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Artificial Intelligence Platforms Market to grow by USD 64.9 Billion (2024-2028), driven by rising AI solution demand, Report on AI’s market transformation – Technavio

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NEW YORK, Dec. 9, 2024 /PRNewswire/ — Report with market evolution powered by AI – The global artificial intelligence platforms market  size is estimated to grow by USD 64.9 billion from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of  45.1%  during the forecast period. Rising demand for ai-based solutions is driving market growth, with a trend towards increasing interoperability among neural networks. However, rise in data privacy issues  poses a challenge. Key market players include Alphabet Inc., Amazon.com Inc., Amelia US LLC, Baidu Inc., Dell Technologies Inc., Hewlett Packard Enterprise Co., Infosys Ltd., Intel Corp., International Business Machines Corp., Microsoft Corp., Nuance Communications Inc., NVIDIA Corp., Palantir Technologies Inc., Qualcomm Inc., Salesforce Inc., SAP SE, SAS Institute Inc., ServiceNow Inc., Tata Consultancy Services Ltd., and Wipro Ltd., Google, Amazon Web Services, Ayasdi, Absolutdata..

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Forecast period

2024-2028

Base Year

2023

Historic Data

2018 – 2022

Segment Covered

Application (Retail, Banking, Manufacturing, Healthcare, and Others), Deployment (On-premises and Cloud-based), Geography (North America, APAC, Europe, South America, and Middle East and Africa), Component, Tools, Service and End-User.

Region Covered

North America, APAC, Europe, South America, and Middle East and Africa

Key companies profiled

Alphabet Inc., Amazon.com Inc., Amelia US LLC, Baidu Inc., Dell Technologies Inc., Hewlett Packard Enterprise Co., Infosys Ltd., Intel Corp., International Business Machines Corp., Microsoft Corp., Nuance Communications Inc., NVIDIA Corp., Palantir Technologies Inc., Qualcomm Inc., Salesforce Inc., SAP SE, SAS Institute Inc., ServiceNow Inc., Tata Consultancy Services Ltd., and Wipro Ltd, Google, Amazon Web Services, Ayasdi, Absolutdata.

Key Market Trends Fueling Growth

In the modern business landscape, interoperability between computer systems is essential for enterprises. The inability of different data science tools and frameworks, particularly those using neural networks, to communicate with each other is a significant barrier to the widespread adoption of Artificial Intelligence (AI). To tackle this issue, major tech companies including AWS, Facebook, and Microsoft collaborated to create the Open Neural Network Exchange (ONNX) in 2017. ONNX is a standard format designed to enable the transfer of fully trained deep learning (DL) models between various frameworks, eliminating the need for developers to build neural networks from scratch for each one. For instance, developers may prefer using PyTorch for image processing tasks but choose Apache MXNet for data collection. ONNX’s benefits include framework interoperability and shared optimization, which streamline the development and execution of neural networks’ computation graphs. The growth of the global AI platforms market is expected to be driven by the increasing importance of ONNX as the standard runtime for various industry players, from researchers to edge device manufacturers, leading to increased innovation in AI. 

Artificial Intelligence (AI) is revolutionizing various industries by automating tasks, enhancing decision-making, and providing personalized experiences. In Digital Technologies, AI software powers data mining, machine learning, and pattern identification for business intelligence. Healthcare benefits from AI in medical imaging analysis, drug discovery, and patient care. AI also transforms Finance through algorithmic trading, fraud detection, and credit risk assessment. Industry adoption of AI is expanding in sectors like Food and Beverages, Banking, and Manufacturing. AIaaS (Artificial Intelligence as a Service) and Cloud Computing Platforms offer cost-effective solutions. However, challenges include complex implementation, integration, and high costs. Ethical considerations, data privacy, and security concerns are crucial. AI brings operational efficiency, product innovation, and improved customer experience. In Finance, AI powers conversational AI interactions through Intelligent Virtual Assistants and Chatbots using Natural Language Processing and Speech Recognition. AI is revolutionizing industries, but businesses must weigh the potential ROI against the uncertainty and costs. 

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Market Challenges

Artificial Intelligence (AI) platforms have gained significant attention in business due to their ability to process large volumes of data quickly and automate tasks. However, the adoption of AI solutions comes with concerns surrounding data privacy and security. The risk of information leakage and misuse is high in AI applications, leading to potential legal and security ramifications. These issues include organizational infrastructure, access control, identity management, risk management, regulatory and legislative compliance, auditing, and logging. Data privacy is a growing concern as AI systems rely on high volumes of data for insights. The credibility and source of data are crucial to ensure the accuracy and relevance of insights. The emergence of advanced AI systems has increased the importance of data privacy. The European Union’s General Data Protection Regulation (GDPR) is an example of a regulation that requires companies to ensure the privacy and legal use of data. The increasing adoption of cloud-based solutions also poses security risks. Cyber attackers can easily access cloud-based data storage systems due to their open architecture and shared resources. Data security breaches and data loss are potential risks in public cloud architecture. Additionally, businesses outsourcing their IT functions to reduce costs have less control over data management, increasing the risk of data breaches. The reliability of data sources and trust are essential for obtaining qualitative data. Data quality and Machine Learning (ML) approaches to data processing significantly impact decision-making within companies. AI’s cognitive capabilities enable it to learn from data and implement this learning in future actions. Data quality can significantly impact AI’s learning capabilities. For instance, Microsoft’s Tay chatbot is an example of how an algorithm’s decision-making capability can be reshaped by the data provided. The COVID-19 pandemic has further increased cybersecurity and data privacy risks for businesses. With the rapid deployment of remote-working solutions, hackers are attempting to exploit the weaknesses of organizations due to reduced IT staffing. Growing data regulations and privacy concerns discourage companies from integrating AI platforms with their business operations. These factors are expected to hinder the growth of the global AI platforms market during the forecast period.Artificial Intelligence (AI) platforms are revolutionizing industries by enabling automation, process optimization, and product innovation. However, industry adoption faces challenges. In sectors like Banking, Business Intelligence, and Customer Experience, AI platforms offer significant benefits, but complexities like regulatory support, ethical considerations, data privacy, and security concerns persist. In Finance, AI solutions are transforming operations, but high implementation costs and uncertain ROI remain hurdles. In contrast, Medical Imaging Analysis, Drug Discovery, and Autonomous Systems are seeing impressive results, despite challenges in industry-specific AI solutions, complex integration, and AIaaS in Cloud Computing Platforms. Large Enterprises and AI start-ups alike are investing in AI platforms, with Google Maps, Aerospace, Security & Surveillance, and CPU industries adopting AI for competitive advantages. Challenges include automation, conversational AI interactions via Intelligent Virtual Assistants and Chatbots, and Natural Language Processing, Speech Recognition, and Conversational AI interactions. Despite these challenges, AI platforms deliver business value through AI solutions and frameworks, improving operational efficiency, enhancing customer experience, and driving product innovation.

Insights into how AI is reshaping industries and driving growth- Download a Sample Report

Segment Overview 

This artificial intelligence platforms market report extensively covers market segmentation by

Application 1.1 Retail1.2 Banking1.3 Manufacturing1.4 Healthcare1.5 OthersDeployment 2.1 On-premises2.2 Cloud-basedGeography 3.1 North America3.2 APAC3.3 Europe3.4 South America3.5 Middle East and AfricaComponentToolsServiceEnd-User

1.1 Retail-  Artificial Intelligence (AI) platforms enable businesses to develop, deploy, and manage intelligent applications. Major players in this market include IBM, Microsoft, Google, and Amazon Web Services. These companies offer various AI solutions such as machine learning, natural language processing, and robotics process automation. The global AI platforms market is expected to grow significantly due to increasing demand for automation and data analytics. Companies are investing in AI to enhance productivity, improve customer experience, and gain a competitive edge.

Download complimentary Sample Report to gain insights into AI’s impact on market dynamics, emerging trends, and future opportunities- including forecast (2024-2028) and historic data (2018 – 2022) 

Research Analysis

Artificial Intelligence (AI) platforms are transforming various industries by enabling advanced data processing, pattern identification, and decision-making capabilities. AI technologies, including machine learning and deep learning, are being adopted across sectors such as healthcare, food and beverages, banking, and aerospace, among others. In healthcare, AI is revolutionizing diagnosis and treatment plans through data mining and analysis. In the food industry, AI is used for supply chain optimization and product innovation. Digital technologies and the internet are key enablers of AI adoption, allowing for real-time data processing and analysis. AI software is being integrated into business intelligence systems to enhance customer experience and operational efficiency. The security & surveillance industry is leveraging AI for advanced threat detection and response. The CPU market is witnessing significant growth due to the increasing demand for powerful processors to support AI applications. Overall, AI is driving innovation and productivity across multiple industries.

Market Research Overview

Artificial Intelligence (AI) platforms are transforming various industries by enabling advanced data processing, pattern identification, and decision-making capabilities. AI is making significant strides in sectors like Healthcare, where it’s used for medical imaging analysis, drug discovery, and patient care. In Food and Beverages, AI is used for supply chain optimization and product innovation. Digital technologies, the Internet, and Data mining are the backbone of AI, with Machine Learning algorithms powering AI software. Cloud platforms are increasingly being used for AI as a Service (AIaaS), providing access to advanced AI solutions and frameworks. AI is revolutionizing industries like Banking with algorithmic trading, fraud detection, and credit risk assessment. Business Intelligence is being enhanced with AI-driven insights, while Customer Experience is being personalized with Intelligent Virtual Assistants and Chatbots. However, there are challenges to AI adoption, including Ethical Considerations, Data Privacy, and Security Concerns. Complexity and Integration Challenges can make implementation costly, and there’s uncertainty around the Return on Investment. Despite these challenges, AI platforms and solutions continue to deliver business value in areas like Automation, Process Optimization, and Industry-specific AI Solutions. Finance, Aerospace, Security & Surveillance, and Autonomous Systems are other industries benefiting from AI. AI is also being used in areas like Natural Language Processing, Speech Recognition, and Conversational AI interactions. The future of AI is bright, with advancements in areas like Regulatory Support, Personalization, and Regulatory Compliance. Despite the progress, there are still challenges to overcome, such as High Implementation Costs, Uncertain ROI, and the need for CPU-intensive processing power. AI platforms and frameworks continue to evolve, with Large Enterprises and AI start-ups driving innovation. Google Maps and other everyday applications are just the tip of the iceberg for AI’s potential. The future of AI is exciting, with endless possibilities for innovation and growth.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

ApplicationRetailBankingManufacturingHealthcareOthersDeploymentOn-premisesCloud-basedGeographyNorth AmericaAPACEuropeSouth AmericaMiddle East And AfricaComponentToolsServiceEnd-User

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio’s report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio’s comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/

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SOURCE Technavio

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP, on the Model You Choose

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data staying inside their own network

LONDON, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that must keep operational data within a defined boundary, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model itself can run entirely inside your own network.”

“IT teams across the United Kingdom and EMEA tell us the same thing: they want the benefit of AI without losing sight of where their data goes,” said Greg McLaughlin, Account Executive for EMEA at JAMS Software. “JAX and JAMS MCP let them keep operational data inside their own network and still get answers in plain language. That combination is what makes this practical for the teams I work with.”

JAX and JAMS MCP are available now to all JAMS Web customers across the United Kingdom and EMEA, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software

Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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Video: CNPC offers green chemical answer

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BEIJING, July 24, 2026 /PRNewswire/ — A news report from chinadaily.com.cn:

Located on the edge of the Taklamakan Desert in Northwest China’s Xinjiang Uygur autonomous region, the Tarim 1.2 MTA Phase II Ethylene Project and its supporting green and low-carbon demonstration facility of PetroChina Dushanzi Petrochemical Company, a subsidiary of China National Petroleum Corporation, are offering a new example of China’s low-carbon industrial transformation.

Watch the video to discover how CNPC is exploring a cleaner and more circular future for the industry.

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SOURCE chinadaily.com.cn

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Shanghai Electric showcases embodied intelligence robot matrix and AI-native smart factory solutions at WAIC 2026

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Featuring humanoid robots with 41 degrees of freedom, pipe‑inspection robots with ±1mm positioning accuracy, and 51 industrial‑grade AI agents

SHANGHAI, July 24, 2026 /PRNewswire/ — Operations in high-end equipment manufacturing often involve confined spaces, complex objects, and fine manipulation tasks that demand sustained and stable precision. At the recent 2026 World Artificial Intelligence Conference and High-Level Meeting on Global AI Governance (WAIC 2026), Shanghai Electric (SEHK: 02727, SSE: 601727) showcased its comprehensive portfolio of embodied intelligence solutions tailored to a range of industrial scenarios.

Themed “AI for All: Smart Squad, Shining Without Limits,” Shanghai Electric highlighted its capabilities across embodied AI robots, robot core components, and AI-native smart factory solutions, demonstrating end-to-end capabilities spanning complete robot systems, critical parts, industrial software, and smart factory architecture.

“The true value of embodied intelligence lies in understanding real industrial tasks: combining the strength, precision, and stability of machines with human experience and judgment to drive a genuine paradigm of ‘machine-assisted, human-machine collaboration,'” said Wang Chunlei, deputy general manager of the Robotics Business Unit at Shanghai Electric Automation Group.

Shanghai Electric’s robotics portfolio covers five key industrial scenarios: connector insertion, electrical operations, flexible sorting, intelligent assembly, and pipe processing. Highlights include:

“SUYUAN” bipedal humanoid robot: With 41 degrees of freedom for enhanced mobility, it is equipped with a multimodal visual sensing system on the head and torso, along with a dual-battery hot-swap system. It is well-suited for inspection, material handling, and assembly tasks.”TUOYUAN” industrial wheeled humanoid robot: Powered by an embodied intelligence foundation model and force-position hybrid control, it is capable of multi-spec connector insertion, material sorting, and loading/unloading of automotive sheet metal parts.”Mermaid” bionic wheeled humanoid robot: Capable of autonomously identifying buttons, knobs, and air switches, it generates real-time operation paths.Autonomous pipe inner-wall chamfering robot: Designed for confined spaces, it can position and process thousands of hole edges with accuracy within 1 millimeter while transmitting data in real time.

Shanghai Electric also showcased its portfolio of core components ranging from power-output to end effectors. Among them, the planetary roller screw offers more than three times the load capacity of traditional ball screws, while the DexHand dexterous hand is designed to meet diverse gripping and manipulation requirements.

Shanghai Electric launched 51 AI models and agents under its “StarCloud Intelligent Manufacturing” series across three domains: R&D and design, production and manufacturing, and operations and maintenance—covering critical equipment processes such as process optimization and wind power facility maintenance.

These industrial agents are embedded in robotic decision-making systems and the operational logic of AI-native smart factories, transforming industrial expertise into digitized, reusable capabilities. They support production-line scheduling, quality inspection, and predictive maintenance, driving the evolution of manufacturing systems from experience-driven to data-driven operations.

Shanghai Electric also released the “AI-Native Smart Factory Technology White Paper,” proposing an active evolution architecture that enables real‑time, closed‑loop optimization of production data, giving the factory self‑perception, self‑decision, and self‑execution capabilities. Built on First Principles, the AI‑native smart factory vertically integrates process flows, industrial software, agents, and smart equipment to dismantle traditional hierarchies while horizontally bridging data silos. The architecture features three core layers: the AI factory brain as the “control center,” industrial agents and embodied robots as the “execution network,” and the physical twin as the “digital mirror.”

Leveraging its deep industrial expertise and comprehensive solution capabilities, Shanghai Electric will continue to drive the implementation of AI in industrial settings, tackle technical challenges facing embodied intelligence in complex scenarios, accelerate the large‑scale deployment of AI‑native smart factories, and deliver replicable solutions across diverse manufacturing environments.

SOURCE Shanghai Electric

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