Intelligent Automation with Decision AIgents
Decision Automation agents (AIgents) automate decision-making based on decision models, prescriptive analytics, and intelligent automation combines AI/ML methods without human intervention. Decision automation integrates with business applications, workflow management systems, and Robotic Process Automation (RPA tools) to execute actions.
Automations Empowered by Decision Intelligence
Decision Intelligence and Decision Automation Agents (AIgents) empower business teams with data-driven decision-making and operational efficiency. Decision Intelligence leverages composite
intelligent automation (IA) models for prescriptive insights.
Decision automation streamlines operational decisions with business process automation.
Multi-modal interface
Decision Automation Agents (AIgents) engage with users and the environment through various communication and input modes, including voice recognition, text-based communication, touch and gestures, computer vision, and audio output. Seamless interactions using different modalities adapt to users' needs, enhancing the overall user experience and making AI interactions more versatile and accessible.
Natural Language Processing
Decision Automation Agents (AIgents) utilize Natural Language Processing (NLP) capabilities provided by Large Language Models (LLMs) to comprehend, communicate, and process textual information. NLP enables AIgents to analyze and interpret text, recognize sentiment, extract named entities, perform language translation, engage in conversations through chatbots and virtual assistants, retrieve information from large text datasets, classify text into categories, answer questions, transcribe spoken words, and generate human-like text.
Data Fabric
The Data Intelligence platform and Data Fabric framework provide centralized access to data from diverse sources, facilitating seamless integration and preprocessing of data for artificial intelligence (AI) model training and decision-making. The Data Fabric platform ensures your unstructured data is monitored for quality, security, and governance, scales to accommodate the growing data requirements of AI workloads, enhances data discovery and access using data cataloging and virtualization, and enables real-time data processing.
Standard Decision Models
Decision Models are created using the Decision Model and Notation (DMN) standard to bolster decision automation by offering a standardized visual framework for representing and executing business decisions. Decision models are executed by DMN-compliant engines, ensuring consistent, automated, and efficient decision-making processes. Decision Automation Agents integrate with Business Processes using BPMN standards to enable the seamless automation of end-to-end business processes, further enhancing operational efficiency.
Composite AI
Decision Automation Agents leverage Composite AI, a powerful approach to decision automation, to harness the synergy of various AI models and techniques. Composite AI enhances the accuracy, reliability, and adaptability of automated decision-making processes. By combining machine learning algorithms, expert systems, risk assessment models, and multi-modal data processing, Decision AIgents make more informed decisions while mitigating risks.
Intelligent Automation
Decision Automation Agents integrate with Robotic Process Automation (RPA) systems to enable intelligent automation tools by infusing automated processes with data-driven decision-making, contextual adaptability, and compliance assurance. Decision AIgents analyze data, prioritize tasks, and determine the best course of action based on context, predictive models, and prescriptive analytics. When combined with RPA, these decisions guide bots in real time, enabling more efficient and flexible process execution.
Decision Controls
Decision Automation Agents operate within defined decision controls and constraints to ensure trust and confidence in decision automation systems. These control mechanisms establish boundaries and safeguards to adhere to regulatory compliance, ethical guidelines, and data privacy. Human oversight and explainability are emphasized for critical decisions, and predefined thresholds and fail-safe mechanisms prevent unintended outcomes. Continuous monitoring, feedback loops, and scenario analysis using simulation contribute to ongoing compliance and risk mitigation.
Decision Orchestration
Decision Orchestration Center streamlines the entire decision-making process, enhancing decision flow, monitoring, governance, and optimization. Real-time monitoring, alerts, and audit trails provide transparency and compliance, while performance metrics enable organizations to assess decision quality. Decision orchestration leverages data-driven insights, machine learning, and scenario modeling for continuous optimization, allowing organizations to refine decision models and improve outcomes over time.
Significant impact on efficiency, accuracy, and business performance
Decision automation offers a wide range of benefits, including increased efficiency, reduced errors, cost savings, and improved decision quality. These advantages enable organizations to operate more effectively, respond to challenges more rapidly, and achieve better business outcomes.
Benefits of intelligent automation
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Increased Efficiency
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Improved Decision Quality
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Enhanced Customer Experience
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Competitive Advantage
Increase an organization’s efficiency by 20–60%
Decision automation eliminates the need for manual tasks, including decision-making processes, reducing the time and effort required to make decisions. This leads to faster response times, shorter decision cycles, and improved workflow efficiency.
Automation software and decision systems can handle a high volume of decisions and scale easily to accommodate growing data and decision complexity. This scalability is crucial for businesses experiencing growth and global expansion.
By streamlining decision processes and reducing the need for manual intervention, decision automation can lead to cost savings. This includes savings in labor costs, reduced operational expenses, and better resource allocation.
Improve Quality, Accuracy, and Compliance
Automated decisions are based on predefined rules, data analysis, and machine learning models. This data-driven approach often results in more accurate and data-backed decisions compared to those made solely by humans.
Automated decisions consistently follow predefined rules and criteria, eliminating the variability that can occur with human decision-makers. Automation minimizes the risk of human errors in decision-making. This consistency ensures that similar situations are handled consistently every time.
Automated decision systems are designed to consistently adhere to regulatory and compliance requirements, reducing the risk of non-compliance and associated penalties.
Automated decision systems can assess and mitigate risks by continuously monitoring data and applying predefined risk management strategies.
Faster Response to Customer requests and 24/7 availability
Automation can improve customer service by enabling faster responses to customer inquiries, personalized recommendations, and efficient order processing. This leads to higher customer satisfaction.
Automated decision systems can operate continuously, providing decision-making support around the clock. This is particularly beneficial in industries where timely decisions are critical, and 24/7 availability is expected.
Automation can trigger rapid responses to events or changes in real-time data. Automated decisions can respond swiftly to market fluctuations or disruptions in your value chains.
Focus on Innovation and Value-addition
Organizations that implement decision automation can gain a competitive edge by making quicker, more accurate decisions, optimizing processes, and staying agile in a fast-paced business environment.
Automation allows human employees to focus on strategic and creative tasks rather than repetitive, routine decisions. The majority of employees don’t feel engaged in their work as they often feel that it is too repetitive and not fulfilling. Intelligent automation frees employees from these tedious tasks by automating them, allowing people to focus on more value-adding activities. This can lead to increased innovation and value-added activities.
Decision automation optimizes resource allocation by making data-driven decisions on resource allocation, workforce scheduling, and process management. This ensures resources are used efficiently and effectively.
Need clarification?
What is Intelligent Automation?
Intelligent automation integrates AI and automation to revolutionize business processes. This transformative technology empowers systems to learn, adapt, and autonomously make informed decisions. Embracing intelligent automation unlocks operational efficiency, accuracy, and decision-making speed. It drives productivity and fosters innovation, making it essential in navigating the complexities of the modern business landscape. Incorporate intelligent automation to propel your organization towards unparalleled success.
How is Decision Automation/Intelligent Automation Related to Robotic Process Automation (RPA)?
Decision Automation and Robotic Process Automation (RPA) are related concepts in the field of automation, but they serve different purposes and operate at different levels of an organization's processes. Decision automation focuses on automating complex decision-making processes, while RPA specializes in automating repetitive, rule-based tasks.
Decision Automation can be integrated into various systems and processes to provide decision support or automate decision-making steps within larger workflows. RPA bots are often integrated into existing applications and systems to perform tasks like a human operator would, interacting with user interfaces.
Decision Automation can handle complex decision-making scenarios by analyzing data, considering multiple factors, and applying sophisticated AI and machine learning algorithms to enhance decision quality. While RPA can handle data, its primary focus is on data entry, retrieval, and transfer between systems.
Decision Automation and RPA can complement each other within an organization's automation strategy, with Decision Automation guiding processes as part of Intelligent Automation and RPA executing tasks efficiently.
How Can We Monitor and Govern Automated Decisions?
Automated decisions are rigorously managed and governed by the Decision Orchestration Center to ensure transparency, accountability, trust, and compliance. The Orchestration Center logs every automated decision, maintaining a comprehensive record that includes inputs, logic, and outcomes.
Comprehensive audit trails are kept for all decision-related activities, enabling traceability and compliance audits. Decision governance policies are enforced, ensuring that decisions align with organizational standards, policies, regulatory requirements, and ethical guidelines.
Version control, authorization, and permissions management safeguard decision logic, while performance metrics help assess decision effectiveness. Compliance checks, dynamic reevaluation, feedback loops, and reporting tools further contribute to the governance of automated decisions.
Circuitry.ai enables businesses to manage decisions in three modes: Analysis, Augmentation, and Automation, allowing decision-makers to select the level of automation and fine-tune decision-making processes over time.
Can We Incorporate Our Own AI Models into the Decision-Making Processes?
Yes. Companies can use AI models developed by internal teams or 3rd parties by integrating them into the Decision Circuits provided by Circuitry.ai.
Circuitry.ai's Decision Intelligence platform supports a combination of various AI techniques and algorithms. You can design, train, and use AI models using the same platform. You also have the option to integrate with AI/ML services running on other platforms and integrate them into the Decision Circuits.
Circuitry.ai's support for standards such as Decision Model and Notation (DMN) and Predictive Model Markup Language (PMML) provides you the flexibility, openness, and robustness to adopt the decision-making processes into your organization.