Organizations are encountering significant challenges in AI adoption, specifically in utilizing domain-specific knowledge to yield reliable results. Knowledge graphs emerge as critical tools that enhance AI’s efficacy by providing a 'truth layer' to otherwise probabilistic outputs. Despite the surge in generative AI investments, many deployments fail to meet expectations for business value, prompting a recommendation from Gartner that organizations incorporate knowledge graphs into their strategies, particularly as they are seen as pivotal in advancing generative AI models. Market forecasts suggest substantial growth in the knowledge graph sector.
Knowledge graphs provide the essential "truth layer" for reliable AI systems, transforming probabilistic outputs into real world business acceleration.
Context is what gives meaning to pretty much everything. All graphs have inherent potential to bring knowledge because they acknowledge the interconnectivity of information.
Investment in AI reached a new high, with a focus on generative AI; however, this has yet to deliver the anticipated business value.
Knowledge Graphs are at the heart of Critical Enabler technologies, with Gartner recommending them as crucial in building and advancing GenAI models.
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