Dresner Advisory Services released its full 2025 AI, Data Science, Machine Learning, and ModelOps research and found that AI is shifting beyond a technology of experiment into a strategic business enabler by organizations across the world. The study illustrates a strong AI adoption growth rate, where generative and agentic AI are driving an interest like no other time we have ever seen, as businesses look to better their operations, expand their forecasting abilities, and create innovation through data-driven decision making and automation of functions.
The patterns of AI use are low, but widespread
According to the 2025 AI, Data Science, and Machine Learning Market Study, one of the reports in the series of Dresner Advisory Wisdom of Crowds, artificial intelligence (AI) is playing a direct strategic or supporting role, and even driving the business, according to the 2025 AI, Data Science, and Machine Learning Market Study, according to Yahoo Finance.
As demonstrated in the Dresner study, the current use of AI, DS, and ML is small but extensive, and the future use of AI, DS, and ML is predicted to be very high. The top three use cases are demand forecasting, customer segmentation, and predictive maintenance, meaning organizations are placing emphasis on practical use cases that can be measured in business impact terms.
Companies adopt strategic artificial intelligence change
According to Howard Dresner, founder and chief research officer of Dresner Advisory, most organizations have identified that AI plays a direct or supporting role in their business and are significantly investing in it due to the need to address inefficiencies, experiment, and be prepared for disruption. Momentum is increasing, and overall organizations are wary but optimistic and moving, at different paces, to more mature and widespread use of AI, DS, and ML to create business value, explained Dresner, noting how cautiously companies are approaching the use of AI.
A companion market study, ModelOps, the practices, procedures, and technologies employed to manage and operationalize machine learning models throughout their life cycle, has been published by Dresner Advisory. ModelOps encompasses but is not restricted to Machine Learning and Artificial Intelligence models that include Large Language models (LLM) and less-complex analytical models.
The 4th annual ModelOps General Market Study analyzes the issue of managing and governing AI and machine learning models, and the operational priorities in relation to maximizing the value of these technologies and ensuring the efficiency and effectiveness of AI efforts.
The challenges of visibility and governance are still there
We continue to observe the increased number and types of models in production, and we continue to struggle with visibility, control, and ownership issues, but the proper management of models is of paramount importance as AI utilization increases, stated Howard Dresner. The increasing role of agentic AI highlights the urgency of the soundness of ModelOps practices, not only in the field of AI and machine learning, but also in all analytical models. ModelOps will become the key to model adaptability, model accountability, and model value in production as adoption grows.
Wisdom of Crowds research is informed by the information gathered regarding usage and deployment patterns, products, and vendors. The users of all ranks and across all industries were involved to present the full picture of the realities, plans, and perception of the market and provide extensive coverage of the existing AI landscape.
According to the research by Dresner Advisory conducted in 2025, AI has progressed to a strategic business requirement rather than an experimental technology, and organizations express cautious optimism as they can reach a mature implementation of AI. The companion ModelOps research demonstrates the vital importance of sound governance practices when the range and diversity of production models are already increasing in terms of numbers, indicating the necessity of adequate control of the AI value and avoiding operational risks.