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OutSystems and KPMG Announce New Survey Exploring AI and the Future of Software Development

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Redefining “impossible” legacy projects, 75% of software executives see up to a 50% reduction in development time with increased AI and automation

SINGAPORE, Sept. 11, 2024 /PRNewswire/ — As the reach of artificial intelligence (AI) expands, IT leaders are exploring new use cases for technology used throughout the software development lifecycle (SDLC), according to a new survey launched today titled “AI in software development: Exploring opportunities and uncertainties” by OutSystems, a global leader transforming how companies innovate through software, and KPMG, a multinational professional services network.

The research surveyed 555 software executives around the world whose companies span IT consultancy services, manufacturing, banking, financial services and insurance among others. 84 percent of respondents reported that their organizations first began to incorporate AI technologies in their SDLCs between six months to five years ago, with the earliest adopters primarily being IT services companies. Across regions, EMEA and North America remain roughly on equal footing, while APAC is steadily catching up.

The findings show that testing, quality assurance, and security vulnerability detection are by far the most widely adopted use cases for AI in software development. Nonetheless, generative AI (GenAI) is set to transform the industry by significantly enhancing these processes and introducing unprecedented capabilities.

75% of software executives have seen up to a 50% reduction in development time by implementing AI and automation. 

Early adopters are planning to increase their use of AI in other stages of the SDLC, such as user interface design, code generation, DevOps optimization, and application maintenance. Nearly all respondents are planning to increase their investment in AI-augmented SDLC management over the next two years, indicating that AI will play a central role in driving innovation and competitive advantage in the software industry.

“AI is redefining the impossible,” said Paulo Rosado, CEO and founder at OutSystems. “I’m laser-focused on helping teams compress multi-year legacy modernization projects into just a few months. The latest AI disruptions have brought us the potential to compress these development timelines into even shorter and faster projects. With AI, historically impossible transformation projects are not only possible but easier, cheaper, and faster to accomplish.”

71% of respondents are planning to incorporate AI into application development and SDLC management workflows.

“Right now, the developer’s role is shifting from code writer to code reviewer,” said Rodrigo Coutinho, Co-founder and AI Project Manager at OutSystems. “Large language models (LLMs) are a big help, but they still make mistakes. But as these models evolve, and trust in the resulting code improves, the developer’s role will be more akin to that of an orchestrator and acceptance tester of AI-generated outputs.”

Despite being a nascent technology a couple of years ago, the report found that confidence in the quality of AI-generated code has risen substantially—half of respondents said that the implementation of AI has improved software quality, enhanced decision-making, and increased efficiency in software testing and quality assurance.

But confidence is also paired with risk awareness surrounding tech debt in the form of orphan code and hallucinations, a lack of context for an organization’s specific coding needs, and scalability concerns. With strategy baked into AI in SDLC processes, 56% of respondents said they experienced or expected to experience a higher quality of applications, with fewer bugs and improved performance.

Data privacy and security concerns remain the primary barriers to broader adoption.

The AI opportunity is undeniably huge, but its wider adoption in other areas of the SDLC beyond software testing and vulnerability detection still face some barriers. Chief among these are data privacy and security concerns (56% of respondents) and regulatory and compliance challenges (42%). Moreover, 38% of executives cite difficulties integrating generative AI into existing workflows as the primary barrier to adoption.

“There’s a lot of speculation on what will change with the rise of GenAI,” said Michael Harper, Managing Director at KPMG U.S. “While there will be challenges, those with effective change management initiatives will reskill and upskill their workforces, leading to AI and jobs evolving in tandem.”

One-third of respondents said they had a backlog of between 150 and 800 use cases for GenAI. 

The speed and sprawl of AI, namely GenAI, is paving the way for an increase in investments for nearly all respondents.

But risks concerning the reliability of AI-generated code persist, though they can be mitigated with existing approaches, such as user acceptance testing, unit testing, and regression testing. “It’s up to the developer working with AI to guarantee the quality of the deliverables, but this becomes way more efficient with AI,” said Coutinho. “AI is, in fact, a great partner in creating tests in synthetic data.”

Other oft-cited concerns were the limited availability of skilled personnel and difficulties integrating GenAI into existing tech stacks and workflows. Fears of job losses are high as well, with 89% of respondents claiming that certain roles will be eliminated by AI. This falls in line with a broader industry trend over the last couple of years. However, in the longer term, AI may well create more jobs than it displaces, resulting in a new type of developer, equipped with specialized AI skill sets.

For more insights from the 2024 survey “AI in software development: Exploring opportunities and uncertainties,” download the report here.  

About OutSystems

OutSystems is a global leader transforming how companies innovate through software, empowering IT leaders with a better way to build the software that matters most. The OutSystems platform helps companies develop, deploy, and maintain mission-critical applications by unifying and automating the entire software lifecycle. With OutSystems, organizations leverage GenAI to deliver software instantaneously, adapt faster to changing requirements, and reduce technical debt by building on a future-proof platform. Helping customers achieve their business goals by addressing key strategic initiatives, OutSystems delivers software up to 10x faster than traditional development. Recognized as a leader by analysts, IT executives, business leaders, and developers around the world, global brands trust OutSystems to tackle their impossible projects and turn their big ideas into software that moves their business, people, and the world forward.

Founded in 2001, the company’s network spans more than 750,000 community members, over 500 partners, and active customers in 75+ countries across 21 industries. Learn more at www.outsystems.com.

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SOURCE OutSystems

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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.

View original content to download multimedia:https://www.prnewswire.com/apac/news-releases/video-cnpc-offers-green-chemical-answer-302834036.html

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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