Shelf Blog: AI Deployment

Successful AI projects require more than just cutting-edge technology. They demand a clear vision, robust data governance, ethical considerations, and an adaptive organizational culture. In this article, we delve into the common pitfalls that can derail AI projects. We also offer insights and...

Hallucinations and ungrounded results are a significant challenge in Content Processing systems. When AI-generated content contains statements that are inconsistent with the input data or knowledge base, it can lead to the spread of misinformation and erode trust in the system. Microsoft Azure’s...

Subject Matter Experts (SMEs) are the architects of quality and precision in AI development. But how can you be the best SME for your organization’s AI output review initiatives? SMEs are presented with a great responsibility – to identify discrepancies, biases, and areas for potential...

Output evaluation is the process through which the functionality and efficiency of AI-generated responses are rigorously assessed against a set of predefined criteria. It ensures that AI systems are not only technically proficient but also tailored to meet the nuanced demands of specific...

AI has revolutionized how we operate and make decisions. Its ability to analyze vast amounts of data and automate complex processes is fundamentally changing countless industries. However, the effectiveness of AI is deeply intertwined with the quality of data it processes. Poor data quality can...

The adage “Garbage In, Garbage Out” (GIGO) holds a pivotal truth throughout all of computer science, but especially for data analytics and artificial intelligence. This principle underscores the fundamental idea that the quality of the output is linked to the quality of the input. As...

Evaluating your organization’s tools, leadership, business model, and partnerships can identify areas of improvement needed to find success with your AI strategy. It’s not necessarily the case that AI will lead to automation of all business — and it may be your industry is one of the least-affected by this disruption — but these steps will ensure your organization is prepared for the future.

If you have been waiting for strong evidence before investing your organization’s time and resources into AI, the knowledge most organizations have already taken that step should be convincing. Now is the time to begin your journey into AI for your enterprise.

The CASC approach hopes to provide regulation that is “future-proof” since it gives agencies free rein to keep adapting to the evolution of AI technology, but it doesn’t provide the clear direction some organizations may want (or need) to pursue their AI strategy.

This article was originally published on TotalRetail blog. The busy holiday shopping season is already upon us, bringing today’s brands an important opportunity to strengthen relationships with new and existing customers. As increasing numbers of consumers rely on e-commerce to meet their shopping...