关键字: [Amazon Web Services re:Invent 2023, Anthropic Claude, Consumer Packaged Goods Industry, Generative Ai, Use Cases, Business Alignment, Responsible Ai Framework]
Number of words: 1500, reading time: 8 minutes
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Introduction
Generative AI is poised to disrupt the business landscape and change the way companies operate, innovate and interact with customers. It has the potential to open doors to unprecedented creativity and efficiency—but how do you open those doors within your own organization? Join this lightning talk to learn how generative AI can be applied in the consumer goods industry. This talk will discuss use cases, practical applications and key factors for successful integration.
Highlights of speech
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Michael Connor and his colleagues took to the busy stage of re:Invent. In the spotlight, Michael introduced himself as the leader responsible for the generative AI practice for Amazon’s global CPG retail team covering more than 150 countries. Generative AI has become a hot topic over the past six months. They held in-depth conversations with thousands of executives and directors across various companies and industries. In numerous conversations with more than 1,000 C-level and board members, they gathered many insights into how to leverage and respond to this emerging technology.
Michael described huge predictions about the future impact of generative AI, with some estimates that it will contribute $7 trillion to global GDP. He observed that this was the first time ever that he had seen such deep engagement and interest from board members and senior leaders. Recognizing the potential of generative AI to radically transform their businesses, these decision-makers want to ensure their companies have strategic plans, funding and the basic knowledge of how to use this technology. Michael noted that it was a new experience for him to have these discussions with board members, unlike the generative AI discussions he had been involved in before.
Speakers mentioned some major investments and new product launches related to generative AI, such as Amazon's $4 billion investment in Anthropic and their conversational Claude model running on Amazon EC2 instances. There have also been some high-profile developments, such as Meta's launch of an AI chatbot that emulates the personality of celebrities like Kendall Jenner. Michael reflects on how these types of innovations are rapidly changing the nature of advertising, which he believes will have a permanent impact.
Turning to practical applications, Michael shared research results for CPG and retail companies. According to McKinsey analysis, generative AI has broad application prospects in fields such as marketing, sales, customer operations, product research and development, and software engineering. He emphasized that companies often focus on how to use generative AI to discover new revenue growth opportunities. However, in some cases, such as customer call centers, this technology can significantly reduce costs.
To illustrate the breadth of potential applications, Michael tells a story about an executive client. This client requested a creative workshop to explore how generative AI could be applied throughout their retail operations. In one day of design thinking, Michael's team was able to compile an exhaustive list of more than 40 high-value use cases, ready for implementation. Some examples he shared include applying generative AI to marketing tasks, such as automatically creating ads, product promotions, image generation, etc. He observes that this is common as organizations move from identifying a single proof-of-concept application to building scalable generative AI pipelines that can support hundreds or thousands of use cases company-wide.
Next, Michael walked the audience through the fundamental differences between generative AI and machine learning and deep learning methods. While other methods require training models on large-scale data sets to complete specific tasks, generative AI absorbs information from the vast amounts of diverse data available on the Internet to create models capable of performing countless functions. This enables generative AI to produce human-like output such as generating images, text, code, etc. He emphasized that enterprises still need traditional AI, but generative models are a game changer.
However, Michael warns that realizing the full potential of generative AI requires thoughtful data strategy and management. This is a pressing need for businesses striving to become data-driven companies that need to keep their data in good shape. Michael drew this lesson from his past role as chief architect at The Coca-Cola Company, where he wished they had had more of a data foundation to support advanced analytics and AI. He points out that Coca-Cola has spent hundreds of millions of dollars buying ETL data migration jobs from other companies, which he believes will become unnecessary with the rise of generative AI.
To demonstrate the capabilities of this technology, Michael went on to demonstrate a number of real-world applications of generative AI:
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Running Stable Diffusion on Amazon EC2 can quickly generate various product concept images, 3D models, press releases, product descriptions and related social media content, thereby turning a new product idea into reality.
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Using tools such as RunwayML, users can easily generate thousands of customized advertising images in just a few minutes, including targeted backgrounds, pets, products, etc., thereby improving marketing effectiveness.
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Automatically generate personalized product descriptions for different customer groups. Michael mentioned that one customer with 300,000 products wanted to localize these descriptions into 10 languages through Amazon Translate.
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Use Amazon Comprehend to simplify complex data analysis (such as pricing models) into easy-to-understand common language, making it easier for business executives to understand these insights.
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Use Amazon Cloud Technology Thinkbox Deadline to accelerate the new product development process and generate hundreds of new product concepts as well as CAD models, names, descriptions, etc. Michael pointed out that it would have taken customers six months to bring a product to market, but now it can be done much faster.
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By using Amazon Lex, factory workers are able to get equipment troubleshooting help by reading manuals and answering questions in natural language through an AI system. This service is available in 10 languages, eliminating language barriers.
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Build a conversational product recommendation engine that understands context and customer needs, using Amazon Personalize.
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Analyze call center calls, generate summaries, extract insights, and determine required action follow-up using Amazon Connect and Amazon Comprehend. Michael said typically only 5% of calls require follow-up action.
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By using Amazon Cloud Technology Lambda, the AI system interprets and converts the original COBOL code to migrate to modern languages.
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Use Amazon CloudFormation to generate cloud infrastructure templates that can spin up environments in minutes, not months. Michael gave the example of reducing setup time from 3 months to just minutes, which was achieved using AI-generated CloudFormation templates.
Based on these experiences, Michael recommends responsible practices, such as incorporating human review into the human-computer interaction process, before using generative AI output in practice. He recommends building AI systems from a small scale in areas such as call centers. Michael emphasizes the importance of focusing on high-impact, low-effort use cases that are aligned with the company's business goals and not getting distracted by training models. Finally, he argued for a responsible use framework that encompasses monitoring, risk, bias, diversity, and legal implications.
Overall, Michael believes that generative AI has huge potential across industries to profoundly transform productivity and performance. By adopting a conscious strategy and responsible implementation, and leveraging cloud services such as Amazon Web Services, businesses can unlock their immense value. He encouraged attendees to contact him afterwards to discuss further how they can leverage generative AI in their organizations.
Here are some highlights from the speech:
The chief architect, who comes from The Coca-Cola Company, leads a team dedicated to data science innovation and works closely with consumer packaged goods (CPG) customers.
He shared his backstory.
Amazon Cloud leaders detail how retail companies are quickly moving every aspect of their business to use Amazon Cloud services.
Trained on massive amounts of data, a single generative artificial intelligence (AI) model can perform thousands of tasks, including generating text, images, and code.
Generative AI is pushing enterprises to take data governance seriously in order to deliver more accurate insights.
Summarize
Michael Connor is a leader in generative artificial intelligence at Amazon Cloud Technologies with deep insights into the consumer packaged goods industry (CPG).
He emphasized that there is tremendous excitement and engagement among executive leaders and board members about generative AI, believing it will revolutionize their business models and the way they operate. Connor demonstrated many real-world application examples in areas such as marketing, sales, R&D, and software engineering, such as creating localized ads and generating product concepts.
Connor's key points include:
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Generative AI enables businesses to generate highly customized and localized content, such as advertising and product descriptions, for different markets and languages, quickly and at scale. This brings unprecedented personalization and relevance.
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Through its natural language capabilities, generative AI can automate or enhance many manual processes, from catalog cleaning to producing troubleshooting guides, increasing speed and efficiency.
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Combined with the right tools and human supervision, generative AI allows for rapid experimentation and iteration of ideas, thus accelerating innovation cycles.
Finally, Connor emphasized that successfully leveraging generative AI requires thoughtful change management and aligning initiatives with core business goals. He recommends starting with high-impact applications and establishing an ethical and responsible AI framework.
Original speech
https://blog.csdn.net/just2gooo/article/details/134868209
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