A critical analysis of AI trends, debunking vendor-driven myths and emphasising practical, business-oriented adoption strategies.
Parametric modelling enhances Agile IT project cost prediction through data-driven complexity categorisation. Accurate data, model flexibility, and clear stakeholder communication are crucial for effective implementation.
Graph AI is finally here, and it promises to fix many of the problems in current retrieval, augmenting generation AI services and improving accuracy, context, and reasoning capabilities.
Algorithm selection is crucial for effective machine learning. This guide outlines key algorithms, their strengths, limitations, and practical business applications.
Simplified, cost-effective graph databases are driving AI innovation, enabling advanced applications like RAG and agentic AI.
The era of deep collaboration and democratised data analytics forces two very different disciplines – knowledge management and data management – together. A surplus of idealised hype and a lack of practical technologies add pressure.
While agentic AI is grabbing the headlines, graph databases are quietly revolutionising AI applications with their ability to capture complex relationships.
Adobe’s Creative Cloud leverages AI for targeted productivity gains, setting a precedent for SaaS vendors to deliver tangible, work-specific AI enhancements, not just generic features.
Enterprise platforms now embed industrial AI agents, shifting from bespoke solutions. ServiceNow focuses on governance; Salesforce on proactive automation, simplifying deployment and impacting various enterprise roles.
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