The evolving landscape of AI
2023 has seen business technology on a whole new level, and this great change in business technology came with the rise of AI and data. This trend will continue and develop in 2024, changing the character of businesses’ operation and their strategic vision.
Newly emerging AI revolution, known as Generative AI is widely discussed but still has to demonstrate its profitability. This technology promises a vast amount of transformative power, and businesses eagerly seek to make use of this “magical tool”. Most businesses are still in an experimental mode, trying to develop their experience with its practical application. Companies must figure out how to incorporate these new abilities promoted AI development into their current technological infrastructure, tasks, and operating procedures.
Second, and essential too in this AI-driven revolution is the data science which itself has begun to move into a new phenomenon. What was once individualized and had to be performed manually, it becomes automated. This evolution is reflected in the use of platforms, processes, and methodologies developed to enhance data model adoption and deployment rates. This change is illustrated with the emergence of machine learning operations systems that enable running, managing, and maintaining operational models at scale. This industrialization of data science, therefore, is not only about technology but a shift in the perspective that highlights factuality, resource productivity and scalability.
Additionally, data products are emerging as a significant element in business plans. As a result, data products that try to consolidate the functionality of data, analytics as well as AI into coherent software packages are now finding their usage being internal decision making and external customer engagement. Nevertheless, there is a level of variation in the way organizations have envisioned and adopted these data products. There are those who perceive them as core elements of their analytics and AI strategies, and others who identify them as separate entities. This divergence highlights the importance of having a proper understanding and dependable consistency in data product management.
The role of data scientists is also changing. They had been seen as the center of data science projects, but now they were partially substituted with a variety of professionals on information engineering including machine learning engineers and managers that enforce an implementation product-oriented demarcation via their work. This is partly because of the advent, citizen data science whereby business professionals having access to automated machine learning tools can now provide her input in terms of modeling and analytics.
Finally, the structures around AI and data science are shifting. First, there is the obvious trend for combining many technology and data roles under broader leadership positions. This shift seeks to simplify the process of decision-making, enhance cross collaborations, and ensure that these modern technologies support the overall direction of a business.
