What Challenges Might Leaders Face When Implementing Machine Learning?
Despite its potential, the implementation of ML comes with challenges. These include:
Data Quality: The effectiveness of ML models depends on the quality of the data. Poor quality data can lead to inaccurate predictions. Skill Gap: There is a shortage of skilled professionals who can develop and manage ML models. Leaders must invest in training and development. Integration: Integrating ML into existing systems can be complex and may require significant changes in infrastructure. Ethical Concerns: Leaders must ensure that ML is used ethically, maintaining transparency and fairness in decision-making processes.