Your 90-Day AI-Ready Plan: Skills, Systems, and Proof

Welcome & Series Setup

Hello, and welcome to the Designing with Love Podcast. I am your host, Jackie Pellegrin, where my goal is to bring you information, tips, and tricks as an instructional designer. Hello, instructional designers and educators. Welcome to episode 129 of the Designing with Love Podcast. As we continue through the 2026 lineup, we're also moving through the AI Ready Designer Series. Last time we measured AI's impact on workflow so you can show improvements with real metrics. Today we'll turn the whole series into a 90-day plan so you know what to do next and how to document it. So, grab your notebook, a cup of coffee, and settle in as we explore this topic together. Before we jump in, a quick note. This is a 12-episode arc, and each episode builds on the last. In this 12-episode AI ready designer series, we'll move through five AI ready checkpoints each time. So you always leave with something practical you can apply right away. Alright, let's jump into checkpoint one.

AI Readiness As A Practice

Here's the shift. Becoming AI ready is not about attending one webinar, testing one tool, or saving a few prompts. AI readiness is becoming part of how instructional designers work. It touches how we analyze problems, design practice, create assets, review quality, protect data, collaborate with stakeholders, measure impact, and document our value. So the goal is not to master AI in some big, overwhelming way. The goal is to build enough skill, structure, and evidence that you can use AI responsibly and confidently in your instructional design work. That's why this final episode is about a 90 day plan. Not because 90 days will make you an expert in everything, but because 90 days is enough time to build momentum. It's enough time to practice, enough time to create a few systems, enough time to collect a little proof, and enough time to stop feeling like you're just reacting to whatever new tool shows up next. AI readiness is not a destination, it's a repeatable practice. So that's the big shift. AI readiness is ongoing. Now let's ground ourselves in what has stayed true throughout this entire series. Across this series, we have talked about tools, prompts, guardrails, workflows, QA, knowledge vaults, practice spots, collaboration, and measurement. But underneath all of that, one truth keeps showing up. Your value is not the tool. Your value is your design judgment. AI can help you draft. AI can help summarize. AI can help generate options. AI can help speed up parts of the work. But you still decide what problem is worth solving, what learners need to practice, what quality looks like, what should be reviewed by a human, what data should be protected, what evidence matters, and what kind of learning experience will actually help people perform. That's the heart of the AI ready designer, not someone who uses every tool, not someone who prompts perfectly every time, but someone who can combine instructional design thinking with responsible AI use. That is the constant. And when we forget that, it's easy to fall into a few traps that make AI readiness feel scattered instead of strategic.

Three Traps That Derail Progress

The risk at this stage is not lack of interest. Most instructional designers are interested in AI. Many are curious, experimenting, or already using it in small ways. The bigger risk is that the learning stays random. Here are three traps to watch for. Trap number one, random skill building. This is when you learn whatever pops up next. A new tool demo, a new prompt trick, a new article, a new feature, and a new platform. That can be useful, but it can also become scattered. You may know a lot of pieces but not have a clear system for using them. Track number two, random tool adoption. This is when tools get added because they are exciting, not because they solve a clear learning or workflow problem. And when that happens, you can end up with too many tools, inconsistent practices, unclear guardrails, duplicated work, and confusion about what is actually approved or useful. Trap number three, no proof of growth. This is the quiet one. You may be learning a lot, you may be improving your workflow, you may be using AI responsibly, but if you are not documenting anything, it is hard to show that growth later. That matters for performance reviews, portfolio examples, internal leadership conversations, client work, consulting offers, and your own confidence. So the risk is not just falling behind, the risk is growing quietly and having no evidence of the progress you made. So if those are the traps, the upgrade is to make AI readiness practical. Build skills, create systems, and collect proof. Here's the upgrade.

The Three Lanes Framework

Instead of trying to do everything, focus on three lanes for the next 90 days. Skills, system, proof. That is your 90 day structure. Lane number one, skills. Skills are what you practice. The AI ready designer, those skills might include writing stronger prompts, reviewing AI generated content, identifying hallucinations or bias, autonomizing information before prompting, designing AI supported practice activities, using AI to summarize feedback, measuring workflow impact, collaborating with IT, security, or procurement. The key is to choose a few skills that connect to your actual work, not random skills, relevant skills. Lane number two, systems. Systems are what make your skills repeatable. This is where you create or refine things like your prompt library, your ID knowledge vault, your QA checklist, your AI approval brief, your measurement tracker, your human in the loop review workflow, your templates for lessons, scripts, job aids, or scenarios. Systems matter because they keep you from starting over every time. If skills help you improve, systems help you stay consistent. Lane number three, proof. Proof is how you document progress. This could include before and after examples, time saved, fewer revision cycles, stronger SME feedback, cleaner drafts, improved consistency, a refined template, a shorter case study, or one portfolio artifact that shows your AI ready workflow. Proof does not have to be dramatic. It just needs to show what changed because of the way you worked. Now let's turn those three lanes into a simple 90-day roadmap you can actually follow.

The 30-60-90 Roadmap

Here's your next move. Use a 3060-90 roadmap. Each month has a focus. Days one through 30, build the foundation. The first 30 days are about clarity. Choose items like one AI use case, one skill to practice, one workflow to improve, one guardrail to follow, one place to document your work. This is where you keep it small. For example, you might decide I'm going to use AI to help draft scenario options, but I will use my QA scan before anything goes to a SME. That is clear. That is manageable. That is a real starting point. Days 31 through 60. Build the system. The second 30 days are about repeatability. This is where you create your support structures. You might build a reusable prompt pattern, a review checklist, a small, knowledgeable vault, a scenario template, a measurement tracker, or a one-page approval brief. The goal is to make the work easier to repeat. You are not just using AI, you are building a better workflow around it. Days 61 through 90. Build the proof. The final 30 days are about documenting impact. Look back and ask yourself, what improved? What got faster? What got clearer? What required less rework? What did I learn? What evidence can I keep? Then turn that into a short proof artifact. That might be a one-page case study, a portfolio example, a project reflection, a before and after comparison, a short internal update, or a personal AI ready progress log. The point is to make your growth visible, because the work you are doing deserves to be seen.

A Real-World Field Note Example

Let me give you a quick field note so you can hear what this might look like for a real instructional designer. Imagine an instructional designer who wants to use AI more confidently but feels scattered. In the first 30 days, they choose one use case. I will use AI to draft scenario options for a customer service module. They practice one skill, writing better spec prompts. They use one guardrail, no real customer names, no private data, and human review before sharing. In days 31 through 60, they turn that into a system. They save their best spec prompt. They create a scenario review checklist. They add a few strong examples to their knowledge vault. Now they are not starting from scratch each time. In days 31 through 90, they collect proof. They compare the old workflow to the new one. They notice scenario drafting is faster, SME feedback is more focused, the examples are more consistent, and revision rounds are lighter. Then they write a short case study. Using AI supported scenario drafting with human review, reduce first draft time, and improve consistency across three practice activities. That is AI readiness, not hype, not perfection, just thoughtful, documented progress.

Final Checkpoint Challenge

Alright, let's make this practical with a final checkpoint challenge of the series. This week's checkpoint challenge is your final series challenge. Create your own 90 day AI ready plan using three headings Skills, Systems, and Proof. Under each heading, write one sentence. Skill, what will I practice? System, what will I build or improve? Proof, what will I document? Then add a thirty sixty ninety timeline. Days one through thirty, build the foundation. Days thirty one through sixty, build the system. Days sixty one through ninety, build the proof. That is your plan. Simple enough to start, strong enough to guide you.

Companion Resource & Subscribe

And to help you map out that plan, I created one final companion resource for the series. Before you go, I made an interactive companion called 90 Day AI Ready Compass. It's a quick click-through guide you can use to plan your next 90 days, choose your focus areas, and document your growth as an AI ready instructional designer. If this episode helped you, please follow or subscribe and share it with a designer who wants to move from AI curiosity to AI confidence. And if you've been following this full AI ready designer series, thank you. I hope this arc gave you practical language, workflows, and guardrails you can carry into your next season of work. As we close this series, remember this you do not have to become an AI expert overnight. You just need a thoughtful path. Build the skill, create the system, document the proof. That is how you move from experimenting with AI to working with it intentionally.

Quote, Closing, & Support

Before I conclude this episode, here's an inspiring quote by Arthur Ash, a tennis champion and civil rights advocate. Start where you are, use what you have, do what you can. Thanks for spending time with me today and for moving through this AI Ready Designer series with me. Until next time, keep it practical, keep it human, and keep designing with love. Thank you for taking some time to listen to this podcast episode today. Your support means the world to me. If you'd like to help keep the podcast going, you can share it with a friend or colleague, leave a heartfelt review, or offer a monetary contribution. Every act of support, big or small, makes a difference, and I'm truly thankful for you.