Magdalena Picariello recently shared something that caught my attention: she "almost feel[s] guilty" because she did not solve the client’s problem. Her data pipeline did.

She wrote that the client probably imagined a heroic scene: analysts reading 500 documents, highlighting rows, and drinking coffee. But the reality was simpler and more powerful: they did not read the documents. They wrote a script. It ingested raw data, structured the chaos into a clean format, and identified patterns in about 30 seconds.

That post resonates because it names a shift many teams are living through but still struggle to explain: the value is moving away from visible effort and toward engineered leverage. And that leverage often starts with one unglamorous capability: data structuring.

The uncomfortable truth behind "thorough" work

Picariello makes a point that should be obvious, yet often gets ignored in project postmortems:

The "thoroughness" did not come from a brilliant analyst. It came from the machine’s inability to get tired or bored.

In many organizations, thoroughness gets conflated with time spent. If someone spent two weeks combing through documents, we assume the outcome must be high quality. Sometimes it is. Often it is simply the best humans could do under fatigue, deadlines, and messy inputs.

Machines do not get bored, and scripts do not lose focus at 4:47 pm. When the task is repetitive and rule-driven (extract, normalize, classify, cross-reference), "thorough" is not a personality trait. It is a property of the system.

The hard part is admitting that the best process is frequently the one with the least human touching the raw material.

Why data structuring is the hidden multiplier

When Picariello says "This isn’t human genius. This is Data Structuring," she is highlighting the work that makes everything else possible.

Data structuring is the process of turning inconsistent, semi-structured, or unstructured inputs into a format that downstream systems can reliably use. That might mean:

Extracting fields from PDFs, emails, forms, or scans
Standardizing names, dates, units, and identifiers
Deduplicating entities and resolving conflicts
Enforcing schemas and validating values
Producing tidy tables, JSON, or event streams that analytics and ML can consume

If your inputs are chaotic, your outputs will be fragile. If your inputs are structured, even simple models and simple logic can look like magic.

A lot of teams attempt to jump straight to "AI" when the real bottleneck is upstream. The biggest ROI often comes from boring steps done well: ingestion, normalization, and quality checks.

Silicon is faster than neurons (and that changes economics)

Picariello also cuts through hustle culture with a blunt physics lesson: speed does not come from hustle. It comes from the fact that silicon processes information faster than biological neurons.

That matters because it flips the cost curve. If the same work would take two weeks manually and costs 10x more, then automating it is not just a productivity win. It changes what projects are even worth pursuing.

Here is the key economic idea: once you automate the examination, you can afford to examine more.

You can analyze 500 documents instead of 50.
You can run the pipeline every day instead of once a quarter.
You can broaden scope without proportional headcount.

The value is not that the first run is fast. The value is that repeat runs are nearly free.

What a "script" really represents

When someone says "we wrote a script," it can sound small, like a quick hack. In mature teams, that script is the tip of an iceberg:

Reliable ingestion (APIs, file drops, scraping with safeguards)
Parsing and extraction (OCR, layout-aware parsing, regex, LLM-assisted extraction)
A schema or canonical model that represents the domain
Validation rules and anomaly detection
Logging and traceability (so you can explain results)
Test fixtures (so the pipeline stays correct as inputs change)
Deployment, scheduling, and monitoring

The "all-nighter" myth is seductive because it is easy to visualize. The pipeline reality is less cinematic, but it is where durable value lives.

When you should keep humans in the loop

Picariello’s line, "sometimes, the best way to solve a problem is to make sure a human doesn’t touch it," is directionally right. But it is worth adding an important nuance: some problems demand human judgment, even if the pipeline does most of the work.

Keep humans in the loop when:

The cost of an error is high (compliance, safety, financial reporting)
The definition of "correct" is subjective or changes frequently
You need defensible explanations for decisions
Inputs are adversarial or prone to manipulation

In practice, the winning design is often "automation-first, review-by-exception."

Review-by-exception in one sentence

Let the pipeline process everything, then route only the uncertain, novel, or high-risk cases to humans.

That approach preserves speed while focusing human attention where it is actually valuable.

The real KPI shift: from hours to outcomes

One of the most important lines in the post is the simplest:

We are entering an era where hard work is no longer the metric for value.

If you lead analytics, data engineering, or applied AI, this is not just philosophy. It changes how you scope projects and how you communicate impact.

Instead of reporting:

Hours spent
Number of analysts involved
Number of documents reviewed

Start reporting:

Cycle time reduced (two weeks to 30 seconds)
Cost per processed document
Coverage gained (500 docs processed consistently)
Error rate and auditability
Repeatability (daily refresh vs one-off analysis)

This is how you teach stakeholders to praise the right thing: the system, not the suffering.

A practical blueprint for building this kind of leverage

If you want to replicate the kind of outcome Picariello describes, here is a simple path that works across industries.

1) Start with the messiest, highest-volume input

Pick a dataset that hurts because it is repetitive: invoices, contracts, support tickets, claims, RFPs, reports. Volume is your friend because it makes automation pay back quickly.

2) Define a clean target schema

Decide what structured output you actually need. Avoid boiling the ocean. A small canonical schema that captures the 20 percent of fields driving 80 percent of decisions is enough to start.

3) Build extraction plus validation, not extraction alone

Extraction gets attention. Validation prevents disasters.

Required fields
Range checks
Cross-field consistency (dates, totals, IDs)
Confidence scores and fallbacks

4) Instrument everything

Log inputs, versions, and outputs. Capture why a record failed. You cannot scale trust without traceability.

5) Design the human touchpoint as a queue, not a workflow

Humans should review exceptions, not manually process the full set. That is where leverage comes from.

Why this post went viral (and what to learn from it)

Picariello’s post is not just a lesson in automation. It is also a strong piece of LinkedIn content because it combines:

A surprising confession ("I almost feel guilty")
A relatable mental image (analysts, coffee, 500 documents)
A clear twist ("We didn’t read the documents. We wrote a script.")
A broader takeaway (value shifts from effort to engineered systems)

That structure works because it teaches without preaching. It invites the reader to update their mental model.

The takeaway: build systems that cannot get tired

My biggest takeaway from Picariello’s point is this: if you can turn a knowledge task into a repeatable structuring pipeline, you should. Not to replace people for the sake of it, but to stop wasting human attention on the parts of the job that are fundamentally machine-shaped.

Use humans for judgment, product thinking, and defining what "good" looks like. Use pipelines for the grind.

And when the client praises your speed, accept the compliment, then gently redirect it: the real hero is the model, the script, and the structured data that made the answer instant.

This blog post expands on a viral LinkedIn post by Magdalena Picariello, ROI from GenAI in 3-6 Months | ex-IBM, Lecturer. View the original LinkedIn post →