Artificial intelligence (AI) has been a topic of discussion and utilization for centuries, from ancient Greek mythology to modern-day technology. The concept of AI gained practical significance in the 1950s when British mathematician Alan Turing questioned the possibility of machines thinking, and American computer scientist John McCarthy coined the term “artificial intelligence.” Since then, AI has made significant progress, with the first Ph.D. program in AI established in the 1960s at Stanford University.
While generative AI, like ChatGPT, has recently garnered attention, adaptive AI, encompassing machine learning, computer vision, and forecasting, is being applied across various industries to solve real business problems. The availability of vast amounts of data, coupled with advancements in cloud technology, has played a crucial role in the development of AI. However, the effectiveness of AI algorithms relies on the availability of good, clean, aggregated, and analyzed data.
To maximize the benefits of AI, two strategies can be employed. Firstly, focusing on specific domains and data science can supercharge AI. By applying AI to a particular industry or problem domain, it can become an expert in solving related challenges. The collaboration between AI, data science, and domain experts is essential for optimal results. Data scientists provide the algorithms and analyze the data, while AI experts leverage the insights to address real-world business problems.
Secondly, marrying exposure and experience is crucial. Embedding AI data science experts within the organization allows them to understand the domain and its challenges, enabling them to create relevant AI solutions. This approach ensures that data scientists have a comprehensive understanding of customer problems and can make insightful decisions regarding data application.
In conclusion, AI has matured as a technology and is being widely applied across industries. The success of AI depends on the integration of domains, data science, and AI expertise. By employing strategies that focus on specific domains and fostering collaboration between data scientists and domain experts, organizations can harness the full potential of AI to solve business problems effectively.
