EnterpriseZone.cc’s Post

Beyond the Hype: 8 Practical Truths About AI Implementation from Chip Huyen pEight practical lessons on implementing AI, emphasizing user experience, data quality, and pragmatic approaches over theoretical perfection or the latest tec…/p

Many relatable points about how to successfully bring AI solutions to Financial Institutions. One in particular: "AI systems work best when embedded into existing workflows, not layered awkwardly on top." https://lnkd.in/dYuK3afT

Quantexa Makes Its Decision Intelligence Platform ‘Agent Ready’ to Solve the Hardest Problems in AI: Data Fragmentation & Context Read more: https://lnkd.in/dm3fsqaM #IndiaTechnologyNews #Quantexa #DecisionIntelligence #AIInnovation #AgentReady #DataFragmentation #ContextualAI #EnterpriseAI #DataAnalytics #ArtificialIntelligence

If there's one thing tech leaders agree on, it's this: AI won't fix a broken business. Rather, it will expose it. Find out how high-quality, connected, context-rich data clears your path to AI that works. http://spklr.io/6048BzVwA #TeamBoomi

Everyone’s talking about AI. But not everyone’s ready for it. It’s not the tech holding companies back: it’s culture, mindset, and trust. This article by Branex nails the 7 biggest barriers stopping AI from deployment. Because AI won’t replace people. But people who know how to use AI will. https://lnkd.in/eFeF5-te

New inspiring AI insights from our colleague, Branislav Popović, AI & ML Expert and Principal Research Fellow! Learn how the model context protocol enhances AI’s strategic agility through context-aware orchestration, and why choosing the right client, balancing performance trade-offs, and ensuring strong governance are essential for effectively deploying adaptive, intelligent AI systems. Find out more here: https://lnkd.in/dtwmJNjw

Everyone talks about AI models, GPUs, and generative breakthroughs. The real bottleneck isn't the model. It's the data. Siloed systems, inconsistent labels, and weak governance are the quiet blockers that stall AI projects before they even begin. Learn more about how these problems undermine AI adoption and how to avoid them at the link below! https://lnkd.in/ee6sMq42 #AI #DataScience #DataEngineering

2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU

2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU

2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU