Automating Information Management for Enterprises

Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various industries and aspects of our lives. However, the true potential of AI lies not in the novelty of chatbots or their integration with existing systems of record, but rather in data-centric work execution.

Data-in-Place Architecture for Autonomous Work Execution

The future of AI is characterized by a shift towards data-in-place architecture, where data remains in its original location and is processed when needed to execute work. This approach eliminates the need for data movement and consolidation, reducing latency and improving efficiency. By leveraging a data-in-place architecture, organizations can unlock the full potential of AI for autonomous work execution (AWE).

AWE refers to the ability of AI Agents to perform tasks and processes with little or no human intervention. This is achieved through the combination of Constitutional Expert Foundational Models (CEFMs), real-time streaming data, and reference grounding to proven work processes.

Constitutional Expert Foundational Models (CEFMs)

CEFMs are models that have been trained on vast amounts of data for specific purposes. They possess a deep understanding of language, process and data and can perform a wide range of tasks based on data augmentation and reference grounding. By incorporating CEFMs into AI systems, organizations can enable them to understand and interpret complex business processes and execute tasks autonomously.

Real-Time Streaming Data

Real-time streaming data provides AI Agents with a continuous flow of information about the current state of the business. This data is used to update CEFMs and ensure that they are always operating with the most up-to-date, cross-functional information. By leveraging real-time streaming data, AI agents can make informed decisions and execute tasks more effectively.

Reference Grounding to Proven Work Processes

To ensure that AI Agents execute tasks in a consistent and reliable manner, they must be grounded in proven work processes. These processes define the steps that need to be taken to complete a task and the criteria that must be met. By referencing proven work processes, AI Agents ensure that they are executing tasks in a way that is aligned with business objectives while seamlessly maturing business practices across and organization.

The Transformative Power of Autonomous Work Execution

The combination of CEFMs, real-time streaming data, and reference to proven work processes enables AI Agents to execute work autonomously across business functions. This has the potential to reduce overhead costs by 60-90%, as AI Agent can automate tasks that are currently performed manually in a massively parallel scale, around the clock.

Autonomous work execution improves efficiency and productivity, as Autonomous Agents work 24/7 without the need for breaks or vacations, providing organizations with the ability to expand their operations without the need for additional staff.

Conclusion

To bolt a transformative technology like AI on to old systems is to deny its transformative power. Leave the chatbots behind and focus your AI aspirations on transformative business impacts that make your organization more competitive, sustainable and efficient. The future of AI is data-centric work execution. By leveraging data-in-place architecture, Constitutional Expert Foundational Models, real-time streaming data, and reference to proven work processes, organizations can unlock the full potential of AI to automate tasks, reduce costs, and improve efficiency. Autonomous work execution is the future, and it is poised to transform the way we work and live.