Executives believe that overall the general public will be using AI agents more than websites or apps from 2031 onwards, though most believe it will happen from 2036 and later.

 Introduction

Agentic AI Statistics: Agentic AI is a segment of artificial intelligence designed to reach specific goals with little human supervision. It works through AI agents, which are machine learning models that act like human decision-makers to handle problems instantly. Agentic AI builds on generative AI techniques and relies on large language models (LLMs) to work well in dynamic situations.

As interest in this area increases, innovation is speeding up, with major tech firms, startups, and research groups putting strong efforts and resources into its progress. Statistics highlighting adoption trends, funding flows, market growth, and real-world applications that provide valuable insights into how agentic AI is evolving and influencing industries worldwide.

In the year 2024, the market value of agentic artificial intelligence (AI) stood at *** billion U.S.dollars. It is anticipated that this market value will surpass ** billion U.S.dollars, with a compound annual growth rate of over ** percent, as reported by Capgemini. This tremendous growth demonstrates the potential of agentic AI to transform industries through autonomous action and decision-making.

The Agentic AI Market Report is Segmented by Component (Solution, Services), Deployment Mode (On-Premises, Cloud, Hybrid), Organization Size (Small and Medium Enterprises, Large Enterprises), End-User Industry (Healthcare, BFSI, and More), Agent Architecture (Single-Agent Systems, Multi-Agent Systems), Application (Autonomous Process Automation, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

In 2024, less than *** percent of the enterprise software applications included agentic AI. Which means most of the applications with AI integrated rely heavily on user prompts. However, this is projected to change drastically by 2028, with almost ******* of all enterprise software applications incorporating agentic AI, enabling a higher level of autonomy in decision-making.

Agentic AI Market is estimated to reach USD 196.6 billion By 2034, Riding on a Strong 43.8% CAGR throughout the forecast period.

Agentic AI Market Size

According to Market.us, The Global Agentic AI Market is set for extraordinary expansion, with its value expected to rise from USD 5.2 billion in 2024 to nearly USD 196.6 billion by 2034. This trajectory represents an exceptional CAGR of 43.8% during 2025 to 2034, reflecting the transformative role of autonomous and decision-making AI systems across industries. The surge is being fueled by increasing adoption of AI agents in enterprise automation, financial services, healthcare, and customer engagement, where demand for intelligent, context-aware, and self-improving solutions is accelerating at scale.

The Agentic AI Market refers to the sector focused on the development and deployment of autonomous AI systems capable of independently reasoning, planning, and acting towards specific goals with minimal human intervention. These systems are distinct from traditional AI by their ability to interact dynamically with environments, continuously learn, optimize workflows, and coordinate actions across multiple agents. Agentic AI is redefining enterprise automation by embedding real-time decision-making, contextual awareness, and outcome-driven autonomy directly into core business processes.

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The adoption of agentic AI is accelerating at a pace that marks a clear shift in enterprise automation strategies. Based on data from DigitalDefynd, more than 60% of new enterprise AI deployments in 2025 are expected to integrate agentic capabilities. This trend signals a movement away from static models toward autonomous, goal-driven systems capable of handling multi-layered and adaptive workflows. Between 2023 and 2025, the use of AutoGPT and agentic frameworks increased by 920% across developer repositories, underscoring strong momentum among engineers seeking scalable automation tools.

Agentic AI has already demonstrated remarkable efficiency gains in practical applications. These systems are capable of reducing human task time by up to 86% in multi-step workflows. By 2025, nearly 45% of Fortune 500 companies are piloting agentic systems, reflecting growing trust in self-directed AI agents for enterprise use. Compared to traditional LLMs, agentic frameworks can perform up to 12 times more complex tasks due to their reliance on dynamic feedback loops and autonomous decision-making. This advantage is positioning them as indispensable tools for large-scale digital transformation.

The investment climate surrounding agentic AI further highlights its disruptive potential. Since 2023, more than USD 9.7 billion has been invested in agentic AI startups, with major technology firms leading innovations in this space. Google DeepMind’s SIMA agent has exhibited the ability to learn over 600 skills across 9 different game engines, showcasing adaptability and versatility. Microsoft’s AutoGen framework has been adopted by 40% of Fortune 100 companies for automating IT operations and compliance-related tasks.

Agentic AI in Telecom Market Size

The Global Agentic AI in Telecom Market is undergoing a major transformation, driven by the rising demand for autonomous systems that can adapt, plan, and make decisions with minimal human input. In 2024, the market stood at USD 3.6 billion, and it is projected to grow at an exceptional CAGR of 48.5% through 2034, ultimately reaching around USD 187.7 billion. This significant growth is being fueled by telecom operators’ increasing reliance on intelligent agents to optimize network traffic, automate customer service, and enhance predictive maintenance across complex infrastructures.

Agentic AI represents a major shift in how telecommunications companies optimize, automate, and enhance their operations. Unlike simple automation, Agentic AI brings in autonomous agents capable of making informed, goal-driven decisions across network and customer domains. These agents are not just task bots; they act independently within defined guidelines, continuously monitoring vast and complex networks, analyzing real-time data, and taking proactive steps to resolve issues or improve services. Their mission is not limited to carrying out instructions but extends to achieving operational goals, improving performance metrics, and personalizing experiences for millions of users at once.

A core driver behind the adoption of Agentic AI in telecom is the need for agility in a highly competitive and quickly evolving market. Rising operational costs, surging network complexity, and ever-higher customer expectations make traditional processes inefficient. Service providers grapple with massive data flows, fluctuating network demands, and the pressure to deliver smarter, more personalized offerings. Agentic AI enables real-time, data-driven decision-making and self-managing network operations that adapt on the fly, all while controlling costs and improving reliability.

The demand for Agentic AI is propelling the sector forward primarily because of mounting pain points. Legacy systems struggle with modern network congestion, and customer support teams face overwhelming volumes of requests. Customers now expect instant, personalized service, and telecom operators risk losing out if they cannot respond swiftly. The rise of 5G, IoT devices, and cloud-driven services boosts data traffic and expands the surface area for potential problems or fraud. Agentic AI directly addresses these issues, providing scalable solutions that anticipate trouble, automate maintenance, and deliver ultra-responsive customer support.

Scope and Forecast

Snapshot img

AI Agent Platform Market Size 2025-2029

The AI agent platform market size is valued to increase by USD 23.56 billion, at a CAGR of 41.1% from 2024 to 2029. Rapid advancements in foundational AI models and reasoning capabilities will drive the AI agent platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 43% growth during the forecast period.
By Technology - ML segment was valued at USD 285.30 billion in 2023
By Type - Single agent systems segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 3.00 million
Market Future Opportunities: USD 23562.70 million
CAGR from 2024 to 2029 : 41.1%

Market Summary

The market is experiencing significant growth, with recent estimates suggesting a value of over USD10 billion by 2025. This expansion is driven by the rapid advancements in foundational AI models and reasoning capabilities, enabling agents to learn and adapt more effectively. As a result, there is a shift from single-purpose agents to collaborative multi-agent systems, which offer increased efficiency and flexibility. However, ensuring reliability and mitigating agentic hallucinations remain key challenges. These issues arise due to the complexity of AI systems and the potential for unintended consequences. Despite these hurdles, the future direction of the market is promising, with continued innovation and refinement expected to address these challenges and unlock new opportunities for businesses.
The integration of advanced natural language processing and machine learning algorithms will further enhance the functionality of AI agents, enabling them to understand and respond to human queries more accurately and intuitively. Overall, the market represents a significant investment opportunity for businesses seeking to leverage the power of AI to streamline operations, enhance customer experiences, and drive growth.

What will be the Size of the AI Agent Platform Market during the forecast period?

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How is the AI Agent Platform Market Segmented ?

The AI agent platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Technology

  ML
  NLP
  Others


Type

  Single agent systems
  Multi agent systems


End-user

  Financial services
  Retail and eCommerce
  IT and telecommunication
  Healthcare
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Technology Insights

The ML segment is estimated to witness significant growth during the forecast period.

The market is witnessing continuous evolution, with machine learning algorithms playing a pivotal role as the foundational intelligence layer. These platforms leverage advanced techniques, such as reinforcement learning, to enable autonomous decision-making and adaptation. Agents learn from interaction feedback, refining strategies through trial and error, much like humans. The sophistication of these ML models allows for advanced reasoning, long-term planning, and self-correction. In turn, this empowers platforms to handle dynamic, unpredictable tasks, optimize complex supply chains, and manage financial portfolios by predicting market signals. Conversational AI, a key component, integrates dialogue management, response generation, and named entity recognition for enhanced user experience.

Data annotation techniques, data privacy regulations, and bias mitigation strategies ensure ethical AI practices. Scalability challenges are addressed through automation workflows, model versioning, and API integration methods. Performance evaluation metrics and user experience design further enhance agent capabilities. A recent study reveals that 80% of enterprises have already adopted or plan to adopt AI agents by 2022. This underscores the growing importance of these platforms in various industries.

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The ML segment was valued at USD 285.30 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 43% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market is experiencing significant evolution, with North America leading the global landscape. This region's dominance is driven by a unique comb

Global Agentic AI For Data Engineering Market size is expected to reach USD 63.8 Bn by 2033, from USD 3.4 Bn in 2024, growing at a CAGR of 36.2% from 2024 to 2033

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

Generative AI adoption has surged across industries, with the technology sector leading the charge at an impressive ** percent usage rate across functions in 2024. This rapid integration of AI technologies is reshaping business operations, particularly in marketing and sales, where AI has found widespread application as a creative assistance tool. However, this swift adoption has not come without challenges, as concerns about regulatory compliance have grown in tandem with the increased usage. Varied adoption rates across sectors While the technology industry stands at the forefront of generative AI adoption, other sectors are not far behind. Professional services, advanced industries, and media and telecom all report adoption rates of around ** percent across functions. Interestingly, in the tech, media, and telecom industry, IT departments lead in generative AI application usage at ** percent, followed by product development at ** percent. This trend differs in the energy, resource, and industrial sector, where operations take the lead at ** percent, with IT following at ** percent. Evolving landscape of AI implementation As organizations increasingly integrate generative AI, the landscape of implementation is evolving. Automation and agentic AI have emerged as the most intriguing technological developments for organizations in 2024. This shift is accompanied by a notable increase in technical skills related to AI, indicating broader usage. However, the rise in regulatory concerns suggests that governments and authorities are stepping up their oversight of the industry. This dual trend of increased adoption and heightened regulatory scrutiny underscores the complex environment in which AI technologies are being deployed and developed.

Snapshot img { margin: 10px !important; } AI In Geospatial Technology Market Size 2025-2029

The ai in geospatial technology market size is forecast to increase by USD 87.2 billion, at a CAGR of 25.3% between 2024 and 2029.

The global AI in geospatial technology market is expanding, driven by the exponential proliferation of geospatial data sources. This surge in data from satellites, drones, and sensors creates a compelling need for AI-driven solutions capable of processing and interpreting vast information streams. A significant development shaping the industry is the rise of geospatial foundation models and generative AI, which are democratizing advanced analytics through more intuitive, conversational interfaces. These advancements in ai in smart cities and geospatial analytics are enabling the development of sophisticated applications, including ai in simulation for urban planning and environmental modeling. However, the inherent complexity and quality issues of this data present considerable integration challenges that can slow adoption.The growth in AI in infrastructure and platforms as a service is pivotal, as it provides the scalable computing power necessary for these advanced applications. The increasing sophistication of autonomous AI is also a key factor, particularly in areas like remote sensing and dynamic monitoring. These capabilities are crucial for the artificial intelligence (AI) in IoT market, where real-time spatial intelligence is essential. Despite these advancements, the creation of high-quality, accurately labeled training data remains a significant bottleneck. This scarcity of reliable training material can hinder the performance of AI models, posing a persistent challenge to realizing the full potential of GeoAI solutions across various sectors, including the artificial intelligence (AI) in military market.

What will be the Size of the AI In Geospatial Technology Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market's evolution is shaped by the interplay between data proliferation and analytical sophistication, where advanced AI models for object detection and feature extraction are becoming essential. The integration of generative AI is redefining user interaction, enabling conversational GIS and making complex spatial analysis more accessible. This shift is particularly relevant for agentic AI in digital engineering, where natural language interfaces can streamline design and simulation workflows. However, progress is tempered by the ongoing need for high-quality ground truth data creation and robust data harmonization processes to ensure model accuracy and reliability.The development of geospatial foundation models signifies a move toward more versatile and scalable solutions, reducing the reliance on task-specific model training. This trend supports ai in learning and development by allowing for rapid fine-tuning for diverse applications, from environmental monitoring to infrastructure management. The utility of these models in ai in simulation is growing, as they can generate synthetic data and model future-state scenarios with greater fidelity. Progress in this area is closely tied to advancements in AI accelerators and cloud-based platform-as-a-service models that provide the necessary computational power.

How is this AI In Geospatial Technology Industry segmented?

The ai in geospatial technology industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. DeploymentCloud-basedOn-premisesEnd-userGovernment and defenseTransportation and logisticsNatural resourcesUtilitiesOthersTechnologyMachine learningComputer visionDeep learningNatural language processingGeographyNorth AmericaUSCanadaAPACChinaIndiaJapanSouth KoreaAustraliaEuropeGermanyUKFranceMiddle East and AfricaUAESouth AmericaBrazilArgentinaRest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.The cloud-based deployment model is the dominant and fastest-growing segment, driven by its scalability, cost-efficiency, and accessibility to high-performance computing. Organizations are increasingly migrating geospatial workflows to the cloud to manage the petabyte-scale datasets generated by modern remote sensing technologies. Cloud platforms offer an elastic environment for processing this data, a task often infeasible for on-premises systems. This model is democratizing access to sophisticated GeoAI capabilities, enabling organizations of all sizes to derive insights without extensive in-house resources.Leading public cloud providers are at the forefront of this trend, conti

Agentic Workflow Orchestration Platform Market Outlook

According to our latest research, the global Agentic Workflow Orchestration Platform market size reached USD 3.24 billion in 2024, underscoring its rapid adoption across industries seeking to automate and optimize complex workflows. The market is expected to grow at a CAGR of 18.7% from 2025 to 2033, with the forecasted market size projected to reach USD 17.15 billion by 2033. This robust growth trajectory is primarily driven by the increasing demand for intelligent workflow automation, digital transformation initiatives, and the proliferation of artificial intelligence and machine learning capabilities within enterprise operations.

The primary growth factor for the Agentic Workflow Orchestration Platform market is the accelerating digital transformation across various sectors. Enterprises are under immense pressure to enhance operational efficiency, reduce costs, and deliver superior customer experiences. Agentic workflow orchestration platforms offer a unified solution to automate, monitor, and optimize end-to-end business processes by leveraging advanced AI, machine learning, and analytics. As organizations shift from traditional manual processes to intelligent automation, the adoption of these platforms has surged, enabling businesses to respond to market changes with agility and precision. The integration of low-code and no-code tools within these platforms further democratizes automation, empowering business users to design and deploy workflows with minimal IT intervention.

Another significant driver is the exponential growth of data and the need for seamless data management across distributed systems. Modern enterprises generate vast volumes of data from multiple sources, including IoT devices, customer interactions, and internal processes. Agentic workflow orchestration platforms facilitate the orchestration of data pipelines, ensuring timely data processing, synchronization, and governance. This capability is particularly critical for sectors such as BFSI, healthcare, and retail, where data integrity and compliance are paramount. Additionally, the rise of hybrid and multi-cloud environments necessitates sophisticated orchestration tools to manage workflows across diverse infrastructures, further fueling market demand.

The increasing complexity of IT operations and the shift towards hyper-automation present another compelling growth avenue for the Agentic Workflow Orchestration Platform market. Organizations are deploying a multitude of applications, microservices, and APIs, often spanning on-premises and cloud environments. Orchestration platforms provide centralized control, visibility, and automation of these interconnected workflows, reducing manual errors and accelerating time-to-market. As businesses strive to achieve end-to-end automation, the integration of advanced AI-driven decision-making within orchestration platforms enables proactive issue resolution, resource optimization, and continuous process improvement, thereby enhancing overall business resilience and competitiveness.

The introduction of the Automation Co-Pilot Platform is transforming how businesses approach workflow orchestration. This platform acts as a digital assistant, guiding users through the automation process with intuitive interfaces and real-time feedback. By leveraging AI and machine learning, the Automation Co-Pilot Platform enhances decision-making capabilities, allowing organizations to automate complex tasks with greater accuracy and efficiency. This innovation is particularly beneficial for industries with intricate workflows, as it reduces the learning curve and accelerates the deployment of automation solutions. As enterprises continue to embrace digital transformation, the role of co-pilot platforms in streamlining operations and improving productivity becomes increasingly vital.

From a regional perspective, North America continues to dominate the Agentic Workflow Orchestration Platform market, accounting for the largest share in 2024. The regionÂ’s leadership is attributed to the early adoption of digital technologies, a mature IT infrastructure, and significant investments in AI and automation. Europe and Asia Pacific are also witnessing substantial growth, driven by the rapid digitalization of enterprises and supportive government initiatives. In Asia Paci

Europe Generative AI in Testing market is set to rise from USD 0.21B in 2024 to USD 3.75B by 2034, growing at a CAGR of 34.21%. Explore drivers, trends and opportunities.

Global Generative AI in Testing market is set to grow from USD 0.71B in 2024 to USD 14.15B by 2034,at a CAGR of 34.2% (2025–2034). Explore trends, opportunities and drivers.

Snapshot img { margin: 10px !important; } AI Procurement Intelligence Market Size 2025-2029

The ai procurement intelligence market size is forecast to increase by USD 14.5 billion, at a CAGR of 42.9% between 2024 and 2029.

Enterprises are increasingly adopting AI procurement intelligence to enhance operational efficiency and achieve significant cost savings in response to persistent economic pressures. This drive for strategic cost management is met by the proliferation of generative AI and hyper-automation, which are being integrated into advanced procurement software. These technologies are enabling a shift toward predictive sourcing functions, allowing teams to forecast market conditions and automate complex decision-making processes. By leveraging natural language prompts and cognitive capabilities, these tools make sophisticated data analysis more accessible, empowering procurement professionals to focus on higher-value activities like negotiation and strategic supplier relationship management. The focus is on creating autonomous and strategic sourcing capabilities through industrial ai software.However, realizing the full potential of these advanced systems is often constrained by foundational issues related to data integrity and accessibility. Many organizations grapple with a fragmented data landscape, where procurement information is trapped in disparate silos with inconsistent taxonomies, making the creation of a unified data view a significant hurdle. Without meticulous data cleansing and normalization, the insights generated by AI algorithms can be skewed or misleading, which erodes user trust and undermines the business case for the technology. This highlights the importance of robust AI governance tools to manage data quality, security, and integration effectively within the framework of agentic AI for data engineering.

What will be the Size of the AI Procurement Intelligence Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market is defined by a strategic shift toward proactive risk mitigation and enhanced supply chain resilience. Organizations are leveraging predictive analytics and real-time monitoring to anticipate disruptions from geopolitical or climate-related events. This move from a reactive to a proactive stance is enabled by AI-powered platforms that provide deep visibility into multi-tier supplier networks. The integration of predictive ai in supply chain systems is becoming standard practice for ensuring business continuity and managing complex global trade dynamics. This focus on foresight and preparedness underscores a fundamental change in procurement strategy.Operational efficiency is being transformed through procurement workflow automation and the adoption of hyper-automation. These technologies are streamlining routine tasks like invoice processing and purchase order generation, freeing up procurement professionals for more strategic activities. The use of generative AI is also changing user interaction via natural language prompts, making complex data analysis more accessible. This focus on intelligent automation and ai in project management helps organizations reduce sourcing cycle times and improve overall productivity.Supplier relationship management is evolving with the use of sophisticated AI tools for performance evaluation and strategic decision-making. AI-powered platforms assist in supplier discovery and vetting, ensuring that new partners meet rigorous standards for quality and compliance. These systems analyze supplier performance metrics to inform consolidation strategies and negotiation tactics. The ongoing development of ai for sales, from a procurement perspective, allows for more dynamic and data-driven interactions, fostering a collaborative and resilient supplier ecosystem.

How is this AI Procurement Intelligence Industry segmented?

The ai procurement intelligence industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. ComponentSoftwareServicesDeploymentCloud-basedOn-premisesEnd-userLarge enterprisesSMEsGovernment and public sectorGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceThe NetherlandsItalySpainAPACChinaJapanIndiaAustraliaSouth KoreaIndonesiaSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaUAESouth AfricaTurkeyRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.The software segment forms the core of the market, comprising digital platforms and applications that enable data-driven procurement. These solutions, predominantly delivered via a Software-as-a-Service model, provide func

North America Generative AI in Testing market is set to grow from USD 0.31B in 2024 to USD 5.8B by 2034, at a CAGR of 33.91%. Explore trends, drivers, and opportunities.

Middle East & Africa Generative AI in Testing market is set to grow from USD 221.08M in 2024 to USD 884.75M by 2034, at a CAGR of 15.35%. Explore trends, drivers, growth.