Enterprise AI Agents vs. Consumer AI Agents

Breaking Analysis: The Rise of Agentic AI in Enterprises
Early discussions around agentic AI have primarily revolved around consumer applications, where AI acts as a digital assistant. However, the complexity and open-ended nature of consumer settings make it a challenging endeavor. Instead, there is greater near-term potential for agentic AI in enterprise contexts, where tasks are well-defined and outcomes are more predictable.
Consumer vs. Enterprise AI
Consumer agents aim to navigate the vast World Wide Web autonomously, posing significant challenges due to the lack of structured guidance. In contrast, enterprise agents operate within well-mapped systems, making their tasks more achievable and beneficial in the short term.
The Evolution from Generative AI to Agentic AI
Agentic AI builds upon generative AI by not just responding to queries but actively pursuing goals under human supervision. This involves planning, coordinating with other agents, and orchestrating actions to achieve specific objectives.
Example: Amazon’s Supply Chain
Amazon’s use of agentic AI in its supply chain illustrates the concept well. Different AI agents handle various tasks, from long-term planning to real-time order fulfillment, all while coordinating their actions to meet overarching corporate goals like profitability and delivery efficiency.
Scaling Across Industries
The vision is to create software capabilities that can be leveraged by all enterprises, enabling them to build systems of agency similar to those used by Amazon. This involves evolving today’s AI stacks to include orchestration, action models, and a digital representation of the business.
Building Blocks and Challenges
Key components of agentic AI include orchestration layers that call applications and use data within those apps to inform actions. Robotic Process Automation (RPA) will play a crucial role, evolving to support more resilient and intelligent automation.
Future Prospects
The next generation of enterprise applications will be custom-built to suit specific business processes, rather than relying on off-the-shelf software. This will involve harmonizing data into a common language and creating a digital representation of the business.
Key Players and Market Dynamics
Companies like Salesforce, Google, Microsoft, Amazon, Palantir, and Celonis are leading the way in developing the components necessary for agentic AI. These firms are converging on similar goals but through different paths, aiming to build the infrastructure needed to make agentic AI a reality.
Conclusion
Agentic AI holds significant promise for enterprises, offering a more structured and achievable scope compared to consumer applications. The focus now is on developing the necessary tools and frameworks to make this vision a reality, with potential for substantial enterprise value in the near future.
What’s Next?
Agentic AI is poised to revolutionize how enterprises operate, but its success hinges on overcoming current technical challenges and harmonizing data across legacy applications. The journey has just begun, and the industry is keenly watching how these developments unfold.
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