Adam Reisfield
Last updated:
April 17, 2025
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30 actionable AI use cases across supply chain, finance, and operations

Learn about the most impactful AI use cases being deployed by operators across brands like Brooklinen, On Running, and Flexport.

Most people don’t realize just how much operators are responsible for. 

They’re the ones building the processes that keep everything moving—connecting tools, maintaining systems, unblocking teams, and laying the foundation for growth. At Parabola, we’ve always built for them. Our workflow builder makes it easy to organize and transform messy data from anywhere—even PDFs, emails, and spreadsheets—so teams can automate the work they thought would always be manual.

Today, those teams are doing more than ever with AI. They’re using Parabola’s AI-powered steps to extract unstructured data, standardize inputs from dozens of sources, generate summaries and alerts, and simply create complex logic in their workflows—without writing a line of code. And they’re seeing real, measurable results: lower costs, faster resolutions, cleaner reporting, and better decisions across the business.

In this post, we’ll outline the most impactful AI use cases we’ve seen from top operators at companies like Brooklinen, On Running, Caraway, and Flexport—so you can bring the same level of clarity, control, and speed to your team.

Continue reading to learn more about actionable use cases across the following steps:

  1. Pull from inbound email
  2. Standardize with AI 
  3. Categorize with AI
  4. Custom transform
  5. Extract with AI 
  6. Experiment with AI

Pull from inbound email

The Pull from inbound email step is Parabola’s most powerful tool for organizing messy data—whether it's coming in via an unstructured email body or a CSV, Excel, or PDF attachment. With this step, you can use AI to automatically translate messy data into neat tables, which you can then use to build downstream logic. Whether you need to pull line items from an invoice or extract shipment IDs from the body of an email, this step is the go-to tool for operators organizing messy data received via email.

Use case title Typical format Description Function Goal Business impact
Extract carrier tracking updates from emails
Email body
Pull revised ETAs and delivery statuses from carrier email bodies to update internal systems and trigger internal alerts
Freight Supply chain
Action on ETA updates in real-time while ensuring data quality Triage potential SLA breaches immediately while improving compliance and CSAT
CIPL (commercial invoice and packing list) digitization
Excel PDF
Parse line item details such as quantities, prices, HS codes, and invoice numbers from messy PDFs or Excel files
Freight Supply chain
Combine CIPL data with ERP records before updating your ERP Recoup leakage by verifying these critical customs clearance and duty documents with internal records and shipment data
Freight invoice digitization
PDF Email body
Extracts and standardizes line-item level details from messy freight invoices across multiple formats
Freight Finance
Cleanly structure freight invoice data to reconcile against rate cards to uncover overages Reduce freight spend by identifying invoicing discrepancies
3PL report ingestion
Excel CSV PDF
Pulls reports received via email from 3PLs containing information like received inventory, open orders, and receiving discrepancies
Operations
Create integration between email and internal systems without tech resources; action immediately on discrepancies Improve data quality across systems and reduce risk of stockouts and SLA breaches
Automated freight order entry
Email body
Ingests order request emails and attachments, extracts key details, and post structured data to your TMS or ERP systems
Freight
Streamline order creation and ensure data quality across systems Reduce data entry errors and focus headcount on more strategic initiatives
Parse shipment details
Email body
Pull data from carrier shipment confirmation emails, extracting structured details around container numbers, ports, and delivery dates
Freight Supply chain
Enrich TMS, ERP, and other internal systems with complete shipment records Enable real-time shipment visibility across carriers
Freight quote request email parsing
Email body
Extract quote request details from email bodies and triggers Slack/Teams alerts or push request details directly to quoting systems
Freight
Respond faster to incoming quote requests Increase win rates and reduce missed opportunities

Standardize with AI

The Standardize with AI step helps teams normalize inconsistent values across systems and vendors. From SKU naming to warehouse locations, this step looks for similar values to automatically clean your data—resulting in improved reporting accuracy, cleaner ERP data, a reduction in sync failures, and improved performance tracking across partners.

Use case title Description Team Goal Business impact
Normalize column names from vendor-provided files Standardize headers across CSVs, Excels, and PDFs received from third-parties to ensure clean ingestion into ERPs, WMS, or TMS
Operations Supply chain Finance
Prevent ingestion errors and sync failures Reduce order and invoice ingestion failure rates; improve data integrity
Normalize carrier names Standardizes naming across carrier and 3PL reports to enable accurate carrier-level performance tracking
Supply chain
Build holistic carrier scorecards that take every data source into account By constantly evaluating your carrier network, operators can improve SLA performance and reduce costs.
Standardize SKU naming across vendors Aligns SKUs from multiple vendors to a single internal format
Operations
Ensure accurate inventory and replenishment Reduce inventory integration failures resulting in inaccurate inventory levels across systems.
Align invoice terms Standardizes payment terms across vendor invoices
Accounting
Improve AP consistency and vendor payment strategy Improve cash flow forecasting and early payment discount capture
Harmonize product categories Maps vendor category labels to internal taxonomy
Operations
Enable consistent product-level performance tracking Identify high-margin vs. low-margin categories more effectively
Clean store names across reports Unifies store names from various platforms (POS, Shopify, marketplaces)
Operations
Consolidate store-level reporting Improve store-level P&L analysis and reduce reporting errors

Custom transform

The Custom transform step is Parabola’s most powerful and flexible AI step designed for logic-based data transformation. It enables teams to describe what they want to do in plain language to generate custom data transformation steps—from reformatting complex tables to performing statistical analysis and generating API-ready JSON bodies structures. This step dramatically lowers the barrier to entry for automation: If you can describe your intent in words, this step translates your intent to a production-grade step.

Use case title Description Persona Goal Business impact
Reconcile shipments across systems using PO-level logic Fills in missing customer data using logic based on PO numbers, enabling root cause analysis across shipment sources
Operations Supply chain
Enable error investigation and reconciliation Reduce cost from fulfillment errors and customer support escalations
Custom cost allocation logic Applies logic to allocate overhead or shipping costs by rule (e.g. % of volume, % of weight) across orders
Accounting
Improve landed cost and COGS accuracy Increase accuracy of profitability reporting and visibility into landed cost components; support financial audits
Summarize historical sales for forecasting Aggregates sales order data by quarter and year to support forecasting and planning workflows
Finance Supply chain
Improve forecast accuracy Enable more accurate inventory and revenue planning — preventing stockouts and improved cash flow
Analyze shipping destinations and costs Identifies top destinations and average shipment weights to inform shipping rate negotiations and regional strategies
Supply chain
Improve visibility into shipping costs across regions and carriers Reduce shipping spend and improve carrier strategy
Calculate SKU-specific lead times Performs complex date logic by SKU to create reference tables used in production and planning flows
Supply chain Procurement
Create accurate production timelines and ensure POs are being placed in time Prevent stockouts while improving cash flow management
Calculate margin by SKU or channel Calculates gross margin across SKUs, channels, or vendors using cost, discount, and pricing data
Finance
Identify most and least profitable SKUs or channels Improve product mix and pricing strategy to increase margin
Pivot-style table transformations Reconstructs large datasets into summaries (e.g. cost per unit by vendor, volume by month, OTIF by warehouse)
Operations
Enable performance benchmarking by dimension Optimize vendor performance, fulfillment SLAs, and planning
Generate API-ready JSON or HTML payloads Converts row-level data into structured formats like JSON or HTML for API integrations
Operations IT
Enable operators to integrate systems with reduced reliance on IT Accelerate system integrations; reduce cost of IT projects

To learn more about what's possible with Parabola's Custom transform step, check out this overview: 

Categorize with AI

The Categorize with AI step helps teams group, label, and tag operational data to uncover root causes, enable strategic reporting, and automate routing decisions. Whether analyzing spend, classifying customer feedback, or routing documents, categorization enables clean structure and insight at scale.

Use case title Description Persona Goal Business impact
Spend categorization across invoice line items After parsing invoice line items from PDFs, assign them into spend categories such as duties and tariffs, warehousing, transportation, etc.
Finance Procurement Supply chain
Enable clean, granular spend analysis across vendors Improve visibility into spend composition across vendors to uncover strategic cost-cutting initiatives
Customer ticket categorization Analyze tickets across systems like Zendesk and Gladly and classify them based on the content of the email (damaged item, delay, etc.)
Customer support Supply chain
Identify patterns across your negative customer experiences, and tie those tickets to the 3PL or carrier that handled the order. Improve CSAT while reducing return rates — ultimately boosting profitability.
Document type classification (e.g., invoice vs BOL) Classify documents received via emails into types based on document contents (e.g., invoice, CIPL, ASN, BOL)
Supply chain
Automate document triage and routing to update relevant systems and notify teammates Improve SLA compliance for document processing and reduce manual review time
Inbound product categorization Categorizes new SKUs or product lists into internal categories during onboarding or merchandising
Operations
Maintain clean product taxonomy Improve category-level margin tracking and promotional planning

Extract with AI

The Extract with AI step helps teams pull structured data from messy sources like PDFs, support tickets, and emails. This step excels when working with large bodies of raw text, such as email bodies or paragraphs of text.

Use case title Description Persona Goal Business impact
Address parsing and enrichment Clean and enrich address data received from customers, carriers, and third-parties
Supply chain Operations
Improve data quality by enriching partial addresses with complete address details Prevent delays stemming from address-issues — resulting in improved SLA performance
Pull return reasons from warehouse notes Extract structured return reasons from freeform warehouse notes or logs
Operations
Identify and address common return causes Reduce return rates
Extract order IDs from support tickets Parse Zendesk or Gladly tickets to extract order numbers, products ordered, and other identifiers from unstructured text
Customer support
Get a full picture of your returns by linking customer tickets to the original order, 3PL, and carrier Improve visibility into return reasons to reduce return rates and improve SLA performance

Experiment with AI

The Experiment with AI step is a beta step that helps teams generate summaries, notifications, and contextual content from structured data. Whether it’s summarizing return reasons, generating Slack alerts, or enriching CRM records, this step enables operators to turn data into action faster.

Use case title Description Persona Goal Business impact
Create Slack alerts based on fulfillment issues Generate proactive messages to post in Slack when data shows an order or fulfillment discrepancy, or is about to breach SLA
Supply chain Operations
Drive real-time operational awareness Reduce SLA violations and improve time-to-resolution
Generate product descriptions from attributes For high-SKU-count businesses frequently receiving new inventory from vendors, auto-populate descriptions to push directly to your ERP/OMS
Operations Supply chain
Accelerate product onboarding and listing creation Improve conversion rates on product pages and scale product catalog
Generate summaries of return reasons Converts raw return data or notes into concise summaries for reporting or alerts
Operations
Provide actionable summaries to cross-functional teams Improve issue resolution speed and reduce repeated return causes
Write CRM company descriptions from domain Enriches CRM records by generating a short company blurb based on the domain or name
Customer support
Improve the completeness of your CRM Improved data quality enabling better CRM reporting

How to deploy AI across your team's operations

After reading this, you might be thinking it's time to incorporate AI into more of your own team's processes—but where do you start? I'd recommend checking out Parabola University, and specifically the lesson on transforming data with AI: 

Finally, to see these steps in action—and explore real examples from teams automating complex workflows—check out Parabola’s Use Case Library.

Happy building!

Adam Reisfield
Last updated:
April 17, 2025
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