How Artificial Intelligence can Enhance Your Business Process

How can AI and automation help you improve your business processes? Learn how AI can effectively enable process orchestration.
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It looks like Artificial Intelligence (AI) is here to stay, so we might as well embrace it and get educated on how it can help us do our jobs more effectively and efficiently.

AI has become very important in many aspects of business, but it stands out when it comes to improving process orchestration. This can be seen in ways to reduce errors, optimize cycle times, and improve accuracy in our processes. Using AI, we can automate additional processes to keep the business running smoothly and optimally.

General Uses of AI

Companies are embracing AI in several ways, including enhancing self-service applications, predicting trends and buying behaviors, helping decipher radiographs to predict medical outcomes, responding to requests, routing information to the appropriate individuals for processing, and more.

Some common areas where AI is already helping organizations are:

  • Automating high volume tasks and repetitive tasks
  • Enhancing the ability to understand unstructured data within company assets
  • Improving customer engagement
  • Providing holistic views to improve decision making
  • Improving data quality and insight

There are three main types of artificial intelligence:

  • Generative AI
    Most people are familiar with Generative AI, which is the ability to use AI to generate text, images or other types of content using models designed for this purpose. These models are trained by learning patterns and other details from training sets. Using this information, the models will create new data that follows similar patterns.
  • Predictive AI
    Predictive AI uses machine learning (ML) to find trends and patterns in historical data and then uses this information to predict future events.
  • Assistive AI
    Assistive AI incorporates the use of ML to assist humans in the completion of human tasks. This can be in the form of detailed instructions, recommendations, etc.

Each of these types of AI can be helpful in improving your business and enhancing your business processes. You can get an overview about each one and how to apply them in our Practical Guide to Leveraging AI Automation in Your Process Orchestration Workflows.

Ways AI can help your business processes

Certain business areas can benefit more from adopting AI than others, but the key thing to remember is that adding AI into your businesses can improve efficiency and also help you streamline your processes.

Analyzing data

One of the first ways that AI can help with business processes is around working with data to find, interpret and do something with your data. This includes things like:

  • Interpreting data for requests to automatically route data
  • Automating credit scoring and risk management
  • Finding information in audio, video and other media formats
  • Locating and redacting Personal Identifiable Information (PII)
  • Matching data for faster processing
  • Predicting trends, issues and detecting fraud

Let’s take a look at each one of these in a bit more detail.

Interpreting data for requests

AI is readily used to analyze data. This can have a positive impact on your customer interaction. For example, AI can accelerate your ability to analyze incoming data from customers so that decisions can be made promptly improving the overall customer service.

Let’s assume that you receive a significant number of incoming customer requests on a daily basis—some that require human interaction and some that do not. This is a great place to implement artificial intelligence to streamline how these incoming requests are handled. By passing the request to an AI engine, AI can analyze the context of the request and provide guidance to route the request for assistance to the correct team, or even generate a response to the customer including links to specific instructions for self-service. This prompt response, accomplished by including AI, improves your customer service experience.

In the simple BPMN example below, you can see a service task that accesses an AI engine to read a customer request and make a determination as to the nature of that request so it is properly routed.


This fairly simple example shows how time can be saved, as well as staff effort, while also improving the customer experience with quick and efficient service by optimizing the business process for request handling. This can be used for just about any customer service area.

Credit scoring and risk management

When determining if an applicant has the proper credentials for a loan or insurance policy, it is typical to review the risk by weighing different inputs and variables about the candidate against risk assessments and credit scores.

AI can make a difference in this assessment by reviewing all the gathered information to guide you to an insightful decision based on the parameters outlined by the company as well as the minimum requirements for acceptance. Taking the inclusion of AI into consideration when implementing your business process can streamline the acceptance (or rejection) of new applicants with credible decision making.

Finding information

Similar to data interpretation, there may be situations where finding specific information in a document can help to route something to the correct group or even flag the instance for possible fraud. Using AI to locate data that could indicate fraud—like duplicate information submitted previously—can be used to automatically spawn a fraud investigation process.

Another implementation that focuses on finding information with AI is within Human Resources. As you scour through resumes looking for candidates that have the correct skills for an open position, AI can help you find the right applicants as part of a business process by only presenting the best matched candidates for review.

Personal Identifiable Information (PII) determination

In today’s world, it is very important to protect personal information and companies must put this in the forefront of their minds in order to retain customer confidentiality. AI can play a role in limiting access to PII as part of your business process.

For example, if you have customer transcripts or documents, these can be passed to an AI engine that specializes in redaction to find any PII and redact it, with the proper review, as part of a business process. This redaction can be done on audio, video and documents to help companies preserve customer confidentiality.

Adding a component or service to your process that will transcribe, locate, and redact PII information can both improve accuracy and accelerate processes like insurance claim and financial account processing.

Matching data

AI can also be used to minimize the manual tasks related to data matching including correlating invoices to receipts or even matching individual values to user entered data. For example, there may be situations where it is necessary to verify values entered by a user, such as, when someone claims a car repair is $7,500, you may need to review receipts provided to confirm this total amount. Another example related to data matching is invoice processing and reconciliation which involves matching invoices to purchase orders.

These traditionally manual tasks could be automated with the use of AI and then added to your invoice processing, insurance claims, and approval process to streamline processing as well as minimize errors.


AI can also be used to examine previous performance, behavior and other metrics that can then be used to improve and optimize your business processes and results. For example, this is very important when looking at customer buying behavior, predicting trends or detecting fraud.

Some of what we see often in our daily online experience is the prediction of what we might purchase. If we buy something like an outdoor grill, AI combined with machine learning can predict that we might also want accessories or cooking utensils to accompany that purchase. This is something that can be part of a purchase process where recommended additional products are presented to be added to an order based on the original purchase.

However, the prediction can be used to assist in other business problems as well. For example, if there is a product that has received many issue reports with a specific component—a trend might be uncovered allowing the early trigger of a manufacturer’s defect or other quality issue. Knowing this information can help us proactively reach out to existing customers with a fix or replacement before any failure which can plummet customer satisfaction.

Finally, reviewing previous information can help to detect fraud. If you see some trends of new requests for assistance from the same address using different names and identification numbers—this can be a trigger that fraud may be at play and should be investigated.

Guided Assistance and Recommendations

Artificial intelligence is a wonderful tool to help decipher requests and use its knowledge database to provide guidance and recommendations. This is no different when it comes to process orchestration and where AI can help.

Here are some areas where this guided assistance can help improve your process models and components as well as provide some on-the-job training to your developers.

Guided Recommendations

Documentation can be leveraged by AI to provide help for building process assets. Smart responses to questions can give additional guidance. Developers can make better decisions with a more complete picture of recommended scaling architectures, complex topics and feature functionality. A large variety of data sources can be leveraged such as documentation, blogs, forums, and more to make the best recommendations.

Assistance in Building Processes

Building business processes can be complex and AI can minimize this complexity with providing guidance and assistance along the way. With AI, a process developer can get insight into what might be the appropriate next component in the process or which gateway to use when building a process and why it is the best option to use in this scenario. AI can review the previous steps in the process and use that information to enhance the training and historical database using that to make recommendations based on that data.

Assistance in Building Process Assets

This assistance or guidance is not just limited to building processes, but can be more definable business process assets that are part of the overall process model. This can include assets like connector recommendations or creations, user interfaces (UI) for human tasks, documentation, and more. In these cases, the process modeling tool can provide a simple prompt for the user and take that information to build a UI that reflects the business context and helps to improve the user experience.

With business processes, there are often scripting tasks that perform various tasks or integrations in the process instance. With assistance from AI, process designers can receive direction for building these scripts and the best use of process variables. For example, generating a simple script through prompts to parse and manipulate process data or identify variable mappings through the process model.

Compliance and Adherence to Company Policies

Using historical process modeling information, recommendations around best practices, model design, and suggestions to build compliant processes can be made with AI. In fact, predictive autocompletion of models based can also be made based on historical models with similar tasks and branches. With a focus on adhering to company standards and policies, AI can help to keep your processes in compliance.

How Camunda embraces AI for process orchestration

AI Form Generation

Camunda Forms provides AI Form generation, which allows users to build forms up to ten times faster from a simple prompt in under a minute. By entering simple and natural language into the provided prompt, forms are created in business context that properly describe the use case.


The result is a well constructed layout with the relevant form widgets such as checklists, drop downs, and date and time components.


Variable Creation

With the use of AI Form generation, variable names (keys) are auto generated that are appropriate for the data fields that appear on your form. As shown below, using a key of “email” for the email field on the form.



Camunda provides out of the box Connectors, including one for Open AI. Using this Connector within your process models allows you to incorporate AI into any business process. For example, passing a combination of process variables and natural language to the OpenAI prompt allows the creation of text that can be used as communication or even to populate additional variables in your business process.

In the example below, the OpenAI Connector is being used to generate a targeted and specific email to a customer providing a status update.


For more on this, check out another great example of using the OpenAI Connector to facilitate human workflows.

Optimize your processes

With Camunda Optimize, intelligence is built into the product in many ways to help you analyze and improve your processes. By reviewing your implemented workflow for efficiencies and possible bottlenecks, you can design better and more optimized processes. You can leverage historical trends, identify seasonality and use that for forecast changes that need to be made to various tasks and decisions.


Optimize AI helps you redesign your processes structure and test them against Service Level Agreements (SLAs) and Key Performance Indicators (KPIs).

Taking it a step further with ML-ready data sets

However, there are many additional data streams and information that may need to be analyzed in order to provide a more holistic view of customers, trends and more. Optimize also provides ML-ready data sets that can be exported for further analysis and incorporation into your machine learning models.

You have the option to export some or all of the data about a business process to an ML-ready data set to leverage process instance information along with additional data within your organization to build models to predict outcomes within your business.

For example, if you are reviewing buying behavior within a particular geographic region, you may want to incorporate demographic data, weather patterns, population, annual salaries, etc. as well as ordering information and processing captured by your process model. Having all the information in an ML-ready format can help your data scientist spend more time perfecting their prediction models for the best possible and optimized outcomes.

What to read next

Camunda continues to research and review how AI and ML can assist in making our products better, more efficient while taking advantage of new and erupting technologies. Keep reading to learn more about how you can use artificial intelligence to improve your business processes.

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