INBOX INSIGHTS: Stop Chasing Use Cases, AI Command Line Tools Part 2 (2026-05-06)

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INBOX INSIGHTS: Stop Chasing Use Cases, AI Command Line Tools Part 2 (2026-05-06)

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Stop Chasing Use Cases

Two weeks ago, I wrote about the strategy/execution gap. Last week, I wrote about why most AI projects start in the wrong place — at the tool, instead of at Purpose. Today, I want to talk about the single most common form of “execution” that isn’t really execution at all.

Pilots.

I’m going to say something a little uncomfortable. Most AI pilots running inside companies right now are not experiments; they are procrastination with a project plan.

I know, because we’ve done it more than once. I’ll come back to that.

The pattern

A leader gets serious about AI. They do the strategy work. They commit budget. They approve three or four small pilots. One will focus on marketing, another on customer service, a third on finance, and maybe one on HR. The thinking is sensible. “Let’s try a few things, see what works, and double down on the winners.”

Six months later, the pilots are all still running. None of them has produced a result anyone can show to the board. None of them has a real owner. They all have “champions” who are doing this on top of their actual jobs. None of them has a budget for what comes after the pilot. They have not measured any of them against the same baseline twice.

This is pilot purgatory. This is what you’ve been hearing about. It’s the most costly type of AI fake progress in the industry.

Why “let’s try a few things” usually fails

It feels prudent. It is not prudent. Here’s what is actually happening when you spread one organization across five small pilots:

Attention dilutes. Real change requires sustained focus from the leader and the team. Five pilots get one-fifth of the attention each. That’s not enough to push any of them through the inevitable hard middle.
Ownership disappears. A pilot with five “stakeholders” and no single owner is a pilot that nobody is responsible for finishing. Everyone can claim a partial role; nobody has to deliver an outcome.
The metric never lands. Each pilot has a different goal, on a different timeline, with different baselines, in a different part of the business. You can’t compare them. You can’t add them up. The board can’t act on them.
There’s no organizational learning. Five tiny experiments at 20% effort produce five inconclusive results. One real experiment at 100% effort produces a result you can build on — even when the result is “this didn’t work, here’s why.”

The cure is the most counterintuitive thing I tell clients: pick one. Not the most ambitious one. Not the one with the loudest internal advocate. Not the one your CEO read about in a magazine. The one that has the clearest path to a real outcome.

The discipline of picking one

This is where TRIPS comes in. TRIPS is a scoring framework we built specifically for this decision and walk through in The AI-Ready Strategist. Think of AI as an outsourcing partner. TRIPS tells you what to actually put in the contract. Five criteria, one question each:

T — Time. How much time is this task actually consuming? Add it up over a year. The “only 45 minutes a week” task is a full work-week a year. The tasks that feel fast — because they’re habitual — are usually where the real time is hiding.
R — Repetition. Does the task follow the same pattern every time, or does every instance require unique judgment? AI loves patterns. This is the criterion that separates a real use case from a science experiment.
I — Importance. This one works in reverse. The more important the task, the more human oversight it needs. A wrong word in an internal Slack summary is nothing. A wrong number in a board deck is career-defining. High-importance tasks can still use AI — but a human makes the final call.
P — Pain. How much do people dread the task today? This is the emotional dimension most AI frameworks skip entirely. People don’t resist AI because they don’t understand the ROI. They resist it because they’re afraid it will take the parts of their job they love. Start with the parts they hate, and buy-in takes care of itself.
S — Sufficient Data. Are there enough templates, past outputs, and documented examples for AI to learn from? If the knowledge lives in one person’s head, you’ve got a documentation gap to close before AI can help. Often, finding that gap is the first win.

When you put your candidate use cases through those five questions, the field collapses fast. Most of the things you were considering are interesting but not important. A few are important but premature. One or two are usually clear. They’re high-volume and repeatable. They’re also painful to do now and well-documented, so AI can learn from them.

That’s the one you ship.

The rest go on a list. They’re not “no.” They’re “not yet.” There’s a real difference between those two answers, and protecting that difference is part of the leader’s job.

The compound benefit of finishing one

Here’s the part I wish someone had told me earlier. When you finish one AI workflow — really finish it, all the way to “this is in production, the metric is moving, the team isn’t using the old way anymore” — three things happen that don’t happen with five half-finished pilots.

First, you have proof. You can walk into any meeting in your company and say, “this is what AI did for us in this workflow.” Skeptics get quiet. Budget gets easier. Recruiting the next champion gets easier.

Second, the team learns how to do this. Not the tool — the change. The hand-offs. The QA. The retraining. The data work. Those skills compound. The second workflow goes faster. The third goes faster still.

Third, you build the muscle of finishing. Most companies have plenty of starting muscle. Almost none have finishing muscle when it comes to AI.

Next week is the last issue in this series — the third of the three frameworks, and the one that makes or breaks everything else. The data work, and the 6C Framework we use to audit it, without which the best strategy and the best use case still produce garbage. Until then:

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If you had to pick one use case, what would it be? Reply to this email or join the conversation in our Free Slack community, Analytics for Marketers!

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to build functional agentic AI systems using professional organizational frameworks. You’ll learn how to apply the 5P framework to create precise job descriptions for your AI agents. You’ll discover why vague instructions lead to costly errors in autonomous workflows. You’ll master the art of decomposing complex tasks into granular instructions that machines actually understand. You’ll explore the TRIPS framework to identify which business processes you should delegate to AI.

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In this week’s Data Diaries, let’s dig into the actual command line tools themselves. Last week, we covered the necessary installation systems to make them work, such as Homebrew and Chocolatey; be sure to review part 1 so you’re setup.

Now that we’re ready, let’s start looking at some of the power tools I use.

ffmpeg. ffmpeg is a media converter that can convert just about anything to anything – turn a video into an MP3, change the format of a video from h.264 to h.265 (a much more efficient format), resize a video from HD to SD. If you do any kind of work with video, it’s absolutely essential, and tools like Claude Code can run it for you and write the appropriate commands. You’d use a prompt like this, once you’ve installed it by Homebrew or Chocolatey:

Using ffmpeg in the terminal, help me convert this video from standard MP4 to HEVC format, and upscale it to 4K using ffmpeg. {video location on your computer} Our priorities are visual, video quality, and file size in equal balance. We want small files that are great looking. ffmpeg is installed and you must use it.

pandoc. pandoc converts most documents to most other documents, especially things like Markdown, most Microsoft office documents, ePub for ebooks, etc. Got a Word document you want to convert to an eBook for Amazon Kindle? Pandoc can do that. Got a mysterious file? Ask Pandoc to see if it can convert it. The only file type Pandoc has a hard time with is PDF. Here’s an example prompt you’d use with something like Claude Code:

Using pandoc in the Terminal, help me convert this Word document into an ebook in EPUB format for Amazon Kindle. Along with the image file, build the necessary front matter and metadata, and create the final e-book in EPUB format in the same folder. DOCX: {Word file name} Cover Image: {image file name} pandoc is installed and you must use it.

yt-dlp. Got a YouTube video that you want to download for posterity? yt-dlp will let you do that. Want to download an entire playlist? What about just the audio of videos? How about just the captions? yt-dlp does all that. It’s one of my favorite ways to grab a lot of data all at once; closed captions files in particular can be a rich source of useful information. Download a big batch and put them in NotebookLM to have as a personal reference encyclopedia. Here’s an example prompt:

Using yt-dlp in the terminal, help me download the English language subtitles for every video on this YouTube playlist. Store the subtitles in a subfolder named {folder name}. After the subtitles are downloaded, rename all the file extensions to .txt files. yt-dlp is installed and you must use it.

These three command line tools work with many of the media files that we marketers and business folks want to work with often. Video, office documents, audio, et cetera. By providing command line tools to our Claude code or similar agentic AI systems, we can dramatically reduce the token usage and get consistent, reliable results by having AI be the operator of the software rather than trying to write its own.

In the next issue, 3 information tools that are essential for maxing out what AI tools can do for you. Stay tuned!

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

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