Akshay Patel’s Post

New blog post from Matthias Patzak and myself on the strategic role of centralization and decentralization in generative AI adoption! "Centralizing or Decentralizing Generative AI? The Answer: Both" We explore how organizations can leverage a hybrid model - centralizing the core infrastructure and foundations, while empowering business domains to rapidly innovate with customized AI solutions. This nuanced approach combines the strengths of centralization (governance, scalability) and decentralization (agility, alignment), positioning companies to maximize the transformative impact of generative AI. Check out the full blog post for our recommendations on navigating this critical strategic decision: https://lnkd.in/ePMKNfNc Let us know your thoughts on the right organizational structure for generative AI in your company! #AWS #AWSEnterpriseStrategy #generativeAI #AIML

🤖 The AI Revolution: Centralize or Decentralize? 🤖 As Generative AI continues to disrupt industries, the question arises: should organizations centralize or decentralize their AI efforts? 🤔 In this insightful article, AWS explores the pros and cons of each approach. Centralization offers consistency, governance, and economies of scale, while decentralization fosters agility, innovation, and domain expertise. The answer? A hybrid model that balances the benefits of both! 🔥 By centralizing core AI capabilities and decentralizing domain-specific applications, organizations can unlock the full potential of Generative AI. Dive into this thought-provoking read to discover how to navigate the AI revolution and future-proof your business. Don't miss out on the insights that could shape your AI strategy! 📚

Centralize or decentralize the management and deployment of generative AI capabilities in your company? A key strategic decision with long-term implications.

Centralizing or decentralizing Generative AI? The answer: both For business and IT decision-makers, the question is no longer whether to adopt generative AI but how to structure its implementation for maximum impact and minimum risk. Whether to centralize or decentralize the management and deployment of generative AI capabilities is a key strategic decision with long-term implications. Learn more: https://lnkd.in/g9BhBehT #GenerativeAI #AI #ArtificialIntelligence #Innovation #DigitalTransformation

While the details will vary, there are 4 common principles that help companies to successfully deploy AI at scale.

Scaling AI: Platform best practices #AI #AIinfrastructure

Did you know that 84% of business executives believe they need to use #AI to achieve their growth objectives? Dive into our latest blog to explore the #ML and AI robust capabilities of Databricks vs. Microsoft Fabric https://hubs.ly/Q02yVvJx0

🔍 Exciting news in the AI world! Ontotext and Semantic Web Company (SWC) have merged to form Graphwise - a game-changer in making AI truly enterprise-ready. The challenge? While 70% of organizations use AI, only 11% deploy it successfully at scale. The missing piece? Knowledge structures that ensure accuracy and reliability. Graphwise's platform combines advanced knowledge graphs with AI, featuring their innovative GraphRAG technology that boosts accuracy from 60% to 90%. Their semantic layer seamlessly connects enterprise data, content, and domain knowledge - essential for trustworthy AI implementation. Read the full story to learn how Graphwise is transforming the Graph AI landscape: https://hubs.ly/Q033xrFx0 #TridionPartners

🌟 Make Your Content AI-Ready: A New Era of Knowledge Management https://lnkd.in/dhgjed5j Organizations rushing to implement AI are hitting a critical gap: accuracy and reliability. Without proper knowledge structures, generative AI can hallucinate responses, misinterpret data, or fail to leverage core organizational knowledge effectively. Here's how to bridge the gap: 🚀 Knowledge Graphs Are the Key Gartner highlights knowledge graphs as critical for trustworthy AI. They connect the dots across unstructured, semi-structured, and structured data, enabling organizations to unlock actionable insights. 💡 The Semantic Layer: A Game-Changer Partners like Graphwise are transforming AI readiness by creating a "semantic layer" that ties enterprise content and data together through: ◻️ Advanced classification and text mining ◻️ Intelligent tagging with Named Entity Recognition ◻️ Automated discovery of relationships between content items ⚙️ From Raw Data to Actionable Knowledge ◻️ Technologies like GraphRAG (Retrieval Augmented Generation) enrich generative AI with domain knowledge, boosting accuracy and reliability—from 60% to over 90% in some implementations. 🌍 Industry-Specific Solutions Graphwise is tackling challenges in: ◻️ ESG & Sustainability: Navigating regulatory landscapes ◻️ Pharma & Life Sciences: Accelerating research and discovery ◻️ Enterprise Knowledge Management: Building holistic knowledge architectures 🗝️ For AI to work smarter, not harder, organizations must prepare their content for the future. Ready to take the leap? Explore how to implement these solutions in your enterprise. #AIReady #KnowledgeGraphs #SemanticAI #CCA #ComponentContentAlliance https://lnkd.in/dz29xwWK #TridionPartners

🔍 Discover how companies like Databricks are transforming business value through AI, moving 1,342% more models from lab to real-world applications. Forbes' latest insights highlight how it's not just about quick wins; it's about setting up for sustainable growth and transformation 🚀 📈 💻 Read the article here: https://bit.ly/3RZQ6PA #DataLeaders #DataSecurity #ResponsibleAI