isn't trying to be everything to everyone – and that's exactly why it might be worth $200 for the right user. For most teams, though, a Perplexity Pro + Gemini Deep Research combo ($40 total) hits the sweet spot of capability and cost.

Think of it this way: You're not just paying for a tool; you're investing in time saved and accuracy gained. Choose based on your specific needs, not the price tag.

Adapt & Create, Kamil

P.S. Using any of these tools in your workflow? Hit reply with your real-world experience – especially interested in hearing about concrete time savings and ROI calculations.

Sources & Additional Reading:

* OpenAI Launches Deep Research Model for ChatGPT

* Google Gemini Deep Research Feature Details

* STORM AI Launch Announcement by Stanford University

* Perplexity Pro Review & Subscription Perks

* OpenAI Deep Research Launch Coverage

* Hands-on with Gemini Deep Research

* STORM AI for Academic Teaching

* OpenAI Deep Research Roadmap

* Performance Metrics – OpenAI Deep Research

* User Experience Studies on ChatGPT’s AI Tool

* Enterprise Implementation of ChatGPT Deep Research

Whether you’re aiming to become the indispensable AI champion at work or building a profitable AI advisory service for your clients, this is your playbook.

Which Path Will You Choose?

We have two clear paths for ambitious leaders. Read both and see which one describes you.

.

PATH 1: Become the AI Champion at Work

($249/year or $49/month)

You see the potential of AI, and you're ready to lead the charge inside your company. Your goal is to translate AI capabilities into measurable ROI, make your team faster, and become the go-to expert for your organization.

Our Premium Membership is designed for you.

Every week, premium members get the battle-tested assets to make them look like wizards to their bosses:

* Monday: Business-Ready AI Workflows & PromptsSteal proven AI workflows that solve real business challenges, complete with step-by-step implementation instructions and expert analysis.

* Thursday: Deep-Dive Case StudiesGet the frameworks, key metrics, and actionable lessons from real companies implementing AI successfully.

This is your fast track to becoming the most valuable person in the room.

PATH 2: Launch Your AI Advisory Business

($499/year)

You're a consultant, advisor, or entrepreneur. You don't just want to use AI; you want to sell it. You need client-ready tools to build a new revenue stream and position yourself as a market leader.

The Strategic Partner Tier is your business-in-a-box.

This is our elite tier for those who are building a business with AI. It includes everything in the Premium plan, PLUS:

* The Complete AI Consultant's ToolkitInstantly access our library of client proposal templates, pitch decks, project scoping documents, and discovery questionnaires.

* White-Label RightsAdapt and use all our frameworks in your client engagements under your own brand. We do the work, you get the credit.

* The ROI Justification PackGet the spreadsheets and models to build a bulletproof business case for your clients, making your services an easy sell.

One new client engagement pays for this tier 10 times over.

Start Your Journey for Free

Not ready for a premium plan? Start with our free subscription. You'll get curated insights on the most impactful AI developments for business and see the quality of our work firsthand.

This is the first step to moving from a passive observer to an active leader.

About The Club

I'm Kamil Banc, and I help business leaders cut through AI complexity. After years of seeing professionals struggle with vendor promises and technical jargon, I've developed a practical approach that turns AI from a buzzword into your competitive advantage. The AI Adopters Club is where I share the playbook.

Your company is looking for an AI leader. Your clients are looking for an AI guide. Choose your path and start today.

Still Not Sure?

Check out these popular issues:

Join today and get instant access to our complete archive of case studies and prompts.

Questions? Book a call | Sponsorship Inquiries

"It's not about replacing experts—it's about amplifying their impact," explains Professor Jeffrey Bussgang. "We needed to transform how knowledge flows through our organization."

Their solution, ChatLTV, became Harvard's way to scale expertise without sacrificing quality. The results spoke volumes:

* One query answered every 42 minutes

* 70% reduction in administrative emails

* Real-time insight into student learning patterns

* 99% private usage rate

The Execution Playbook: Your 4-Week Implementation Guide

Become a premium subscriber to get the full case study and analysis.

Remember that colleague who still uses Comic Sans in their presentations? Well, today we're going to make sure you're not the one getting side-eyes in your next meeting. I've been testing Napkin AI, and let me tell you – it's like having a secret weapon for turning those sleep-inducing text documents into visuals that actually keep people awake.

The Truth About Business Visuals

Let's be honest: most of us aren't graphic designers, and our attempts at "jazzing up" presentations usually end up looking like a PowerPoint template from 2003. But here's the thing – in today's fast-paced business world, clear visuals aren't just nice-to-have anymore. They're essential for getting your point across before everyone starts checking their phones under the table.

Why Napkin AI Is Different

This isn't just another AI tool that promises to revolutionize your workflow (and then crashes during an important demo). NapkinAI actually delivers, especially if you're in that sweet spot of being too small for a design team but too ambitious to stick with bullet points.

Here's what makes it particularly interesting:Instead of spending hours wrestling with shapes in PowerPoint, you can literally paste your text and get a professional-looking flowchart in seconds. It's like having a designer on speed dial, minus the creative differences and revision rounds.

Let me paint you a picture (pun intended):Your boss dumps a 10-page process document on your desk (or in your Slack, because it's 2025). Instead of forwarding this wall of text to your team, you feed it to Napkin AI and out comes a clean, intuitive flowchart. Suddenly, you're not just passing along information – you're the person who makes complex things simple.

Who Should Care (And Why)

This tool is perfect for:

* Project managers tired of explaining the same workflow for the fifth time

* Marketing folks who need to make data look sexy (without the graphic design degree)

* HR professionals who want their training materials to be read by actual humans

* Team leads who need to communicate complex ideas without writing novels

The Bottom Line

Here's what you really need to know: Napkin AI is free during its beta phase. That means you can test it out, look like a visualization wizard, and not spend a dime of your department's budget. Even when they introduce paid plans, it'll likely cost less than your monthly coffee budget.

Quick Wins to Get Started

* Take your most confusing process document

* Paste it into Napkin AI

* Watch it transform into a clear visual

* Share with your team and accept the praise

* Remember to mention where you learned about it 😉

What's Next?

While everyone else is still creating bullet-point slides, you could be revolutionizing how your team communicates. No design skills needed, no expensive software required, and definitely no Comic Sans involved.

Adapt & Create,Kamil

If you found this helpful, forward it to that colleague still using Comic Sans. We're all in this together.

Your CEO probably forwarded you that glowing piece about DoorDash's AI transformation last week. You know, the one where they casually mention their AI system processing 1,400 messages per minute across dozens of languages? Let's talk about what this actually means for those of us who don't have Silicon Valley's pocket change to play with.

Here's the thing: While everyone's swooning over DoorDash's AI wins, they're missing the real story. It's not about their fancy "knowledge graphs" or the fact that they're using every AI buzzword in the book – it's about how they chose their battles.

👉 Premium subscribers get immediate access to:

* Complete DoorDash AI Implementation Case Study (20 pages)

* Comprehensive implementation timeline from pre-2023 to 2025

* Technology stack and architecture overview

* Complete FAQ addressing common implementation challenges

* Study guide with key insights and lessons learned

I noticed that the final video is not mirrored, the preview is …. next time I’ll know better.

What you can expect

* Minutes, Not Hours Turn complex Excel tasks into quick AI conversations.

* Pro-Level Solutions Get expert formulas and automation with zero guesswork.

* Learn & Scale Fast Progress from basics to advanced while building real solutions.