Building a foundation for AI success: Technology and data strategy | The Microsoft Cloud Blog – Microsoft
This is part three of a six-part blog series. See part one, part two, and download the white paper.
AI is redefining the boundaries of what’s possible, but the unique demands of AI—from vast data volumes and high-speed processing requirements to complex security and compliance challenges—require a strategic approach to data and technology infrastructure.
“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,” says Wangui McKelvey, General Manager, Microsoft Azure Data Analytics.
In this post, we’ll focus on the five data and technology fundamentals required to deliver meaningful, sustainable, and responsible value creation with AI:
Building a Foundation for AI Success: A Leader’s Guide
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’s 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.
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.
Microsoft cloud
“Understanding how your business strategy maps to your product strategy, and then how your product strategy maps to your infrastructure, is key,” says Omar Khan, General Manager, Microsoft Azure Product Marketing. “AI-optimized infrastructure will help accelerate both building AI solutions and integrating AI into applications.” From a technology perspective, the most critical requirement is access to infrastructure that is built for AI—with the ability to run large AI workloads and perform securely and reliably at scale.
Data is the fuel that powers AI technology, so planning for any successful AI implementation requires that you identify the right data sources and ensure that the data is complete, of high quality, in the right format, and representative of your target customers and business objectives.
Microsoft AI
“Organizations continue to see their data as their competitive advantage,” says Wangui McKelvey. “Unlocking insights from their data, across their organization, in a single, integrated platform, empowers businesses to take advantage of AI. If you don’t understand the insights you can deliver from your data with AI, then you’re going to be left behind.”
The decisions you make today to buy, build, or modernize your technology infrastructure can significantly influence your ability to execute on future goals, so it’s essential to choose a path that is supportive of your organization’s vision. Here are some of the considerations and trade-offs:
Regulatory compliance, particularly with the General Data Protection Requirement (GDPR) and the coming EU AI Act, introduces a set of technical, legal, and operational considerations for infrastructure choices.1 Factors such as data sensitivity, data residency, scalability, and governance all play a part in architecture decisions, whether organizations choose on-premises, cloud, or co-located deployments. One critical factor to consider is access to sophisticated security measures that utilize machine learning, AI, and global threat intelligence databases to contribute to a more robust defense against cyber threats.
“Contrary to common belief, on-premises environments are often less secure than the cloud,” says Omar Khan. “Despite the sense of control associated with in-house infrastructure, cloud-based security solutions have evolved significantly, offering more advanced tools and technologies for threat detection and mitigation.”
Stay tuned for the next post in our series, “Building a Foundation for AI Success: AI strategy and experience,” in which we will explore the factors that contribute to a successful AI strategy and experience for customers. We will follow the next entry with dedicated posts focusing on organization and culture, and AI governance.
Download a copy of “Building a Foundation for AI Success: A Leader’s Guide.”
1EU AI Act.
The AI Strategy Roadmap shares what Microsoft has learned about the emerging best practices that organizations are using to create sustainable value with AI.
The Cloud Cultures series explores Mexico, a leading country in cloud adoption in Latin America.
We have collected a set of resources that encompass best practices for AI governance, focusing on security, privacy and data governance, and responsible AI.
Discover how the most trusted and comprehensive cloud can help you meet the challenges of a rapidly changing world.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
AI is redefining the boundaries of what’s possible, but the unique demands of AI—from vast data volumes and high-speed processing requirements to complex security and compliance challenges—require a strategic approach to data and technology infrastructure.
“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,” says Wangui McKelvey, General Manager, Microsoft Azure Data Analytics.
In this post, we’ll focus on the five data and technology fundamentals required to deliver meaningful, sustainable, and responsible value creation with AI:
Building a Foundation for AI Success: A Leader’s Guide
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’s 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.
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.
Microsoft cloud
“Understanding how your business strategy maps to your product strategy, and then how your product strategy maps to your infrastructure, is key,” says Omar Khan, General Manager, Microsoft Azure Product Marketing. “AI-optimized infrastructure will help accelerate both building AI solutions and integrating AI into applications.” From a technology perspective, the most critical requirement is access to infrastructure that is built for AI—with the ability to run large AI workloads and perform securely and reliably at scale.
Data is the fuel that powers AI technology, so planning for any successful AI implementation requires that you identify the right data sources and ensure that the data is complete, of high quality, in the right format, and representative of your target customers and business objectives.
Microsoft AI
“Organizations continue to see their data as their competitive advantage,” says Wangui McKelvey. “Unlocking insights from their data, across their organization, in a single, integrated platform, empowers businesses to take advantage of AI. If you don’t understand the insights you can deliver from your data with AI, then you’re going to be left behind.”
The decisions you make today to buy, build, or modernize your technology infrastructure can significantly influence your ability to execute on future goals, so it’s essential to choose a path that is supportive of your organization’s vision. Here are some of the considerations and trade-offs:
Regulatory compliance, particularly with the General Data Protection Requirement (GDPR) and the coming EU AI Act, introduces a set of technical, legal, and operational considerations for infrastructure choices.1 Factors such as data sensitivity, data residency, scalability, and governance all play a part in architecture decisions, whether organizations choose on-premises, cloud, or co-located deployments. One critical factor to consider is access to sophisticated security measures that utilize machine learning, AI, and global threat intelligence databases to contribute to a more robust defense against cyber threats.
“Contrary to common belief, on-premises environments are often less secure than the cloud,” says Omar Khan. “Despite the sense of control associated with in-house infrastructure, cloud-based security solutions have evolved significantly, offering more advanced tools and technologies for threat detection and mitigation.”
Stay tuned for the next post in our series, “Building a Foundation for AI Success: AI strategy and experience,” in which we will explore the factors that contribute to a successful AI strategy and experience for customers. We will follow the next entry with dedicated posts focusing on organization and culture, and AI governance.
Download a copy of “Building a Foundation for AI Success: A Leader’s Guide.”
1EU AI Act.
The AI Strategy Roadmap shares what Microsoft has learned about the emerging best practices that organizations are using to create sustainable value with AI.
The Cloud Cultures series explores Mexico, a leading country in cloud adoption in Latin America.
We have collected a set of resources that encompass best practices for AI governance, focusing on security, privacy and data governance, and responsible AI.
Discover how the most trusted and comprehensive cloud can help you meet the challenges of a rapidly changing world.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
