AI in 2024: Leaders Perspective

In 2023, generative AI emerged as a game-changer across various domains, offering heightened productivity and revolutionizing content creation. The extensive capabilities of these advanced tools created immense excitement and anticipation. Looking ahead to 2024, the focus is shifting towards a new phase: AI measurement. It involves moving beyond the technical prowess of Large Language Models (LLMs) and delving into their tangible impact on a company's profit and loss (P&L) statements. This shift signifies a deeper exploration into these powerful AI technologies' real-world implications and financial outcomes.

The integration of generative AI is expected to drive significant changes in 2024, with many companies experimenting or expanding with this technology. The pace of advancement in generative AI is crucial for technology executives to understand and keep pace with the year ahead. However, to make the most of AI, it is essential to consider more than just the technology itself.

Leaders will adopt a more evidence-based approach in 2024

In 2024, leaders are expected to adopt a more empirical approach to AI, recognizing the need to move beyond the hype and base their decisions on data-driven insights. This change indicates an increasing recognition of the significance of evaluating the advantages of AI tools and gauging their impact on organizational success. As a result, executive decision-makers will focus on identifying the most influential metrics for evaluating AI tools and delving deeper into generative AI tools' return on investment (ROI). This strategic emphasis on empirical analysis is poised to guide organizations in making informed and impactful decisions regarding integrating and utilising AI technologies.

Everybody generally talks about AI, incredibly generative AI, which will have a real impact on everything data. It is here to change many things. So, if you look at the last 15 years of AI and bucket, it has largely been based on Supervised learning where supervised learning can sort of predict; people use people call it data labelling. So it’s very good at labelling stuff. Self-driving cars are in application, quality control applications. You’re labelling defective components. But now, with Generative AI, organizations will rush into processing far more unstructured data than ever. So, the analytics field was mainly about structured data that’s about to change with Generative AI.