Core Insights - OpenText has introduced the OpenText AI Data Platform (AIDP) to unify data management and AI, aiming to provide secure and scalable enterprise capabilities [1][2][14] - The platform addresses the growing need for organizations to manage and activate their proprietary data effectively, especially in light of the negative consequences reported by 51% of organizations using AI [2][3] AI Strategy and Innovation - OpenText's AI strategy is built on 35 years of experience in data management, emphasizing the importance of contextual understanding for effective AI deployment [4][5] - The company aims to create a new generation of secure AI agents that can operate within specific business contexts, enhancing accuracy and reducing false confidence [3][4] Product Features and Capabilities - OpenText Aviator, built on the AIDP, enables automated workflows and adheres to open architecture standards, allowing organizations to customize their AI strategies [6][8] - The platform supports multi-cloud, multi-model, and multi-application deployments, ensuring compatibility with various AI models and enterprise systems [9] Partnerships and Collaborations - OpenText is expanding its partnership with Databricks to co-innovate on the AIDP, focusing on technical integrations that will enhance data governance and analysis capabilities [7][8] - The collaboration aims to empower customers to unlock trusted AI insights and drive innovation at scale [7] Upcoming Innovations - At OpenText World 2025, the company outlined its 18-month roadmap for innovations that will redefine enterprise interactions with data and AI [10][11] - OpenText Aviator will be included in upgrades to existing management systems, providing practical use cases for various business applications [11]

Core Insights - Financial institutions are rapidly adopting AI agents for customer-facing processes, significantly transforming interactions with banks and insurers [1][2] - The Capgemini Research Institute predicts that AI agents could generate up to $450 billion in economic value by 2028, highlighting a substantial opportunity for the financial services sector [2][7] - Despite optimism, only 10% of firms have implemented AI agents at scale, indicating a large potential for growth in this area [5][6] Adoption and Implementation - Key processes for AI agent deployment in banks include customer service (75%), fraud detection (64%), loan processing (61%), and customer onboarding (59%) [1] - Insurers also prioritize customer service (70%), underwriting (68%), claims processing (65%), and onboarding (59%) [1] - 80% of financial services firms are in the ideation or pilot stage for AI agent deployment, with 33% developing proprietary agents in-house [5][6] Economic Impact and Investment - Nearly two-thirds of executives (61%) view cloud-based orchestration as critical to their AI strategy, transforming cloud platforms into innovation engines [3] - C-suite executives are aligning investments with AI agent technologies, with nearly two-thirds indicating that up to 40% of their generative AI budget is allocated to these technologies [7] - By 2028, one in four firms expects to increase spending on AI agent solutions by up to 60% [7] Challenges to Adoption - Executives cite a skills gap among business leaders and employees (92%) and regulatory compliance burdens (96%) as major roadblocks to AI adoption [8] - High implementation costs are also a barrier, leading 25% of firms to consider a service-as-a-software model for AI consumption and monetization [9]

Core Insights - Financial institutions are rapidly adopting AI agents for customer-facing processes, transforming interactions in banking and insurance sectors [2][3] - AI agents are projected to deliver up to $450 billion in economic value by 2028, indicating significant opportunities for the financial services industry [3] - A majority of firms are in the ideation or pilot stage of AI agent deployment, with only 10% having implemented them at scale [6] Adoption and Implementation - Key processes for AI deployment in banks include customer service (75%), fraud detection (64%), loan processing (61%), and customer onboarding (59%) [2] - Insurers prioritize customer service (70%), underwriting (68%), claims processing (65%), and onboarding (59%) [2] - 33% of banks are developing proprietary AI agents in-house, while nearly half are creating new roles to supervise these agents [3][8] Benefits and Opportunities - Executives believe AI agents can enhance efficiency in customer onboarding, KYC, loan processing, and claims management, with benefits including real-time decision-making (96%), improved accuracy (91%), and faster turnaround times (89%) [7] - 92% of executives see AI agents facilitating expansion into new geographies without heavy infrastructure costs [9] - 79% believe cloud-native AI agents can enable dynamic pricing and offers, enhancing revenue potential [9] Investment Trends - Nearly two-in-three executives allocate up to 40% of their generative AI budget to agent technologies, with expectations of increasing spending on AI agent solutions by up to 60% by 2028 [10] - A growing number of firms (25%) are considering a service-as-a-software model to manage AI costs effectively [12] Challenges to Adoption - Executives identify a skills gap (92%) and regulatory compliance burdens (96%) as major obstacles to AI adoption [11] - High implementation costs are also a barrier, prompting firms to explore new consumption and monetization models for AI [12]

Core Insights - Financial institutions are rapidly adopting AI agents for customer-facing processes, with significant deployment in customer service, fraud detection, loan processing, and onboarding [1][2][5] - AI agents are projected to deliver up to $450 billion in economic value by 2028, prompting banks and insurers to develop proprietary AI solutions and create supervisory roles [2][7] - The integration of AI and cloud technologies is transforming operational strategies, with a focus on real-time decision-making and improved efficiency [3][4][6] Adoption and Implementation - Currently, only 10% of financial services firms have implemented AI agents at scale, while 80% are in the ideation or pilot stages [5][7] - Executives identify customer onboarding and KYC processes as the most inefficient, with high expectations for AI agents to enhance accuracy and turnaround times [6][10] Economic Impact and Opportunities - 92% of executives believe AI agents will facilitate expansion into new markets without significant infrastructure costs, and 79% see potential for dynamic pricing strategies [8][9] - Nearly two-thirds of leaders allocate up to 40% of their generative AI budget to agent technologies, with expectations of increasing spending by up to 60% by 2028 [9] Challenges to Adoption - Major barriers to AI adoption include a skills gap among employees (92%) and regulatory compliance issues (96%) [10] - High implementation costs are prompting firms to consider a service-as-a-software model, focusing on outcomes rather than traditional licensing [11]

Group 1 - Financial institutions are actively transferring significant customer processes to AI agents, transforming customer interactions with banks and insurance companies [1][4] - Key processes where banks deploy AI agents include customer service (75%), fraud detection (64%), loan processing (61%), and customer onboarding (59%) [1] - Insurance companies also follow a similar trend, with customer service (70%), underwriting (68%), claims processing (65%), and customer integration (59%) being the primary areas of deployment [1] Group 2 - AI agents could generate up to $450 billion in economic value by 2028, indicating substantial opportunities for the financial services sector [2] - 33% of banks are developing their own AI agents internally, while 48% are creating new roles to supervise these agents [2][5] - Only 10% of companies have deployed AI agents at scale, despite 80% being in the design or pilot stages [4][5] Group 3 - Nearly two-thirds (61%) of executives consider cloud orchestration essential to their AI strategy, transforming cloud platforms into innovation engines [3] - The combination of AI and cloud technology allows banks and insurance companies to provide more accurate, faster, and efficient services [4] - Executives see AI agents as capable of real-time decision-making (96%), improved accuracy (91%), and shorter processing times (89%) [6] Group 4 - Major challenges in adopting AI include a lack of skills among executives and employees (92%) and regulatory constraints (96%) [7] - High implementation costs hinder a true return on investment for AI, leading 25% of companies to consider a Service-as-a-Software model in the next 12 to 18 months [8] Group 5 - Executives are optimistic about AI agents helping to expand into new geographic areas without massive infrastructure investments (92%) [11] - 79% believe cloud-integrated AI agents will enable dynamic pricing and personalized offers, maximizing revenue and staying ahead of competition [11] - 75% see opportunities for multilingual support tailored to local regulations and cultural standards [11]

Core Insights - The deployment of the first intelligent humanoid robot, named Hoxo, in the nuclear sector represents a significant technological advancement for the industry [2][3] - This initiative aims to enhance industrial performance and safety by integrating robotics with human operations in challenging environments [4][5] Company Overview - Orano is a leading international operator in nuclear materials, focusing on innovative solutions to address global energy and health challenges, with a workforce of 18,000 employees [7] - Capgemini is an AI-powered global business and technology transformation partner, with nearly 60 years of experience and a workforce of 420,000 across more than 50 countries, reporting global revenues of €22.1 billion in 2024 [8] Technological Innovation - Hoxo is equipped with advanced AI and sensors for real-time perception, autonomous navigation, and interaction, designed to replicate human movements and assist teams in nuclear facilities [3][4] - The project combines robotics, AI, computer vision, and digital twins, redefining human-machine interaction in sensitive environments and pushing the boundaries of industrial automation [6]

Core Insights - Capgemini has successfully closed the acquisition of Cloud4C, enhancing its leadership in cloud managed services and expanding its capabilities in automation and AI-driven solutions [3][5]. Group 1: Acquisition Details - The acquisition of Cloud4C, a provider of automation-driven managed services for various cloud environments, was finalized after meeting all regulatory requirements [3]. - Cloud4C's team of 1,600 specialists will contribute to Capgemini's growth in the cloud managed services market, particularly in developing industry-specific frameworks and generative AI solutions [3][4]. Group 2: Strategic Implications - The integration of Cloud4C will bolster Capgemini's offerings in SAP services, particularly in end-to-end SAP services and SAP RISE, by combining Syniti's data expertise with Cloud4C's capabilities [3]. - Capgemini aims to address the increasing demand for AI-powered cloud automation platforms and scalable industry-specific solutions, providing tangible business value to clients [4]. Group 3: Market Context - The global demand for managed services in hybrid cloud environments is rising, with many clients seeking assistance for complex migrations [4]. - Cloud4C's comprehensive service offerings, including consulting, stack migration, and hybrid cloud solutions, will enhance Capgemini's ability to meet client needs in this dynamic market [4]. Group 4: Company Overview - Capgemini is a global business and technology transformation partner with a workforce of 420,000 across more than 50 countries, reporting global revenues of €22.1 billion in 2024 [6]. - The company focuses on delivering end-to-end services leveraging its deep industry expertise and strong partner ecosystem [6].

Core Insights - Capgemini has successfully closed the acquisition of Cloud4C, enhancing its leadership in cloud managed services and expanding its capabilities in automation and AI-driven solutions [2][3][4] Company Overview - Capgemini is a global business and technology transformation partner with a workforce of 420,000 across more than 50 countries, reporting global revenues of €22.1 billion in 2024 [5][6] Acquisition Details - The acquisition of Cloud4C, announced on August 26, 2025, fulfills all regulatory requirements and adds a team of 1,600 specialists in automation-driven managed services for various cloud environments [2][4] - Cloud4C's offerings include consulting, stack migration, and hybrid cloud solutions, which will complement Capgemini's existing services [3] Strategic Implications - The integration of Cloud4C will allow Capgemini to develop industry-specific frameworks and generative AI solutions, enhancing its SAP services and addressing client demand for cloud automation [2][3] - The acquisition is expected to facilitate smoother migrations for clients and provide access to advanced cloud managed services [3]

Core Insights - Siemens and Capgemini are expanding their strategic partnership to co-develop AI-native digital solutions for product engineering, manufacturing, and operations, focusing on 16 high-impact capability areas to enhance production efficiency, time-to-market, quality, and sustainability [1][2] Partnership Details - The collaboration aims to address long-standing challenges in integrating IT and operational systems by leveraging technologies such as industrial AI, digital twins, and next-generation automation [2] - The partnership will utilize orchestrated AI agents to enhance collaboration across engineering and manufacturing silos [2] Leadership Statements - Siemens' CEO Cedrik Neike emphasized the partnership's role in guiding customers through digital transformation with speed and precision, while Capgemini's CEO Aiman Ezzat highlighted the ambition to help clients achieve operational efficiency and tangible business impact [3] Client Case Studies - For Airbus, the partnership is focused on decarbonizing four industrial locations, targeting a 20% reduction in energy consumption and an 85% reduction in Scope 1 and 2 emissions by 2030, utilizing energy system twins for optimal decarbonization roadmaps [3][4] - In the case of Sanofi, the collaboration is standardizing production processes and accelerating the rollout of Manufacturing Execution Systems (MES), resulting in a 70% reduction in review time and an 80% decrease in deviations [4] - For GravitHy, the partnership aims to digitalize industrial processes, targeting a hydrogen production cost reduction of up to 10% [6] Industry Focus - The joint initiative will concentrate on key industries such as aerospace, automotive, and life sciences, as well as emerging markets like hydrogen and water/wastewater [7] - Capgemini plans to expand its pool of certified experts to enhance its Siemens technology capabilities [7] Company Background - Siemens generated revenue of €75.9 billion and net income of €9.0 billion in fiscal 2024, employing around 312,000 people globally [10] - Capgemini reported global revenues of €22.1 billion in 2024, with a workforce of 420,000 team members across more than 50 countries [11]

Oct 29, 2025 9:15 AM Eastern Daylight Time xtype Launches 2026 State of ServiceNow Operations Survey at World Forum New York Share The company invites ServiceNow customers to contribute critical and influential insights on governance, AI adoption, and platform ROI xtype, 'State of ServiceNow', Survey Launch COVINA, Calif.--(BUSINESS WIRE)--xtype, the Technical Governance Platform for ServiceNow, today announced the launch of its 2026 State of ServiceNow Operations Survey, launching at The ServiceNow World F ...