{"id":3067,"date":"2023-10-23T08:00:00","date_gmt":"2023-10-23T15:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/innovation\/blog\/ms-industry\/from-discussion-to-deployment-4-key-lessons-in-generative-ai\/"},"modified":"2026-04-09T08:57:33","modified_gmt":"2026-04-09T15:57:33","slug":"from-discussion-to-deployment-4-key-lessons-in-generative-ai","status":"publish","type":"ms-industry","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/microsoft-cloud\/blog\/retail-and-consumer-goods\/2023\/10\/23\/from-discussion-to-deployment-4-key-lessons-in-generative-ai\/","title":{"rendered":"From discussion to deployment: 4 key lessons in generative AI"},"content":{"rendered":"\n

A year ago, few had heard the term \u2018generative AI\u2019, but with the launch of OpenAI\u2019s ChatGPT that all changed very rapidly. Within 64 days of launch, ChatGPT had over 100 million users1<\/sup> and this interest changed completely the conversations between business teams and their IT counterparts. <\/p>\n\n\n\n

It also changed the conversations Microsoft customer-facing teams were having as organizations scrambled to exploit the technology. There are very few customer conversations and major projects where some form of generative AI does not have a part to play. <\/p>\n\n\n\n

Microsoft teams have worked hard to advise customers on the best way forward and there have been lessons learned from all sides. Conversations are now starting to change, however. They are moving away from being primarily focused on \u2018What can we do with generative AI?\u2019 toward \u2018How should we approach generative AI?\u2019. <\/p>\n\n\n\n

There are some lessons that have been learned in customer interactions that are now being applied to projects to maximize the return on investment for our customers. Each of these four areas you may wish to give consideration to as you explore possibilities within your organization. <\/p>\n\n\n\n

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Microsoft Cloud for Retail<\/h2>

Lean into change and thrive during times of uncertainty.<\/p>

Discover more<\/a><\/div><\/div>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t
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1. Be clear on the problem <\/h2>\n\n\n\n

There can be a temptation to see generative AI as if it were a \u2018new toy\u2019 waiting to be played with. It is critical however to be focused on the problem you are trying to solve and to use this to \u2018paint the vision\u2019 of what you want to achieve. <\/p>\n\n\n\n

An approach that can work well is to identify several candidate initiatives or use cases where generative AI may be able to help. Examining these more closely could reveal that there are other ways to solve some of them. There may also be some that you can cluster together because they could warrant similar approaches to address their needs. <\/p>\n\n\n\n

Prioritizing based on likely business impact<\/a> versus the effort required is an excellent way to determine which use case, or use cases, you should start with.\u00a0This is a technique Microsoft Industry Architects use with customers engaging with both technical and business teams at the same time.\u00a0Placing each use case on a simple four-quadrant matrix with \u2018Effort\u2019 on the x-axis and \u2018Impact\u2019 on the y-axis gives a simple yet effective visualization approach to setting priorities.\u00a0<\/p>\n\n\n\n

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Figure 1<\/em>: Impact and Effort Matrix <\/em><\/figcaption><\/figure>\n\n\n\n

When considering impact, consider things like speed or time saving, improved decision making, and cost savings but also consider how long it will take to reap the benefits. When considering effort, think about the time and money you would need to invest to achieve the desired outcome while being conscious of the different systems and personas that would be involved. <\/p>\n\n\n\n

Once you have determined your target use case or use cases, you must be clear about what \u2018good\u2019 looks like. This means determining the metrics and values by which you will measure your success. Using quantitative terms can deliver real focus. Your metric could be based on time, effort, or money\u2014but try to make it as measurable and meaningful as possible. <\/p>\n\n\n\n

2. Work inside-out <\/h2>\n\n\n\n

As a customer-centric organization, it might feel counter-intuitive to focus internally first. With new innovative technologies like generative AI, this is a great way to try out the technology and your approaches on a \u2018friendly\u2019 audience\u2014your own internal teams\u2014before applying to your external customers. <\/p>\n\n\n\n

This also acts as the foundation for a roadmap of projects and initiatives all centered around generative AI. You can think of this roadmap in three phases\u2014which you could align to a \u2018Crawl-Walk-Run\u2019 approach. <\/p>\n\n\n\n

Phase 1: Crawl<\/h3>\n\n\n\n

Use cases inside the organization<\/h4>\n\n\n\n

This focuses on ‘human in the loop’ reviews of content that is generated. In a retail and consumer goods environment example use cases might include: <\/p>\n\n\n\n