By Matej Bukovinski, CTO of Nutrient

As we embark on the journey of understanding AI's impact on productivity, we find ourselves at an exciting crossroads. Currently, generative AI is revolutionizing content creation, enabling us to produce new material with unprecedented ease. Additionally, AI's capability to access and summarize text from images has transformed our interactions with documents, making them more intuitive than ever.

Here are a few ways that I expect AI to reshape our daily interactions in various industries in the coming year.

LLM and LAM Industry Use Cases

Large Language Models (LLMs) and Large Action Models (LAMs) are poised to revolutionize various industries. These advanced technologies are not just enhancing efficiency but also transforming the way businesses operate and interact with their customers. Here are some key predictions on how LLMs and LAMs will reshape different sectors:

Software: LLMs will speed up the development process by generating code which will soon only need minimal review. They will also aid the generation of test cases, documentation and other supporting materials. LLMs are also positioned well to assist even with more complex tasks like refactoring legacy systems and streamlining debugging by analyzing errors and suggesting fixes. Meanwhile, LAMs will be able to autonomously perform historically manual tasks such as resolving issues identified in automated testing, updating dependencies, resolving merge conflicts, taking corrective actions on production infrastructure, and even performing initial incident response and recovery actions.

Retail: Retailers will leverage LLMs for personalized customer interactions, such as product recommendations and support, while LAMs will automate inventory management, order fulfillment, and supply chain logistics. This will not only improve customer satisfaction but also streamline operations, reducing costs and increasing efficiency.

Legal: Law firms will utilize LLMs to assist with legal research, performing semantic searches on large corpora of documents, tasks that currently take paralegals days to complete. LAMs will further process these documents, adding highlights, redacting sensitive information, and more. This will significantly reduce the time and effort required for legal professionals to prepare cases, allowing them to focus on more strategic aspects of their work.

Customer Support: LLMs will understand customer inquiries and generate personalized responses, while LAMs will execute actions like processing refunds, booking appointments, or managing logistics without human intervention. This will lead to faster resolution times and improved customer satisfaction, as well as freeing up human agents to handle more complex issues.

Finance: LLMs will analyze market trends and provide recommendations, while LAMs will autonomously execute trades or manage portfolios, reducing latency and improving decision-making in real-time market conditions. This will enhance the ability of financial institutions to respond to market changes quickly and effectively, potentially leading to better investment outcomes.

Industrial: In industrial settings, LLMs will optimize production schedules and predict maintenance needs, while LAMs will control robots and automated systems on the factory floor, improving efficiency. This will result in reduced downtime, lower maintenance costs, and increased productivity, ultimately boosting the competitiveness of manufacturing operations.

Healthcare: The healthcare sector will also benefit from LLMs and LAMs. LLMs can assist in diagnosing diseases by analyzing patient data and medical literature, while LAMs can manage administrative tasks such as scheduling appointments and processing insurance claims. This will allow healthcare professionals to spend more time on patient care and less on paperwork.

Education: In education, LLMs can provide personalized tutoring and feedback to students, while LAMs can automate administrative tasks such as grading and attendance tracking. This will enable educators to focus more on teaching and less on administrative duties, potentially improving educational outcomes.

In summary, the advancements in AI technology are set to revolutionize our experiences, making interactions more dynamic and personalized. By automating routine tasks, providing intelligent assistance, and facilitating better communication, these models will enable users to focus on higher-level problem-solving and creative endeavors. As the technology continues to evolve, staying informed and adapting to these changes will be crucial for businesses to remain competitive in the rapidly changing landscape.

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ABOUT THE AUTHOR

Matej is a software engineering leader from Slovenia. He began his career freelancing and contributing to open source software. Later, he joined Nutrient, where he played a key role in creating its initial products and teams, eventually taking over as the company's Chief Technology Officer. Outside of work, Matej enjoys playing tennis, skiing, and traveling.