NWI 2: Talent and tech stack implications of adopting applied AI

What happens when we present all the artificial intelligence developed in the lab into the real world? This is what is referred to as Applied AI. The application of AI boosts software by leveraging machine learning techniques to ensure precise and adaptive performance over time. Applied AI uses computational techniques, like machine learning, deep learning, natural language processing, and computer vision to address challenges in business and this is reshaping business strategies and priming workflows while transforming interaction with the world around us.

Organizations are using applied AI to optimize processes, make predictions, improve decision-making, etc. This begs the question - where do we place the talent and tech stack that managed these businesses till now? 

Getting skilled in AI as we knew it involved getting a degree in CS or other technical fields, learning how to code, and hopefully joining an organization that appreciates this effort and helps you grow. In recent times, however, it has become more competitive and a tad complex for even talent from some of the best colleges in the world. The integration of advanced AI systems requires a workforce equipped with specialized skills and a robust, adaptable tech infrastructure to support innovation.

There has been a skill demand shift from generic software development engineers to data science, data engineering, and AI/ML engineers who can design, develop, and manage AI algorithms and models. Organizations are now seeking experts who need help understanding programming languages but can design and manage language models. Continuous learning has also been emphasized in the field of AI which has been ever-changing and evolving over the last decade, to compete in AI implementation effectively requires a blend of traits, including domain-specific knowledge, software engineering, and statistical analysis. This calls for a diverse talent pool capable of interdisciplinary skills.

Consequently, adopting applied AI requires advancements in the technology stack, making sure that organizations and businesses have the infrastructure needed to support complex AI systems. Applied AI demands scalable, high-performance computing resources, cloud services, and data storage solutions capable of handling petabytes of data. The tech stack should also be scalable and secure to accommodate the growing computational needs of AI applications.

To seamlessly adopt applied AI in industry, integration with existing technologies, data management, and security measures are required to leverage AI's potential and drive innovation fully.