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

3. Decision Automation

Not all process decisions fit neatly into a standard business rule. Many repetitive decisions within your enterprise's processes rely on human decision making and aren't entirely predictable or straightforward. This makes them difficult to automate with traditional methods.

Consider handling credit blocks. When a credit block is set, a credit manager typically reviews several pieces of information to decide on the next steps. Whether they’re examining sales orders or a customer's payment history, the decision to keep, remove, or internally escalate a block is often subjective, relying on your company's existing business guidelines–leaving individuals to make a decision that is often subjective.

This use case is exactly where you can start to leverage GenAI applications and the reasoning models behind them to create solutions that provide recommendations about what to do with the block and the reasoning behind the suggestion. For example, a leading manufacturer of design and architectural surfaces, used Celonis to create an AI Assistant for credit block management. 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). Credit managers can accept or reject the recommendation with the click of a button and even provide feedback to the assistant to learn from.

Celonis helps automate judgment-based decisions like this by feeding third-party agentic systems with Process Intelligence–via Process Intelligence APIs or directly through the AI Annotation Builder.

4. Prediction & Forecasting

From avoiding inventory stockouts to accurately predicting customer deliveries, planning and forecasting are critical business functions. Think about how many times you reacted, for example, to a delay, a cancellation, or an escalation, when all of the signs were there that this could potentially have happened, or confidently predict your sales forecast for the following month.

These are scenarios that Celonis customers are familiar with, and Cortez says we can bucket them into two categories:

• Outcome-Based Predictions: These are predictions where we want to forecast the likely result of an event or situation–like which orders will be delivered late based on activity data, which customers will churn based on behavior and activities, which tickets are likely to be escalated.
• Time-Series Forecasting: In these problems, we will monitor the change of a variable over time, such as estimating invoice payment volumes per month to optimize cash flow or the number of expected service requests/tickets.

To tackle these use cases, 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.

5. Data Deduplication & Matching

Duplicate or inconsistent data can clog up your processes–causing rework, confusion, delays, and other negative consequences for your business. According to Cortez, there are two types of use cases that companies encounter the most:

• Spelling Matching (Fuzzy Matching): This challenge often occurs with similar, but not identical, entities–like vendor names. For instance, if you have five vendors with similar abbreviations but different payment terms, it's tough to quickly pinpoint the right information. Fuzzy matching helps solve this by detecting duplicates in invoices, customer data, and vendor master data.
• Meaning Matching (Semantic Matching): Unlike fuzzy matching, sometimes the problem lies with entities that are actually identical but appear different. This occurs when the same entity exists in various formats or even different languages. These variations, though minor, can create significant data integrity issues, leading to confusion and errors. Semantic matching helps detect material master data duplicates, clustering free-text POs, and matching customer complaints to those that refer to the same issue.

Celonis helps identify and cluster duplicate or similar records with solutions like the Duplicate Invoice Checker App (part of the Celonis Finance Solution Suite), which prevents overpayments and duplicate payments by detecting and managing duplicate invoices using AI-driven intelligent matching and real-time ERP integration.