{"id":655,"date":"2023-11-01T08:00:00","date_gmt":"2023-11-01T15:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/microsoft-cloud\/blog\/?p=655"},"modified":"2024-04-25T07:01:02","modified_gmt":"2024-04-25T14:01:02","slug":"building-a-foundation-for-ai-success-business-strategy","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/microsoft-cloud\/blog\/2023\/11\/01\/building-a-foundation-for-ai-success-business-strategy\/","title":{"rendered":"Building a foundation for AI success: Business strategy"},"content":{"rendered":"\n

This is part two of a six-part blog series\u2014see part one<\/a> and download the white paper<\/a>.<\/p>\n\n\n\n

Business strategy is the first step toward AI success<\/h2>\n\n\n\n

AI is applicable to so many different use cases, from content generation to code generation to prediction to summarizing vast amounts of data. But what makes AI valuable is the impact it can have on business goals.<\/p>\n\n\n\n

In this post, we\u2019ll focus on business strategy\u2014the first of five categories that support the ability to deliver meaningful, sustainable, and responsible value creation with AI. Subsequent posts in this series will cover best practices for the remaining categories: data and technology strategy, AI strategy and experience, organization and culture, and AI governance.<\/p>\n\n\n

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Building a Foundation for AI Success<\/h2>\n\n\t\t\t\t\t
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Learn about the pillars of AI success<\/p>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tDownload the white paper<\/span>\n\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\n\t\t\t\t\t\t\t
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Five steps to building a successful business strategy for AI<\/h2>\n\n\n\n

AI has tremendous potential to transform multiple business functions, from marketing to product development to customer service to operations. But, like any consequential technology, it needs to support business objectives to drive meaningful business value.<\/p>\n\n\n

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\n\t\t\tAI use cases\t\t<\/p>\n\t\t\n\t\t\tRead customer stories<\/span> <\/span>\n\t\t<\/a>\n\t<\/div>\n<\/div>\n\n\n\n

In our conversations with customers, partners, and external and internal experts, we identified five steps that can help you develop a strategy for AI that will help you meet your goals.<\/p>\n\n\n\n

1. Define and prioritize business needs<\/span><\/h3>\n\n\n\n

Successful AI projects begin with a clear, prioritized\u2014and most importantly, valued\u2014set of business needs. \u201cWe\u2019ve only just begun to understand the potential for AI business transformation across organizations,\u201d said Alysa Taylor<\/a>, Corporate Vice President of Azure & Industry Marketing at Microsoft. \u201cWhile customer use case adoption of AI varies by industry, we are seeing clear momentum around core business opportunities like employee experience, customer engagement, and internal business processes, as well as a focus on areas where AI can help bend the curve on innovation.\u201d<\/p>\n\n\n\n

Starting with the business need is crucial because it helps pinpoint the use cases that are best equipped to drive meaningful impact and garner executive visibility, support, and, critically, resources. This can help you avoid \u201cperpetual proof-of-concept” and scale the initiatives with the greatest potential to become a force multiplier for your organization.<\/p>\n\n\n\n

2. Identify AI use cases that support business objectives<\/span><\/h3>\n\n\n\n

Once your business needs are clear, it\u2019s time to identify the use cases best suited to meeting your needs. Some of the top use cases<\/a> we\u2019re seeing for generative AI include:<\/p>\n\n\n\n

Business need<\/th>Generative AI use cases<\/th><\/tr><\/thead>
Advance productivity<\/td>\u00b7 Streamline employee tasks
\u00b7 Speed up communication with AI-generated content
\u00b7 Accelerate service delivery<\/td><\/tr>
Maximize efficiency<\/td>\u00b7 Anticipate future needs with predictive analytics
\u00b7 Accelerate operations with amplified automation
\u00b7 Avoid downtime with predictive maintenance and AI-powered incident management<\/td><\/tr>
Improve business outcomes<\/td>\u00b7 Generate new products and services
\u00b7 Personalize customer experiences
\u00b7 Enhance decision-making with intuitive business reporting<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n
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\n\t\t\tMicrosoft AI\t\t<\/p>\n\t\t\n\t\t\tExplore solutions<\/span> <\/span>\n\t\t<\/a>\n\t<\/div>\n<\/div>\n\n\n\n

Of course, the best use cases are ones that deliver value in multiple ways. Conversational search, for example, is a great time-saver but also improves customer experience, while call summarization can help front-line employees and surface issues or opportunities that can lead to product or service improvements, or even new features and products.<\/p>\n\n\n\n

3. Establish a set of criteria you\u2019ll use to prioritize use cases<\/span><\/h3>\n\n\n\n

The next step is to establish a set of criteria that you will use to evaluate use cases with the highest likelihood of success. It\u2019s critical to engage a diverse group of stakeholders and teams spanning multiple areas of expertise within your organization. These insights can help to identify use cases from different perspectives and inform the potential impact of each one, so you have the broadest possible view of success from stakeholders across the business.<\/p>\n\n\n\n

Following are five criteria to consider. Implementing these can be as simple as a discussion or as rigorous as a scorecard that you use at the beginning of each project.<\/p>\n\n\n\n