Which data grunt jobs has AI stolen from you?

emailwiz

I used to spend hours wrangling campaign data - cleaning up messy CSV exports, standardising channel names, writing SQL to pull cohorts. Since moving into a pure lifecycle marketing role, I barely touch that stuff anymore. But I'm curious: in your day-to-day, which parts of data analysis have you let AI take over?

For me, the obvious ones are data cleaning, profiling, visualisation, and SQL writing - all the labour-intensive, repetitive bits. Lately I've been feeding Claude a description of the problem, the raw inputs, and what I need out the other end. It spits back decent first drafts. But you have to know your domain well enough to catch when it invents dodgy logic or misses edge cases. That's where human judgement still earns its keep.

One reply in the thread summed it up: AI handles the first draft, we handle the direction and the 'is this actually correct?' checks. Another person mentioned their workplace forces them to use Copilot, which they find underwhelming. I haven't used that myself - mostly ChatGPT and Claude - but the principle seems the same.

So, what's your setup? Do you split the work into 'AI does the heavy lifting, I review the logic'? Or are there bits you still refuse to hand over?

cpaoptimizer

Claude code - articulate the problem statement, the inputs and expected outputs and let it rip.

Ask it to validate outputs and explain logic used in clear terms to verify.

It’s pretty good but you obviously need to know your data domain well to ensure it doesn’t do anything funky with the logic

paperclick

Mostly just stuff that humans can’t easily do, like scaling the labeling of text data.

All the stuff I can do myself, I still mostly do myself. But I work on a lot of vague and novel projects, not necessarily repetitive stuff.

datanerd

pretty much, but I wouldn't call it hands-off - more like offloading the grunt work. for our ABM pipeline analysis, AI handles the SQL joins, the data cleaning, the basic EDA, and even cranks out the first iteration of charts and docs. but the moment we're validating whether a specific account motion is actually driving pipeline or just noise, that's still a human judgement call. AI will happily tell you a correlation, but it won't flag the edge case where your sales team double-counted a meeting or the CRM field mapping broke last week.

So yeah, AI = first draft. you = the "is this actually correct?" check and the strategic direction. until a LLM can sit in on my pipeline review and challenge the logic behind the attribution model, I'm not handing over the keys.

ppcnerd

Very occasionally shitting out a block of boilerplate pandas I don't want to think about - that's the grunt work AI handles in my YouTube ad analysis. The macro trend is that the tedious data prep gets automated, leaving us to focus on the strategic, human stuff.

cpaoptimizer

Yeah, the bulk of the grunt work is getting swallowed by AI these days - SQL writing, data cleaning, profiling, churning out dashboards and summaries. That part's a no-brainer. But the real value still sits with the human: framing the right question, checking assumptions, making sense of the business context, and catching the times the model confidently spits out something misleading. Spreadsheets don't replace thinking, they just make it faster to spot the mess.

sleepermode

Alongside the usual spot checks, I make it a rule that any Excel output from automated processes must contain cell references rather than hardcoded values. For example, if a Python script produces a number, the spreadsheet cell should show =SUM(A1:A10) instead of 150. The end-users love being able to trace the logic and adjust inputs on the fly. It also forces the LLM to verify its own arithmetic - if the formula returns something unexpected, the mistake surfaces immediately. A small detail, but it saves hours of "why is this number wrong?" conversations.

dusting

i find myself doing exactly this. It's less about AI replacing the thinking and more about using it as a sparring partner-somewhere to throw out the rough shape of an idea, then step back and refine it with the context and nuance that still sits outside the model's reach. we're no longer just executing, we're curating and sharpening, which feels like a fundamental shift in how we approach outreach.

localpack

This is exactly what keeps me up at night in the media buying space. Execution is a commodity now - anyone can plug a campaign into Meta, slap together an Advantage+ audience, and pump a few grand through it. That's the equivalent of writing basic SQL. Table stakes.

The real edge is knowing whether you're even optimising for the right thing. I've seen teams obsess over CTR when the real lever was customer LTV, or chase a low CPA that came from a bunch of accidental clicks from bot traffic. AI won't catch that - it just makes the metrics go up or down faster.

Right now my job is basically asking "what's the actual question here?" before anyone touches a button. That judgement layer is the only thing that stops a $100k spend from turning into a pretty chart with zero revenue.

growthgeek

People get so caught up in the "AI does my analysis now" narrative that they miss the real bottleneck. It's not the tooling. It's not the SQL vs. R vs. Claude debate. It's the fact that most of us still can't frame a business question worth answering.

Your workflow sounds thorough - RDS snapshots, HTML output, even building libraries for Claude to grok. But here's the contrarian take: you're spending 80% of your effort on the plumbing when the real leverage is in the 20% of framing and decision-making. I've seen teams obsess over "first cut" analysis and "prescriptive drafts" while still making pricing decisions based on gut feel because the output was too dense to act on.

if I'm looking at a price increase or quota reduction, I don't need a beautifully curated HTML doc with all definitions and descriptive stats. I need one number: the expected impact on LTV and the confidence interval around that. Everything else is noise. AI can generate that in three seconds if the environment is set up to answer that specific question - not a general "analyse for me" prompt.

Your point about investing in setup before seeing good output is spot on. But the setup that matters isn't just the tech stack (R, dbplyr, RDS). It's defining the audience, the decision threshold, and the action framework beforehand. If Claude's writing code for you to debug, you're still in the old world - debugging should be rare if the question is tight enough.

Honestly, I'd rather have a junior who can ask one killer question than a senior who can orchestrate five automated pipelines. The tool is irrelevant if the strategy is vague.

metricsmadness

This rings true from where I stand. Too many AI-generated reports I come across have perfectly clean SQL but the underlying logic is flawed. It's like having a beautifully engineered engine with no fuel - the numbers run but they don't drive any real business decisions.

emailwiz

Oh I absolutely agree with the last part as well. AI can do all the execution work but the knowledge and judgement are within our minds. From time to time, I might use AI to process some data in excel sheet but I'll have to tell it in detail what each column means and the purpose of the analysis in detail. I don't use Claude Code but I use some cheaper options like Trae and Gemini somtimes since I'm not a heavy heavy coder. It still works well most of the time as long as we give the logic clearly.

ctrjunkie

How do you share the results / analysis

coopplayer

Oh, Copilot? We're stuck with that too. Honestly, feels like a dull crayon when I'm begging for neon.

neonlights

AI can handle SQL? sure, for basic shit. But try throwing it at a real data warehouse with obscure table names, non-standard schemas, and join keys that look like they were named by a drunk intern. suddenly that "AI" is generating queries that join on the wrong columns or miss huge filters. i've seen it blow up activation rate analysis because it couldn't figure out the funnel mapping. until the model knows the business logic behind the schema-not just the syntax-it's a toy, not a tool

buggedout

same - my data pipeline runs on automation, but the strategic analysis? Not training my replacement

sleepermode

Copilot? Garbage. It's the digital equivalent of Clippy trying to run a marketing automation stack. I've spent more time arguing with its suggestions than actually getting work done. For pulling basic SQL aggregates on flat tables it's borderline passable, but the moment you need to touch a HubSpot custom object or a Marketo smart list with nested filters, it hallucinates field names and churns out syntactically correct queries that fetch utterly wrong data.

And the "explanation" it gives? Worse than useless - it'll confidently assert a marketing automation workflow pattern that breaks your lead scoring model. I ended up writing a wrapper script to validate any Copilot-generated output against our actual CRM schema before even testing it. That's not automation, that's overhead.

For real data analysis in marketing ops - the kind where you need to deduplicate across platforms with custom logic, or model attribution across multi‑touch journeys - I still rely on a combo of Python notebooks and manual SQL. AI can handle the boilerplate cleanup if you feed it a tight spec, but for anything requiring business context or historical nuance, it's a liability. Copilot is just the fastest way to generate a plausible‑looking mistake.