If there is one thing AI should be genuinely good at, it is analytics. Pattern recognition across large data sets is more or less what it was built for. But just because AI can look at your data does not mean it knows what you are actually trying to learn from it. That is the part most people skip.

In this episode, Cole brings in a framework from a LinkedIn post by Tim Stoddart that puts the problem into clear terms: data is cheap, insight is expensive, storytelling is priceless. The Lego analogy Stoddart uses is a good one. You can sort a pile of bricks by color, arrange them beautifully, and end up with something completely meaningless if you started with the wrong bricks. The same is true with analytics. Before AI can help you, you have to be honest about whether you are even pulling from the right data to begin with.

Virgil has been testing this directly using the podcast's own analytics across Google Analytics, Captivate, YouTube, Apple Podcasts, Spotify, SoundCloud, and their mailing list. The challenge is not a lack of data. It is that the data lives in separate places, each with its own reporting logic, and none of them talk to each other. When he ran actual queries against the data he could access, the results were uneven. One question surfaced a genuinely useful insight about engagement that he would not have found on his own. Another hit a wall that no amount of follow-up prompting could get past.

The bigger point underneath all of it is about starting with the outcome rather than the data. Virgil has applied this same logic to web strategy for years. The last page you should build is the homepage. The same principle applies here. If you cannot clearly name what you want to understand before you open your analytics, the data is not going to organize itself into an answer.

The tools for cross-platform AI analytics are not quite where they need to be yet, but the direction is clear. AI is already starting to suggest its own follow-up questions, which changes the dynamic considerably for people who do not know what to ask next. The dashboard as a destination is fading. What replaces it is a conversation with your data - one that only works if you walk in knowing what you are trying to find out.

Previously in the Intentional AI series:

Episode 1: Intentional AI and the Content Lifecycle

Episode 2: Maximizing AI for Research and Analysis

Episode 3: Smarter Content Creation with AI

Episode 4: The role of AI in content management

Episode 5: How much can you trust AI for accessibility

Episode 6: You’re asking AI to solve the wrong problems for SEO, GEO, and AEO

Episode 7: Why AI can make your content personalization worse

Episode 8: The real value of AI wireframes is NOT the wireframes

Episode 9: Just because AI can create images doesn't mean you should use them

Episode 10: The Super Bowl didn't sell AI, it exposed it

Episode 11: AI video rewards planning, not your ideas

Episode 12: AI might struggle with creativity, but coding isn't creative

Episode 13.1: What the rise of conversational search means for your website

Episode 14: AI agents are only as good as your workflow

Episode 15: AI can't fix your social media if you have nothing to say

New episodes drop every other Tuesday.

For more conversations about AI, design, and digital strategy, visit https://www.highmonkey.com/podcast and subscribe on your favorite podcast platform.

(0:00) - Intro

(0:48) - Today's topic: AI and data analytics

(1:51) - Virgil example: 3 million rows, one question

(4:02) - The Lego analogy: from a pile of bricks to a story

(5:04) - What if you're sorting the wrong bricks?

(6:26) - Building with Legos from multiple sets

(8:59) - You have to know what you're building

(11:00) - A live example with podcast analytics

(13:59) - Where AI can name the problem but not solve it

(16:04) - AI that tells you what to ask next

(17:40) - Stop reading your data, start asking it questions

(20:36) - The tools landscape today and what's coming

(22:00) - Outro

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