INBOX INSIGHTS: Realistic 90 Day AI Plans, AI Command Line Tools Part 3 (2026-05-13)

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INBOX INSIGHTS: Realistic 90 Day AI Plans, AI Command Line Tools Part 3 (2026-05-13)

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A Realistic 90-Day Plan

This is the last issue in the series. Thank you for sticking with me through the last three pieces. You’ve learned more about the unglamorous side of AI work than most in our field are willing to share. That matters.

Quick recap of where we’ve been:

Issue 1: The gap isn’t in your strategy. It’s in the execution. Most teams have the deck and not the workflow.

Issue 2: Most AI projects start at the tool, which is the wrong place. The 5P Framework by Trust Insights™ forces you to start with Purpose, the measurable question, and end with Performance. That’s how you know whether any of it actually worked.

Issue 3: Stop running five pilots. Use TRIPS — Time, Repetition, Importance, Pain, Sufficient Data — to pick the one that pays. Finish it. Then earn the right to do the next one.

Today, data is the part that can ruin everything. People rarely mention it until something goes wrong.

The problem with most AI roadmaps

Most of the AI roadmaps I see are built like the IT roadmaps of 2015. Twelve months. Quarterly milestones. A Gantt chart with a lot of color. Someone spent a long weekend in Smartsheet making it look beautiful. Don’t get me wrong; those roadmaps have a special place in my heart and that’s how I always want to get started as well.

It is, however, almost without exception, fiction in 2026.

The technology you’re planning around will change in the time it takes to print the deck. The team that’s supposed to execute the plan will hit a snag in week three that wasn’t in the model. The vendor you bet on will release a feature in month two that makes half of your plan obsolete. The reorg you didn’t see coming will reshuffle your owner.

Twelve-month AI roadmaps don’t fail because the planners were bad. They fail because the planning unit is wrong for the rate of change.

Ninety days is a better unit. Short enough that the technology won’t outrun you, the org won’t reshuffle out from under you, and the team will remember what they committed to. Long enough that one real workflow can go from “we picked it” to “it’s in production and the metric is moving.”

One workflow. One owner. One metric. One decision at the end. That’s the plan.

And under all that sits the one thing that decides whether any of it actually works.

The data conversation almost nobody has

Here’s the part I really need you to hear. You can have the best strategy and model. You can also pick the right use case. But if your data is flawed, the AI will still produce junk.

Data quality is the silent killer of AI initiatives, and it’s the one thing leaders consistently underinvest in because it’s invisible until it isn’t.

In the AI-Ready Strategist course, we walk through what we call the 6C Data Quality Framework. It’s six dimensions you have to interrogate before you let AI near your business data:

Clean: Free of errors, duplicates, and formatting inconsistencies. Garbage in, garbage out isn’t a cliché — it’s a law.
Complete: No missing information. A dataset that’s 80% complete might look fine in a dashboard. The missing 20% is often the 20% that changes the conclusion.
Comprehensive: The data answers your question directly, not just any question with available data. Most teams go wrong here, analyzing the data they have instead of the data they need.
Calculable: Usable by a business person, not just a data engineer. If your revenue column has dollar signs and commas mixed with text, it is not calculable. Numbers should be numbers. Dates should be dates.
Chosen: The opposite of comprehensive. Where Comprehensive asks “do I have enough?” Chosen asks “do I have too much?” A dataset with 200 columns when you need 10 isn’t thorough — it’s overwhelming.
Credible: Sources are trustworthy and collection methods are sound. You could defend them in a conversation with a skeptical executive. If you can’t, the rest doesn’t matter.

If the answer to any of those is “I don’t know” or “mostly,” you have a data project before you have an AI project. That’s not a setback. That’s the actual order of operations. Skipping it creates the AI horror stories you’ve heard. For example, a chatbot confidently shares false info. A forecasting model might suggest ordering double the needed inventory. A report can be wrong, and no one notices until the board meeting.

Good data governance isn’t bureaucracy. It is the thing that makes the AI safe enough to actually use.

Where this leaves you

Four issues in, here’s the honest summary.

You probably have a strategy. You probably haven’t run it through the 5P lens, so Purpose and Performance are still fuzzy. You probably have too many pilots and not enough owners. TRIPS would have told you to pick one. And you probably haven’t done the 6C data work that keeps the rest from producing garbage.

That isn’t a criticism. It’s the situation almost every team is in right now, including a lot of teams much bigger than yours with more resources. The companies that are going to pull ahead in the next twelve months aren’t the ones with the cleverest strategy. They’re the ones who do the unglamorous middle work first.

That’s what The AI-Ready Strategist is for. It’s the three frameworks, 5P, TRIPS, and 6C, that turn “we have a strategy” into “we have an AI workflow producing measurable results.” You’ll also have access to the tools and templates we use with our own consulting clients. Not a magic wand. Not another deck. A structured walk through the actual work.

If you’ve read this far, you already know whether this is for you.

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

Thank you for reading this series. It’s the most honest look at AI adoption in a real business. If any of it resonated, forward it to one person on your team who needs to hear it. That’s the most useful thing you can do with it.

How is your AI adoption going? Reply to this email or join the conversation in our Free Slack community, Analytics for Marketers!

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to stop your AI agents from producing inconsistent results by treating them like human team members. You’ll learn how to apply the 5P framework to build standard operating procedures that keep your automated tasks on track. You’ll discover the secrets of defining “done” so your AI agents stop guessing and start delivering exactly what you need. You’ll understand why your current management style might be the reason for your AI failures. You’ll master the art of delegation by including essential feedback loops that force AI to ask for clarification before it wastes your time and tokens.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we looked at setting up Hermes Agent. Catch the episode replay here!

This week on So What? we’ll be exploring the state of the art in local AI models. Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, we’re continuing our series at looking at command line tools to help us get the most out of the data we have, using agentic AI systems like Claude Code or OpenCode. In issue one, we looked at installing package management software such as Homebrew and Chocolatey. In issue two, we looked at major media conversion tools. This issue, let’s look at command line tools that AI specifically can make use of.

graphify. Graphify is by far one of the most powerful tools you can add to a system like Claude code because it dramatically reduces the amount of time Claude spends rereading files. Instead, it uses a knowledge graph to help Claude quickly locate just the key parts of a code base that it needs to reference. In your Claude Code settings, either globally or on a project level, add this, especially to your hooks:

Always use graphify if available. Prefer graphify over grep. Run graphify update . whenever a task is completed.

As a side bonus, Graphify generates nice knowledge graphs of your project so you can see for yourself visually how interconnected your project is. A project that has low levels of interconnection means you’ve got a lot of dead code or orphaned objects.

tmux. tmux adds multi-session and multiple location terminal connections to any Claude Code or OpenCode session. Once you install tmux and configure it properly, you can remotely connect to any terminal window session and pick up where you left off.

This is especially handy when you are using tools like Claude Code or OpenCode with models that are not official Anthropic models. Claude Code, for example, can be used with nearly any API provider if you change the provider in the settings. However, some Claude features like remote control become unavailable. Tmux picks up that gap, allowing you to remote control into any terminal session.

Get started easily by adapting this prompt:

You’re a software MDM expert. I want to install tmux on my computer with the goal of helping me resume sessions in Claude Code/OpenCode and other similar command line applications on different devices. I use {MacOS / Windows / Linux} and my level of technical skill is {beginner / intermediate / advanced}. Build me a step by step guide for downloading, installing, and using tmux on my computer. Ask me questions until you have enough information to succeed at the task.

Tailscale. Tailscale creates a virtual private network of devices that you install it on. This allows devices even across different networks function as though they were on the same LAN, allowing for free, private, and secure data transfer. If you’ve ever been doing work on your home computer and you’ve thought to yourself, “oh gosh, I have to connect to my computer while I’m away from it, how can I do this safely?”, Tailscale is your answer. I use this a ton on my laptop, my local development server, and my mobile devices. They all behave as though they are on the same network, even if they are thousands of miles apart.

Tailscale is especially useful in combination with tmux. You can set up Tailscale on your phone or tablet computer as well as on your main computer. Start a session in tmux on your main computer and then seamlessly pick up where you left off on any device – all for free, all safely, all privately.

Get started easily by adapting this prompt:

You’re a software MDM expert. I want to install Tailscale on my computer with the goal of helping me resume sessions in Claude Code/OpenCode and other similar command line applications on different devices across different networks and WANs. I use {MacOS / Windows / Linux} and my level of technical skill is {beginner / intermediate / advanced}. Build me a step by step guide for downloading, installing, and using Tailscale on my computer. Ask me questions until you have enough information to succeed at the task.

These three applications, Graphify, tmux, and Tailscale, give you much-needed infrastructure that supports using tools like Claude Code or OpenCode anywhere you go on any device, all while keeping your data safe and private. Commercial companies charge tens of thousands of dollars for services like this; you don’t need to spend that money when you can have a tool like Claude Code walk you through how to install these things.

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.

👉 Take this course now to become an AI leader

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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