General

In a GenAI-driven world, language editors and supervisory analysts remain vital in financial services, ensuring clarity, compliance, and accuracy where AI tools may fall short.

This article explores how AI and ML transform data quality management by tackling inaccuracy and incompleteness, with insights on automation and real-world case studies.

Learn how Explainable AI (XAI) enhances transparency and trust in AI systems by revealing their decision-making processes, crucial for sectors like healthcare, finance, and criminal justice.

In the fast-paced QSR industry, generative AI is becoming a game-changer for consultants. By personalizing client interactions, enhancing decision-making, and driving competitive advantages, AI is reshaping how we deliver value.

Learn how AI is revolutionizing knowledge management, enabling rapid information retrieval, automated organization, and personalized knowledge delivery for competitive advantage by 2025.

When your role requires you to generate a high volume of documents, particularly when those documents follow familiar formats, that’s a great opportunity to have AI assist in the process. Its simply faster and more efficient to use intelligent automation to do the leg work.
Document completion tools fall into two categories – simple templates for basic document creation and composite templates which synthesize information from a variety of sources and combine them into one finished product.

Generative AI (GenAI) solutions can seem so all-encompassing and innovative that it can be difficult to know where to begin with them. We’ve addressed this dilemma by shaping two specialized uses of GenAI: virtual experts and document completion tools. We’ll discuss the latter soon in a companion post. For now, let’s consider two case studies of virtual experts, find out what they’re for, and how they can contribute to your workflow and data analysis.

In the corporate realm, knowledge is power. But what happens when that power remains cloistered, unable to reach the very people who need it? Siloed departments have long been the bane of large corporations.

In the constantly evolving generative AI sector, there’s significant competition between companies to create ever more relevant and consistent models. The challenge in this particular use case lies in ensuring that AI, when tasked with generating a company profile, adheres to these standards. This attention to detail provides the client with the confidence that the resulting profiles can be consistently compared and assessed.

Knowledge management (KM) plays a pivotal role in today’s organizations by ensuring that information is harnessed, disseminated, and leveraged for better decision-making. However, with the explosion in data, people across all roles are navigating a complex maze of knowledge management platforms, making content discovery increasingly more time-consuming and inefficient.

Recently, like most other analytics providers, we’ve been working with clients on incorporating more AI into their strategies and roadmaps. Generative AI is a large piece of it, but we’re seeing overall more focus from business leaders on using emerging technologies to dig deeper into their data and extract more valuable insights.

Data scarcity can be a barrier for companies looking to implement AI. But generative AI has a huge potential to resolve data scarcity issues.

AI is maturing rapidly and each year, novel applications of AI for business and everyday living are debuted or gain traction.

Machine learning (ML) algorithms are already commonplace in the business world, fueled by the extreme amounts of data now available and

As ChatGPT takes over social media and gets picked up everywhere, we asked our in-house experts their thoughts on the innovative technology!