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You won't find many bigger fans of generative AI than yours truly—and Claude in particular.

Plenty of learned folks believe that single-purpose tools, like note-taking apps, will go the way of the dodo. In short, I agree. I'm on record predicting most niche software vendors will go poof in the near future. But what about enterprise systems? In this post, I'll explain why most of them will survive.

How Are You Using That App?

I will never claim to be an expert on every task management, productivity, and note-taking app out there. Popular choices include Coda, Bear, Obsidian, and Todoist. I do, however, know a thing or six about Notion. For the sake of argument, say that the Notion universe consists of only two groups: basic and power users.

Group #1

A software vendor's app relies heavily upon casual users. The majority of these folks use it in a limited way. Maybe 70 percent of people just scribble down the occasional idea in the note-taking app.

You needn't be a sorcerer to know how this movie ends: Claude Cowork and similar tools will make these simple apps obsolete. (Zapier, Make.com, and other third-party glueware are in similar boats.)

Group #2

Now let's move to power users. I'm talking about people who use the app or system for much more than taking notes. Notion performs dozens of functions for folks in this cohort. Claude, ChatGPT, Gemini, or one of their competitors will ultimately cannibalize such a versatile tool anytime soon.

🖥️ Let Me Count the Ways
I firmly put myself in this camp. Organizations and individuals hire me to build bespoke systems for them. Notion serves as my CRM. I generate quotes and manage complicated projects (such as books) from it. It helps me evaluate service providers and plan trips. I could go on, but you get my drift.

For Notion power users, the idea that a newfangled AI tool can replace it is absurd.

Now, let's kick it up a level: If a single individual is unlikely to supplant Notion with Claude, then will even a medium-sized enterprise forgo its core systems?

want to learn more about citizen development?

Check out my 2023 book Low-Code/No-Code: Citizen Developers and the Surprising Future of Business Applications.

BUY THE BOOK A Key Software Distinction

Generative AI tools are inherently non-deterministic. Sure, they'll continue to improve, reduce hallucinations, and ultimately reduce tool sprawl.

By contrast, CRM and ERP systems of record are deterministic. They are far too important to leave to chance. “I usually want to get employees' checks right,” said no payroll manager ever. What's more, these vendors behind these enterprise systems recognize the need for their wares to evolve.

CRM and ERP systems won't disappear.

Connecting the dots leaves one answer: enterprise systems will certainly morph. In particular, their UIs will undergo massive makeovers. It's folly, though, to think that they'll completely disappear. And plenty of others feel the same way.

Claude and CardioApple Health’s data visualizations are surprisingly basic, so I used AI to generate real insights into my workouts.Phil SimonPhil SimonSimon's Law of AI Disruption

The more diverse the software, the more likely it is to survive the AI tsunami.

Feedback

What say you?

Perhaps no technology in history has morphed quicker than generative AI. Hell, a week seems like a year for a single company. Even if you spend a good deal of time teaching yourself how to use newfangled tools, it's virtually impossible to keep up.

Against this backdrop, no-code automation giant Zapier recently made news by releasing the second iteration of its AI fluency rubric. TL;DR: If you don't embrace AI, don't bother applying for a job. (Zapier is hardly alone here. Meta grades its workers on their degree of “AI nativeness.”)

Observations

Zapier's post is thoughtful and well-written, but you won't find anything meaningful about how employees can level up their AI proficiency. Noticeably lacking are specifics on any of the following:

Not that long ago, the decision of which application or system to use was typically a no-brainer.

Take the early aughts. Back then, you'd be silly to create a training manual in anything but Microsoft Word. You wouldn't think twice. Ditto for PowerPoint—the de facto standard for presentations. We performed ad hoc data analyses in Excel. You could eschew Salesforce because you preferred your bespoke Microsoft Access CRM. (That choice, though, probably didn't sit well with your boss.) Google's productivity apps worked similarly. You get my drift.

Brass tacks: Only an amateur would use a screwdriver to hammer a nail into the wall.

What about today?

The rise of GenAI has muddied the waters. In today's quick post, I'll offer a few tips on how Claude users can successfully navigate this new world.

Prompt #1

When I begin a new task or project of consequence, I prompt Claude as follows via a Raycast snippet:

When it comes to embracing new technologies, I've always been an early adopter. My friends in grad school still fondly remember my exuberance over the nascent web.

I'll take the Pepsi challenge against anyone. (Thank you, Carnegie Mellon.) At the same time, though, I have to admit: I'm struggling to keep up with Anthropic's crazy pace of innovation.

Recent Additions

As I write these words, here are just some of the features the company has dropped in the last few days:

The ability to fully control your computer on whatever device you like—subject to a few caveats. (Cool, but yikes!)Claude Cowork Projects.Claude Code Channels.Memory 2.0.Auto mode to grant or restrict Claude Code's access on your computer. (It's a safer alternative to --dangerously-skip-permissions.

To be fair, you may not have seen any or all of these new releases just yet for whatever reason. Possible culprits include:

Anthropic's gradual rollout of the feature.The responsibilities of your day job.A plan restriction.A combination of all three.

Maybe it's something else.

Hell, by the time you read these words, Anthropic may have released two more insanely useful capabilities.

Unexpected UI Changes

Yesterday, I was testing Claude's cool new Dispatch feature when I noticed a lightbulb icon in Claude Cowork:

Claude Cowork Screenshot | Click on the image to enlarge it.

I went all Krusty the Klown:

Even Claude couldn't confidently give me an answer. Here's my prompt:

What's a Claude Cowork idea?

Reply:

The "Ideas" label on the lightbulb icon likely shows suggested tasks or prompts Claude thinks might be useful — similar to how Claude.ai sometimes surfaces conversation starters.

It's squarely aimed at non-developers who want automation without writing code or using Claude Code.

Likely shows. Even Anthropic's own chatbot can't keep up with its torrid pace of innovation.

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Looking at the Data

Always curious, I looked at the Claude Code changelog and asked my AI bestie to visualize the number of recent changes in that tool alone. In other words, I omitted Claude Cowork, vanilla Claude, and change Anthropic classified as a fix. Here's the breakdown:

Claude Code Recent Features by Day | Source: Antropic | Click on the image to enlarge it.

For some reason, Claude omitted changes after March 21.

Of course, some enhancements are more meaningful than others. Still, nary a weekday goes by without something new appearing in the Claude suite of tools.

GoDaddy and Microsoft Failed Me. Then Came Claude.Switching email hosting services neutered my intricate Outlook system. AI helped me find a new provider and reimagine how to prevent spam.Phil SimonPhil SimonSimon Says

If I have learned anything over my three decades in enterprise technology, it's this: Tech typically moves much faster than people and organizations can handle. The rise of AI in the last few years reflects that reality in spades.

Is it possible to keep a workforce current on tools that change by the day?

No one is more critical of organizations failing to train their employees properly on new tools than I. (It's a key point in Project Management in the Hybrid Workplace.) At the same time, however, is it possible to keep a workforce current on tools that morph by the day?

In a word, no.

⚾ Correction: The first version of this post incorrectly stated that nine people played all positions before. Swing and a miss...

On the diamond, we call individuals who can effectively play more than one position utility players. Fun baseball fact: five people have played all nine in a single game.

I was recently thinking about this term in the context of AI because Claude helped me solve four very different problems before lunch.

Mixologist and Grocery Store Attendant

I know nothing about mixing drinks—even two-ingredient ones. At least I'm self-aware enough to ask Claude:

I learned early on in my career that there is no such thing as perfect software. For decades, I've found different systems and apps limiting in small but critical ways. Out of the box, different tools often can't do exactly what my clients or I need them to do. As a result, I've had to create a variety of workarounds over the years—some of which have lasted far longer than I anticipated.

In today's post, I'll describe one of the most fascinating and powerful aspects of agentic AI: the ability to overcome a software product's core limitations.

The Fleas No Longer Come With the Dog

Notion's native automations are ridiculously useful. I use them with all my clients, but they cannot trigger other automations. Evidently, it would cause a glitch in The Matrix.

Fortunately, there's been a quick, relatively simple fix for a long time:

Create an integration with a third-party tool. Zapier and Make.com are popular choices. Link the new integration to the Notion database in question.

Voilà!

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Pros and Cons

On one hand, as I write in Low-Code/No-Code, it's never been easier to get these tchotchkes to play nice with other apps. On the other, they add another layer of complexity—not to mention potential technical complications and licensing issues down the road.

Up until recently, most people accepted the limitation and went with the third-party tool. The fleas came with the dog.

The rise of AI agents, however, has changed the game.

For example, consider a simple recurring task. Notion lets you add them to existing databases according to set schedules (daily, hourly, or weekly). If a tree falls in a forest, though, would you hear it? Less abstractly, you need to use one of those third-party tools to alert you of that new task.

Photo by Alex Reynolds / Unsplash

Thanks to Notion custom agents, those days are over.

Solving the Problem via AI Agents

Here's a test database after a Notion recurring task auto-populated it:

Test Notion Database | Click on the image to enlarge it.

I had also created a simple notification. If another user added a task to that database, then Notion would notify me:

Notion Alert Setup | Click on the image to enlarge it.

But here's the rub: That notification would not work on auto-generated recurring tasks without a third-party integration. Without one, I'd never know about the new task unless I manually checked that database. (Again, Notion notifications can't fire based on other notifications.)

To circumvent that limitation without a third-party integration, I created this simple Notion custom agent:

Notion Custom Agent | Click on the image to enlarge it.

True to form, Notion dutifully alerted me of the new task at 4:15 pm my time:

Total time spent: maybe three minutes.

Technically, a native Notion notification did not trigger the notification. Rather, a Notion custom agent triggered the notification. The semantics don't matter to my clients. More important, I have solved their problem.

Notion Formulas Based on AttachmentsImpossible? Think again.Phil SimonPhil SimonSimon Says

This is just one of many examples of how AI agents can be useful—and not just in Notion. Stay tuned. They're only becoming more useful. No, they're not perfect, but it's downright silly to ignore them.

Need help building a Notion system?

LET'S CHAT

In mid-January of this year, I was catching up with a friend on the phone. He asked me if I received his recent email about a job reference. Oddly, I hadn't. Time to do some digging.

I soon learned that I wasn't special. Untold GoDaddy customers were no longer receiving their business emails. The company was experiencing what would turn out to be yet another multi-day Outlook service outage. Even more infuriating, GoDaddy again didn't deign to alert affected individuals. No messages to their backup email addresses. Nary a text. No automated phone calls. Zilch.

GoDaddy's lack of communication irritated just as much as the technical disruption. Just like Pacino in The Insider, I was pissed off and curious. I enlisted Claude to start investigating alternative email providers. Eventually, I decided to kick the tires on Fastmail (affiliate link). As you'll see in this post, I ultimately came out ahead.

An Early Obstacle

I began my migration by changing my MX records. I then exported my emails from GoDaddy to FastMail. Next up, I downloaded my existing blacklist from my personal GoDaddy quarantine page:

GoDaddy Blocked Email Addresses

To replicate my previous setup, though, I needed to migrate all my existing Outlook email rules. How hard could that be?

Impossible, because Microsoft prohibits Mac users from taking this action. Can someone say vendor lock-in? Maybe the Redmond behemoth only cares about data sovereignty in Switzerland. Ultimately, my hundreds of email filters remain trapped in Outlook and currently unusable.

My MX records propagated. I was now officially on Fastmail, but all new messages arrived in my inbox—not in their carefully curated folders and subfolders. What's more, I received absurd emails from icky domains and scammers that I had previously blocked through my advanced keyword-based filters in Outlook.

I spent about a week intermittently recreating prior filters as I received a variety of messages. By early February, though, I had figured out Fastmail. Why not start noodling with its advanced features?

I soon began to sense that there might be a better way to attack the problem than playing Whac-A-Mole. I then asked myself the following question:

Was replicating my previous rules-based approach to fighting spam really right way to go?

Perhaps my GoDaddy/Microsoft fiasco actually represented an opportunity to build a more sophisticated mousetrap.

A More Modern Approach to Fighting Spam

I'm preparing for my first speaking gig of 2026. The topic shouldn't surprise anyone these days: AI with a dash of citizen development.

As I do with all my talks, I bring my receipts in the form of data. Lots of data. To that end, why not enlist AI to help me generate compelling visualizations?

It's not a difficult ask. Standalone AI image generators have existed since 1973. These days, you need neither a dedicated tool to produce computer-generated JPG and PNG files nor a design program like Canva. Put differently, mainstream AI chatbots are multimodal. As you'll see in this post, they continue to make plenty of baffling non-text mistakes.

Prompting Gemini

I used Claude's deep research feature to generate some recent surveys and studies on AI in the workplace. I threw the results into Notion, took a screenshot of one promising survey, and then crossed over to Gemini. Here's my initial prompt:

Turn this picture into a bar chart.Notion Image Uploaded to Gemini, January 28, 2026 | Click to enlarge it.

Here's its initial response:

Gemini Result, January 28, 2026 | Click on the image to enlarge it.

That's the data in a structured format. It's a good start, but Gemini failed to produce the requested chart. I responded with the following prompt.

Where's the bar chart? I just see a table.

And Gemini then gave me the abomination below:

Gemini Result, January 28, 2026 | Click on the image to enlarge it.

See a problem?

Sometimes, it's just quicker to pretend that Gemini, Perplexity, and ChatGPT don't exist.

I had to go old school.

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AI as a Designer

For a recent post's featured image, I gave Google's much-hyped Nano Banana the following prompt:

Draw a picture in this style of Arnold Schwarzenegger watching a movie.

Nano Banana required four attempts to return something passable. Pictures of decapitated celebrities aren't my jam.

The Fourth Time Was a Charm | Image Sources: Google Nano Banana

The Enshittification of Tennis Viewing: An AI InfographicExplaining the madness that today’s fans suffer when they try to watch a match.Phil SimonPhil SimonThe AI Paradox Is Alive and Well

So smart one minute and so dumb the next. Expect plenty of hallucinations—and not just in text-based responses.

Such is life in the age of AI. Sometimes, it's just quicker to pretend that Gemini, Perplexity, and ChatGPT don't exist.

Feedback

What say you?

Have you ever talked to your favorite actor, author, musician, celebrity, or athlete? Probably not, but would you fork over a small monthly fee to interact with that individual's digital clone?

Plenty of startups are betting that you will. Here's Claude's quick take on the competitive digital self landscape.

Claude generated the following analysis. Digital Clone Startup Landscape Market Overview

The digital cloning market splits into two distinct segments: conversational AI clones and video/avatar generation tools.

Personal AI Clone Market

These companies build tools to create digital replicas of individuals for text-based interaction:

Delphi.ai

Creates personalized digital clones from uploaded content (PDFs, videos, podcasts). Raised $2.7M in seed funding in September 2023 from Founders Fund, Lux Capital, Xfund, MVP Ventures, and SaxeCap. Founded in 2022 by Dara Ladjevardian and Samuel Spelsberg. Based in Miami.

Character.AI

Top competitor according to CB Insights. Builds conversational AI characters.

Inflection

Major competitor in the conversational AI space.

Sensay

Creates autonomous digital replicas for dementia support, education, and fan engagement. Focuses on privacy and consent.

PeopleAI

Builds personalized digital twins for people and brands to connect with audiences.

SuperMe

Creates AI clones for sharing expertise and experience.

MegPT

Focuses on shareable AI clones with natural conversation capabilities. Users can distribute clones via short links.

Elysian AI

Builds conversational AI to create digital replicas available 24/7/365.

Video and Avatar Creation Market

These companies overlap with Delphi's visual representation capabilities:

HeyGen

Generates professional videos using AI avatars and voices. Eliminates need for expensive equipment.

D-ID

Creates 3D avatars and videos with customizable options. Offers API for developers.

Rephrase.ai

Makes videos from text inputs using facial mapping and audio cloning.

DeepBrain AI

Offers AI avatar video generation with multilingual voiceovers. Used in education, marketing, broadcasting, and customer service.

Colossyan

Provides AI-driven video creation for workplace learning using AI avatars and text-to-speech.

Key Differentiators Across Competitors Conversational Focus: Delphi, Character.AI, Sensay, and PeopleAI prioritize text-based interaction and personality replication. Video Focus: HeyGen, D-ID, and Rephrase.ai emphasize visual avatar creation and video generation. Specialized Applications: Some competitors target specific use cases (Sensay for dementia care, Colossyan for corporate training). Market Maturity

The digital clone market remains in early stages. Most competitors raised seed or Series A funding between 2022-2024. The proliferation of competitors suggests strong investor interest but unclear market leaders.

 

Source: Claude, January 4, 2026

There's no shortage of hype and investment, but can these sophisticated AI chatbots separate fact from fiction?

Let that question serve as the starting point for today's post.

Hello, Arnold

I chose one prominent startup from Claude's list. Delphi.ai allows you to “create your digital self.” You needn't be a celebrity like Arnold Schwarzenegger.

Arnold Bot | Source: Delphi.ai

I decided to ask Arnold Bot about the actor's best movie. As you'll see below, the chatbot immediately hallucinated—and not about some obscure Predator or Red Heat quote.

Delphi AI Chat With Arnold Schwarzenegger, 1st Attempt | Click on the image to enlarge it.

One simple question immediately yielded one incorrect response. At least AI Arnold copped to the error. Perhaps Delphi should train its actor chatbots on accurate and comprehensive . Hey, at least Delphi's no outlier. Plenty of other AI tools make basic yet glaring mistakes.

A Second Attempt

I wondered if Arnold Bot would learn from its titanic gaffe (pun intended).

Getting a proper sweat going every day doesn't just keep me in shape; it keeps me sane. I religiously log my workouts via Strava and Apple Health. Those two apps provide basic information on my workouts, not penetrating analysis. (Admittedly, I'm on the free plan for each one, and Apple may enhance its Health app at some point this year.)

Data Deficiencies

At present, neither one lets me easily slice and dice my workout data. Now, I don't need to know the total number of devil's presses I've done. I would, however, like answers to specific questions. Examples include:

What's the average number of calories I burn during my Thursday HIIT workouts? Has the number changed over the last few years?What about my walks since last April? I notice the signs at my gym that contain grammatical errors and confusing sentences. Fine, but what else do I do there during a typical week? How much time have I spent getting my ass kicked in Heated Warrior Sculpt since I decided to torture myself?How do my workouts break down by time? A simple histogram would answer that question.How did my 2024 ankle injury affect my exercise routine?Do I really chill on Sundays—my scheduled active recovery?

You get the drift.

Beyond specifics, on a macro level, I can't observe general trends beyond those apps' decidedly limited views.

What to do?

Building a Better Workout Analysis Mousetrap

Fortunately, we live in an era of citizen development. Any curious soul with minimal tech chops can easily create interactive data visualizations, all without a pricey Tableau license or JavaScript background.

After exporting my Apple Health data to a messy XML file on my MacBook Pro, I let Claude—my favorite tool—go to work on the massive file. It created a Python script that parsed the data to a proper, much cleaner CSV format. I ran the script in Claude Code, and it provided summary stats:

Summary of Workout Data | Sources: Claude Code and Apple Health | Click on the image to enlarge it. want to learn more about citizen development?

Check out my 2023 book Low-Code/No-Code: Citizen Developers and the Surprising Future of Business Applications.

BUY THE BOOK

Interesting, but not remotely visual. Surely we could do better than this simple table. Much better. After all, it's not 1997.

It was time to take it up a notch.

Forty minutes of additional futzing in Claude and Claude Code resulted in a slick interactive dashboard:

Images From Interactive Dashboard | Source: Claude Code | Click on one to enlarge it.

A chart or dashboard to answer simple queries is overkill, though. I fired up Claude and asked the dataset questions in natural language.

Simple Prompt and Response | Click on the image to enlarge it.

Of course, you might decide later that you want a graph after all. Ask, and ye shall receive:

Seconds later:

Chart Based on Claude Prompt and Answer | Click on the image to enlarge it.

Mouse over a part of the bar chart, and the date and the number of calories burned appear.

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Flexes and Limitations

My DIY workout tool represents a vast improvement over Apple Health and Strava, but it's hardly perfect.

For starters, the dashboard doesn't automatically sync workouts. In other words, it won't update after I complete a one-hour GTX class. As a workaround, I would have to either:

Run this cumbersome ETL process each time I want to examine my most recent workout data.Pay for the third-party Mac app Health Auto Export or some alternative.Get creative with HealthKit.

In a related vein, my solution also won't alert me if my routine suddenly changes. Finally, it inexplicably fails to count the two or so miles I sprint run on a treadmill during each HIIT workout. Evidently, Apple Health infuriatingly ignores mileage data during these classes. From Claude:

Apple Health simply doesn't record distance for strength and HIIT workouts — those fields are empty in the CSV for all 1,315 sessions across those two types. That makes sense: neither workout type involves covering ground, so Apple Watch never captures a distance value for them.

Sorry, Claude, it doesn't make sense. Apple's omission flunks the logic test. (See above.) By my math, the company's decision has effectively cost me over 2,000 miles of treadmill pounding since I started wearing its watch. Call me vain if you like, but I conducted this exercise explicitly to gain better visibility into my workouts.

What a great time to be a citizen developer.

Confidently WrongI asked Claude to evaluate my recent Claude Code activities. Its response infuriated me.Phil SimonPhil SimonSimon Says

Despite these limitations, my little creation provides insights into my exercise history that Apple Health and Strava—apps with tens of millions of users—currently don't.1 It's not even close.

I shared the dashboard with a few of my instructor friends. They dug it and want me to show them how to use it after they kicked my ass during one of their classes this week. Along these lines, I decided to publish it on GitHub as a public repo. (My friend and fellow Claude nut Brad Feld gave me the nudge I needed.) Steal it and fork away. Setting it up yourself is pretty straightforward.

If I can vibe code something so useful in a short period, so can you.

What are you waiting for?

🤖 Disclosure: I wrote this post myself, but Claude helped me finesse its title. Footnotes At least to my knowledge and at the time of my writing. ↩