{"id":170,"date":"2024-01-29T08:00:00","date_gmt":"2024-01-29T16:00:00","guid":{"rendered":""},"modified":"2026-04-11T13:11:00","modified_gmt":"2026-04-11T20:11:00","slug":"building-a-foundation-for-ai-success-technology-and-data-strategy","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/microsoft-cloud\/blog\/2024\/01\/29\/building-a-foundation-for-ai-success-technology-and-data-strategy\/","title":{"rendered":"Building a foundation for AI success: Technology and data strategy"},"content":{"rendered":"\n

This is part three of a six-part blog series. See <\/em>part one<\/em><\/a>, <\/em>part two<\/em><\/a>, and <\/em>download the white paper<\/em><\/a>.<\/p>\n\n\n\n

AI is redefining the boundaries of what’s possible, but the unique demands of AI\u2014from vast data volumes and high-speed processing requirements to complex security and compliance challenges\u2014require a strategic approach to data and technology infrastructure.<\/p>\n\n\n\n

“It’s been over a year now since generative AI became mainstream. We are through the science experiment phase and leading companies are now putting AI into action,\u201d says Wangui McKelvey, General Manager, Microsoft Azure Data Analytics.<\/p>\n\n\n\n

In this post, we\u2019ll focus on the five data and technology fundamentals <\/span>required to deliver meaningful, sustainable, and responsible value creation with AI:<\/p>\n\n\n\n

    \n
  1. Align technology strategy and business strategy<\/li>\n\n\n\n
  2. Assess infrastructure needs and goals<\/li>\n\n\n\n
  3. Prepare your data estate to smooth the path from proof-of-concept to production<\/li>\n\n\n\n
  4. Consider build-versus-buy decisions<\/li>\n\n\n\n
  5. Determine a strategy for regulatory compliance and safeguarding AI assets<\/li>\n<\/ol>\n\n\n\n
    \n\t\n
    \n\t
    \n\t\t
    \n\t\t\t
    \n\t\t\t\t\n\n

    Five pillars of AI success<\/h2>\n\n\n\n

    Building a Foundation for AI Success: A Leader\u2019s Guide<\/p>\n\n\n\n

    \n
    Get the white paper<\/a><\/div>\n<\/div>\n\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t\t\t\"\"\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n<\/div>\n<\/div>\n\n\n\n

    Align technology strategy and business strategy<\/h2>\n\n\n\n

    Successful AI projects begin with a clear, prioritized, and valued set of business objectives, such as maximizing productivity and efficiency, improving customer experience, or growth-oriented objectives such as revenue acquisition or product innovation. These goals will help you prioritize use cases against likelihood of impact and realistically assess feasibility based on data and infrastructure requirements. It\u2019s also useful to think of your technology investments in aggregate as an investment portfolio, which will enable you to set clear success criteria and reallocate resources across projects as needed.<\/p>\n\n\n\n

    Assess infrastructure needs and goals<\/h2>\n\n\n\n

    Moving successfully from proof-of-concept (POC) to production with AI depends on a mix of technology and business factors that, ideally, must work together.<\/p>\n\n\n\n

    \n\t
    \n\t\t

    \n\t\t\tMicrosoft cloud\t\t<\/h2>\n\t\t

    \n\t\t\t\t\t\t\t\n\t\t\t\t\t\tLearn more\t\t\t\t\t\t\t\u2197<\/a>\n\t\t\t\t\t<\/p>\n\t<\/div>\n<\/div>\n\n\n\n

    \u201cUnderstanding how your business strategy maps to your product strategy, and then how your product strategy maps to your infrastructure, is key,\u201d says Omar Khan, General Manager, Microsoft Azure Product Marketing. \u201cAI-optimized infrastructure will help accelerate both building AI solutions and integrating AI into applications.\u201d From a technology perspective, the most critical requirement is access to infrastructure that is built for AI\u2014with the ability to run large AI workloads and perform securely and reliably at scale.<\/p>\n\n\n\n