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Generative AI

The new wave of generative AI systems, such as ChatGPT, have the potential to transform entire industries. To be an industry leader in five years, you need a clear and compelling generative AI strategy today.

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.


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.

 

Our Generative AI Collaborations

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BCG’s Collaboration with OpenAI

BCG is collaborating with OpenAI to help our clients realize the power of OpenAI technologies and solve the most complex challenges using generative AI—responsibly.

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BCG Advances Partnership with Google Cloud

BCG and Google Cloud are excited about generative AI’s transformative capabilities, devoting significant resources to jointly help customers apply this breakthrough technology.

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BCG’s Collaboration with Intel

The ability to scale AI applications continues to challenge businesses across industries. Our collaboration with Intel brings together BCG’s transformation expertise, BCG X’s engineering capabilities, and Intel’s AI hardware and software in order to rapidly create enterprise-grade generative AI solutions for our clients—securely and responsibly.

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.

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.