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
Valuable information locked in webform comments, PDF free-text fields, and email threads requires labor-intensive manual categorization before it is usable.
▾ source quote
“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
The AI Annotation Builder uses GenAI to reason through unstructured and structured data and generate decisions and action recommendations.
▾ source quote
“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 recommendations”
3
Process Copilot answers queries
Ai action
Process Copilots are GenAI chatbots that let users ask natural language questions and get real-time answers about KPIs, status, or process steps.
▾ source quote
“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
An AI 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 with reasoning.
▾ source quote
“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.
▾ source quote
“Credit managers can accept or reject the recommendation with the click of a button”
6
Manager feedback improves assistant
Feedback loop
Credit managers can provide feedback to the assistant to learn from.
▾ source quote
“provide feedback to the assistant to learn from”
7
Prediction models flag issues early
Ai action
The Prediction Builder enables training and deploying outcome prediction models to anticipate issues like late deliveries before they happen.
▾ source quote
“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
The Duplicate Invoice Checker App prevents overpayments and duplicate payments by detecting and managing duplicate invoices using AI-driven intelligent matching.
▾ source quote
“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
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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.
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.