Pulses deliver forward-looking insights into the evolution and impact of science, technology, and trends on global transformation. By engaging with our Pulses, you will gain a deeper understanding of each topic's significance, the key innovators driving change, and the future direction we anticipate. These insights are designed to stimulate discussions within your teams, challenging you to consider your preparedness for impacts on new product development, innovation, vision, strategy, R&D, and beyond.

AI and ML have transformed the way enterprises manage, access, and use information. As modern-day enterprises endlessly generate and process data, it becomes increasingly difficult to manage data access across multiple systems, maintain its quality and reliability, and ensure security compliance. Enterprise search has emerged as a key solution to these problems as modern-day enterprise search solutions combine managing, governing, and searching for data under one offering. The integration of AI and generative AI technologies has evolved enterprise search solutions, allowing them to be queried in natural language and generate key insights and personalized recommendations based on the searched data. In this report, we explore the enterprise search technology market, provide a high-level view of notable market offerings, and gauge the technology’s industry adoption. Scope All industries and geographies Contents In this report, we examine: Enterprise search’s evolution Modern enterprise search solutions’ core components Key enterprise considerations influencing enterprise search adoption Technology provider landscape and the growing investment capital raised by providers Enterprise search solutions’ maturity and use across industries Key adoption challenges enterprises face Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

Pulses deliver forward-looking insights into the evolution and impact of science, technology, and trends on global transformation. By engaging with our Pulses, you will gain a deeper understanding of each topic's significance, the key innovators driving change, and the future direction we anticipate. These insights are designed to stimulate discussions within your teams, challenging you to consider your preparedness for impacts on new product development, innovation, vision, strategy, R&D, and beyond. Membership(s) Advanced SciTech

Regulation in the AI space is about to change significantly with the introduction of the EU AI Act. How will this new legislation affect enterprises in the EU and beyond? This webinar, hosted by Everest Group Partners Anil Vijayan and Nitish Mittal alongside Mark Lewis, Senior Consultant at Stephenson Harwood LLP, delved into this first-of-its-kind regulation and its implications for enterprises, tech providers, and tech service providers. They discussed how to prepare for the legislation’s effects, including its extraterritorial impact, as the introduction of this comprehensive AI-focused legislation affected other global locations. Watch this event and gain insights into the potential repercussions of this legislation, with advice for best practices and key considerations to make when implementing AI tools in light of this new Act. What questions did the webinar answer for the participants? What is the state of the global regulatory environment with regards to AI? What is the anticipated impact of the EU AI act for various stakeholders? How can enterprises prepare for the Act coming into effect?

Research on advanced intelligent systems is underway to solve critical business problems and world issues. Intelligent loan approval, automatic medical diagnosis, autonomous cars, and predictive criminal defender identification are some of the revolutionary applications of AI that promise to deliver immense potential in terms of cost savings and performance improvement. While the high potential is appreciated by enterprises, our research suggests that only one in five enterprises have adopted AI at scale to deliver meaningful business results. Is the training data bias free? How did the system arrive at these results? How do I validate the results for accuracy and ensure compliance? Is there an exceptional situation where the system may fail? Such decisive questions have been left unanswered by AI systems leading to muted trust and limited large-scale adoption. This key roadblock is the black box nature of AI systems that limits explainability. Enterprises increasingly demand trustable, ethical, and repeatable behavior from AI algorithms that can ensure fairness and accuracy of results. In this paper we detail out the meaning and need of explainable AI systems. In addition, we: Identify enterprise expectations from an explainable AI system Establish a framework for enterprises to evaluate the extent of explainability required Explore the existing approaches to enable explainability at training data stage, functionality stage, and interpretation stage Comprehend the limitations and challenges with existing AI systems and the road ahead Membership(s) Digital Services