INBOX INSIGHTS: Planning Without Doing, Citizen Analyst Part 3 (2026-02-25)

INBOX INSIGHTS: Planning Without Doing, Citizen Analyst Part 3 (2026-02-25) :: View in browser

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INBOX INSIGHTS: Planning Without Doing, Citizen Analyst Part 3 (2026-02-25)

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Why We Plan, But Don’t Execute

Let me ask you something, and I want you to be honest with yourself: How closely are you actually tracking that Q1 roadmap you sat through endless hours of meetings to put together?

I’ll wait.

I posed this question to our free Slack community, Analytics for Marketers, last week. The responses were both hilarious and painfully relatable. One member shared that they were almost done setting the Q1 roadmap—and half of what was on it had already changed. Another admitted that their team loves to spend more time planning than doing. And honestly? I think most of us are nodding along right now.

But then the conversation took a turn that really made me stop and think. When I asked why companies seem so afraid to actually do something, the answer was blunt: they’re afraid to be held to the results, especially failure.

Yikes. But also… yeah. That tracks.

What Is CYA Paralysis?

One of our community members coined a term that I immediately wanted to steal for this newsletter: CYA Paralysis.

It’s the idea that organizations get so caught up in covering themselves—making sure no one person can be blamed if something goes wrong—that they end up doing… nothing. Or at least, nothing meaningful. Instead, they plan. And then they plan some more. They create another version of the roadmap. They schedule a meeting to discuss the roadmap. They form a committee to review the meeting notes about the roadmap.

Sound familiar?

Here’s what’s actually happening: planning feels productive. It looks productive. You’ve got documents, spreadsheets, slide decks, and Gantt charts. You can point to all that and say, “Look, we’re working on it.” But working on the plan isn’t the same as working on the thing.

Why We’d Rather Plan Than Do

Let’s break down what’s really going on because I think it goes deeper than just fear of failure.

Planning is safe. Nobody ever got fired for having a really thorough project plan. But the second you start executing, you’re exposed. Results can be measured. Outcomes can be judged. And if it doesn’t work? That’s on you (or at least, that’s what it feels like).

Planning feels like control. When everything is still in the planning phase, it’s all potential. Nothing has gone wrong yet. Every idea is still a good one. Execution is where reality shows up uninvited and starts poking holes in your beautiful strategy.

Planning has no accountability deadline. Think about it: how many times have you heard “we’re still finalizing the plan” used as a perfectly acceptable reason for not having started? There’s always one more stakeholder to consult, one more scenario to model, one more competitor to analyze. The plan is never quite done enough to act on—conveniently.

The Real Cost of Not Executing

Here’s the part that should make you uncomfortable: while you’re perfecting your roadmap, someone else is executing a mediocre one and learning from it. They’re iterating. They’re collecting real data. They’re finding out what actually works instead of guessing.

Meanwhile, your team is on version seven of a plan that’s already half-irrelevant because the market moved while you were in your planning meeting.

I’ve seen this play out more times than I can count, both at Trust Insights and in my consulting work with clients. The companies that consistently outperform aren’t the ones with the best plans. They’re the ones who are willing to execute an imperfect plan, measure the results, and adjust.

How to Break the Cycle

Ok, so what do you actually do about this? Here’s where I’ll pull out the 5P Framework, because (surprise) it’s designed for exactly this kind of problem.

Purpose: Start by asking, “What is the one thing we need to accomplish this quarter?” Not ten things. One. If you can’t articulate a single, clear goal, that’s your first problem.
People: Who is responsible for execution—not planning, execution? Assign a single owner. Not a committee. Not a “cross-functional team.” One person who will be accountable for making sure work gets done.
Process: Set a hard deadline for when planning stops and doing starts. I mean it. Put it on the calendar. “Planning phase ends February 28. Execution begins March 1.” No exceptions, no extensions.
Platform: What tools do you actually need to get started? Not which tools would be nice to have. Not which tools you should evaluate. What can you use right now to start moving?
Performance: Define what “good enough” looks like before you begin. Not perfect. Not ideal. Good enough. Because here’s the thing—you can always optimize later, but only if you have something to optimize.

The Permission to Be Imperfect

I think the biggest mindset shift teams need to make is this: an executed plan that’s 70% right will always beat a perfect plan that never launches. Always.

You will learn more from doing than from planning. You will uncover problems you never would have anticipated in a planning meeting. And yes, some things will fail. That’s not a bug—that’s the whole point. Failure with data is infinitely more valuable than theoretical success on a whiteboard.

So here’s my challenge to you: look at your Q1 roadmap (assuming you can find it). Pick one thing on it. Just one. And commit to executing it this week. Not planning it further. Not socializing it with more stakeholders. Doing it.

Because we’re halfway through the quarter and the clock doesn’t care about your planning timeline.

Are you stuck in planning paralysis? 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 why most Q1 plans stall and how hidden fear holds teams back. You’ll learn simple ways to turn a big roadmap into tiny actions you can start. You’ll discover how generative AI can suggest low‑risk steps that keep momentum without a big budget. You’ll explore how to break the blame cycle and build real progress even in risk‑averse companies. Watch the episode to start moving your plan forward.

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

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In this week’s Data Diaries, let’s continue our series on the Citizen Analyst. Parts 1 and 2 are available here.

Last time, we talked about the ETL pipeline – get the data, work with the data, do something useful with the data, and we used Google Trends as an example for our charity of choice, Baypath Humane Society. Generative AI identified Google Trends and a few other sources as great data for us to work with as citizen analysts.

That brings us to working with the data, and this is where generative AI tools really shine. Many of us know of different ways to work with data, but we may not know the technical specifics. For example, Google Trends is called time-series data – data where time itself is a primary dimension.

Last time, we downloaded the CSV file. Our remit was to help Baypath use the data to forecast adoptions, for the purposes of both marketing and staffing. How would we do this? In the old days, we’d fire up a statistical language like R and write statistical code to do that forecasting.

Today, we ask generative AI to write the code. We have a conversation with it, and have it ask us the tough questions. Once we’ve done that, we let it do the typing. That’s the key to making generative AI work well: we do the thinking, it does the typing.

Here’s a prompt example, using last week’s files. You’ll note that we’re asking it to do very specific tasks, like anomaly detection and ensembling (mixing methods together) – a good citizen analyst would know the vocabulary for AI to build with.

You’re a time series prediction and forecasting expert. Today, we will forecast likely interest in animal adoption for Baypath Humane Society, a no-kill shelter in Massachusetts. I have assembled a collection of CSV files of Google Trends data, weekly data about common pet adoption terms dating back 5 years, restricted to Massachusetts-based queries. We want to forecast 1 year ahead, taking into account the anomalies of 2020-2022 which was the COVID pandemic; we should give more weight to more recent searches. The files have commented headers which will mess up a straight import, so you’ll need to trim the first row of each file. I’d suggest doing that first, then unifying the files into a single data table. Because Google Trends data is all relative, one search term in each file will remain the same, so you will need to deduplicate things. You’ll likely need to use Python code and libraries for this work; what Python time series forecasting libraries would best suit this kind of data? Ensembling is okay too, if it works. We want to help Baypath understand when demand for pet adoption would increase or decrease over the year so they could staff appropriately and know when to spend scarce marketing dollars to increase adoptions. Ask me questions until you have enough information to successfully complete the task.

Putting this in a tool like Google Gemini, we can see today’s AI tools following the instructions and using Python code right in the browser – nothing to download or install.

Next week, we’ll take these forecasts and look at what to do with them in our final part of the series.

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