- AI, Data Science, and Machine Learning are growing in significance but remain in the early stages of deployment.
- Key focus areas include customer segmentation, demand forecasting, and customer lifetime value analysis.
- Generative AI is rapidly expanding, with production use and experimental applications rising.
- ModelOps is critical for managing the lifecycle of machine learning models, including governance and operational efficiency.
- The market is preparing for increased adoption and innovation in AI technologies over the next 12 months.
- Comprehensive data from various industries points to a growing demand for AI-driven solutions.
Main AI News:
The 2024 AI, Data Science, and Machine Learning Market Study, part of the Dresner Advisory Wisdom of Crowds series, highlights the current deployment of AI, data science, and machine learning use cases as limited but poised for growth. AI/DS/ML encompasses various techniques, including statistics, modeling, machine learning, neural networks, and data mining, which are applied to predict future or unknown events. Key areas with solid potential include customer segmentation, demand forecasting, and customer lifetime value analysis.
The study notes the increasing prominence of these technologies as organizations seek to enhance operational efficiency, improve forecasting capabilities, and foster innovation. A significant trend is the rapid expansion of generative AI, with growing adoption for production use, experimental applications, and planned integration over the next 12 months.
In conjunction with this, Dresner Advisory published a companion study on ModelOps, focusing on the practices, processes, and technologies needed to manage and operationalize machine learning models throughout their lifecycle. ModelOps encompasses machine learning and AI models, including Large Language Models (LLMs) and less complex analytical and decision-making models.
The 3rd annual ModelOps Market Study explores the management and governance of AI and machine learning models, emphasizing the operational priorities necessary to maximize their value. ModelOps provides a framework for managing model lifecycle processes, scalability, and performance, making it an essential component of AI, data science, and generative AI initiatives.
The Wisdom of Crowds research is based on comprehensive data collection, capturing usage trends, deployment patterns, and market perceptions from users across various industries. The research presents a detailed view of the current realities, future plans, and market sentiments regarding AI and data science.
Dresner Advisory Services, founded by Howard Dresner, an expert in business intelligence and performance management, focuses on delivering forward-looking insights to help organizations navigate the evolving landscape of AI, data science, and machine learning technologies.
Conclusion:
The findings indicate a clear trend of growing interest and investment in AI, data science, and machine learning technologies, especially in high-value areas like customer segmentation and demand forecasting. The rise of generative AI and the increasing focus on ModelOps suggests that businesses prioritize not just the development but also the operationalization and scalability of AI models. It signals a shift toward more widespread adoption of AI-driven solutions shortly. For the market, companies successfully integrating AI and data science into their operations will likely gain a competitive edge. At the same time, those lagging may struggle to keep pace with the innovation curve.
