It’s Not You! AI Can’t Do Everything (Alone)

Everywhere you look, someone’s telling you that AI can do everything. Just throw your data at it, and it’ll magically spit out insights, predictions, reports – whatever you want!

But behind that promise is a growing sense of panic. Smart people are wondering: Am I falling behind? Am I doing it wrong?

A big part of the problem is what we don’t see. We see someone say, “I built this with AI!” but we don’t see the stack, the APIs, the orchestration, or the hours of prep work, failed attempts, and supporting tools that made it actually work.

So someone learning how to use AI uploads a CSV to ChatGPT, asks for some complex transformations on the data, it fails, and they think they’re doing something wrong.

Here’s the truth: AI is amazing at some things and terrible at others. Once you know the difference, you can stop fighting the tool and start using it (or other tools) right.

What LLMs Are Good At (All by Themselves)

Essentially, LLMs are supercharged autocomplete engines trained on vast amounts of text. Here’s where they truly shine:

Brainstorming ideas

Need 10 subject lines for an email campaign? Done. Want to explore “what if” scenarios? AI loves hypotheticals. Stuck on how to explain something complex? It’s great at analogies.

Why this works: Creative brainstorming doesn’t require precision – it requires variety and inspiration. LLMs are pattern-matching machines that naturally surface connections you might miss.

Writing and rewriting

From turning bullet points into polished emails to summarizing long reports or drafting SQL queries you half-remember, LLMs are strong writing partners. They can even walk you through existing code and highlight possible errors.

Why this works: LLMs have absorbed billions of examples of good writing. They know what “professional but friendly” sounds like, or how to structure a compelling argument.

Remember: SQL and Python are languages, not engines. LLMs generate code the same way they generate headlines: by pattern-matching across examples. They speak the language of data but don’t execute the work.

Synthesizing information

Need to understand a complex topic quickly? LLMs can explain unfamiliar concepts, compare different approaches, or provide context for decisions you’re making.

Why this works: They’ve absorbed information from millions of sources and can synthesize patterns across everything they’ve learned. Just keep in mind: this is background knowledge, not real-time research. For current events or the latest data, you’ll need web search (or use a model that searches the web).

Converting small formats

Whether it’s JSON to CSV, meeting notes to action items, or Markdown to HTML, LLMs handle small-scale transformations with ease.

Why this works: LLMs understand the structure and patterns of different formats, so they can translate between them effectively.

Note: Anything beyond a few hundred rows or complex transformations will hit memory limits or produce incomplete results. For large datasets or complex transformations, you need dedicated tools that can handle the full workload reliably.

What LLMs Can’t Do (Alone)

Here’s where the magic breaks down – and why you’re not failing when AI can’t help:

1. AI isn’t deterministic

AI is probabilistic, not deterministic. Deterministic tools (like a calculator or SQL query) follow clear, rule-based logic and always return the same result. Probabilistic tools (like LLMs) generate likely answers, which can vary. That’s fine for creative work – not fine for payroll or financial projections.

For example, as a calculator 2 +2 and you’ll always get 4. Ask an LLM the same question and it might reply 4, four, or “the sum is four.” All are correct in spirit, but they’re not identical. And that small gap between precise and plausible is what makes LLMs powerful for creativity but risky for exact calculations.

Why your approach feels broken: You’re expecting AI to be a calculator when it’s actually a pattern-matching consultant. When AI gives you “about $47,283” instead of exactly $47,283.42, that’s not wrong – that’s just how it works.

How it actually works at scale: Companies using “AI for financial analysis” aren’t letting AI do the math directly:

AI designs the financial model and suggests formulas
Spreadsheets or accounting software handle the actual calculations
AI interprets results and suggests insights
Humans verify critical numbers before any business decisions

You should try this: Use AI to design your financial models and suggest formulas, then input those formulas into Excel, Google Sheets, or dedicated accounting software to get precise calculations. Always double-check critical numbers before making business decisions.

2. AI can’t remember everything

AI can only hold so much information at once. If you upload 100,000 keywords and ask it to categorize them all, it’ll either fail or forget half your data partway through.

Some AI tools now offer memory features – ChatGPT can remember your preferences and Claude Projects can maintain context. But they still can’t process massive datasets in one go.

Why your approach feels broken: You’re trying to do in one conversation what scaled systems do with hundreds of automated micro-conversations. When you upload massive datasets and it fails, that’s not your fault – you’re just missing the infrastructure that makes AI work at scale.

How it actually works at scale: When you see companies saying “we use AI to categorize millions of keywords,” they’re not feeding everything to ChatGPT at once. They’re running automated pipelines that:

Break the data into small chunks (100-200 keywords)
Send the same prompt repeatedly with each chunk
Automatically collect and compile all the results
Handle errors and retries when the AI fails on certain batches

You should try this: Break your data into smaller chunks (100-200 items at a time). Process each chunk separately, then combine the results. For automation, look into tools like Zapier or Make that can handle the repetitive processing for you.

Before reaching for AI for a large-scale repeatable task, pause and ask yourself: is this a deterministic task that could be solved more efficiently with well-crafted regex, a SQL query, or a quick Python script? Could an existing API handle it faster, cheaper, and (dare I say it?) more reliably or repeatably?

3. AI can’t handle the heavy lifting with data

AI can analyze a summary report and give great insights. But hand it 1,000,000 customer records to clean and dedupe? It’ll freeze or give you incomplete results. It can’t execute code by itself.

Why your approach feels broken: You’re asking AI to be both the strategist and the worker. When you upload a massive CSV and ask for complex transformations, that’s not your failure – you’re missing the execution layer that actually processes the data.

How it actually works at scale: When companies say “we use AI to process millions of records,” they’re using AI to write the instructions, then other systems do the actual work:

AI generates SQL queries for data cleaning
Those queries run in BigQuery, Snowflake, or similar databases
AI writes Python scripts for analysis
Those scripts execute in proper data environments like Jupyter or Databricks
Results flow back to AI for interpretation and insights

You should try this: Use AI to generate the code or SQL queries you need, then copy & paste the code into Google Colaboratory, Google Sheets with App Scripts, Excel with Power Query, or ask someone technical if they can help you with cloud functions or database tools like BigQuery or Snowflake for larger datasets.

4. AI can’t see or debug what’s happening

When AI-generated code fails, there are no breadcrumbs to follow. No logs, no error tracking, no way to see what went wrong. It just…didn’t work.

Why your approach feels broken: You’re running AI code blindly and hoping it works. When that Python script fails with no explanation, you’re not bad at AI – you’re just missing the debugging infrastructure that makes code reliable.

How it actually works at scale: Teams building large-scale systems aren’t letting AI run rampant.

AI writes code in development environments with full logging
Code gets tested in sandboxes where errors are visible
Successful scripts move to automation platforms that track every step
When things break, engineers can trace exactly what happened

You should try this: Start with simple code in environments where you can see what's happening - like Google Colab for Python or the formula bar in Excel. Test small pieces first, then build up to more complex automation. Add logging, error handling, and test heavily! An easy way to test is to do something “the old fashioned way” with the same input, then put it into your new AI-driven approach and compare the outputs. Then do it again!

For mission-critical code, work with someone technical to help you - quality assurance (QA), user acceptance testing (UAT), data tests, logging, monitoring are probably second nature to them and they can give you great pointers.

How AI Can Use Other Tools

When you see someone say “I built this amazing thing with AI,” it’s rarely AI alone. Here’s what’s really happening:

AI writes the instructions, other tools do the work

AI might draft Python code, but something else still has to run it. AI might suggest Excel formulas, but Excel does the math. Think of AI as the director – it gives instructions, but the cast and crew (your tools) bring the show to life.

The missing piece: Integration tools

To connect AI with your other systems, you need translators in the middle – APIs, automation platforms like Zapier or Make, or webhooks. These don’t replace your tools; they let AI “talk” to them. If AI can’t access your CRM or calendar directly, that’s not AI’s fault. It just means you’re missing the connectors.

Action MCPs are great for simple tasks, not reliable workflows

MCPs (Model Context Protocols) are pre-built connections that let AI interact with your software without you needing to write code or understand APIs. They help AI “speak” to other tools – your calendar, CRM, spreadsheets and come in two flavors: Action MCPs and Data MCPs

Action MCPs excel at one-off tasks and can even kick off workflows: “Send this as a direct message,” “Create a calendar event,” “Save this document,” or “Trigger this automation when X happens.”

But Action MCPs hit the same conversational limits – no retry logic when things fail, no audit trails, no monitoring when workflows break. They’re perfect for internal tasks where occasional failures are just inconvenient, but you wouldn’t want to rely on them for mission-critical processes (unless you incorporate a way to handle errors and retries).

Data MCPs are built for conversation, not computation

Data MCPs are perfect for exploration: “What’s in this database?” or “Get me the search volume for these 20 keywords.” They’re built for the kind of back-and-forth conversation where you refine what you’re looking for.

But when you need to pull and process large amounts of data, MCPs can hit computational limits fast. They’re optimized for conversation, not computation. So if you’re using an LLM + an MCP + API there’s no storage mechanism involved – you can either use this group of tools as-is for lightning fast but bite-sized answers or incorporate storage into the mix.

If you know exactly what data you need and how you want it processed, you might be better off looking at API documentation and using AI to write focused code and adding in some compute rather than using out-of-the-box MCPs. You’ll get exactly what you want without hitting the conversational and computational limits of MCPs.

💡 If you can describe your API request as:

"I need these 50 specific columns from this endpoint, filtered by some_parameter and > 30 days, grouped by some_specific_field, with calculated fields using this formula for my_custom_measure,"

...you've outgrown what most basic data MCPs can handle efficiently alone. At that point, just build your own MCP with some compute in a cloud function.

Stop Blaming Yourself

If AI couldn’t solve your problem, you probably didn’t fail – you just asked it to do something it can’t do by itself.

The solution isn’t to “get better at AI.” It’s to know what it’s good at, then pair it with the right tools for everything else.

Start with your problem, not with AI. What are you actually trying to accomplish? Sometimes AI is perfect. Sometimes you need a database. Sometimes you need both.

💡 Before assuming AI should handle everything, ask it: "What's the best tool for this job?" You might discover you need a database, automation platform, or specialized software - and that's exactly right.

And the next time you see a “I built this with AI” post, remember: AI probably sketched the prototype, but real systems did the heavy lifting. The best solutions aren’t always the newest ones – they’re the ones that actually work.

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