INBOX INSIGHTS: AI Project Starts, AI Command Line Tools Part 1 (2026-04-29)

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INBOX INSIGHTS: AI Project Starts, AI Command Line Tools Part 1 (2026-04-29)

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You’re Starting in the Wrong Place

Last week I wrote about the gap between having an AI strategy and actually doing anything with it. The response was a lot of variations on the same email: “Yes. This is us. Now what?”

So let’s get into it. Let’s begin with the most common mistake I see. It’s so frequent that I’d wager your current AI project is making it too.

Most AI projects start with the tool.

Someone brings a demo to leadership. Or a vendor comes in with a deck. Or a board member forwards an article. Budget gets allocated for a platform. A pilot gets spun up. A cross-functional team gets pulled together. Six months later, the tool is in place, the team is “using it,” and nobody can tell you what actually changed.

That’s not a coincidence. That’s an architectural problem, and it’s sitting right at the beginning of your project.

The 5P lens

We run every strategic project, AI or otherwise, through a five-step lens called the 5P Framework by Trust Insights™. It’s five questions, in order: Purpose, People, Process, Platform, Performance.

The order matters more than anything else in the framework, and the order is almost always violated.

Here’s what the five mean in plain language:

Purpose. What is the measurable question we’re trying to answer? Not “use AI.” Not “modernize our stack.” A specific, testable question. “Can we reduce the time the team spends drafting first-pass customer emails by 50% without reducing response quality?” That’s a Purpose. “Leverage GenAI for customer engagement” is not.
People. Who is involved, what skills do they have, what skills do they need, what are they willing to change, and do they have the authority to change it? Skills alone aren’t enough. Willingness alone isn’t enough. Authority alone isn’t enough. You need all three.
Process. How does the work flow through the organization today — and how will it flow through it after AI enters the picture? What are the handoffs, the QA steps, the decision points, and the escalation paths?
Platform. Which tools, systems, and data will support this? This is where most AI projects start, and this is the fourth question, not the first.
Performance. How will we measure whether any of it worked? Back against the Purpose we started with. The loop closes, or we learn nothing.

The 5P Framework by Trust Insights™ stands out because it starts and ends with Purpose and Performance. Most other frameworks skip these crucial steps. Most frameworks start somewhere in the middle and stop before the end. You execute the plan faithfully and still have no idea whether it was worth doing.

Why most AI projects start at Platform

Because Platform is the fun part. Demos are shiny. Pilots feel like progress. Picking a tool feels like a decision. Nobody gets fired for running a pilot.

Purpose is hard. Purpose requires you to commit to a specific, measurable outcome before you know whether you can hit it. People is hard. People requires you to look at your team honestly and say “do we actually have the skills and the willingness and the authority to make this change.” Process is hard. Process means you have to map how the work is really done, which is almost never how the org chart says it’s done.

Platform is easy. Platform is a Gartner quadrant and a vendor shortlist.

So teams skip the hard parts and start at the Platform. They then try to reverse-engineer a Purpose. This leads to vague, unmeasurable goals like “improve productivity” and “drive innovation.” Then they discover that the People pillar is actually the hard part and start retrofitting training. Then they try to bolt a Performance metric on at the end and realize they have no baseline to compare against.

This is how six months disappear.

The diagnostic

Take one AI initiative you’re running right now. Answer these five questions out loud, in order:

What is the specific, measurable question we are answering?
Who is doing the work, and do they have the skills, willingness, and authority to do it differently?
How does the workflow actually change when AI is in the loop?
Which tool or system supports this? And did we pick it because it fit the answers to 1, 2, and 3, or did we pick it first and work backward?
How will we know in 90 days whether this actually worked?

If Purpose is unclear and Performance is uncertain (and question 4 feels off ) your project isn’t failing due to execution. It’s failing because it was architected backwards.

The fix isn’t to add more budget or more training or a better vendor. The fix is to go back to Purpose and answer the five questions in the right order. That’s what the 5P Framework is for, and it’s why Purpose and Performance are not negotiable bookends.

Where this plugs in

The 5P Framework by Trust Insights™ is the readiness framework that anchors The AI-Ready Strategist. Next week, I’ll discuss TRIPS. It’s the scoring system we use in Step 3 (Process). This helps us decide which tasks are suited for AI and which are best for humans. Until then:

👉 The AI-Ready Strategist: trustinsights.ai/aistrategycourse

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In this week’s Data Diaries, let’s walk through a few of my favorite command line tools in a new series, something that we’ve discussed recently on the podcast and livestream.

As a refresher, command line tools are terminal-based pieces of software that typically do specific functions or tasks. Every computer has a command line or terminal. Every computer has the ability to install and run command line tools.

There are hundreds of thousands of different tools available to us and our AI agents every single day – and that’s where command line tools shine the most. Not for us, the humans to use (though we certainly can) but for machines to use.

Why? Because in the current era as we’re hitting more rate limits with AI than ever, the more we can NOT use AI for tasks that don’t need it, the more efficient and effective we become. Letting AI handle what it’s best at – probabilistic tasks – and forcing it to use best-in-class existing tools for deterministic tasks (tasks with clear outcomes and processes) is the best way to make the most of limited AI usage quotas.

The first two tools are meta-tools, in that if you install them, it’s much easier to do everything else in this series. They’re called package managers; what they do is enable your computer to install other command line tools and keep them up to date.

The best in class tools are:

For Macs: Homebrew. This is the ultimate Mac software package manager and it can install both command line tools and regular apps. It’s maintained well and kept up to date, and is the easiest way to install most tools.
For Windows: Chocolatey. Similar to Homebrew, it’s a terrific package manager.
For Linux: Every Linux distribution comes with its own package manager built in, so use the one that comes with your operating system (apt, deb, yum, etc.)

Not sure how to do this? Ask any AI system with search enabled such as ChatGPT, Copilot, Gemini, Claude, etc. Here’s a simple starting prompt; choose which options are appropriate for you in the curly braces:

You’re a software MDM expert. I want to install a package manager on my computer with the goal of helping me install command line tools for use with agentic AI systems that can call them from the command line. I use {MacOS / Windows / Linux} and my level of technical skill is {beginner / intermediate / advanced}. Build me a step by step guide for installing {Homebrew for Mac / Chocolatey for Windows} on my computer. Ask me questions until you have enough information to succeed at the task.

Once you answer any clarifying questions, you’ll have a great tutorial to get started.

If you work inside an organization with IT policies about what you’re allowed to install, ask them for permission and help; feel free to forward on this newsletter issue to them to help explain why you’re doing what you’re doing.

Next week, we’ll start in on the different command line tools that can make a huge difference in how you use AI efficiently; future issues in this series are predicated on this issue, so get that package manager installed!

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.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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