Artificial intelligence is no longer in an experimental phase but has transcended into operational environments. Organizations are embracing AI technology in an attempt to increase efficiency and effectiveness, and deliver better services. However, when one considers deploying artificial intelligence in a real-world setting, it is evident that there is a disconnect with regards to the anticipated results and actual performance. This paper will discuss challenges AI technology tries to address, limits within which an organization can work, and requirements to promote AI technology effectively.

Generally, firms mainly implement AI solutions in order to counter ineffectiveness in operations, improve predictive analysis, and deal with increased complexities in data. In industries, AI technology is presented as a way to improve operations which have always proved to be resource-consuming or error-ridden. In supply chain management, AI can be used in demand forecasting, inventory management, and logistics. With predictive analysis, companies can forecast decreases and increases in demand, thus eliminating inventory shortages or overflows. In healthcare, AI can be used in accelerating medical diagnostics, interpreting patient information, and creating synthetic research datasets. In financial institutions, AI can aid in real-time risk analysis, credit card fraud analysis, and stock analysis. Additionally, learning institutions can use AI in learning assistance.

Although AI holds such promise, a significant number of AI projects have not lived up to expectations. Several projects have derailed because of a mismatch in project aims, wrong assumptions, or implementation. Failures in high-profile projects have highlighted these dangers. AI systems learning from biased datasets have produced unsafe or discriminatory results, such as when self-serving algorithms perpetuated gender bias or produced non-reliable recommendations. In most companies, AI models function well but remain unused due to poor integration with business operations or a lack of end-user trust in those systems. A lack of measurable return on investment is where projects frequently stagnate. While capabilities were overestimated, integration complexities such as data privacy, compatibility, and change management have dampened effects. In this way, a good system can be rendered powerless in an organizational setting.

Today, data is the basis of all AI solutions, which is largely impacted by data limitations in terms of accuracy and reliability. Poor-quality data with errors, inconsistencies, or noise will have models learn incorrect patterns based on which they fail to generalize in real-world settings. The availability of data is another challenge. A lack of access to different quality datasets limits an organization to poor sources, making progress a challenge. With reduced numbers of quality datasets available in reliable sources, scaling AI systems will become a challenge.