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Despite the surge in AI technology within the enterprise software market, many organizations are finding themselves disappointed as newly implemented AI tools fail to deliver the anticipated breakthroughs. Employees still find themselves manually copying data between systems, tracking approvals through email, and maintaining status updates in spreadsheets. Standalone AI solutions are not enough to effectively transform business processes on their own.
Nearly every software application on the market is now labeled as "AI-powered," including chatbots, document recognition tools, and predictive models. While these tools can be beneficial on their own, issues occur when they operate as isolated point solutions, disconnected from the organization’s other systems. An AI’s data extraction capability becomes significantly less valuable if the extracted data must then be manually transferred, processed, and integrated with other information.
Isolated AI tools cause three critical problems:
A service task is an automated process step that performs a specific task without the need for human intervention. The essential aspect of this process is its automation; however, it doesn't have to be based on artificial intelligence.
The core idea of intelligent process automation lies in using each component appropriately within its context. It's important to note that not every task requires AI to be effective.
If-then logic: Executes specific actions based on predefined business rules. It directs the workflow path by evaluating variables such as transaction amounts, authorization levels, or record statuses.
OCR (Optical Character Recognition): Converts scanned documents and images into machine-encoded text. While it excels at high-fidelity data extraction, it focuses on character recognition rather than contextual interpretation.
RPA (Robotic Process Automation): Automates manual tasks by interacting with user interfaces (UI). It is the ideal solution for bridging gaps where APIs are unavailable or when dealing with legacy systems that lack modern integration capabilities.
API calls: Facilitate seamless, structured communication between disparate systems. They are used to query, validate, and synchronize data in real-time across the software ecosystem.
Webhooks: An event-driven mechanism where one system provides real-time data to another as soon as a specific event occurs, triggering downstream processes without the need for manual polling.
AI Agent: Intelligent entities capable of interpreting unstructured data within their specific context. They process natural language and execute complex decision-making in non-deterministic environments where rules are fluid.
True automation is created as an integrated network of microprocesses. These microprocesses are smaller, well-defined processes that serve specific business goals and consist of reusable components. Each microprocess is built from service tasks - such as API calls, if-then logic, AI agents, and RPA steps - as well as human tasks. These elements connect in a modular way.
This approach offers three key advantages:
Scalability: The same microprocess can be utilized across different departments, allowing document processing logic to be applied to invoices, contracts, and resumes.
Gradual development: There is no need to implement full automation all at once. The level of automation can be individually adjusted for each microprocess, enabling the system to evolve in small, controlled steps.
Flexibility: If new requirements arise, a new microprocess can be easily added to the existing network without the need to rebuild the entire system.
The strategic advantage of gradual automation is that it minimizes the likelihood of errors and allows employees time to adjust to the changes. It is advisable to elevate only one or two micro-processes at a time to ensure that the transition remains manageable and safe.
When service tasks are connected in an integrated process, manual bridges and data silos disappear:
Continuous data flow: Data extracted by OCR (Optical Character Recognition) is forwarded to the AI agent for interpretation. The AI's categorization is then verified by the system through an API call, ensuring that there is no loss of data between steps.
Automatic continuation: Each step automatically triggers the next one, requiring no human intervention. The process continues uninterrupted until it reaches the endpoint or a decision point that requires human input.
Complete transparency: Every step of the process is logged. Users can see in real-time where each process stands, how long it has been running, and what the next step will be.
Fluenta One is designed using a microprocess-based, service task-centric approach. The platform focuses on intelligent process automation rather than just selling AI, with AI being one important component among many, but not the sole focus.
The key features of the system are its flexibility and gradual implementation. The level of automation can be adjusted for each microprocess, allowing for easy integration of new microprocesses into the existing network. Additionally, changes can be made incrementally with minimal risk. This enables organizations to advance in their digital transformation journey at their own pace while maintaining consistent progress.
The AI revolution is genuine, but it's not solely about using AI to solve every problem. True digital transformation involves the smart integration of AI with other automation technologies within a network of microprocesses, where each component executes the appropriate task in the right place.
The era of point solutions has ended. The future lies in process-oriented, modular, and gradually developable automation, where AI and traditional automation technologies collaborate harmoniously.
Curious where your company stands on the automation journey? Complete our automation maturity survey and receive personalized recommendations for next steps.
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