RIP to RPA: The Rise of Intelligent Automation
By Kimberly Tan
As AI turns labor into software, the opportunity to productize external professional services (e.g., in legal or accounting) has become a hot topic. However, we believe there is also substantial opportunity in productizing internal work within organizations. These responsibilities often fall under the umbrella term of “operations” and can range from full-time data entry and front desk roles, to routine operational tasks embedded in every other role. This work generates fewer media headlines, but they are the internal stitching that holds companies together.
These ops roles involve critical, but often repetitive and mundane tasks. Companies have historically attempted to automate these tasks by using Robotic Process Automation (RPA), but with generative AI, we believe true automation through agents is now possible. We’ve already seen early examples of agents working in production, such as Decagon’s automated customer support. And with companies like Anthropic launching capabilities like computer use to enable models to meaningfully interact with existing software, there is a clear emerging infrastructure stack for founders to build verticalized intelligent automation applications.
These examples preview a world in which AI agents are able to fulfill the original promise of RPA, turning what used to be operations headcount into intelligent automation and freeing workers to focus on more strategic work.
Welcome to LLMflation - LLM inference cost is going down fast
To a large extent, it is a rapid decline in cost of the underlying commodity that drives technology cycles. Two prominent examples of this are Moore’s Law and Dennard scaling, which help to explain the PC revolution by describing how chips become more performant over time. A lesser known example is Edholm’s Law, which describes how network bandwidth increases — a key factor in the dotcom boom.
In analyzing historical price data since the public introduction of GPT-3, it appears that — at least so far — a similar law holds true for the cost of inference in large language models (LLMs). We’re calling this trend LLMflation, for the rapid increase in tokens you can obtain at a constant price.
In fact, the price decline in LLMs is even faster than that of compute cost during the PC revolution or bandwidth during the dotcom boom: For an LLM of equivalent performance, the cost is decreasing by 10x every year. Given the early stage of the industry, the time scale may still change. But the new use cases that open up from these lower price points indicate that the AI revolution will continue to yield major advances for quite a while.
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Global Medical Manager Digital & Technology
2wFor me, it is easier to set up agentic workflows than RPA – it is a matter of preference.
CEO at xLM | Transforming Life Sciences with AI & ML | Pioneer in GxP Continuous Validation |
2wIt's fascinating to see the potential of AI in transforming internal operations. In the life sciences sector, AI-driven automation can significantly enhance GxP compliance and validation processes. For instance, using AI to analyze manufacturing data can predict deviations before they occur, ensuring continuous compliance and reducing downtime. This proactive approach not only improves efficiency but also ensures higher standards of quality and safety. The integration of AI in these areas could revolutionize how we maintain compliance and streamline operations. Looking forward to more developments in this space.
Energy & Supply Chain Executive | Technology, Strategy, Supply Chain | AI Adoption | Strategic Decision Making | Doctoral Candidate | Views are my own
1moThis post underscores a critical opportunity: using generative AI to revolutionize supply chain operations, turning repetitive tasks into strategic enablers. Supply chains thrive on efficiency, yet roles like inventory management and demand forecasting remain manual and reactive. Generative AI can change that. Imagine AI agents dynamically predicting stock needs by analyzing trends, weather, and geopolitics, or adapting to disruptions by rerouting logistics and renegotiating supplier terms. Unlike RPA’s rigid workflows, these agents respond in real time, adding agility and foresight to operations. Declining LLM inference costs ('LLMflation') are lowering adoption barriers, enabling mid-sized firms to deploy advanced tools previously reserved for industry giants. The challenge now is building AI systems that are efficient, transparent, and trustworthy—essential for a field as complex as supply chains. By turning operational headcount into intelligent automation, generative AI is transforming supply chains from reactive to anticipatory networks, unlocking resilience and unprecedented efficiency.
Accelerated Inference & Edge Computing @ NVIDIA | Deep Tech Investor 🚀
1moChristopher Lyons check out Superface…