Latest news with #IDC
AU Financial Review
an hour ago
- Business
- AU Financial Review
Agentic automation takes aim at Australia's productivity slump
'It augments things that you could not do with existing automation – especially things that cannot be defined in rules or direct approaches. It's more for dynamic goals, where agents can do a little bit of dynamic planning and decision-making along the way in order to perform the task.' The recent UiPath Agentic Automation Summit in Sydney brought together business and tech leaders to showcase how agentic automation is already reshaping the way Australians work and engage with AI. One summit attendee described agentic automation as a collision of technologies – a convergence of artificial intelligence and traditional automation that's already reshaping how businesses approach cost, quality and decision-making. 'Agentic automation is really the convergence of two trends,' says Dr Chris Marshall, vice president of data, analytics, AI, sustainability, and industry research at IDC. 'You've got generative AI, which is powerful but unpredictable and expensive, and traditional automation like RPA, which is reliable but limited. Together, they're becoming something more useful than either alone.' Improved decision making Marshall says this hybrid approach is helping businesses unlock a broader range of tasks that humans used to do. 'Suddenly you have a degree of reliability and intelligence that covers more work, more consistently, and often with better quality outcomes,' he says. 'It's not just productivity – it's also improved decisions and process quality.' While the efficiencies are compelling, he says the longer term value comes from how agentic systems interact with companies' underlying processes. 'You start to learn where the blockages are,' says Marshall. 'You can simulate improvements, chop up tasks differently, or assign them to different agents or people. That's where innovation starts.' He says the future of agentic automation isn't just advice from a bot – it's systems that act. 'The bot gets stuff done. The agent thinks so that people lead,' says Marshall. 'Combining advice, action and leadership – that's where agentic automation is heading, and that's what sets it apart from older forms of automation.' Marshall says that Australia's approach to these tools may differ from other markets. 'Australians are more sceptical about technology – and that's not a bad thing,' he says. 'Especially in regulated sectors like financial services, that demand for privacy, data sovereignty and compliance is driving better questions and, ultimately, better outcomes.' That's translating into real use cases. Businesses are starting to embed AI agents in the final stages of processes that were already largely automated, removing friction and extending the value of earlier automation programs. While much of the conversation around agentic automation remains abstract, a recent deployment in Australia's energy sector shows what this technology looks like in action – and why businesses are moving fast to scale it. Oil and gas take the lead Resources giant Woodside Energy recently used agentic automation to overhaul its procurement communications, where staff had been manually sorting messages from more than a dozen channels. 'Employees were spending up to 20 hours a week just triaging communications,' says Peter Graves, area vice president ANZ at UiPath. 'With agentic tools, those hours are reclaimed, freeing people up to make decisions rather than sift through noise.' The deployment used Communications Mining, a platform that applies natural language processing to unstructured inputs like emails and text messages and routes them intelligently across systems. 'Where traditional automation stops at structured systems, agentic automation bridges the gap into unstructured data,' says Graves. By combining robotic process automation with AI and human input, the system enabled real-time decision-making while cutting delays and costs. 'It's about people, robots and AI agents working synergistically in the same workflow,' says Graves. 'That's the real shift.' Woodside's implementation was powered by a bespoke dispatcher framework integrated with UiPath's CommPath language model. 'It's all run through a trust layer that makes the system secure and predictable,' says Graves. Graves says the same architecture is already being replicated across other communication-heavy sectors. 'We're seeing this as the new standard for intelligent automation in Australia's energy sector and beyond,' he says. 'That's where the initial success is coming from,' says Graves. 'It could be a final step, or it could be a number of steps inside a process where you needed a human to jump in and make a decision. An agent can do that quite effectively.' Recent IDC research shows strong Australian uptake, with more than half of organisations surveyed saying they are already using AI agents, and a further quarter plan to adopt them within the year. In practical terms, this means fewer isolated bots and more systems capable of context-aware orchestration – taking in unstructured data, making decisions, and collaborating with humans across workflows. The shift is particularly visible in sectors such as healthcare and financial services, where long processes and regulatory compliance create friction that traditional automation struggles to handle. Unstructured data 'In healthcare, there are often human-in-the-loop steps that an agent can now manage,' says Graves. 'And in banking and financial services, there's lots of unstructured data. An agent can review, make a decision and move on – something it would have been impractical to code into a robot.' This pressure is not just about productivity, but also about creating systems that are safe, governable and sustainable at scale. While automation has long been associated with fears about job losses or burnout, early experiences with agentic tools suggest the opposite: by removing low-value cognitive load, they're helping people focus on more engaging work. Removing the stress 'When I look at some of the things we have agents do right now, it's not typically the stuff people enjoy doing,' says Hao. 'It's the next level up from mindless work – stuff where you have to think about what to do next, but doing it isn't that interesting. It's almost always the same four steps, just ordered differently.' Graves says that's having a measurable impact. 'We're seeing the ability for agentic automation to remove that stress, so someone can get their work done in the allotted time instead of staying back for two or three hours.' Still, the shift is not without its barriers. Education, trust and clarity around what agentic AI can actually do remain issues for many organisations. 'It's still a very new area, and not a lot of workers have been exposed to it,' says Graves. 'There's a lot of proof of concepts, a lot of pilots – but not a lot that's gone into production yet.'
TECHx
6 hours ago
- Business
- TECHx
Qlik Trust Score for AI Now Available in Talend Cloud
Home » Tech Value Chain » Global Brands » Qlik Trust Score for AI Now Available in Talend Cloud Qlik®, a global in data integration, data quality, analytics, and artificial intelligence, has announced the general availability of Qlik Trust Score™ for AI. This innovation is now included within Qlik Talend Cloud®. It helps organizations assess whether data is truly ready for AI before it reaches a model. The Qlik Trust Score for AI introduces purpose-built scoring across AI-specific dimensions. It is designed to help companies establish strong data foundations for responsible and scalable AI. As AI adoption grows, many businesses face a critical challenge they don't know if the data feeding their models is trustworthy. Qlik's solution addresses this by offering a single, intuitive score that reveals where data trust breaks down. This helps prevent bias, drift, and faulty outcomes. Drew Clarke, EVP of Product and Technology at Qlik, stated, 'Most companies still treat data trust like an IT hygiene issue. It's not. It's the foundation of every AI decision a business makes.' He added that without the ability to measure trust, organizations are gambling with outcomes, compliance, and customer experience. The Qlik Trust Score for AI provides a real-time signal that data is fit for purpose. This tool builds on Qlik's existing Trust Score framework with three new AI-focused dimensions: Diversity: Measures representativeness and balance to reduce training bias. Timeliness: Assesses freshness of data for relevant decision-making. Accuracy: Flags values that break business rules or lack reliability. These features combine with metrics like Discoverability and Usage. The solution supports AI training, Retrieval-Augmented Generation (RAG) pipelines, and intelligent automation. Security and LLM Readiness metrics are expected in future updates. Qlik also revealed plans for Trust Score historization, enabling users to track trends over time. This helps correlate trust shifts with issues like model drift or performance drops. In addition, Qlik reported an early access program for an AI-native Data Stewardship experience in Qlik Talend Cloud. Launching this fall, the feature aims to detect and resolve data issues earlier. It will include automated rules, human-in-the-loop workflows, and platform-wide governance. Ritu Jyoti, Group VP/GM at IDC, noted that many AI initiatives fail because of untrustworthy data. 'Without visible metrics for data trust, organizations risk costly failures and stalled adoption,' she said. Charles Link, Senior Director of Data and Analytics at Reworld, emphasized that trust in data is critical. 'The hardest part of AI is rarely the model. It's trusting the data behind it,' he said. A recent Qlik survey revealed only 42% of executives fully trust AI-generated insights, despite nearly 90% seeing AI as vital to their strategy. Qlik Trust Score for AI helps bridge this trust gap with an objective and repeatable framework. It aligns with emerging governance standards. Qlik is the first to deliver a unified, AI-specific trust signal directly into the data pipeline. It integrates measurement, monitoring, and remediation in one platform. The feature is now generally available for Qlik Talend Cloud Enterprise Edition customers.
Swift Newz
12 hours ago
- Business
- Swift Newz
Experience at the edge: Redefining CX for Saudi Arabia's Vision 2030
In 2016, Saudi Arabia launched Vision 2030, a sweeping national agenda to future-proof its economy, reduce reliance on oil and enhance quality of life for citizens. Built around the pillars of a vibrant society, a thriving economy and an ambitious nation, the initiative has become synonymous with rapid innovation—driven by investments in AI, cloud computing, smart cities and digital government. Today, these efforts are visible everywhere, from biometric border controls to AI-powered health platforms and cashless pilgrimages. Yet amid all this progress, a critical question has emerged: as infrastructure transforms and services go digital, is the customer experience transforming with th ailability of advanced technologies, many businesses—and even institutions—still rely on em or being left behind? While government platforms like Absher and Tawakkalna have raised the bar for ease and accessibility, not all sectors have kept pace. Despite the surge in connectivity and the av fragmented, one-size-fits-all communication systems that fall short of citizen expectations. A connected nation, rising expectations In 2025, Saudi Arabia stands as one of the most digitally advanced nations in the region: It has 36.84 million internet users, with 99% penetration About 94.3% of the population is active on social media, with 35.1 millions About %93.18% use smartphones to go online (Statista) . But this level of access is only the beginning. The real transformation lies in what people are now demanding—seamless, intelligent and hyper-personalised digital interactions across every touch point. What's needed now is a shift from reactive CX models—common in sectors like finance, where customer issues are often addressed only after complaints—to proactive engagement. The current CX gap stems from organizations' inability to break down data silos, integrate touchpoints and provide seamless service across channels. Despite having the infrastructure for advanced digital engagement, many businesses still lack the tools and processes to deliver personalised, anticipatory service that customers now expect. This gap not only hampers customer satisfaction but also limits the ability of businesses to fully leverage their investments in digital transformation. As highlighted in IDC's Future of Customer Experience 2024 predictions, businesses must unify data, channels and insights to meet rising expectations and fully capitalise on their digital infrastructure investments. In short, the infrastructure is advancing, but experience delivery hasn't fully caught up. To realise Vision 2030's full potential, closing this CX gap must become a priority, not just in government, but across every industry shaping Saudi Arabia's future. Saudi Arabia's journey to 2030 is one of extraordinary ambition and real, measurable progress. And if the goal is a society that's not just digital, but truly human-centred, then the way people experience these services must evolve alongside the infrastructure itself. By closing communication gaps and enabling smarter engagement, solutions like engageX are helping organisations transform Vision 2030 from a strategic vision into a lived reality, one meaningful interaction at a time. Customer experience that is real, responsive and relevant is no longer a competitive advantage, it's a national imperative. Whether in the public or private sector, organisations must now rethink not just how they serve, but how they connect, listen and adapt.
Business Standard
15 hours ago
- Automotive
- Business Standard
TVS Motor launches new variant of TVS iQube with 3.1 kWh battery
TVS Motor Company, today launched a new variant of its flagship electric scooter, TVS iQube with 3.1 kWh battery that offers an IDC range of 123 km, hill hold and dual tone design. This latest addition builds on the company's refreshed EV portfolio with enhanced range and features. With this, the TVS iQube portfolio now offers an array of six variants, making this one of the widest and most compelling portfolio in the segment. With over 600,000 units sold and presence in over 1900 touchpoints, TVS iQube continues to be India's favourite family EV, helping accelerate India's transition to clean mobility. Designed for everyday commuting, the TVS iQube 3.1 kWh starts at an effective ex-showroom price of INR 1,03,727 (Delhi) and offers an IDC-certified range of 123 km on a single charge. It comes equipped with Hill Hold for added safety and a refreshed UI/UX interface for a more intuitive riding experience. It is offered in four attractive colours: Pearl White, Titanium Grey, and two dual-tone optionsStarlight Blue with Beige and Copper Bronze with Beige. The new introduction compliments the diverse TVS iQube portfolio, which was also recently upgraded with improved battery capacity, range capability and design upgrades like dual tone colours and backrest. TVS iQube is inspired by three fundamental principles: Giving customers the POWER OF CHOICE for range, connected technology, charging solutions and price points; COMPLETE ASSURANCE through vehicle safety and overall ownership experience and the SIMPLICITY OF USAGE through easy-to-use features that enhance the riding experience of customers.
Associated Press
15 hours ago
- Business
- Associated Press
IDC Releases Report on AI-Powered Adaptive Education Industry, Revealing Opportunities and Future Trends of AI in Education
SHANGHAI, July 2, 2025 /PRNewswire/ -- In March 2025, IDC (International Data Corporation) released the report 'AI-Powered Adaptive Education: Opportunities and Trends,' which provides a comprehensive analysis of the current trends, application scenarios, case studies, and industry insights in the AI education sector. The report showcases the immense potential of AI to disrupt traditional education models and empower the education industry, offering detailed references for educators, investors, and all parties interested in the development of the education sector. Current Situation Analysis: AI in Education—Immediate Assistance and Long-Term Promotion With the rapid development of technology, artificial intelligence is gradually permeating and profoundly changing various industries, and the education sector is no exception. The report suggests that the AI intelligence level in the education industry is steadily advancing along a six-level evolution path from Level 0 to Level 5, marking a profound transformation of the education model from a basic digital stage to a full-scenario intelligent education agent. At the Level 0 basic digital stage, the emergence of electronic textbooks with static content storage and zero adaptive capabilities, online education resource libraries, and educational apps (for querying and browsing content) has enabled the migration of educational resources from paper to electronic media. Entering the Level 1 response-based intelligent stage, the system is upgraded to an AI system with basic response capabilities, capable of performing keyword-based responses and standardized resource pushing. Online learning machines and question-and-answer analysis tools have become typical applications of this stage. At the Level 2 context-aware stage and Level 3 simulated interaction stage, the level of AI intelligence has gradually been upgraded to systems with elementary cognitive abilities that can analyze learning behaviors and dynamically adjust difficulty (Level 2), and AI educational agents with human-like conversational abilities (Level 3) that can perform natural language processing, generate teaching strategies, deeply understand learning situations, and conduct multi-turn instructional conversations. With the advent of the Level 4 educational large-model stage, the level of AI intelligence has gradually evolved to feature multi-modal teaching understanding and adaptive education. Based on a strong cognitive system of vertical large models in education, products such as comprehensive adaptive learning platforms and integrated virtual-real teaching spaces have successively entered the market. Finally, at the Level 5 stage, represented by AI intelligent teachers, the full-scenario educational intelligent agent is the ultimate form of expression for ed-tech companies after entering the intelligent development stage of education. Level 5 full-scenario educational intelligent agents integrate the capabilities of Level 4 large models, incorporating functions such as personalized learning, emotional support, situational awareness, adaptive teaching, and MCM (Mode of Thinking, Capacity Building, and Methodology) training, enabling cross-disciplinary knowledge integration and dynamic teaching strategy generation. In the intelligent development process of the education industry, Level 5 is equivalent to the highest level in the autonomous driving field, representing an advanced state of full autonomy and no need for human intervention. Squirrel Ai's intelligent teacher is a typical representative case. Sullivan's '2024 China Intelligent Learning Machine Industry White Paper' has evaluated Level 5 representatives, pointing out that Squirrel Ai's intelligent teacher is the only application to reach Level 5. Ernst & Young's 'China Adaptive Education Industry White Paper' also lists Squirrel Ai's intelligent teacher as a representative application that has reached a fully adaptive education level. With core drivers such as recognition and interaction, data processing and learning, and cognition and intelligent reasoning, Squirrel Ai's intelligent teacher has successfully realized an ideal adaptive education model where all aspects from teaching preparation to teaching implementation and feedback are independently handled by AI, truly achieving a state where no human teachers are needed for additional intervention, covering various scenarios such as classroom teaching, home tutoring, and social practice. Under this growth rate, the core impact of AI-powered education is also becoming increasingly apparent. It is mainly manifested in two major aspects: On the immediate assistance level, AI can achieve performance improvement in the short term. A variety of AI products have built an intelligent tool matrix that can quickly respond to students' personalized learning needs. With the help of intelligent devices, the real-time monitoring and feedback mechanism for students' learning behaviors and environmental changes can effectively enhance the learning experience. At the same time, AI technology supports seamless switching and integration of various learning models online and offline, and across different terminal devices, ensuring that students can obtain the required learning resources in the most convenient way, meeting the development of personalized needs. On the long-term promotion level, AI can achieve long-term capability improvement by changing learning habits. This mainly includes five major aspects: the transformation of learning methods, the fairness of educational resources, the popularization of adaptive education, immersive learning, and intelligent supervision. The report points out that AI technology, as a bridge, can transport high-quality educational resources to remote and resource-poor areas, narrowing the educational gap. At the same time, adaptive education, as an important technology and application for personalized education, has its widespread popularization, which can provide students with personalized learning path planning. Combined with gamification and competitive interaction elements, it creates an immersive learning environment, stimulates learning interest, and guides students to transition from surface learning to deep understanding of knowledge and cultivation of critical thinking and innovation abilities. In addition, in Google's 'Future of Education' report, adaptive education is regarded as one of the three major technological trends and the core technology for global ed-tech companies to transform and invest in research and development since the beginning of the AI era. With the dual support of advanced large-model technology and deep learning algorithms, AI has gradually taken on the role of an 'intelligent guardian,' providing students with all-round guidance and support. Application Scenarios: Multi-modal Interaction and Adaptive Large Models Lead the Trend As AI technology continues to permeate the education sector, its application scenarios are also becoming increasingly rich. From in-school to out-of-school, from teaching to learning, generative AI is integrating into various aspects and scenarios of education in multiple ways. In in-school scenarios, generative AI demonstrates broad application potential. On the one hand, it can be used to build virtual experiments and simulated environments, providing students with practical operation opportunities; on the other hand, it can assist teachers in optimizing teaching design, improving teaching effects, and at the same time, playing a positive role in scientific research assistance. In out-of-school scenarios, generative AI can provide 24-hour online tutoring, accelerate language skill improvement, customize personalized learning paths, and provide emotional companionship. For example, the 'Intelligent Learning Companion' function launched by Onion Academy, by creating a virtual character named 'Nuannuan,' interacts with students through audio and video courses, helping them deal with emotional problems during the learning process. Facing the challenges of students' self-management and emotional adjustment, Squirrel Ai's intelligent teacher has a built-in 'intelligent mentor' in the system, which can accurately identify students' emotional states, conduct emotional exchanges, and, when necessary, refer them to professional psychological counselors, forming a closed-loop emotional support system and creating a learning environment with 'technical efficiency + humanistic warmth.' In scenarios that take into account both in-school and out-of-school contexts, generative AI plays a key role in intelligent homework grading and feedback, education management and decision support, and intelligent adaptive learning content generation. The report, taking Squirrel Ai Intelligent Teacher as an example, points out that it relies on a self-developed full-subject multi-modal adaptive education large model to accurately locate students' knowledge gaps, customize personalized learning plans, and thus effectively improve performance. In the homework grading and feedback process, it captures homework information in all aspects, integrates and deeply analyzes multi-modal data such as text and graphics, achieving a grading accuracy rate of over 90%, significantly reducing the teacher's workload. At the same time, with the help of draft paper content intelligent analysis technology, it accurately locates the cause of students' errors and provides students with targeted guidance in problem understanding and logical reasoning. Case Studies: Vertical Domains and Independent R&D Build Brand Differentiation The report, through in-depth research on domestic and foreign artificial intelligence education brands, has selected five major AI-powered education best practice cases. DreamBox Learning focuses on the fields of mathematics (K-8) and reading (K-12), and with the help of AI technology, it deeply analyzes students' learning styles, pace, and understanding levels to tailor personalized course content for them. Through the clever integration of game elements, such as challenges, rewards, and achievement mechanisms, it significantly improves students' learning engagement and successfully creates an immersive learning experience. Squirrel Ai Intelligent Teacher, as a representative of the Level 5 full-scenario educational intelligent agent, is the first unicorn technology innovation enterprise in China to introduce adaptive learning technology into the education field, focusing on the K-12 education field. Its self-developed world's first full-subject multi-modal adaptive education large model LAM (Large Adaptive Model), with a 'data layer - model layer - application layer' technical architecture, has created an AI intelligent teacher with 'eyes, ears, a mouth, and wisdom.' It can not only accurately locate students' knowledge gaps and provide personalized learning paths but also interact with students, solving the 'one-size-fits-all' pain point of traditional education. Khanmigo is built on OpenAI's large language model (LLM) and uses ChatGPT & GPT-4 as its training model, providing a strong foundation in language processing and the ability to respond to various different human inputs. In addition, Khanmigo comprehensively covers various subject areas from elementary school to university, providing students with a personalized and adaptive learning experience, guiding them to think independently. It can also empower educators by guiding them to create lesson plans through dialogue, effectively improving lesson preparation efficiency and teaching quality. TAL Education Group's business layout is extensive, covering quality education, adult education, smart books, smart hardware, and many other fields. Based on the different growth characteristics of children and adolescents, it has carefully developed a scientific cultivation system suitable for ages 3-18. In the field of mathematics, with problem-solving and explanation algorithms as its core technology, it has independently developed the Jiuzhang Large Model, injecting innovative power into mathematics education. iFlytek deeply integrates artificial intelligence and big data technology to create a product and service system that covers all scenarios of school teaching, teacher development, smart exams, quality education, and independent learning. Its launched world's first cognitive large model AI learning machine has 8 core large model functional features, closely meeting the needs of primary, junior high, and senior high school students and parents, comprehensively covering key learning links such as preview, review, test preparation, and homework tutoring, and providing students with all-round learning support. Industry Insights: AI Reshapes the Education Paradigm, the Ability to Evolve Teaching is Key Through an in-depth analysis of the current situation of AI education applications and practical cases, it is not difficult to find that AI technology is reshaping the education industry landscape. Against this backdrop, understanding and mastering the future development trends of the industry is crucial. For educational institutions intending to enter the AI education track, they need to focus on product algorithm optimization and personalized learning capability improvement. They should deeply examine the accuracy and effectiveness of the intelligent evaluation system and adaptive teaching functions, and strengthen the real-time feedback and dynamic teaching content adjustment mechanism to adapt to the high-standard requirements of the education market for intelligent education products and services. For investors, when considering investment projects, they should comprehensively analyze the stability of the enterprise's technical barriers, the accuracy of its market positioning, the feasibility of its business model, the comprehensive strength of its team, and the adaptability of its policy environment. They should prioritize enterprises with independent intellectual property rights, outstanding technical advantages, and strong sustainable innovation capabilities to ensure investment returns and the sustainable development of the industry. View original content: SOURCE Squirrel Ai Learning
