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Author: McKinsey & Company

Key Insights

1. Generative AI could significantly boost the global economy. It's estimated that generative AI could add between $2.6 trillion to $4.4 trillion annually across various use cases. To put this in perspective, the entire GDP of the UK in 2021 was $3.1 trillion. This means generative AI could potentially add an amount larger than the entire economy of the UK to the global economy every year.

2. Generative AI could deliver most of its value in four areas: Customer operations, marketing and sales, software engineering, and R&D. These areas could benefit from AI's ability to interact with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among other tasks.

3. Generative AI could have a significant impact across all industry sectors. Industries like banking, high tech, and life sciences could see the biggest impact as a percentage of their revenues from generative AI. For example, in the banking industry, the technology could deliver value equal to an additional $200 billion to $340 billion annually if fully implemented.

4. Generative AI could change the nature of work. It could augment the capabilities of individual workers by automating some of their activities. Current generative AI and other technologies have the potential to automate work activities that take up 60 to 70 percent of employees' time.

5. The pace of workforce transformation is likely to accelerate. With the potential for technical automation increasing, it's estimated that half of today's work activities could be automated between 2030 and 2060.

6. Generative AI could substantially increase labor productivity. However, this will require investments to support workers as they shift work activities or change jobs. If managed well, generative AI could contribute significantly to economic growth and support a more sustainable, inclusive world.

7. The era of generative AI is just beginning. While there's a lot of excitement around this technology and early pilots are promising, fully realizing the technology's benefits will take time. There are still considerable challenges to address, including managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas, and it has the potential to add a lot of value to the global economy. It could change the way we work by automating many tasks, and it could increase productivity. However, this will require support for workers who may need to learn new skills or change jobs. While there's a lot of excitement about the potential of generative AI, it's still early days, and there are many challenges to overcome.

1) Generative AI as a technology catalyst

Generative AI, which includes tools like ChatGPT and GitHub Copilot, is a result of significant investments in recent years that have advanced machine learning and deep learning. These AI applications are now embedded in many of the products and services we use daily, from the tech powering our smartphones to autonomous-driving features on cars.

Generative AI has been gradually permeating our lives, and its progress was almost imperceptible. However, it has now captured the public's attention due to its broad utility and ability to perform a range of tasks, such as reorganizing and classifying data, writing text, composing music, and creating digital art. This has led to a broader set of stakeholders grappling with generative AI's impact on business and society.

Generative AI is typically built using foundation models, which contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. These models are part of what is called deep learning, referring to the many deep layers within neural networks. Unlike previous deep learning models, foundation models can process extremely large and varied sets of unstructured data and perform more than one task.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It's like a super-smart computer program that can do things like write text, make music, and create digital art. It's been gradually becoming a part of our everyday lives, and it's now getting a lot of attention because of how useful it can be. Generative AI is built using really complex models that are inspired by how our brains work. These models can handle a lot of different types of data and can do more than one task at a time.

2) Generative AI use cases across functions and industries

Generative AI is a significant step forward in the evolution of artificial intelligence. It has the potential to deliver substantial value across various functions and industries. The report identifies and catalogs generative AI use cases across different industries and business functions.

1. Customer Self-Service: Generative AI can power chatbots to provide immediate and personalized responses to complex customer inquiries. This could automate responses to a higher percentage of customer inquiries, enabling customer care teams to focus on inquiries that require human intervention.

2. Software Engineering: Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding. They can train AI models to develop applications that generate code when given a natural-language prompt describing what that code should do.

3. Resolution during Initial Contact: Generative AI can instantly retrieve data a company needs to resolve customer issues during the first contact. This could significantly improve customer satisfaction and reduce the cost of service.

4. Testing and Trials: Generative AI can help to reduce testing time of complex systems and accelerate trial phases involving customer testing through its ability to draft scenarios and profile testing candidates.

5. Multimodal Capabilities: While most of generative AI's initial traction has been in text-based use cases, recent advances have also led to breakthroughs in image generation, and much progress is being made in audio, including voice and music, and video.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in a lot of different ways, like helping customers get answers to their questions, helping software engineers write code, and helping companies solve problems faster. It can also help with testing new products and can even create images, music, and videos.

How customer operations could be transformed

Generative AI has the potential to revolutionize customer operations, improving both the customer experience and the productivity of customer service agents. Here are some ways it could do this:

1. Customer Self-Service: Generative AI can power chatbots to provide immediate and personalized responses to complex customer inquiries. This could automate responses to a higher percentage of customer inquiries, enabling customer care teams to focus on inquiries that require human intervention.

2. Resolution during Initial Contact: Generative AI can instantly retrieve data a company needs to resolve customer issues during the first contact. This could significantly improve customer satisfaction and reduce the cost of service.

3. Virtual Expert: Generative AI could act as a virtual expert, providing always-on, deep technical knowledge to service professionals. It could rapidly access all relevant information such as product guides and policies to instantaneously address customer requests.

4. Agent Support: Generative AI can assist customer service agents by providing automated, personalized insights, including tailored follow-up messages or personalized coaching suggestions.

The application of generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. It's important to note that this analysis only captures the direct impact generative AI might have on the productivity of customer operations. It does not account for potential knock-on effects the technology may have on customer satisfaction and retention arising from an improved experience.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in customer service to help answer customer questions, solve problems faster, and even help customer service agents do their jobs better. This could make customer service more efficient and improve the experience for customers.

How marketing and sales could be transformed

Generative AI could significantly change the way both B2B and B2C companies approach marketing and sales. Here are some ways it could do this:

1. Strategization: Sales and marketing professionals could use generative AI to efficiently strategize. It could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments.

2. Increase Probability of Sale: Generative AI could identify and prioritize sales leads by creating comprehensive consumer profiles from structured and unstructured data. It could suggest actions to staff to improve client engagement at every point of contact. For example, generative AI could provide better information about client preferences, potentially improving close rates.

3. Improve Lead Development: Generative AI could help sales representatives nurture leads by synthesizing relevant product sales information and customer profiles. It could create discussion scripts to facilitate customer conversation, including up- and cross-selling opportunities.

4. Virtual Collaborator: Generative AI can work in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest large amounts of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work.

The application of generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. It could also increase sales productivity by 3 to 5 percent of current global sales expenditures.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in marketing and sales to help come up with new strategies, identify and prioritize potential customers, help salespeople develop leads, and work alongside employees to help them do their jobs better. This could make marketing and sales more efficient and effective.

How software engineering could be transformed

Generative AI could significantly change the way software engineering is done. Here are some ways it could do this:

1. Inception and Planning: Software engineers and product managers could use generative AI to assist in analyzing, cleaning, and labeling large volumes of data, such as user feedback, market trends, and existing system logs.

2. System Design: Engineers could use generative AI to create multiple IT architecture designs and iterate on the potential configurations, accelerating system design, and allowing faster time to market.

3. Coding: AI tools could assist engineers with coding, reducing development time by assisting with drafts, rapidly finding prompts, and serving as an easily navigable knowledge base.

4. Testing: Engineers could employ algorithms that can enhance functional and performance testing to ensure quality and can generate test cases and test data automatically.

5. Maintenance: Engineers could use AI insights on system logs, user feedback, and performance data to help diagnose issues, suggest fixes, and predict other high-priority areas of improvement.

The direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function. This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in software engineering to help with planning, designing systems, writing code, testing, and maintenance. This could make software engineering more efficient and effective.

How product R&D could be transformed

Generative AI could significantly change the way product research and development (R&D) is done. Here are some ways it could do this:

1. Early Research Analysis: Researchers could use generative AI to enhance market reporting, ideation, and product or solution drafting. This could lead to more efficient and effective research processes.

2. Virtual Design: Researchers could use generative AI to generate prompt-based drafts and designs, allowing them to iterate quickly with more design options. This could speed up the design process and lead to more innovative products.

3. Virtual Simulations: Researchers could accelerate and optimize the virtual simulation phase if combined with new deep learning generative design techniques. This could lead to more accurate and efficient testing of product designs.

4. Physical Test Planning: Researchers could optimize test cases for more efficient testing, reducing the time required for physical build and testing. This could lead to faster product development cycles.

The application of generative AI to product R&D could increase productivity at a value ranging from 10 to 15 percent of overall R&D costs. This value would arise primarily from reducing time spent on certain activities, such as generating initial drafts and designs, optimizing virtual simulations, and planning physical tests.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in product research and development to help with analyzing research, designing products, simulating product performance, and planning product tests. This could make product research and development more efficient and effective.

Value potential by modality

Generative AI has the potential to revolutionize the way we conduct business, and text-based AI is on the frontier of this change. Text-based data is plentiful, accessible, and easily processed and analyzed at large scale by Language Learning Models (LLMs), which has prompted a strong emphasis on them in the initial stages of generative AI development.

However, the value of generative AI is not limited to text-based applications. It also has significant potential in other modalities, such as images, audio, and video. For example, generative AI could be used to create new designs in industries like fashion or architecture (these would be LLMs trained on "design languages"). Once trained, such foundation models could increase productivity on a similar magnitude to software development.

Across the 63 use cases analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry's revenue.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in a lot of different ways, like helping with text-based tasks, creating new designs, and even working with images, audio, and video. It has the potential to add a lot of value to different industries, but exactly how much it can add will depend on a lot of different factors.

Generative AI could change the game for retail

Generative AI could significantly change the way retail and consumer packaged goods (CPG) companies operate. Here are some ways it could do this:

1. Customer Interaction: Generative AI can improve the process of choosing and ordering products. For example, a chatbot could pull up the most popular tips from the comments of a recipe, helping customers choose and order ingredients for a meal.

2. Marketing and Sales: Generative AI can help companies brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. This can increase awareness and improve sales conversion rates.

3. Product Discovery and Search Personalization: Generative AI can personalize product discovery and search with multimodal inputs from text, images, and speech, and deep understanding of customer profiles. This would allow companies to improve their ecommerce sales by achieving higher website conversion rates.

4. Customer Care: Retailers can combine generative AI with human agents to provide rapid resolution and enhanced insights in customer care. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information.

5. Disruptive and Creative Innovation: Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design.

The application of generative AI to the retail and CPG industry could increase productivity by 1.2 to 2.0 percent of annual revenues, or an additional $400 billion to $660 billion.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in retail and consumer goods companies to help with customer interaction, marketing and sales, product discovery, customer care, and creating new product designs. This could make these companies more efficient and effective.

Banks could realize substantial value from generative AI

Generative AI could significantly change the way banking industry operates. Here are some ways it could do this:

1. Content Production at Scale: Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. This could lead to more efficient and effective communication processes.

2. Virtual Expert: Banks have started to grasp the potential of generative AI in their front lines and in their software activities. For example, one European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting information from pictures and tables.

3. Risk Management: Generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data.

4. Customer Interaction: Generative AI can improve the process of choosing and ordering products. For example, a chatbot could assess user requests and select the best service expert to address them based on characteristics.

The application of generative AI to the banking industry could increase productivity by 2.8 to 4.7 percent of the industry’s revenues.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in the banking industry to help with producing content, providing expert advice, managing risk, and improving customer interaction. This could make the banking industry more efficient and effective.

Generative AI deployment could unlock potential value equal to 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries

Generative AI could significantly change the way pharmaceutical and medical-product industries operate. Here are some ways it could do this:

1. Content Production at Scale: Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. This could lead to more efficient and effective communication processes.

2. Virtual Expert: Generative AI can be used to develop a virtual expert by synthesizing and extracting information from pictures and tables. For example, one European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert.

3. Risk Management: Generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data.

4. Customer Interaction: Generative AI can improve the process of choosing and ordering products. For example, a chatbot could assess user requests and select the best service expert to address them based on characteristics.

The application of generative AI to the pharmaceutical and medical-product industries could increase productivity by 2.6 to 4.5 percent of the industry’s revenues.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used in the pharmaceutical and medical-product industries to help with producing content, providing expert advice, managing risk, and improving customer interaction. This could make these industries more efficient and effective.

3) The generative AI future of work: Impacts on work activities, economic growth, and productivity

Generative AI could significantly change the way we work and could have a big impact on our economy and productivity. Here are some ways it could do this:

1. Superpowers for Workers: Generative AI can give workers "superpowers" by taking over routine tasks and work, which can increase human productivity. This is especially important as productivity growth has been slow for almost 20 years.

2. Offsetting Aging Workforce: Generative AI can help offset the impact of an aging workforce, which is starting to affect workforce growth in many of the world's major economies.

3. Changing Work Activities: To achieve these benefits, a significant number of workers will need to substantially change the work they do, either in their existing occupations or in new ones. They will also need support in making transitions to new activities.

4. Biggest Impact on Knowledge Work: Generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation.

5. Potential Economic Challenges: The scale and scope of the workforce transitions described in this report are considerable. In the midpoint adoption scenario, about a quarter to a third of work activities could change in the coming decade.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can be used to help workers do their jobs more efficiently, help offset the impact of an aging workforce, and change the way we work. However, it could also lead to significant changes in the workforce, and workers will need support to adapt to these changes.

Actionable Advice for AI Careers (*Not from McKinsey)

Here are some actionable advice for people who are interested in a career in AI or want to adjust their work in the age of AI:

1. Embrace AI Tools: AI is not just for AI specialists. Many jobs will be transformed by the use of AI tools. Embrace these tools and learn how to use them effectively in your work. This could involve anything from using AI to automate routine tasks to using AI to analyze data and make decisions.

2. Continuous Learning: The AI landscape is rapidly evolving. Continuous learning is essential to keep up with the latest developments and to understand how they can be applied in your work. This could involve formal education, online courses, or self-study.

3. Focus on Human Skills: While AI can automate many tasks, there are still many things that humans do better. Skills like creativity, critical thinking, emotional intelligence, and the ability to collaborate effectively with others are all likely to become more important in the age of AI.

4. Adaptability: Be prepared for change. The tasks and skills required for your job are likely to change as AI becomes more prevalent. Be open to learning new skills and taking on new tasks.

5. Understand the Impact of AI: Understand how AI is likely to impact your industry and your job. This can help you anticipate changes and take proactive steps to adapt.

6. Ethics and Responsibility: As AI becomes more prevalent, it's important to understand the ethical implications and to use AI responsibly. This includes understanding issues like bias in AI, data privacy, and the impact of AI on employment.

7. Networking: Connect with others in your industry who are also interested in AI. This can help you learn from each other and stay up to date with the latest developments.

8. Specialize in an AI Application: If you're interested in a career in AI, consider specializing in an application of AI that's relevant to your industry. For example, if you work in the healthcare industry, you might specialize in using AI for medical imaging.

Remember, the goal is not to replace humans with AI, but to use AI to augment human skills and abilities. By embracing AI and learning to work with it, you can make yourself more valuable in the workplace.

4) Considerations for businesses and society

Generative AI, like any new technology, has the potential to reshape societies. It's already changing the way we live and work, and its rapid development could generate trillions of dollars of additional value each year and transform the nature of work. However, it could also deliver new and significant challenges. Here are some considerations for different stakeholders:

1. Companies and Business Leaders: Companies need to move quickly to capture the potential value of generative AI while managing the risks it presents. They need to consider how the mix of occupations and skills needed across their workforce will be transformed by generative AI and other artificial intelligence over the coming years. They also need to consider their role in ensuring the technology is not deployed in harmful ways.

2. Policy Makers: Policy makers need to consider what the future of work will look like at the level of an economy in terms of occupations and skills. They need to consider how workers can be supported as their activities shift over time, and what retraining programs can be put in place. They also need to consider what steps can be taken to prevent generative AI from being used in ways that harm society or vulnerable populations.

3. Individuals as Workers, Consumers, and Citizens: Individuals need to consider how concerned they should be about the advent of generative AI. They need to balance the conveniences generative AI delivers with its impact in their workplaces. They also need to consider how they can have a voice in the decisions that will shape the deployment and integration of generative AI into their lives.

In simpler terms, generative AI is a type of artificial intelligence that can create new content or ideas. It can have a big impact on our lives and our work, but it also comes with challenges. Different groups of people, like companies, policy makers, and individuals, need to think about how they can benefit from generative AI while also managing the risks it presents.