Invoice processing · Production

Operationalizing AI with PI: Five common AI use cases that businesses face

The problem

Businesses face challenges processing valuable unstructured data locked in webforms, PDFs, and email threads; handling repetitive internal queries; automating judgment-based decisions such as credit blocks; predicting outcomes before they occur; and managing duplicate or inconsistent records that cause rework and delays.

First attempt

Traditional rule-based automation fails for judgment-intensive decisions because many enterprise decisions are subjective and unpredictable, making them difficult to automate with standard business rules.

Workflow diagram · grounded in source
1
Unstructured data awaits processing
trigger
“making it useful, especially for AI, often involves the labor-intensive, manual categorization and tagging of specific keywords or details”
2
AI Annotation Builder structures data
ai_action
“Celonis helps turn messy, unstructured content into structured, analyzable data using the AI Annotation Builder, a no-code tool that uses GenAI to reason through data (both structured and unstructured) and generate decisions and action r…”
3
Process Copilot answers queries
ai_action
“Process Copilots are GenAI chatbots that let you ask natural language questions and get real-time answers about key performance indicators (KPIs), status, or process steps–right inside Celonis or in tools like Teams and Slack”
4
AI recommends credit block action
ai_action
“The assistant analyzes each blocked order, pulls together relevant data, such as order value and credit information, and makes a recommendation on what actions to take (along with its reasoning for the recommendation)”
5
Credit manager accepts or rejects
human_review
“Credit managers can accept or reject the recommendation with the click of a button”
6
Manager feedback improves assistant
feedback_loop
“provide feedback to the assistant to learn from”
7
Prediction models flag issues early
ai_action
“Celonis developed the Prediction Builder–allowing you to train and deploy outcome prediction models, so you can anticipate issues like late deliveries before they happen and take proactive action”
8
AI detects duplicate invoices
ai_action
“prevents overpayments and duplicate payments by detecting and managing duplicate invoices using AI-driven intelligent matching and real-time ERP integration”
Reported outcome

Celonis AI tools—AI Annotation Builder, Process Copilots, Prediction Builder, and Duplicate Invoice Checker App—address these five use cases by turning unstructured data into analyzable structure, answering natural language process queries, automating judgment-based decisions with human oversight, predicting issues before they occur, and preventing overpayments through AI-driven duplicate detection.

Reported metrics
Overpayment and duplicate payment preventionprevents overpayments and duplicate payments
Process opportunity identification speedsimplify and accelerate the process of identifying value opportunities
Proactive issue anticipationanticipate issues like late deliveries before they happen
Reported stack
AI Annotation BuilderProcess CopilotsPrediction BuilderDuplicate Invoice Checker AppProcess Intelligence APIsTeamsSlack
Source
https://www.celonis.com/blog/operationalizing-ai-with-pi-five-common-ai-use-cases-that-businesses-face
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Celonis AI tools—AI Annotation Builder, Process Copilots, Prediction Builder, and Duplicate Invoice Checker App—address these five use cases by turning unstructured data into analyzable structure, answering natural la…

What tools did this team use?

AI Annotation Builder, Process Copilots, Prediction Builder, Duplicate Invoice Checker App, Process Intelligence APIs, Teams, Slack.

What results were reported?

Overpayment and duplicate payment prevention: prevents overpayments and duplicate payments; Process opportunity identification speed: simplify and accelerate the process of identifying value opportunities; Proactive issue anticipation: anticipate issues like late deliveries before they happen (source-reported, not independently verified).

What failed first in this deployment?

Traditional rule-based automation fails for judgment-intensive decisions because many enterprise decisions are subjective and unpredictable, making them difficult to automate with standard business rules.

How is this invoice processing AI workflow structured?

Unstructured data awaits processing → AI Annotation Builder structures data → Process Copilot answers queries → AI recommends credit block action → Credit manager accepts or rejects → Manager feedback improves assistant → Prediction models flag issues early → AI detects duplicate invoices.

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