Unleashing the Power of Autonomous GenAI Task Execution: A Guide for Procurement Leaders.
Are you considering how the new generation of AI can transform and enhance your business processes? I am. We know GenAI is a game changer, and companies that don't enact a well-designed AI strategy will suffer. While GenAI text summarization and generation are valuable capabilities, the future lies in autonomous task execution. These new GenAI-powered autonomous task agents leverage three technical capabilities to achieve outcomes where traditional automation and transformation solutions cannot deliver a clear return on investment.
Before distinguishing GenAI-powered autonomous task agents (task agents) from current solutions and explaining why they must be included in your strategic plan, let's explore why traditional automation projects often fail to achieve business objectives. According to a 2023 report on process orchestration, over 72% of respondents believe that mission-critical processes are becoming more complex to maintain. Additionally, 73% say that much of their process design is locked behind custom tooling, and 54% of IT leaders believe their process automation is becoming outdated.
Consider the significant financial and workforce investments in custom workflow automation, only to see critical processes locked into a static set of outdated steps. Stuck with systems that cannot be upgraded due to customizations, the vendors' promises of future-proof ROI become technical debt, leading to a poor user experience and business risk. This juncture is where the new generation of AI technology excels by expanding data access exponentially, incorporating real-time process flexibility, and improving system and user interaction.
- Expanding data access exponentially
- Incorporating real-time process flexibility
- Improving system and user interaction
The first thing to understand is that the new task agents do not rely solely on structured data; they use unstructured data just as effectively. This ability changes how we approach software development and automation. Predefined structured data schemas are the foundation for all major enterprise systems, including ERP, CRM, and HRS. Agreed-upon industry data standards like EDI, ACORD, and HL7 facilitate system interoperability by defining common data structures for data exchange between systems. Without these standards, system integrations, workflows, and data management become even more complex and expensive. However, this still only represents a fraction of the enterprise's data. According to IBM, over 80% of an enterprise's data is unstructured.
With task agents’ ability to use unstructured data, many additional data sources are available to search, analyze, cross-reference, synchronize, and act on during a multi-stage execution process, significantly expanding transformation opportunities.
Next, let's examine the decision-making processes of traditional workflows. Out-of-the-box workflow technology for the enterprise is usually implemented by professional workflow designers and an in-house team that predefines the steps and uses conditional logic rules to automate business rules and decision points. In contrast, task agents can reason around many issues that impede fixed logic, dynamically adjusting to changes in data structure and API while preserving generated code for audit records. This real-time capability changes the workflow definition paradigm, reducing the risk of being locked into a technical stack or outdated workflows. While task agent technology is still in the early days, the benefits of an AI strategy that includes dynamic logic will be significant, including reduced risk, increased adaptability, and improved efficiency.
Finally, let's consider interaction with other entities and people. Traditionally, structured data from trusted sources is used to augment interactions and results. AI developers use this same approach in RAG —Retrieval-Augmented-Generation. RAG combines validated (domain-specific) data with LLMs to improve retrieval quality and domain expertise and lower costs. Because of the task agent's ability to access structured and unstructured data, the augmentation of a task or interaction with outside information and input is uncharted territory for developers and users. Processes may utilize virtually any accessible information to add contextual data. However, it's important to note that selecting the appropriate data sources, validating the accuracy, and controlling access remains a human responsibility, highlighting the continued importance of human oversight in AI-augmented processes.
People are essential in maximizing the value of artificial intelligence. User interaction with AI is increasing, and user expectations are evolving. Users do not want to worry about inaccurate data or AI complicating their work. They want easier control of AI, transparency to see, reuse, and save task logic, and the ability to write or speak instructions in natural language. Well-designed GenAI autonomous task agents should incorporate these requirements, providing seamless experiences that belie the incredible computing power at the user's fingertips.
At Floada, we believe that mainstream enterprises will take 3-5 years to adopt GenAI autonomous task agents. The majority of enterprises have just begun utilizing GenAI's generative capabilities. Early adopters of the generative and summarization capabilities are now exploring RAG capabilities to enhance results and reduce costs. Now is the time to incorporate autonomous task agents into your AI transformation strategy.
Floada specifically seeks trailblazing companies - the 3% of companies recognizing the critical role of AI innovation and transformation in achieving business success. Floada is revolutionizing contract information management. The GenAI native architecture can integrate with various information sources, reducing overhead costs and avoiding technical lock-in. With Floada, you can efficiently access, analyze, cross-reference, and synchronize data without relying on expensive professional services or enduring months-long implementation cycles.
For more information about Floada, please visit our website or email us at [email protected]