Jakob Freund is its CEO Kamundaa software company that innovates end-to-end process orchestration.
Intelligent automation is more than just a buzzword. It represents the merging of artificial intelligence and automation and emphasizes the importance of thoughtfully orchestrating every process from start to finish. While AI is grabbing the headlines, it’s vital to see it not as a “be-all-be-all” but as part of a broader, coherent automation strategy.
Industry analysts have their own terms for this holistic approach. Gartner’s”hyper-automation“focuses on orchestrating multiple automation technologies together, including artificial intelligence. Forrester uses “automation fabric” to describe a unified approach that brings together various tools, technologies, workflows, partners and employees. In both senses, AI is a piece of the automation puzzle, not the whole solution. True intelligent automation means using artificial intelligence to making smarter decisions and streamlining complex tasks within a broader automation framework.
Avoiding AI Silos
A common pitfall is to treat AI as a stand-alone solution instead of integrating it with other systems and processes. This creates silos, where AI tools work in isolation, holding back overall performance. Us 2024 State of Process Orchestration The report notes that 60% of business and IT leaders use more than 26 systems in their automation efforts. Many of these are siled, with 42% of IT leaders citing a lack of integration as a major barrier to digital transformation.
These silos can lead to significant problems, such as slower processes and frustrated customers. For example, if an insurance company’s claims system operates in isolation, it can delay claims processing and push customers to competitors. Instead, every element of a process—all people, systems, and devices—must work together seamlessly for true intelligent automation. Artificial intelligence should be seamlessly integrated into the entire process. Deloitte exhibitions that 92% of organizations are either already implementing end-to-end automation or plan to do so within the next three years. This shows a strong trend toward integrating artificial intelligence and other complementary technologies into broader automation strategies.
Here’s where you can start:
Building a solid foundation
Successful intelligent automation starts with a strong foundation, involving all stakeholders in planning and design. Start by defining your business goals. Some examples may include:
• Making the customer journey more seamless
• Improve internal efficiency to save costs
• Optimization of resource allocation
Engage key IT and business stakeholders to agree on workflows using common modeling standards such as BPMN (Business Process Model and Notation) and DMN (Decision model and notation). These models help align everything—making even the most complex processes readable—and ensure that automated processes meet business needs. You can even use AI in the design phase of the process to build these models using AI co-pilots, just as co-pilots are used in other areas of software development. Doing so speeds up process planning and can make business and IT users more productive.
Applying artificial intelligence to end-to-end processes
To integrate AI into end-to-end processes, it is important to orchestrate AI services and machine learning models. Orchestration ensures that your entire automation technology stack and your people are working together. It also provides visibility into end-to-end process execution data, making it easier to refine and continuously improve your automated processes. Soon, AI will likely be able to learn from process data to autonomously “drive” improvements to your processes, just like a self-driving car.
In all industries, artificial intelligence can have a significant impact on automated processes. For example, in banking and financial services, AI can detect subtle patterns in large data sets, detect fraud in real time and enhance security. In insurance, AI can automate the extraction of data from documents, speeding up claims processing and improving underwriting decisions by analyzing historical data. And in telecommunications, AI can analyze performance data to identify potential problems, helping teams take proactive steps to maintain service quality and minimize downtime. Regardless of your industry, AI could be a critical accelerator for automation if implemented strategically.
Success with Smart Automation
Organizations often rush to implement AI without considering its impact on existing processes. A more effective approach involves understanding where AI can best achieve business goals and improve processes. To succeed with intelligent automation:
• Use BPMN and DMN to understand how AI interacts with other technologies and people in your automated processes.
• Rely on process orchestration to integrate AI into existing workflows, enhancing the effectiveness of AI in automation and maximizing ROI.
• Further optimize customer experiences and your internal processes by continuously improving your processes.
The ultimate goal of intelligent automation is to leverage the latest technology to increase business value. Process orchestration is an essential part of integrating AI into a cohesive, effective automation strategy.
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