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生成AI

AI

ChatGPTのような生成AIシステムの新しい波は、産業全体を変革する可能性を秘めています。5年後に業界のリーダーであるためには、今日、明確で説得力のある生成AI戦略を策定することが必要です。

We are entering a period of generational change in artificial intelligence. Until now, machines have never been able to exhibit behavior indistinguishable from humans. But new generative AI models are not only capable of carrying on sophisticated conversations with users; they also generate seemingly original content.


What Is Generative AI?

To gain a competitive edge, business leaders first need to understand what generative AI is.

Generative AI is a set of algorithms, capable of generating seemingly new, realistic content—such as text, images, or audio—from the training data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks.

For example, GPT-3.5, a foundation model trained on large volumes of text, can be adapted for answering questions, text summarization, or sentiment analysis. DALL-E, a multimodal (text-to-image) foundation model, can be adapted to create images, expand images beyond their original size, or create variations of existing paintings.


What Can Generative AI Do?

These new types of generative AI have the potential to significantly accelerate AI adoption, even in organizations lacking deep AI or data-science expertise. While significant customization still requires expertise, adopting a generative model for a specific task can be accomplished with relatively low quantities of data or examples through APIs or by prompt engineering. The capabilities that generative AI supports can be summarized into three categories:

  • Generating Content and Ideas. Creating new, unique outputs across a range of modalities, such as a video advertisement or even a new protein with antimicrobial properties. 
  • Improving Efficiency. Accelerating manual or repetitive tasks, such as writing emails, coding, or summarizing large documents. 
  • Personalizing Experiences. Creating content and information tailored to a specific audience, such as chatbots for a personalized customer experiences or targeted advertisements based on patterns in a specific customer's behavior.  

Today, some generative AI models have been trained on large of amounts of data found on the internet, including copyrighted materials. For this reason, responsible AI practices have become an organizational imperative.

生成AIの活用方法

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生成AIが小売業界の消費者体験を一変させる

生成AIは、消費者との対話を通じてこれまで掘り起こせなかった潜在ニーズを把握し、その人に合った商品やサービスを提案できます。それを予期させる買い物アプリの例をまじえ、小売・消費財業界の動向を紹介します。

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金融機関では営業や貸出審査の効率化に威力

金融機関は生成AIをどのような領域でどう活用できるのか。営業、審査などにおける代表的な活用例を紹介するとともに、厳しいリスク管理基準のもとで活用するにあたって何を検討するべきか、ヒントを提示します。

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ヘルスケア領域は医薬品開発、患者対応に高い期待

ヘルスケア領域は生成AIの活用が最も期待される領域の一つです。医薬品開発にかかる時間とコストの大幅な削減から、医療機関における「共感力」の高い患者対応まで、現在見込まれている活用事例をご紹介します。

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行政を効率化し働き方も大きく変える

政府や自治体の業務は、法律や規則の専門的な知識が必要とされ、政策立案や文章作成に多大な労力と時間がかかります。生成AIは、こうした業務の効率を高め、行政の働き方そのものを変える可能性があります。


How Is Generative AI Governed?

Generative AI systems are democratizing AI capabilities that were previously inaccessible due to the lack of training data and computing power required to make them work in each organization’s context. The wider adoption of AI is a good thing, but it can become problematic when organizations don’t have appropriate governance structures in place.

The Ethical Issues Tied to Generative AI Governance

What Are the Types of Generative AI Models?

Types of Text Models

Types of Multimodal Models


What Type of Content Can Generative AI Text Models Create—and Where Does It Come From?

Generative AI text models can be used to generate texts based on natural language instructions, including but not limited to:

  • Generate marketing copy and job descriptions 
  • Offer conversational SMS support with zero wait time 
  • Summarize text to enable detailed social listening 
  • Search internal documents to increase knowledge transfer within a company 
  • Condense lengthy documents into brief summaries 
  • Power chatbots 

  • Perform data entry 
  • Analyze massive datasets 
  • Track consumer sentiment 
  • Writing software 
  • Creating scripts to test code 
  • Find common bugs in code 

This is just the beginning. As companies, employees, and customers become more familiar with applications based on AI technology, and as generative AI models become more capable and versatile, we will see a whole new level of applications emerge.

How Is Generative AI Beneficial for Businesses?

Generative AI has massive implications for business leaders—and many companies have already gone live with generative AI initiatives. In some cases, companies are developing custom generative AI model applications by fine-tuning them with proprietary data.

The benefits businesses can realize utilizing generative AI include:

  • Expanding labor productivity 
  • Personalizing customer experience
  • Accelerating R&D through generative design 
  • Emerging new business models 

Our Client Impact with Generative AI

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With GenAI, Customer Journeys Are Reaching the Next Level

As AI innovates new opportunities for business, forward-thinking companies like Allstate are capitalizing on the technology. The longtime BCG business partner used GenAI to gain better insights into customer touchpoints, building stronger relationships and reinforcing its best-in-class customer service.

"Satellite in space"

BCG Announces Collaborations with NASA and USRA

We are privileged to work with NASA’s Ames Research Center and the Universities Space Research Association (USRA) to launch a GenAI lab for science and engineering.

BCGの生成AI関連コラボレーション

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BCGとオープンAIの協働

BCGは、クライアント企業がオープンAIのテクノロジーのパワーを実現し、生成AIの責任ある活用によりきわめて複雑な課題を解決できるよう支援するために、オープンAIと協働しています。

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BCGとインテルの協働

AIアプリケーションを拡大展開する能力は、業界を問わず企業にとっての課題であり続けています。クライアント向けのエンタープライズグレードの生成AIソリューションを、迅速に、かつ安全に、責任をもってつくりあげるために、インテルとBCGの協業により、BCGのトランスフォーメーションの専門能力、BCG Xのエンジニアリングの組織能力、インテルのAIハードウエア/ソフトウエアを組み合わせて支援します。

How Business Leaders Can Get Started with Generative AI


What Are the Industries That Benefit from Generative AI?

Generative AI technology will cause a profound disruption to industries and may ultimately aid in solving some of the most complex problems facing the world today. Three industries have the highest potential for growth in the near term: consumer, finance, and health care.

  • Consumer Marketing Campaigns. Generative AI can personalize experiences, content, and product recommendations. 
  • Finance. It can generate personalized investment recommendations, analyze market data, and test different scenarios to propose new trading strategies. 
  • Biopharma. It can generate data on millions of candidate molecules for a certain disease, then test their application, significantly speeding up R&D cycles.  

Given that the pace the technology is advancing, business leaders in every industry should consider generative AI ready to be built into production systems within the next year—meaning the time to start internal innovation is right now. Companies that don’t embrace the disruptive power of generative AI will find themselves at an enormous—and potentially insurmountable—cost and innovation disadvantage.

 

Our Latest Insights on Generative AI

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video series

Harnessing the Power of AI and GenAI

To unlock the full potential of AI, companies must embrace both predictive and generative AI, align tech investments strategically, and prepare their talent for new challenges. In this video series, BCG experts share key strategies to forge a competitive edge for the future.

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BCG AI Radar: 生成AIの可能性を利益に

1,400人以上の経営層を対象としたBCGの調査で、生成AIが急速にビジネスのあり方を変えつつあることが明らかになりました。企業はどのようにして、生成AIの可能性を利益に変えているのでしょうか。

Meet Our Generative AI Experts

BCG’s generative AI experts have deep experience in AI technology, neural networks, generative models, the benefits of generative AI, and more. Here are some of our experts in generative AI.

Tech + Us: Monthly insights for harnessing the full potential of AI and tech.