Knowledge and work are inextricably linked together regardless of the sector or industry. If harnessed correctly, Learning Analytics (LA) will create an opportunity for Business Schools (BS) to impact not only their participants, but their organizations and overall society. Technological advances that are changing the way we work, communicate, and live. According to a market study, 92% of organizations are expected to increase their investment in EdTech in 2025. Caution is warranted, however. Data is only valuable if it is being collected properly, and adopting new technologies such as Artificial Intelligence (AI) without quality data may result in sub-optimal, costly and potentially harmful results.

AI is the technological frontier transforming the learning in ways that are not yet clear. BS's are already using LA to improve their academic offer, however the use of AI in BS's is not yet widespread. BS's have reason to be worried about the lack of quality data to analyze. External digital learning solutions providers have emerged as key competitors.

Data quantity and quality are both key in LA. This paper shows the extent to which learner data is being harnessed by BS's. The scope is important as the higher the quality of LA, the more meaningful the conclusions. The methodology used was fundamentally qualitative. Information was gathered from Data Officers and Learning Innovation Officers at BS's via survey.

The objective was to answer these questions: Are LA being deployed, for what purpose, and what are the challenges to implement them in BS programs?

Data collected for this study shows that LA are already being used by 71.4% of the institutions surveyed. The primary goals for implementing LA was to improve participant retention and success through personalized learning experiences (64.3%), followed by data-driven curriculum improvements (50%).

As for challenges, 78.6% said that lack of standardized data collection is the biggest challenge. As for implementing LA the challenge is more technical with 57.1%, saying that infrastructure limitations and lack of expertise in LA (42.9%) are the issues. The most common challenge for all respondents was the ability of faculty and staff to use LA effectively, as 50% said they were minimally prepared, while 42. 6% said they were moderately prepared. None said they were highly prepared. This underscores the fact that only 21.4% of those surveyed believe that LA are aligned with their institution’s strategic goals. The timeline for expanding or improving upon LA ranged from 1-3 years (42.9%) to no concrete timeline at all.

From this data, BS's are behind the curve when it comes to using LA to personalize experiences or to measure learning transfer. They are at a crossroads and must adapt accordingly to the new trends in the sector or lose their competitive advantage. For the moment, it seems that they are slowly moving towards quality data collection, but they may not be able to keep pace with the technology if the strategic goals are not aligned.

LA and data will be the foundation upon which future educational programs are designed. There is no reason to believe that BS programs will be an exception. AI may (or may not) completely transform the way we teach and learn, but a data-based approach to training and development is happening, as this study shows. A future study to determine how AI will increase learning transfer in BS's is warranted and is the next step in this research.

Keywords: Learning Transfer, Executive Education, Data, Artificial Intelligence, Learning Analytics, Business Schools.