AI Process Automation
Explore the definition, examples, and benefits of AI process automation
Introduction
In an enterprise IT architecture, the business process orchestration layer is the engine that powers an organization’s critical, end-to-end processes and experiences. When AI is applied to process orchestration, the resulting process automation enables organizations to execute processes and deliver experiences faster, more accurately, at scale, and with greater cost efficiency.
[ 96%]
of all IT decision-makers and business leaders say automation is vital to digital transformation
What is AI process automation?
AI process automation is the implementation of artificial intelligence technologies, such as natural language processing (NLP), machine learning (ML), large language models (LLMs), and data analytics, into an organization’s process orchestration layer to enhance and optimize end-to-end business processes.
The three most common ways organizations are applying AI process automation are to enable:
1. Predictive AI — Using powerful AI analytics to improve overall process flow by predicting patterns and trends from operational data, such as identifying and mitigating process bottlenecks.
2. Generative AI — Using LLMs to create new application code or refine existing application code from natural language prompts, such as, “Add a contact form to this task.”
3. Assistive AI — Using ML to automate tasks and actions and assist humans in decision-making, even for complex tasks. Examples include responding to a customer service inquiry or suggesting and implementing improvements for an entire end-to-end process.
What are the benefits of AI process automation?
AI process automation is helping organizations across industries reduce costs, enhance productivity, and enable new levels of innovation and efficiency.
Organizations are using AI process automation to improve operations and outcomes in many ways. For example:
Building, testing, and orchestrating business processes faster
- Using generative AI to translate developer ideas into Business Process Model Notation (BPMN) and Decision Model and Notation (DMN) or generate data to test application code for efficiency
- Using predictive AI to learn from operational processes and suggest best practices
- Using assistive AI to recognize API specifications and automatically generate the appropriate connectors to speed application development and deployment
Working as an endpoint in a process to intelligently automate steps
- Implementing AI/ML tools and services as a process step, as part of overall process orchestration, or as third-party tools and services via connectors
- Taking advantage of AI’s contextual understanding and decision-making capabilities to intelligently automate workflows and route work to the right people and systems
Improving processes over time through intelligent execution and optimization
- Continually testing and analyzing end-to-end processes to collect data, compare results, and suggest opportunities to deliver better outcomes and deeper value
What are the challenges of AI process automation?
AI process automation has the power to help organizations work more efficiently, unlock new opportunities, and maximize business value. But successful implementations at the enterprise level require an understanding of AI’s biggest challenges and strategies that address them.
Challenges
- Process complexity — According to Camunda’s latest “State of Process Orchestration” report, 72% of IT leaders say real-world, mission-critical processes are becoming more complex to maintain
- Data quality and availability — The ability to collect data from siloed process instances, make the data machine-learning ready, then analyze it to find opportunities for improvement
- Legal and ethical risks — Considerations specific to AI, including hallucination, bias, data security, legal or copyright issues, and model maturity
- Skills gap — Shortage of professionals who know how to develop, implement, and manage AI-enhanced systems
Solution
- A process orchestration platform that ties together endpoints and provides visibility into processes from end-to-end
- An enterprise-wide platform that can collect relevant data across all process instances, such as incidents, tasks, workflow duration, and event duration, and integrate with data cleaning and processing tools
- Clear, established internal guidelines and compliance frameworks plus a human always in the loop to monitor and verify AI decisions and actions
- A program for upskilling current employees and promote a culture of continuous learning
AI process automation use cases
Organizations across industries are using AI process automation to intelligently automate tasks, streamline workflows, and improve customer experiences. Some of the most common applications for AI process automation are in the financial, insurance, and telecommunications industries, as well as the public sector.
AI process automation in banking and financial services
Advancing fraud detection and enhancing security
To help prevent fraud and financial crimes and comply with Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations, banks and financial services organizations must continually scan and analyze vast amounts of data from transactional records, external databases, and the internet. The work can overwhelm risk and compliance teams.
By using AI to scan and analyze disparate data in real time, banks and financial services are increasing their chances of detecting potential fraud early and using process orchestration to automatically flag information for review.
AI process automation in insurance
Streamlining the claims process and enabling more informed decisions
A claims processing workflow in the insurance industry involves data collection, document verification, evaluation, and decision-making. This multi-step process is traditionally highly manual, labor-intensive, and time consuming. That’s why more and more insurers are using AI process automation to help collect and verify data, analyze that data along with historical customer information, and develop recommendations for their insurance agents.
AI process automation is reducing the amount of time it takes to process a claim and providing insurers with the information they need to make critical underwriting decisions.
Discover how AI process automation is helping insurers modernize operations
AI process automation in telecommunications
Empowering high service standards and customer satisfaction
The telecommunications industry continues to face challenges from rapidly advancing technology, changing regulations, customer churn, and cybersecurity threats. To provide their customers with a robust, secure communications platform, telcos are implementing AI to help analyze network performance and improve customer service.
AI process automation is helping predict potential issues and taking proactive actions, such as managing network congestion, security threats, and equipment issues. Telcos are also using AI-enabled chatbots to provide faster customer service and AI-powered analytics to deliver personalized customer experiences.
Read how Telecommunications leaders future-proofing their networks with AI process automation
AI process automation in the public sector
Boosting efficiency while reducing technical debt
Government organizations are tasked with providing responsive services but are challenged with outdated tech stacks, siloed processes, and limited resources. AI process automation is helping government organizations connect data, processes, and systems so that the information and workflows needed to serve citizens efficiently are organized and optimized from end-to-end.
Understanding AI process automation and intelligent automation
AI is evolving, and the way organizations apply AI to their enterprise operations continues to evolve as well. AI process automation, intelligent process automation, and intelligent automation are all ways AI is helping organizations improve processes and workflows, but with distinct operational differences.
Process automation — Uses technology to automate repetitive, rules-based tasks from end to end to improve accuracy and efficiency. Business Process Management (BPM), Robotic Process Automation (RPA), and process orchestration platforms are technologies used in process automation.
AI process automation — Applies AI to process automation technologies to handle more complex processes and decision-based tasks from end to end for even greater efficiency.
Intelligent automation — Also referred to as “intelligent process automation,” combines the capabilities of AI process automation, robotics, advanced analytics, and deep learning technologies to continuously and autonomously analyze complex, operational systems, make decisions, learn, and adapt to changing needs. Intelligent automation systems are designed to operate at enterprise scale without the need for human intervention.
AI process automation trends
The pace of digital transformation is not slowing anytime soon. With AI-based tools making low-code and pro-code development faster and more intuitive, organizations are continuing to evolve their IT ecosystems to achieve greater visibility and control of their enterprise operations.
Over the next five years, IT leaders will continue to modernize systems to further improve their AI-based capabilities for:
- Hyperautomation — An integrated approach to end-to-end automation that relies on multiple technologies, including AI, machine learning, RPA, process orchestration, and advanced analytics.
- AI–assisted application development — Using the natural language processing capabilities inherent in generative AI to instantly translate written or spoken task descriptions into BPMN and DMN application models.
- Agentic AI — The next evolution of AI process automation. Using a combination of powerful analytics, deep learning, and advanced decisioning, AI-based components work in an always-on, autonomous capacity to evaluate overall operations, make decisions, and initiate actions with minimal human supervision.
By 2027, generative AI tools will be used to explain legacy business applications and create appropriate replacements, reducing modernization costs by
[ 70%]
AI process automation tools
Modeler Copilot
Provides guidance that helps you fully leverage the power of BPMN process modeling and Camunda Connectors
AI-assisted Form Builder
Use natural language to describe the form you want to design, then customize and extend for your use case
AI Docs Chatbot
LLM-powered search that learns from its sources and delivers quick answers to your technical questions
AI-Enabled Blueprint
Interpret customer inquiries and leverage decision automation to route customer requests accurately
AI/ML Connectors
Orchestrate AI services such as OpenAI, Hugging Face, and AWS SageMaker with flexibility and full auditability
ML-Ready Datasets
Export process execution data in a format that’s ready to import into popular ML tools and feed your data models
Read this blog for more insight on how Camunda is helping IT teams create end-to-end process models faster and more efficiently
Process orchestration
Process orchestration is the critical, foundational platform that enables AI process automation, making it possible for organizations to coordinate a wide range of people, systems, and devices across tasks and operational processes from end to end. Process orchestration platforms give organizations greater visibility into operations at enterprise-scale and superior control in automating and executing those processes from start to finish. They are a must-have for any organization-wide implementation of AI.
Getting started with AI process automation
AI process automation empowers organizations with sophisticated capabilities and, therefore, requires a well thought out, strategic plan for implementation. Basic best practices include:
Defining clear objectives — Identify the processes that will benefit the most from automation in measurable ways, such as workflow efficiency, reduced costs, increased accuracy, or better customer experiences.
Selecting the right tools — Choose AI process automation tools that align with project needs and will integrate with your existing infrastructure.
Ensuring availability of necessary data — Choose a project with data that is accessible and machine-readable or integrate with the necessary tools to collect, clean, and prepare data for AI analysis.
Establishing a plan for governance — Implement measures to protect systems and data plus comply with applicable regulations.
