Core Insights - Retail trends in 2026 are shaped by structural shifts that accelerated in 2025, driven by economic pressures, changing consumer behavior, and technological advancements [1] Group 1: Artificial Intelligence in Retail - The adoption of artificial intelligence in retail saw a significant increase in 2025, with large retailers moving beyond pilot projects to implement AI for demand forecasting, inventory management, personalized recommendations, and automated customer service [3] - AI tools are expected to become integral to everyday retail operations in 2026, influencing product discovery and cost control through AI-powered shopping assistants, dynamic pricing engines, and predictive supply chain systems [4] - Data governance and ethical use of AI are gaining importance, with regulators indicating tighter oversight of automated decision-making, making transparency around customer data and algorithmic processes a competitive issue [5] Group 2: Omnichannel Retail Strategies - Omnichannel retail has transitioned from a differentiator to a basic requirement for relevance, with consumers expecting a seamless experience across online platforms, mobile apps, and physical stores [6] - Retailers focused on deeper channel integration in 2025, investing in services like click-and-collect tied to store inventory, mobile checkout systems, and digital tools to support in-store staff [7]

Investment Rating - The report emphasizes that the highest return on investment in 2026 will come from modern data infrastructure rather than the latest AI models [11][14]. Core Insights - The gap between AI ambitions and actual operations is widening across industries, with organizations needing to focus on foundational work to achieve transformative wins [6][5]. - Companies are shifting from broad experimentation to specific, high-value use cases, moving AI from proof-of-concept to enterprise-level deployment [19][15]. - Successful organizations are prioritizing data lifecycle management, modernization, and human capabilities to shape their AI-driven transformation strategies [48][59]. Summary by Sections Overview - The report highlights a significant disconnect between organizational expectations of AI and the foundational work required for successful implementation [6][5]. - Many companies are still relying on outdated systems and manual processes, which hinders their ability to leverage AI effectively [6][9]. 2026 AI Trends - AI success in 2026 will be driven by data infrastructure rather than new models, with a focus on creating accessible and real-time data management systems [11][12]. - Organizations are expected to transition from broad AI experiments to targeted applications that deliver measurable business value [15][17]. Industry-Specific Trends - The report outlines predictions for various sectors, including: - Airlines will require rapid experimentation due to competitive pressures [65]. - Retail will see AI operating behind the scenes, influencing pricing and supply chain decisions [66]. - Healthcare will experience regulatory advancements that promote AI-driven innovations [69]. Preparing for 2026 - Companies need to invest in data management and governance to support AI initiatives effectively [48][51]. - A cultural shift is necessary for organizations to embrace AI as a core component of their business strategy rather than a standalone project [30][60]. Conclusion - The foundation laid in data infrastructure and governance will determine the success of AI initiatives in 2026, with companies that prioritize these areas likely to thrive [87][89].

KPMG has announced a partnership with UK company Aiimi to launch a workplace AI platform for data governance across its operations. The collaboration is designed to manage sensitive information and extract insights from the digital estate of KPMG, supporting the firm’s aim to govern data at scale and encourage adoption of AI. The move comes shortly after the Financial Reporting Council’s annual review was published. Aiimi’s platform leverages AI to assist organisations in locating, governing, and unlock ...

As you probably know, AI is already redefining the world economy from financial services to healthcare to automotive, energy, manufacturing, public sector. We are really starting to see great new AI applications come and this is all in partnerships with Nvidia, our great partner who has been pushing us on the envelope of innovation. So what we are talking about here is yes we've been here for many years 27 years in fact but the last 10 years has been amazing in 2015 almost 10 years ago we were supercharging ...

Core Insights - Data governance is often viewed as an ancillary project rather than a core component of data initiatives, leading to questions about its actual value in the short to medium term [1] - The shift from centralized management to a more decentralized approach in data governance has been driven by advancements in low-cost storage, cloud computing, and artificial intelligence [2] - The need for seamless access to accurate data through various means has led to the rapid adoption of data mesh architecture, which emphasizes business ownership over data rather than IT [2] - Successful data governance now requires a shift in strategy for technical project managers, focusing on business initiatives and mapping them to data products [2][3] - The integration of data governance with business objectives is essential for achieving effective outcomes and realizing short-term benefits [3][4] Summary by Sections - **Data Governance Perception**: Traditionally seen as a supplementary function, data governance lacks immediate value perception, leading to its relegation in overall data strategy [1] - **Evolution of Data Governance**: The last decade has seen a transition to decentralized data governance models, driven by technological advancements and the need for diverse data access [2] - **Decentralized Data Mesh**: The data mesh architecture promotes business department ownership of data, enhancing understanding, traceability, and interoperability [2] - **Strategic Shift for Implementation**: Technical project managers are encouraged to adopt a right-to-left approach, prioritizing business stakeholder involvement in data governance initiatives [2][3] - **Business Alignment**: Effective data governance must align closely with business goals to be successful, moving away from fragmented approaches [3][4] - **Implementation Roadmap**: A viable roadmap for building a business-oriented decentralized data governance framework is suggested [5][6] - **Frequent Value Realization**: The focus is on achieving business value and data governance goals frequently in the short term, contrasting with traditional methods that aim for one-time achievements [8]

Core Insights - Cloudera has announced a significant platform update integrating Trino, Cloudera SDX, and Cloudera Octopai to enhance data access, control, governance, and traceability across data assets [1][2] - The update aims to address the challenge of data accessibility for AI projects, with only 9% of IT leaders reporting all data is available and 38% indicating most data is usable for AI [1][2] - Cloudera's platform leverages AI-driven automation to unify data silos and streamline governance processes without the need to move data, thereby improving efficiency and trustworthiness [1][2] Company Overview - Cloudera is positioned as a trusted data and AI platform for large enterprises, enabling them to integrate AI with data stored anywhere [3] - The company differentiates itself by providing a consistent cloud experience through a validated open-source infrastructure, unifying public cloud, on-premises data centers, and edge environments [3] - Cloudera empowers enterprises to harness AI and gain comprehensive control over all forms of data, enhancing security, governance, and real-time predictive insights [3] Product Features - The Cloudera platform automates key data architecture operations such as data quality checks, classification, and profiling, facilitating efficient data management [2][4] - It offers natural language access to enterprise data, allowing both technical and non-technical teams to utilize data equally [2][4] - Cloudera Octopai's data source tracking feature provides end-to-end visibility and credibility for data transformations, including those from outside the Cloudera ecosystem [2][4]

Core Insights - The 2025 Finance and Accounting Technology Expo highlighted the evolving role of CFOs and the finance tech landscape, focusing on modernization, data governance, and AI adoption [1][2][3] Group 1: Technology Vendor Interactions - Decision makers are demanding clearer and more concrete answers from technology vendors regarding data extraction, integration failure rates, and reporting liabilities, indicating a shift towards higher transparency and due diligence in tech stacks [4] - Trust in cloud-based systems is increasingly dependent on the surrounding technology environment, with organizations favoring a single system of record to minimize complications from multiple tools [5] Group 2: ERP Implementation and Legacy Systems - A recurring theme in discussions was the integration of new technologies with existing systems like NetSuite, reflecting a reliance on legacy vendors for flexibility in finance functions [6]

Core Insights - VCI Global Limited has announced a special dividend of 10% of its cybersecurity and AI subsidiary, V Gallant Limited, to be distributed to all shareholders, marking a strategic move towards unlocking value ahead of V Gallant's planned Nasdaq IPO [1][2][3] Group 1: Dividend Announcement - The special dividend will be distributed on a pro rata basis to all VCIG shareholders, representing a 10% equity interest in V Gallant [1][4] - The record date for the dividend distribution will be announced after administrative procedures are completed, with confirmation expected within 7-14 days [4] Group 2: Strategic Rationale - The distribution aims to broaden shareholder participation in V Gallant, reduce corporate concentration, and enhance trading liquidity, aligning long-term interests across both entities [2][6] - This move is part of VCI Global's value-unlocking strategy as V Gallant prepares for its Nasdaq carve-out IPO, which is projected to have a nine-figure valuation based on internal estimates and market comparables [2][5] Group 3: V Gallant's Positioning - V Gallant is recognized as one of VCI Global's fastest-scaling technology units, focusing on cybersecurity and AI solutions for enterprises and government organizations [3][7] - The company is advancing through key preparatory milestones for its IPO, including audit completion, valuation review, and engagement with institutional investors [5][6]

What does AI need to do to deliver real economic value. Let's talk about it with Charles Sansbury, the CEO of Cloudera, who is here with us in studio for a video brought to you by Cloudera. Charles, great to see you.How are you. >> Great to see you and thanks for having me. >> Thanks for being here.We've been talking on the show so much about the economic value of artificial intelligence. Um whether or not there there will be an ROI on this technology. >> I'm so happy to be speaking with you today because y ...

Core Insights - The integration of artificial intelligence into business processes necessitates CFOs to closely monitor the data that informs AI models for effective scenario planning and forecasting [1][3] Group 1: Importance of Data Governance - CFOs should apply the same level of audit discipline to data as they do to financials, viewing data as a financial asset [2] - Privacy and data trust are essential governance mechanisms that protect and enhance the value of data assets [2] Group 2: AI Integration and Challenges - CFOs are enthusiastic about AI's potential benefits, such as improved forecasting and reduced financial close times [3] - A significant challenge is that enthusiasm for AI often exceeds the maturity of the data being used, leading to a lack of trust in the data [4] Group 3: Data Quality and Operational Impact - Untrustworthy or unclear data can lead to operational issues, such as distorted forecasts and increased material and reputational risks [5] - Ensuring clean data is crucial for CFOs, as strategies depend on accuracy, predictability, and risk mitigation [5]