7 reasons analytics and ML fail to meet business objectives

Foundry’s State of the CIO 2024 reports that 80% of CIOs are tasked with researching and evaluating possible AI additions to their tech stack, and 74% are working more closely with their business leaders on AI applications. Despite facing the demand for delivering business value from data, machine learning, and AI investments, only 54% of CIOs report IT budget increases. AI investments were only the third driver, while security improvements and the rising costs of technology ranked higher.

CIOs, IT, and data science teams must be careful that AI’s excitement doesn’t drive irrational exuberance. One recent study shows that the most important success metrics for analytics projects include return on investment, revenue growth, and improved efficiencies, yet only 32% of respondents successfully deploy more than 60% of their machine learning models. The report also stated that over 50% do not regularly measure the performance of analytics projects, suggesting that even more analytics projects may fail to deliver business value.

Organizations shouldn’t expect high deployment rates at the model level, as it requires experimentation and iteration to translate business objectives into accurate models, useful dashboards, and productivity-improving AI-driven workflows. However, organizations that underperform in delivering business value from their portfolio of data science investments may reduce spending, seek alternative implementation methods, or fall behind their competitors.