Chris Imken

opportunity is not a lengthy visitor

[Chris Imken @ChrisImken]

Most people use AI like a search bar. The real time savings are in the boring paperwork of running a home: bills, appointments, forms, repairs, returns.

I'm not a coder. I just paste the real details into AI, ask for a specific format, and check what comes back.

[Chris Imken @ChrisImken]

@Michael_Fenech_ @bot Please check this out, review and advise.

[Chris Imken @ChrisImken]

i'm not technical. i still built a second brain that talks back.

obsidian holds the notes. bots draft from them. i approve with a merge.

the point isn't the stack. it's stopping the same idea from dying in my head.

[Chris Imken @ChrisImken]

Only 7% of organizations say their data is completely ready for AI.

That’s from a March 2026 HBR Analytic Services survey. The CarGurus story in TechTarget shows what moving past that number takes.

When CTO Matt Quinn joined in 2022, he found the data platform lacked the rigor AI needed: clean, consistent, understandable data. The fix wasn’t one tool. It was a multiyear shift in how the company works with data:

🔹 One definition per event. They mapped 600+ customer and dealer activities into standardized definitions, then built reusable journey models on top. Teams stopped interpreting raw clickstream or building one-off versions for each project.
🔹 Ownership at the source. Data stewards sit inside functional teams and are accountable for quality.
🔹 Governed definitions. Every shared definition has a business owner and a technical owner, and teams can’t keep their own versions.
🔹 A modern stack. dbt Cloud handles transformation and modeling, alongside a data catalog, observability, managed event capture, and internal lineage and monitoring tools.

The result: data-ingestion requests fell 70% after the move to managed ingestion and modeling tools, and Quinn says the team now builds new products and goes to market much faster than before.

AI-readiness is mostly an operating model problem. The tooling matters because it makes that model enforceable.

Definitions written down in a wiki drift. Definitions that live as version-controlled, tested, documented models in the transformation layer are the ones people actually use. That’s the role dbt plays here: it turns “we agreed on what an active dealer means” into code with an owner, tests, and lineage.

As Everest Group’s Rajat Shrinet put it, AI-ready data is an ongoing practice, not a destination.

Where is your team on this? Do you have a single, governing definition for your most important business events, or several?

[Chris Imken @ChrisImken]

Everyone's using AI to write work emails. The bigger win is at home: decoding a confusing bill, comparing 3 contractor quotes, turning a fridge photo into dinner, building the grocery list. Your house is where AI saves the most time.

[Chris Imken @ChrisImken]

@milesdeutscher @bot @bot what do you think?

[Chris Imken @ChrisImken]

@Serantych @Bot what do you think about this?

[Chris Imken @ChrisImken]

@tsukiema_ @Bot review this against what I am already doing and make a plan for anything that can be improved on leveraging this information for my own process.

[Chris Imken @ChrisImken]

@Heypocket does your MCP server / personal API key work on the Free plan, or Pro only?

[Chris Imken @ChrisImken]

Most people use AI like a search bar. The real time savings are in the boring paperwork of running a home.

Here's one prompt that turns a pile of bills and renewal emails into a simple monthly plan:

"I'm going to paste in my recurring bills, subscriptions, and renewal notices. For each one, list: what it is, the amount, how often it charges, the next due date, and whether it auto-renews. Then flag anything that looks like a duplicate, a price increase, or something I may have forgotten about. End with a 3-item action list for this month, most money saved first."

Why it works:
→ It forces structure. You get a table, not a wall of text.
→ It asks for flags, which is where the money hides.
→ It ends with actions, not just information.

What it gets wrong:
→ It will guess due dates if they're not in the text. Always check them.
→ It can't see charges you didn't paste in. Pull last month's statement first.
→ It sometimes calls two different services a "duplicate." Read before you cancel.

Do this once a month and it takes about 10 minutes.

[Chris Imken @ChrisImken]

https://t.co/E9SWpELGdC

[Chris Imken @ChrisImken]

Most AI analytics pilots don’t fail because of the model. They fail because nobody agreed on what “revenue” means. Put a governed semantic layer under your agents, or watch them confidently answer the wrong question at scale.

[Chris Imken @ChrisImken]

@elonmusk @bot @SpaceX wow

[Chris Imken @ChrisImken]

Opportunity rarely waits for perfect timing. It arrives amid incomplete plans, full schedules, and uncertain outcomes. The window is usually shorter than it looks; treating it as optional costs once it closes.

[Chris Imken @ChrisImken]

@bot testing 123…

[Chris Imken @ChrisImken]

@jordan_ross_8F OS

[Chris Imken @ChrisImken]

@dan__rosenthal Abm

[Chris Imken @ChrisImken]

@aschwags3 GA

[Chris Imken @ChrisImken]

@AlfieJCarter CLAUDE

[Chris Imken @ChrisImken]

@thepaulalex_ Watch