How applying Artificial Intelligence helps hyperautomation
Hyperautomation uses cognitive technologies and advanced algorithms to automate individual tasks and end-to-end processes. It leverages AI capabilities to analyze vast data, extract insights, and make informed decisions in real-time. By combining automation with AI techniques, hyperautomation augments human productivity, reduces errors, and drives efficiency across organizations.
Exploring the AI-spectrum
In the early days of 1960, the first reasoning (inference) engine was built and presented by Allen Newell and Herbert A. Simon, applying logical deduction and heuristic search to navigate the problem space and arrive at solutions. It utilized a set of rules or production systems to guide its artificially intelligent reasoning and decision-making. Artificial intelligence was born, and a transformative journey has unfolded in artificial intelligence (AI), propelling us from rudimentary inference engines to ChatGPT, a significant advance in artificial general intelligence (AGI). This trajectory spans a vast spectrum, encompassing breakthroughs, challenges, and remarkable advancements that have reshaped our understanding of what machines can achieve.
Human derived knowledge
In the next decade, techniques and standards like the Standard for Business Vocabulary and Rules (SBVR, 2006), the formal rule specification of RuleSpeak (published in 1996), and the Decision Model and Notation (DMN, 2015) emerged, helping analyze regulations and policies and implement expert-derived knowledge .
Data derived knowledge
As the technology evolved, Machine Learning (ML) techniques matured with the availability of big data and emerged as a game-changer, bringing about a significant shift and ushering into another era of AI. With the ability to derive knowledge from data identifying patterns, machine learning algorithms unlocked tremendous potential. Supervised learning algorithms learn from labeled examples, while unsupervised learning algorithms discover hidden structures within unlabeled data. Reinforcement learning algorithms even ventured into trial-and-error, acquiring knowledge through interaction with an environment.
Applying the core
This application involves three interconnected elements:
Hyperautomating processes
Intelligent Process Automation: AI can automate and optimize complex business processes . Analyzing historical process data allows AI algorithms to identify patterns, extract insights, and suggest process improvements. Machine learning models integrate with Digital Decisioning integrated within processes, reducing the need for human intervention and automating repetitive tasks, decision-making, and process orchestration, resulting in increased efficiency and productivity. AI-powered bots can handle routine tasks, such as data entry or document processing, allowing employees to focus on higher-value work.
Adaptive Workflows: AI can optimize and adapt workflows based on real-time data and changing conditions. By continuously monitoring and analyzing process performance, AI algorithms can identify bottlenecks, inefficiencies, or deviations from expected outcomes enabling the system to dynamically adjust workflows, reassign tasks, or trigger alerts to ensure optimal process execution.
Robotic Process Automation (RPA): RPA involves automating repetitive and rule-based tasks performed by humans. Digital Decisioning invoking ML algorithms can enhance RPA systems by enabling intelligent decision-making capabilities, combining human-derived knowledge with data-derived knowledge. ML models can analyze data, identify patterns, and make informed decisions, allowing RPA bots to handle more complex scenarios and exceptions.
Exploiting data streams
Of course, the successful application of AI and ML in hyperautomation depends on proper data preparation, expert model training, and integration with existing systems. It also requires continuous monitoring, evaluation, and refinement to ensure optimal performance and adaptability to changing business needs.
Intelligent Data Extraction: AI techniques, such as optical character recognition (OCR) and natural language understanding (NLU), can automate extracting information from unstructured data sources, such as documents, emails, or web pages eliminating manual data labeling and enable businesses to extract insights from large volumes of textual data efficiently.
Predictive Analytics: ML models can analyze historical and real-time data for predictions and forecasts , help organizations anticipate demand, optimize inventory management, and improve resource allocation. Predictive analytics can also be applied to maintenance scheduling, enabling proactive maintenance and reducing unplanned downtime. The results then can be fed into Digital Decisioning and be combined with expert-derived knowledge.
Anomaly Detection: ML algorithms can analyze large volumes of data to identify anomalies and potential fraud or security breaches. ML models can detect deviations from normal behavior by monitoring data patterns and flagging suspicious activities in real time. Anomaly detection can be integrated with Digital Decisioning and applied to network security, financial transactions, and fraud prevention .
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Hyperautomating operational decisions
Digital Decisioning, coined in 2019 , emerged by combining and integrating this data-derived knowledge with expert-derived knowledge , increasing the ability to make more sophisticated decisions used by hyperautomation.
Read more in my previous blog: Digital Decisioning, at the heart of hyperautomation creating value
Enabling cognitive capabilities
Deep Learning , inspired by the structure of the human brain, neural networks grew in depth and complexity, enabling machines to handle vast amounts of data and extract intricate features. Convolutional neural networks revolutionized computer vision, while recurrent neural networks breathed life into natural language processing. These advancements led to breakthroughs in image recognition, speech synthesis, and language translation, among other domains, enhancing the possibilities for hyperautomation. ChatGPT, for example, employs deep learning techniques, such as transformer neural networks, to generate coherent and contextually relevant responses and leverages large-scale pre-training on diverse text data to learn patterns and structures in language.
Understanding Natural language
Natural Language Processing (NLP) enables machines to understand and interpret human language . It is employed in chatbots and virtual assistants that can interact with users, understand their queries, and provide relevant information or assistance. NLP techniques like sentiment analysis can also extract insights from customer feedback or social media data, enabling businesses to make data-driven decisions with the help of Digital Decisioning. Chatbots, for example, can handle customer inquiries and automate customer support processes, reducing manual intervention and response times.
Interpreting visual information
Computer Vision uses AI algorithms to analyze and interpret visual data , such as images or videos. In hyperautomation, computer vision can automate tasks involving visual inspection, object recognition, or quality control. For instance, AI-powered systems can analyze images to identify defects in manufacturing processes or extract information from scanned documents.
Cognitive reasoning
Cognitive Automation combines AI technologies like ML and NLP with traditional automation techniques. It enables systems to understand, reason, and learn from data and user interactions and adds the capability of adapting to changing circumstances. Cognitive automation, embedded in Digital Decisioning, can automate tasks requiring judgment, reasoning, or contextual understanding and improve claims processing, loan approvals, and fraud detection.
Conclusion
By leveraging AI technologies in these ways, businesses can achieve hyperautomation by automating and optimizing their processes, enhancing decision-making, and improving overall operational efficiency. AI brings intelligence and adaptability to automation, enabling systems to handle complex scenarios and make real-time informed decisions.
While AGI remains an elusive goal, for now, the quest has revolutionized technology and transformed our understanding of intelligence. The AI spectrum is a testimony to human ingenuity, offering a glimpse into a future where machines and humans walk together on the frontiers of knowledge.
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