Arnlee Vizcayno’s Post

Deploying #AI at scale can be a challenge for enterprises. Capital One has developed a set of best practices and platform standards which boil down to 4 principles others can use to successfully achieve AI at scale. Check out all 4 here! #scalingAI #enterpriseAI #aideployment #aiadoption #artificialintelligence #aiplatforms #enterprisearchiticture #aiuse #bestpractices #genai #aimodels #llms #ml #platformstandards #aigovernance

Deploying #AI at scale can be a challenge for enterprises. At Capital One, they’re guided by a set of best practices and platform standards which boil down to 4 principles others can use to successfully achieve #AI at scale. Check out all 4 here! https://bit.ly/49Bcy9E (Sponsored)

Enterprises are shifting to advanced, scalable platforms to deploy and evolve AI, as highlighted by Capital One's focus on user-centric design and multi-tenant control planes. This approach is transforming how companies manage and scale AI, improving efficiency, collaboration, and accessibility for teams across the organization. Adopting these strategies will enable businesses to stay ahead in the AI race, driving innovation and unlocking long-term competitive advantages. #BusinessGrowth #AI

🚀 Elevate Your AI Strategy with MAIVA 🚀 In my experience, successfully scaling AI from pilot projects to enterprise-wide solutions is about more than just innovation. It's about ensuring strategic alignment, robust security, and scalability. That’s where #MAIVA comes in. This framework guides organizations to align their AI initiatives with core business goals while establishing a secure and responsible platform. By implementing cross-enterprise governance and equipping your team with the right tools, MAIVA helps manage AI operations and integrations seamlessly. The framework also emphasizes agile deployment with a focus on user experience, making it easier to scale solutions using reusable code. Continuous improvement is built into the process, with real-time performance monitoring and automated anomaly detection ensuring that AI continues to deliver value. For organizations ready to unlock the full potential of AI, MAIVA offers a strategic path forward, transforming AI into a powerful business advantage. #AI #DigitalTransformation #TechnologyStrategy #Innovation #BusinessGrowth

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

DataStax CEO: 2025 will be the year we see true AI transformation | While GenAI implementation may be challenging now, the groundwork laid in 2024 will pave the way for transformative applications in 2025? | Must adjust revenue forecast? | Venture Best

Scaling AI: Platform best practices #AI #AIinfrastructure

Are your legacy systems holding you back? Research shows that outdated infrastructure severely hinders businesses’ ability to adopt new technologies like generative AI. However, with 80% of companies recognising GenAI as key to modernising their applications, it’s clear that leveraging platforms to transform outdated systems is essential. Gen AI guides the migration process, ensuring that new frameworks are robust, efficient, and aligned with business needs. #GenAI #Modernisation #IT

Menlo Ventures release a report "2024: The State of Generative AI in the Enterprise". Enterprise AI spending saw dramatic growth in 2024, reaching $13.8 billion - a sixfold increase from 2023. Organizations are moving beyond experimentation, with 72% of leaders expecting broader AI adoption and identifying an average of 10 potential use cases. Funding is shifting from innovation budgets (60%) to permanent allocations (40%), signaling long-term commitment. The top enterprise applications show clear patterns of adoption. Code copilots lead at 51%, followed by support chatbots at 31%. Enterprise search, data transformation, and meeting summarization round out the top five use cases, reflecting a focus on productivity and efficiency gains. Across departments, technical teams command the largest share of spending, with IT and Product/Engineering accounting for 41% combined. Infrastructure trends reveal evolving preferences in the enterprise AI stack. Organizations typically employ multiple foundation models, with OpenAI's market share declining from 50% to 34% while Anthropic doubled its presence to 24%. RAG adoption increased significantly from 31% to 51%, and agentic architectures emerged as a notable new pattern with 12% adoption. I would argue this is the ROI of AI rapidly manifesting. AI is moving away from being just a product, a service, or even an infrastructure. It is effectively becoming a meta-factor of all business operations. Anything that can be optimized in a full cycle of a business has a place for use of AI. This is the big picture of the ROI of AI in the next 5 years. From this perspective, AI has at least a $180 Trillion market (the aggregation of market value for all existing industries globally). Report: https://lnkd.in/ebTV6pP5

In his latest Forbes post, Tray.ai CEO and founder Rich Waldron explains how enterprises can successfully achieve a manageable and scalable AI implementation strategy: ⚖️ Adopt a platform that can become the control center for end-to-end business processes where precise decisions can be made about where and how to infuse AI for the best business outcome 🧩 Build with a composable mindset to create more agile, flexible and efficient AI systems that can be quickly assembled and deployed to address evolving business needs. ✅ Run self-evaluations on historical data and identify when the organization is in beta on real-time AI processes. 🌐 Stay nimble and avoid committing to any one AI vendor. Check out the full post below. https://buff.ly/46Cen4F #ipaas #AIimplementation #integration #AI #automation