AI Starter Kit

Many organizations struggle to fully understand what AI is, how it works, and what its potential applications are. 

Implementing AI requires specialized skills and knowledge that many organizations may not possess in-house. 

AI requires large amounts of high-quality data to function properly. Many organizations may not have access to the necessary data or may struggle with data quality issues. Additionally, in the past organizations may not have had enough data for creating non-bias algorithms, but now we can use GenAI to supplement datasets with synthetic data to fill data gaps. 

AI relies heavily on data, which can raise concerns around data privacy and security. Organizations may be hesitant to implement AI due to these concerns. 

As AI becomes more advanced, ethical considerations become increasingly important. Organizations may struggle with ethical considerations around AI implementation. 

Implementing AI can be expensive, especially for smaller organizations with limited budgets. Taking an approach to implement AI models by business use case can result in a narrower approach and increase ROI. 

AI must be integrated with existing systems and processes in order to be effective. This can be a complex and time-consuming process. 

Many organizations struggle to see a clear return on investment for AI implementation, which can make it difficult to justify the cost and effort. 

Implementing AI requires a significant shift in organizational culture and mindset. Some employees may resist this change, making it difficult to fully implement AI. 

Finally, some organizations may struggle to identify use cases for AI within their business, making it difficult to implement effectively.