Introduction

Agentic AI refers to autonomous systems designed to perform complex, multi-step tasks with minimal human intervention. These AI agents are capable of advanced decision-making, adapting to changes, and self-management. In the context of digital engineering, agentic AI is utilized to automate tasks ranging from coding and testing software to orchestrating entire software deployment cycles. The technology enhances productivity by taking over repetitive and mundane tasks, allowing engineers to focus on more strategic activities that require human creativity and insight​.

According to Market.us, The Global Agentic AI in Digital Engineering Market is poised for significant growth, with the market size expected to reach USD 378.5 billion by 2034, up from USD 3.4 billion in 2024, reflecting a remarkable compound annual growth rate (CAGR) of 60.20% during the forecast period from 2025 to 2034. This growth can be attributed to the increasing adoption of advanced AI technologies in digital engineering, particularly in areas like automation, design optimization, and predictive analytics. As industries continue to evolve, the demand for more intelligent, autonomous solutions is driving this market expansion.

In 2024, North America holds the largest market share, accounting for over 39.4% of the total market. The region generated around USD 1.3 billion in revenue, largely due to its established technological infrastructure, high investment in AI research and development, and strong presence of key market players. North America’s leadership is expected to continue, though other regions are also witnessing rapid growth as AI adoption spreads globally.

The primary drivers for the adoption of agentic AI in digital engineering include the need for increased efficiency and the reduction of human error in complex processes. Organizations are also driven by the competitive advantage gained through faster deployment and innovation cycles. Furthermore, the ability to seamlessly integrate with existing digital infrastructures and the capability to continuously learn and improve processes are significant factors enhancing its adoption​.

Demand for agentic AI is growing across various sectors due to its ability to enhance decision-making and automate complex tasks. Industries such as healthcare, finance, and insurance are increasingly relying on agentic AI to process large volumes of data for better operational efficiency and customer service. In digital engineering, the demand is particularly driven by the need for automating software development processes and improving the quality and speed of engineering outputs​.

Key Takeaways

• The Global Agentic AI in Digital Engineering Market is projected to reach USD 378.5 Billion by 2034, up from USD 3.4 Billion in 2024, growing at a CAGR of 60.20% during the forecast period from 2025 to 2034.
• In 2024, the Generative AI for Engineering Design segment held a dominant position, capturing more than 45.7% of the market share.
• The On-Premise segment also led the market in 2024, holding more than 56.6% of the total market share in digital engineering.
• The Automotive & Aerospace segment maintained a dominant position in the market in 2024, accounting for over 38.9% of the market share.
• The Product Design & Development segment captured a significant portion of the market in 2024, holding more than 35.5% of the total market share.
• North America held a dominant share in the market in 2024, with over 39.4% of the market share, generating revenues of approximately USD 1.3 billion.
• In 2024, the market for Agentic AI in Digital Engineering in the United States was estimated at USD 1.95 billion, with rapid expansion anticipated at a CAGR of 58.4%.

Agentic AI Statistics

• By 2028, approximately 33% of enterprise software applications are anticipated to integrate agentic AI, a significant increase from less than 1% in 2024.
• AI-driven automation is expected to lead to a 25% reduction in customer service costs.
• By 2028, agentic AI will be responsible for autonomously making at least 15% of day-to-day work decisions.
• A European Commission report highlights the growing need for transparency, with 70% of consumers demanding clear explanations of AI decisions that affect their lives.
• A study from Stanford University suggests that explainable AI can improve compliance rates by 30% and significantly enhance stakeholder trust, which is crucial for the widespread adoption of AI.
• The Global Agentic AI Market is projected to reach a valuation of approximately USD 196.6 Billion by 2034, up from USD 5.2 Billion in 2024, growing at a CAGR of 43.8% during the forecast period from 2025 to 2034.
• In 2024, North America held a dominant market position, capturing more than 38% of the market share, with revenues of approximately USD 1.97 Billion. The U.S. Agentic AI Market was valued at USD 1.58 Billion in 2024, with a growth rate of 43.6% CAGR.
• According to a report from IBM, agentic AI is expected to contribute to a 20% reduction in traffic congestion and a 30% reduction in energy usage in urban areas, according to findings from the Smart Cities World Forum.

Analysts’ Viewpoint

Investment in agentic AI technology is seen as highly promising, given its potential to revolutionize multiple industry sectors. Opportunities exist in developing more sophisticated AI agents that can handle increasingly complex tasks with greater autonomy. Additionally, investments in the underlying technologies such as machine learning models and data analytics platforms that support AI agents are also lucrative. These investments are expected to yield significant returns as the technology matures and its adoption broadens​.

Technological advancements in agentic AI include improvements in natural language processing, machine learning algorithms, and the expansion of AI agents capable of performing a wider variety of tasks. Innovations also encompass better integration capabilities with various digital platforms, enhancing the agents’ ability to communicate and function within multi-agent systems. These advancements contribute to more robust, efficient, and adaptable AI systems​.

The regulatory landscape for agentic AI is evolving as its capabilities and implications become more understood. Key concerns include ensuring transparency, accountability, and the prevention of bias in AI decisions. Regulations may also focus on the ethical use of AI, particularly in sensitive sectors such as healthcare and finance. Organizations must navigate these regulations carefully to harness the benefits of agentic AI while ensuring compliance and maintaining public trust​.

Emerging Trends

• Autonomous Operations: There’s a growing trend towards developing AI agents capable of fully autonomous operations within digital systems. These agents can perform tasks, adapt to new information, and resolve issues without human intervention, enhancing efficiency across various industries​.
• Human-AI Collaboration: Increasingly, AI systems are designed to complement and augment human abilities rather than replace them. This collaboration is seen in areas where strategic decision-making is crucial, such as in healthcare and finance, improving outcomes through a combination of human intuition and AI’s data-processing capabilities​.
• Ethical AI Development: As the capabilities of AI agents expand, so does the focus on ethical AI development. This involves programming AI agents to adhere to ethical guidelines and making their decision-making processes transparent and accountable to avoid biases and ensure fairness in operations​.
• AI in Edge Computing: The integration of agentic AI with edge computing devices is a notable trend. This allows AI agents to operate at the source of data collection, such as IoT devices, enhancing the speed and efficiency of data processing and decision-making in real-time environments​.
• Adaptive Learning Systems: AI agents are increasingly equipped with adaptive learning capabilities that allow them to improve their performance over time without explicit reprogramming. This trend is particularly prevalent in dynamic environments like digital marketing and supply chain management, where conditions continuously change​.

Top Use Cases

• Automated Customer Service: AI agents are used to manage customer inquiries and support, reducing response times and improving customer satisfaction by handling routine queries and escalating complex issues appropriately​.
• Supply Chain Optimization: In logistics and supply chain management, AI agents analyze real-time data to optimize routes, predict bottlenecks, and adjust inventory levels dynamically, thereby minimizing costs and improving service delivery​.
• Healthcare Management: Agentic AI plays a crucial role in healthcare by assisting in diagnostic processes, patient data management, and personalized treatment planning, thus enhancing the efficiency and accuracy of medical services​.
• Financial Analysis and Planning: In the financial sector, AI agents help in risk assessment, market analysis, and personal financial planning, providing deep insights that enable better investment decisions and risk management​.
• Intelligent Automation in Manufacturing: AI agents are increasingly used in manufacturing for predictive maintenance, quality control, and production optimization. They monitor equipment and process data to predict failures before they occur and ensure the manufacturing process runs efficiently​.

Attractive Opportunities

• Development of Industry-Specific Agents: There is significant opportunity in developing AI agents tailored to specific industry needs, such as bespoke solutions for healthcare, finance, or retail. These specialized agents can offer more precise functionalities adapted to the unique challenges of each sector​.
• Enhanced Data Privacy and Security: As businesses become increasingly data-driven, the opportunity to use AI agents to enhance data security and privacy protocols is crucial. These agents can proactively manage threats and ensure compliance with evolving data protection laws​.
• Advanced AI Training Simulators: Investing in simulation technology that trains AI agents in virtual environments can lead to more robust and capable AI systems. These simulators can expose AI to a range of scenarios, enhancing their ability to perform under different conditions without real-world risks​.
• Expansion into Emerging Markets: Agentic AI technologies have the potential to revolutionize industries in emerging markets where manual processes are still prevalent. Implementing AI solutions in these markets could significantly enhance operational efficiencies and economic outcomes​.
• Integration with Blockchain Technology: Leveraging blockchain to enhance the capabilities of agentic AI in areas like transaction security, data integrity, and smart contracts offers a promising intersection of technologies. This integration can foster new business models and trust in AI decisions​.

Regional Analysis

In 2024, the market for Agentic AI in Digital Engineering in the United States was valued at USD 1.95 billion, reflecting a rapid expansion of this technology within the engineering sector. The growth trajectory for this market is particularly strong, with a projected compound annual growth rate (CAGR) of 58.4%, indicating significant future growth.

This rapid expansion can be attributed to the increasing adoption of AI-powered solutions in various engineering applications, including design optimization, predictive maintenance, and automation. The integration of Agentic AI in digital engineering is reshaping processes by improving efficiency, reducing errors, and enabling more innovative designs.

In 2024, North America dominated the Agentic AI in Digital Engineering market, capturing more than 39.4% of the total market share, with revenues reaching USD 1.3 billion. Several factors contribute to North America’s market leadership. The region benefits from a well-established technological infrastructure and high levels of investment in artificial intelligence research and development.

Furthermore, North America is home to major industry players in both the technology and engineering sectors, providing a strong foundation for the widespread adoption of Agentic AI. The combination of a favorable business environment, robust R&D initiatives, and the growing demand for AI-driven innovation in engineering has cemented North America’s position as a leader in this market.

Report Segmentation

Technology Analysis

In the year 2024, the Generative AI for Engineering Design sector achieved a significant market presence, accounting for approximately 45.7% of the overall market share. This dominance underscores the substantial role that generative AI technologies play in enhancing and streamlining engineering processes across various industries. The integration of AI in engineering design not only enhances efficiency but also drives innovation by enabling more complex and precise designs at a faster rate.

Deployment Model Analysis

The On-Premise deployment model continued to lead in the digital engineering landscape in 2024, with a market share exceeding 56.6%. This model’s preference indicates a strong inclination towards data security and system control among businesses. Companies favor on-premise solutions to maintain direct oversight over their digital engineering tools and data, which is crucial for sensitive and critical operations.

Industry Vertical Analysis

Within the Agentic AI in Digital Engineering market, the Automotive & Aerospace sector was prominent, capturing more than 38.9% of the market share in 2024. The automotive and aerospace industries heavily invest in agentic AI to drive the development of more advanced, safer, and energy-efficient vehicles and aircraft. This technology’s ability to simulate and analyze complex designs and scenarios significantly contributes to this sector’s growth.

Application Analysis

Focusing on application specifics, the Product Design & Development category held a dominant position in the Agentic AI in Digital Engineering market, securing over 35.5% of the market share in 2024. The deployment of AI in product design and development enables companies to rapidly prototype, innovate, and refine products. This application of AI is essential for maintaining competitive advantage in fast-evolving markets by reducing time-to-market and enhancing product quality.

Key Market Segments

By Technology

• Generative AI for Engineering Design
• Digital Twins & AI-Driven Simulations
• AI in Robotics & Automation
• Explainable AI (XAI) for Engineering
• Others

By Deployment Model

• On-Premise
• Cloud-Based

By Industry Vertical

• Automotive & Aerospace
• Energy & Utilities
• Construction & Civil Engineering
• Electronics & Semiconductor
• Healthcare & Medical Devices
• Others

By Application

• Product Design & Development
• Predictive Engineering Analytics
• Process Automation & Workflow Optimization
• AI-Augmented Simulation & Testing
• Intelligent Infrastructure & Smart Manufacturing
• Others

Top Key Players in the Market

• Google LLC
• OpenAI
• Microsoft Corporation
• Nvidia Corporation
• International Business Machines Corporation
• Siemens
• Autodesk, Inc.
• Dassault Systèmes
• ANSYS, Inc.
• PTC Inc.
• Boston Dynamics, Inc.
• Others

Sources:

• https://blogs.nvidia.com/blog/what-is-agentic-ai/
• https://www.aifalabs.com/blog/agentic-ai
• https://www.digitalocean.com/resources/articles/agentic-ai
• https://telus-international-telus-international-global-production.pr.gke.telus.digital/insights/ai-data/article/agentic-ai-in-the-enterprise
• https://digitaldefynd.com/IQ/agentic-ai-statistics/
• https://www.knime.com/blog/what-is-agentic-ai
• https://builtin.com/artificial-intelligence/agentic-ai
• https://www.uipath.com/ai/agentic-ai

ABOUT AUTHOR

by Yogesh Shinde

Yogesh Shinde is a passionate writer, researcher, and content creator with a keen interest in technology, innovation and industry research. With a background in computer engineering and years of experience in the tech industry. He is committed to delivering accurate and well-researched articles that resonate with readers and provide valuable insights. When not writing, I enjoy reading and can often be found exploring new teaching methods and strategies.