Troy Thomas’ Post

A big blocker to AI ROI today is lack of vision and imagination. I still think people are underestimating the value of AI. Maybe not investors, given Nvidia's market cap, but practitioners. Too often we develop point solutions where potential value is less than change management costs. To generate big AI ROI, redesign a process, don't just augment it. John Lee's work at NBCUniversal Media is a good example. He's breaking silos between customer identify management, media planning, and media measurement. Read more in my blog post below. #AI #aiandml https://lnkd.in/giKwUthA

Microsoft has released a Responsible AI Transparency Report, highlighting their commitment to developing and deploying secure and trustworthy AI applications. The report outlines their strategies for responsibly building generative AI tools, providing customer support, and continuously improving their responsible AI program. Key actions include implementing a governance approach based on their Responsible AI Standard and the National Institute of Standards and Technology's AI Risk Management Framework. This framework helps identify and mitigate risks throughout the development process. Microsoft has also launched 30 responsible AI tools, comprising over 100 features to help customers develop their own AI solutions responsibly. Additionally, they have published 33 Transparency Notes since 2019, providing detailed information about their platform services, such as the Azure OpenAI Service. Collaborating with multi-stakeholder groups like the Frontier Model Forum, Partnership on AI, MITRE, and National Institute of Standards and Technology (NIST), Microsoft seeks to shape a broader responsible AI ecosystem. They fund AI research initiatives, including the National AI Research Resource, and their own Accelerating Foundation Models Research and AI & Society Fellows programs. Get the Full Report: https://lnkd.in/dqHB4AJk Photo Credits: Raimond Spekking | https://lnkd.in/dMA7CGsC

Expectations for generative AI in business are high, and for good reason. According to forecasts, enterprise spending on generative AI solutions is expected to reach $143 billion by 2027. And the potential benefits are staggering - McKinsey research predicts that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion to the global economy every year. But with all this excitement, companies are left with an important question: how can generative AI actually generate real value for customers, employees, and shareholders? As they explore the possibilities, it's important to keep this question front and center to ensure that generative AI is used in ways that truly benefit everyone involved.

AI platforms can help to harness the power of #AI technology, driving a range of benefits for the business, such as increased #automation, #scale, #security and more. These platforms enable businesses to #analyze vast amounts of #data, derive valuable insights and adapt swiftly to changing market dynamics, ultimately fostering innovation and a competitive edge. But…how to choose the best AI platform for your business? 🤔 Selecting a platform can be a challenging process, as the wrong system can drive increased costs as well as potentially limit the use of other valuable tools or technologies. Apart from pricing, there are numerous other factors to consider when evaluating the best AI platforms for your business. Gaining an understanding of available AI tools and their capabilities can assist you in making informed decisions when selecting a platform that aligns with your business objectives. In her recent article Anna Holman discusses about capabilities and best examples of AI platform use cases. Read the article here: 👉 https://lnkd.in/dcQHJ9V9 IBM

Understanding AIaaS: A Brief Overview Before exploring the scalability and flexibility of AIaaS, it is essential to understand what AIaaS is and how it operates. AIaaS Defined: AIaaS refers to the provision of artificial intelligence technologies through cloud-based platforms. It allows businesses to access and utilize AI capabilities without investing in costly infrastructure and specialized personnel. Service Models: AIaaS encompasses various models, including Machine Learning as a Service (MLaaS), Natural Language Processing as a Service (NLPaaS), and more, offering a wide range of AI functionalities. The Scalability of AIaaS Scalability is one of the most significant advantages offered by AIaaS. This section explores various facets of this scalability. Scaling Up and Down with Ease On-Demand Resources: AIaaS enables businesses to scale their AI resources up or down as needed. During peak demand or for large-scale projects, companies can easily access more resources, and similarly, scale down during low-demand periods. Cost-Effective Scaling: This scalability is not only about capacity but also cost-efficiency. Businesses pay for what they use, avoiding the financial burden of maintaining unused infrastructure. Supporting Business Growth Growth Alignment: As businesses grow, their AI needs often increase. AIaaS supports this growth by providing the necessary AI resources without the time and cost associated with traditional scaling. Agility in Market Adaptation: The ability to quickly scale AI resources means businesses can swiftly adapt to market changes, a critical factor in maintaining competitiveness. The Flexibility of AIaaS Flexibility is another cornerstone of AIaaS, allowing businesses to tailor AI solutions to their specific needs. Diverse AI Tools and Applications Wide Range of Services: AIaaS platforms offer a plethora of tools and services, from data analytics to complex machine learning models, catering to diverse business needs. Customization and Integration: Businesses can select and customize AI services to fit their operational processes, ensuring seamless integration with existing systems. Experimentation and Innovation Low-Risk Experimentation: The flexibility of AIaaS encourages experimentation with different AI technologies, fostering innovation without significant risk or investment. Rapid Prototyping and Deployment: AIaaS platforms enable rapid development and deployment of AI models, allowing businesses to quickly test and iterate AI solutions. AIaaS in Action: Use Cases Demonstrating Scalability and Flexibility Real-world examples illustrate the scalability and flexibility of AIaaS. SMEs Leveraging AIaaS for Growth Case Study: A small e-commerce company uses AIaaS for customer data analysis and personalized recommendation systems. As the company grows, it scales its AIaaS usage, enhancing customer experience and driving sales. Large Enterprises Optimizing Operations

🗣 "To be effective, generative models should be fine-tuned on domain-specific data catalogs and their output should be reviewed by humans. They can accelerate human work but not yet safely perform it solo.” — Jonathan Bruce, Vice President of Product Management at Alation. Generative AI holds a wealth of benefits for organizations and their innovation. But only if exercised correctly. ☝️ Generative AI needs to be fed with trusted, governed data and overseen by data analysts. Learn from Jonathan Bruce in this Digital CxO piece how AI can aid in enhanced customer experiences and productivity. #GenerativeAI #AI #Alation

🚀 Ready to Unlock the Power of AI? Check Out Our Latest Blog! 🤖 Artificial Intelligence is transforming industries worldwide, and it's your chance to stay ahead of the curve! Our new blog dives into "How to Choose the Best AI Platform," highlighting the economic impact of AI and key features you need to consider when selecting an AI platform for your business. 💡 Discover how AI can boost automation, enhance scalability, and improve security. 📈 Learn from real-world AI use cases in healthcare, finance, retail, and manufacturing. 🔍 Find out about the latest AI innovations, including IBM watsonX. Join us in exploring the limitless possibilities of AI in our blog! Read it here: https://lnkd.in/ewtBnfTt Don't miss out on this opportunity to harness the power of AI and gain a competitive edge. Read, share, and let's shape the future together! 🌐👩💼👨💼 #AI #ArtificialIntelligence #Innovation #BusinessIntelligence

Discover the pivotal factors and essential considerations crucial for crafting a robust generative AI integration strategy tailored to your business needs. 😎 Illuminate your path forward by reading this blog 👀 and ensure you harness the transformative power of AI while mitigating potential pitfalls. Want to embark on this journey of innovation and growth? 🤔 Then connect with our seasoned experts for personalized support 💪 https://lnkd.in/gN52E-Mn #tangenz #tangenzcorporation #oracle #generativeai #ai #blogreshare #blog #strategy #trending #culturalshift #usecase #usa #unitedstates #management #america #global

"To be effective, generative models should be fine-tuned on domain-specific data catalogs and their output should be reviewed by humans. They can accelerate human work but not yet safely perform it solo.” — Jonathan Bruce, vice president of product management at Alation. Generative AI holds a wealth of benefits for organizations and their innovation. But only if exercised correctly. Generative AI needs to be fed with trusted, governed data and overseen by data analysts. Learn from Jonathan Bruce in this Digital CxO piece how AI can aid in enhanced customer experiences and productivity. #generativeai #ai #alation