Zero-Shot and Few-Shot Learning Market Size, Share, Trends, and Opportunities By Type (Zero-Shot Learning, Few-Shot Learning), By Application (Computer Vision, Robotics, Natural Language Processing, Healthcare Diagnostics, Others), By End-User (Healthcare and Pharmaceutical, BFSI, Retail and e-Commerce, Autonomous Vehicles), And By Geography – Forecasts From 2025 To 2030

Zero-Shot and Few-Shot Learning Market Size:

The zero-shot and few-shot learning market is expected to expand at a significant CAGR over the forecast period.

The zero-shot and few-shot learning market is rising in demand due to the growing advancement in the generative AI (Artificial Intelligence) and increasing scalability and cost effectiveness. The zero-shot learning is used for models to perform tasks without any additional data or training, while few-shot learning uses a small amount of labelled datasets. They are increasingly integrated across industries due to a decrease in dependency on labelled data, and generalization of real-world sparse like rare diseases, from limited data, which is promoting their expansion.

Zero-Shot and Few-Shot Learning Market Overview & Scope:

The zero-shot and few-shot learning market is segmented by:

• Type: The few-shot learning segment is expected to have a significant share in the zero-shot and few-shot learning market, as the segment utilizes a small amount of labelled data to finely tune the model and outperforms the zero-shot learning in terms of accuracy in tasks. The versatility and scalability of few-shot learning enable it to be utilized in diverse applications ranging from healthcare, retail, and finance, where the demand for AI solutions is increasing.
• Application: The natural language processing segment holds a major growth driver in the market, due to growing advancements of the large language models, which are significantly integrating the zero-shot and few-shot learning capacities in the segment. Meanwhile, this segment is followed by the computer vision and healthcare diagnostics segment is expected to hold at a substantial pace due to utilization in autonomous vehicles and improvement of accuracy in disease diagnosis.
• End User: The healthcare and pharmaceutical segment has been projected to hold a substantial share in the end-user segment. This is due to the limitation of data related to rare disease and new drug discovery, along with the costly and time-consuming process for the collection of a large dataset leads to an increase in adoption of these zero-shot and few-shot learning solutions in this segment in the coming years.
• Region: The Europe region is predicted to witness considerable growth in the market for zero-shot and few-shot learning because of the government's stringent data privacy regulations, such as GDPR (General Data Protection Regulation), which poses restrictions on the collection of personal data and its usage. This enables zero-shot and few-shot learning, growing utilization in the autonomous vehicles and healthcare sectors of the region for data analysis and recognition with minimal or no dataset.

Top Trends Shaping the Zero-Shot and Few-Shot Learning Market:

• Advancements in Meta-Learning and Transfer Learning

The meta learning works in improving the few-shot learning by providing models to quickly adapt to new tasks, while transfer learning utilizes pre-trained models, which have minimal fine-tuned data, which works in an upward trajectory for the market.

Zero-Shot and Few-Shot Learning Market Growth Drivers vs. Challenges:

Drivers:

• Addressing Ethical and Bias Concerns: The zero-shot and few-shot learning can efficiently work with compact and smaller datasets or depend on the generalized data which is already provided to the pre-trained models, which decreases the requirement of providing personal data, which promotes the major concerns of the industries, which is ethical and bias issues. Further, with the governments enacting laws and regulations on data protection with strict compliance can lead to increased cost and complexity for companies that are subjected to them, as they need a large amount of data to use in traditional supervised learning.

This can lead to companies adopting zero-shot and few-shot learning as they require very less or no labelled datasets for analysis, which leads to minimal use of personal data and decreases the potential fine or regulatory issues. For instance, in January 2023, Californians approved the California Privacy Rights Act (CPRA) to come into effect, which focuses on data protection, including the data portability rights of the regional consumers. The exemption, along with strict requirements, creates an opportunity for zero-shot and few-shot learning solutions adoption by companies for decreased collection of personal data while minimizing the compliance risk and cost efficiency.

• Rising Advancements in Large Language Models (LLMs) and Generative AI: There is a rise in the advancement of the LLMs and generative AI models such as GPT-4 and Claude 3 and their advancing successors AI models which are pre trained on diverse data such as text, multimodal internet scale data and images which given them a broader dataset. This enables them to integrate zero-shot and few-shot learning for performing tasks without a requirement for a new dataset or additional training. This advancement contributes to the enhancement of their capacity and increases their adoption by diverse industries like healthcare and automotives.

Further, as per the United Nations Trade and Development (UNCTAD) data of April 2025, the AI market is growing rapidly and is expected to expand from 7 percent to 29 percent from 2023 to 2033, which is $189 billion to $4.8 trillion globally by 2033. This is predicted to be a twenty-five-fold rise in 10 years, and the countries are working on integrating AI through government initiatives. This trend of expansion of the AI market globally will provide an opportunity to make zero-shot and few-shot learning techniques more accessible and robust due to the scaling up of LLMs and generative AI model infrastructures.

Challenges:

• Model Interpretability: The complexity of AI models makes it challenging to understand and interfere with the task due to resource restrained, which could slow down zero-shot and few-shot learning adoption.

Zero-Shot and Few-Shot Learning Market Regional Analysis:

• North America: North America holds the major share of the zero-shot and few-shot learning market due to the presence of a large number of leading AI innovators in the region, majorly Google, Meta, OpenAI, and xAI, which increasingly drives the innovation of LLMs that power the utilization of zero-shot and few-shot learning. Additionally, the growing adoption by industries like healthcare for drug discovery and in the diagnosis of diseases, driven by strict regulations like CCPA, which limits personal data usage, will contribute to the regional market growth during the projected period.

Zero-Shot and Few-Shot Learning Market Competitive Landscape:

The market is fragmented, with many notable players, including IBM, Google, Open AI, Meta, Anthropic PBC, Microsoft, Amazon, Databricks, xAI, and Scale AI, Inc., among others.

• Product Launch: In March 2024, Anthropic, a leading AI research company, introduced the Claude 3 family of models. This model consists of Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. These models were designed to advance conversational AI, providing enhanced performance in reasoning, language understanding, and task execution compared to previous models. The Claude 3 family was noted for its zero-shot and few-shot learning capabilities, enabling it to handle tasks like text generation, classification, and question answering with minimal or no task-specific training data.

Zero-Shot and Few-Shot Learning Market Segmentation:

By Type

• Zero-Shot Learning
• Few-Shot Learning

By Application

• Computer Vision
• Robotics
• Natural Language Processing
• Healthcare Diagnostics
• Others

By End-User

• Healthcare and Pharmaceutical
• BFSI
• Retail and e-Commerce
• Autonomous Vehicles

By Geography

• North America
  • United States
  • Canada
  • Mexico
• South America
  • Brazil
  • Argentina
  • Others
• Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Others
• Middle East and Africa
  • Saudi Arabia
  • UAE
  • Others
• Asia Pacific
  • Japan
  • China
  • India
  • South Korea
  • Taiwan
  • Others

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. Zero-Shot and Few-Shot Learning Market BY type

5.1. Introduction

5.2. Zero-Shot Learning

5.3. Few-Shot Learning

6. Zero-Shot and Few-Shot Learning Market BY Application

6.1. Introduction

6.2. Computer Vision

6.3. Robotics

6.4. Natural Language Processing

6.5. Healthcare Diagnostics

6.6. Others

7. Zero-Shot and Few-Shot Learning Market BY INDUSTRIES

7.1. Introduction

7.2. Healthcare and Pharmaceutical

7.3. BFSI

7.4. Retail and e-Commerce

7.5. Autonomous  Vehicles

8. Zero-Shot and Few-Shot Learning Market BY GEOGRAPHY

8.1. Introduction

8.2. North America

8.2.1. United States

8.2.2. Canada

8.2.3. Mexico

8.3. South America

8.3.1. Brazil

8.3.2. Argentina

8.3.3. Others

8.4. Europe

8.4.1. United Kingdom

8.4.2. Germany

8.4.3. France

8.4.4. Italy

8.4.5. Others

8.5. Middle East & Africa

8.5.1. Saudi Arabia

8.5.2. UAE

8.5.3. Others

8.6. Asia Pacific

8.6.1. Japan

8.6.2. China

8.6.3. India

8.6.4. South Korea

8.6.5. Taiwan

8.6.6. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. IBM

10.2. Google

10.3. Open AI

10.4. Meta

10.5.  Anthropic PBC

10.6. Microsoft

10.7. Amazon

10.8. Databricks

10.9. xAI

10.10. Scale AI, Inc.

11. APPENDIX

11.1. Currency

11.2. Assumptions

11.3. Base and Forecast Years Timeline

11.4. Key benefits for the stakeholders

11.5. Research Methodology

11.6. Abbreviations

IBM

Google

Open AI

Meta

 Anthropic PBC

Microsoft

Amazon

Databricks

xAI

Scale AI, Inc.