Where are Banks Prioritising their Tech Investment?
Providing insights into a recently released survey of over 400 bank executives, Isabelle Guis, Chief Marketing Officer, Temenos joined the FinextraTV studio at Temenos Community Forum 2025. She explains what the results tell us about banks' priorities when investing in technology, and how they are looking to adopt Generative AI.
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Geeky Gadgets
an hour ago
- Geeky Gadgets
Google Veo 3 : The Secret Weapon for High-Quality Video Advert Marketing
What if creating professional, eye-catching video ads was as simple as typing out a few ideas? Enter Google Veo 3, a innovative AI tool that's transforming how businesses approach video advertising. Imagine crafting a sleek, eight-second ad that feels like it was made by a top-tier production team—without the hefty price tag or weeks of back-and-forth edits. With its ability to seamlessly integrate audio, text overlays, and polished transitions, Veo 3 is more than just a tool; it's a fantastic option for marketers looking to stand out in a crowded digital landscape. The promise? High-quality, user-generated content (UGC)-style ads that connect with audiences on an authentic level. In this breakdown, Corbin Brown explores how Google Veo 3 is reshaping the video ad creation process, offering businesses a way to produce engaging, professional-grade content with minimal effort. You'll discover the tool's standout features, from script-based customization to its ability to showcase products with stunning visual clarity. But it's not all smooth sailing—Veo 3 has its quirks, and knowing its limitations is just as important as understanding its strengths. Whether you're a small business owner or a seasoned marketer, this guide will help you decide if Veo 3 is the right fit for your advertising needs. After all, the future of video marketing might just be a few clicks away. Google Veo 3 Overview Understanding Google Veo 3 Google Veo 3 is an advanced AI-driven platform designed to streamline the process of video ad creation. It enables businesses to produce short, visually appealing video clips—up to eight seconds in length—tailored to their branding and messaging needs. Its standout features include: Audio Integration: Add background music or voiceovers to enhance the emotional impact of your message. Add background music or voiceovers to enhance the emotional impact of your message. Text Overlays: Highlight key points with customizable text elements that align with your brand identity. Highlight key points with customizable text elements that align with your brand identity. Realistic Transitions: Ensure smooth and professional scene changes for a polished final product. For example, if you're promoting a new product like a smartphone, Veo 3 can generate a dynamic video showcasing its features, complete with branded text and audio. The tool's ability to follow detailed prompts ensures that the final output aligns closely with your creative vision. Performance and Practical Use Google Veo 3 has been tested across various projects, including advertisements for software solutions and physical products. For instance, when tasked with creating an ad for a software company called 'Bumpups,' Veo 3 produced a visually engaging clip featuring a user interacting with the software. The video included a voiceover and branded text, effectively highlighting the product's key features. However, during testing, some challenges were observed. Occasional issues such as audio synchronization errors and minor visual inaccuracies required manual adjustments. Despite these limitations, the tool demonstrated its ability to produce high-quality, professional-looking content with minimal effort. Create AI Video Ads with Google Veo 3 Watch this video on YouTube. Master AI video generation with the help of our in-depth articles and helpful guides. Core Features of Google Veo 3 Google Veo 3 offers a robust set of features that make it a valuable asset for businesses aiming to create impactful video advertisements. These include: AI Video Generation: Advanced algorithms generate high-quality, tailored video content that aligns with your branding. Advanced algorithms generate high-quality, tailored video content that aligns with your branding. Audio Integration: Seamlessly incorporate background music or voiceovers to enhance the emotional appeal of your ads. Seamlessly incorporate background music or voiceovers to enhance the emotional appeal of your ads. Text Overlays: Add customizable text to emphasize key messages or branding elements. Add customizable text to emphasize key messages or branding elements. Realistic Transitions: Smooth transitions between scenes create a professional and cohesive viewing experience. Smooth transitions between scenes create a professional and cohesive viewing experience. Script-Based Customization: Provide detailed scripts to guide the content creation process and ensure alignment with your vision. Provide detailed scripts to guide the content creation process and ensure alignment with your vision. Product Visualization: Showcase physical products effectively with realistic and visually appealing depictions. These features empower businesses to create professional-grade video ads without the need for extensive resources, technical expertise, or large production budgets. Limitations to Be Aware Of While Google Veo 3 offers numerous advantages, it is not without its limitations. Key considerations include: Video Length: The eight-second cap on video length may not be sufficient for more complex narratives. However, combining multiple clips can help create longer videos. The eight-second cap on video length may not be sufficient for more complex narratives. However, combining multiple clips can help create longer videos. Output Accuracy: Occasional issues with audio synchronization or visual details may require manual intervention to achieve the desired quality. These limitations indicate that while Veo 3 is a powerful tool, it may not fully replace traditional video production methods for all use cases, particularly those requiring longer or more intricate storytelling. Applications in Modern Marketing Google Veo 3 is particularly well-suited for businesses seeking efficient and cost-effective video production solutions. Its ability to quickly generate high-quality content makes it an ideal choice for small to medium-sized enterprises with limited budgets. Potential applications include: UGC-Style Video Ads: Create relatable and authentic ads that resonate with your target audience. Create relatable and authentic ads that resonate with your target audience. Product Marketing: Highlight product features and benefits in visually engaging ways to drive customer interest. Highlight product features and benefits in visually engaging ways to drive customer interest. Branding Integration: Seamlessly incorporate your brand identity into video content to enhance recognition and consistency. By using Veo 3, businesses can enhance their marketing efforts, improve audience engagement, and reduce production costs, all while maintaining a professional standard of quality. The Future of AI in Video Advertising As AI technology continues to evolve, tools like Google Veo 3 are set to play an increasingly significant role in the marketing and content creation landscape. The ability to produce video ads that rival human-created content opens up new possibilities for personalization and audience engagement. In the future, advancements in AI may enable these tools to support longer, more complex video narratives, further expanding their applications and utility. Google Veo 3 represents a significant step forward in AI-powered video generation, offering businesses a practical and efficient solution for creating high-quality video advertisements. While it has its limitations, its potential to streamline production processes and reduce costs makes it a valuable tool for modern marketers. As AI technology progresses, tools like Veo 3 are poised to shape the future of advertising, allowing businesses to connect with their audiences in more dynamic and impactful ways. Media Credit: Corbin Brown Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
an hour ago
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Your AI Reading Assistant: 5 Top Pocket Alternatives to Boost Productivity
With Pocket preparing to shut down, finding a reliable alternative to save, organize, and interact with your content has become a priority for many users. Fortunately, several apps have emerged to fill the gap, offering innovative features tailored to diverse preferences. Whether you seek AI-powered categorization, distraction-free reading, or collaborative learning, these five alternatives stand out for their unique capabilities and practical benefits. The awesome video below from Shu Omi walks us through these five apps. Watch this video on YouTube. Recall Recall is an excellent choice for users who value AI-driven tools and seamless organization. Transitioning from Pocket is straightforward, thanks to its one-click import feature that allows you to migrate your saved content effortlessly. What makes Recall particularly appealing is its ability to categorize and summarize articles using advanced AI. The app also includes interactive Q&A features, allowing you to engage more deeply with the material you save. Recall supports a wide range of content formats, including articles, podcasts, PDFs, and videos, making it a versatile tool for managing diverse types of media. Additional features like memory retention tools—such as quizzes and knowledge graphs—help you actively retain what you've read. With cross-device functionality and automatic tagging, Recall ensures your content is always accessible and well-organized, making it a powerful option for users who prioritize both efficiency and engagement. Instapaper For those who prefer simplicity and a distraction-free reading experience, Instapaper is a strong contender. This app allows you to save articles and organize them into folders for easy access. Highlighting key passages is intuitive, and premium features like full-text search and permanent archives enhance its utility. Instapaper integrates seamlessly with platforms like Kindle and Evernote, making it a practical choice for users who already rely on these tools. Its minimalist interface ensures that the focus remains on your content, providing a clean and uncluttered reading experience. At just $6 per month, Instapaper offers an affordable solution for users who value straightforward functionality without unnecessary complexity. Qbox Qbox is a robust option that combines the best features of Pocket and Evernote, offering a comprehensive solution for content organization. The app allows you to store articles, images, and files, all neatly categorized with tags for easy retrieval. Its AI-generated summaries and insights provide a deeper understanding of your saved material, making it a valuable tool for users who want to go beyond basic content storage. Qbox also delivers a clean reading experience and includes annotation tools for marking up content. These features make it ideal for users who want to interact with their saved material more actively. Priced at $70 per year, Qbox strikes a balance between advanced functionality and affordability, catering to those who need a versatile and feature-rich platform. Glasp If you enjoy learning collaboratively, Glasp offers a unique and engaging approach to content interaction. This app emphasizes social learning, allowing you to share and view highlights from other users. Its AI clone feature remembers your highlights and provides personalized insights, enhancing your overall learning experience. Glasp is free to use, with an optional $10 per month subscription for private highlighting. For users who value community-based learning and collaboration, Glasp stands out as an innovative tool that fosters shared knowledge and interaction. Its focus on collective learning makes it particularly appealing for those who enjoy exchanging ideas and insights with others. Reader by Readwise Reader by Readwise is designed for users who want to turn reading into an active learning process. Acting as a centralized hub, it supports various content types, including articles, newsletters, PDFs, and videos. Its AI assistant, 'Ghost Reader,' provides summaries and explanations, helping you quickly grasp key concepts and extract meaningful insights. One of Reader's standout features is its daily review system, which uses spaced repetition to reinforce your learning. This method ensures that the information you consume is retained over time, making it an ideal choice for users focused on long-term knowledge retention. By combining advanced AI tools with a structured approach to learning, Reader by Readwise offers a comprehensive solution for those who want to maximize the value of their reading. Choosing the Right App for Your Needs Each of these apps offers distinct advantages, catering to a wide range of needs and preferences. Whether you're drawn to the AI-powered tools of Recall and Reader, the simplicity and focus of Instapaper, the organizational capabilities of Qbox, or the collaborative features of Glasp, there's an option to suit your requirements. By exploring these alternatives, you can find the perfect tool to enhance your reading, learning, and content organization experience. With these apps, managing and interacting with your saved content becomes not only efficient but also engaging and tailored to your personal goals. Uncover more insights about AI-powered read-it-later apps in the previous articles we have written. Source & Image Credit: Shu Omi Filed Under: AI, Guides, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Finextra
2 hours ago
- Finextra
Inside AI Assisted Software Development and why tools are not enough (Part 1): By John Adam
The recent squeeze on funding and margins is by no means only being felt in the financial services and fintech sectors. But it's fair to say the pinch is particularly hard and the necessity to quickly and effectively innovate is simultaneously more pressing than ever. The good news is, new AI tools can speed up delivery and improve the quality of software projects without adding to headcount. But even if that general statement is true, just using tools is not enough. Especially in a regulated industry like financial services. If there is no pre-approved list of tools and how and where they are applied in an SDLC (software development lifecycle), organisations have governance, observability, measurability and consistency issues. If 'real' gains are not measured by benchmarking against 'before', do they really exist? Tree falling in a forest metaphor. Certainly not in a way that can be scaled across or up an organisation. There is no clear business case, just intuition. Are tools and where and how they are being used compliant with organisational policy and regulatory frameworks? Has anyone read the privacy policies? I'm personally convinced that a big AI company having its Facebook/Cambridge Analytica moment falls under 'when, not if'. And when the first big AI privacy scandal does break, you don't want your organisation published in a list in a newspaper. To benefit from and scale the gains of an AI-assisted SDLC, organisations need a framework for structured, consistent integration + governance, observability and measurability. Just tools isn't enough. Realistic gains from an AI-assisted SDLC It's important to note that at the time of writing, we are in a period of rapid change in AI tooling. A good framework operates at a level or two higher than specific tools and allows for them to be interchangeable with upgrades. The market most of us operate in is at a point in its cycle where resources are at a premium. Most of the organisations I work with are expected to deliver more with less compared with pre-2023. In that context, banking the productivity gains achievable with AI tooling is non-negotiable. Organisations are demanding it in the demand for greater, better output despite fewer resources. Getting it right is also non-negotiable and that means marrying increased productivity with measurability, observability and governance, which I cover in-depth in Part 2 of this article. As an introduction to building a proper framework, I'll start by explaining the realistic improvements AI can provide to each stage of the SDLC: Product prototyping Developers use prototypes to test idea viability and functionality, and to gather user and investor feedback. Historically, the average prototype required 2 to 6 weeks of teamwork to complete. But by amplifying developers' work via low-code/no-code prototyping and AI-generated code and other AI tools, a clickable prototype can now be completed in days or even hours. UX/UI design UX (user experience) and UI (user interface) designers collaborate closely with developers to design website and app interfaces. Using AI tools that can quickly generate multiple design mock-ups and UI components based on foundational style guides and example concepts, designers can visualise ideas and user flows in various contexts to improve design clarity and direction long before designs touch a developer's desktop. Clarity improves the quality of initial designs and reduces designer-developer back-and-forth, meaning larger projects that took 4 to 6 months to complete now require far less effort and time. Even UXR (User Experience Research) is accelerated and refined. User interviews are, by necessity, long and complex, and result in large, qualitative datasets. AI tools can highlight patterns and repetition in datasets and transcripts in seconds—shining a spotlight on insights, false positives or even biased questions that human researchers may have overlooked. Architecture Software architects plan higher-level design, bridging technical and business requirements. Their diagrams include the sum of a products' components and their respective interactions; until recently, the initial design phase alone took 1 to 2 weeks. Using AI, architects can quickly draw up diagrams to easily visualise these relationships and standardise dependency versions across services. AI can also be trained to use PR comments to report architectural violations, and libraries can be unified to encourage stability across features. Better consistency and immediate feedback mean architects can work faster and create fewer iterations of a product before diagrams meet stakeholder expectations. Coding AI-powered tools for coding have a variety of use cases. My team uses a mix of tools and GenAI to: ensure comprehensive project documentation, automate code documentation and README generation, scan for duplicate code and suggest improvements, improve understanding of complex, inconsistent or unfamiliar code bases, unify code styles and standards across different microservices, and perform code completion and check for bugs and inconsistencies based on defined standards. Paired with manual oversight to catch any mistakes, we've accelerated writing and testing code by a minimum of 20% across projects. GenAI makes complex codebases easily understandable—meaning team members can flexibly move to work on unfamiliar projects and diminish time spent on internal comms by about 25%. One tool we use is SonarQube, which reviews code without executing it. It runs automatically in GitLab CI/CD (Continuous Integration/Continuous Delivery and Deployment) pipeline to find bugs, report security vulnerabilities, and enforce code standards to unify style and mitigate potential misunderstandings down the line with better code readability. Testing and QA (Quality Assurance) As they write code, developers write and run unit tests to detect initial bugs and security issues that eat up between 10% and 20% of their time. The SDLC is slowed further by code reviews and PRs, or feedback from experienced colleagues. Tests are postponed by days, sometimes weeks, if various code reviews are required and dependent on busy colleagues. GenAI can augment developers' efforts by writing unit tests, conducting code reviews and PRs in real time, and automatically generating and solving for edge cases to overcome bottlenecks like a lack of expertise or teammates' availability. AI augmented QA can reduce redundancy, unify access to code, and consolidate fragmented knowledge across a project to make a QA team more efficient. And AI-driven tools like Selenium, for example, can automate web app test writing and execution, accelerating product releases and improving product reliability. Automated testing is especially compelling in the context of projects with tight deadlines and few resources. For example, my team's AI toolkit for QA testing includes Llama 3.3 LLM to generate test cases and analyse code and Excel-based legacy documents, IntelliJ AI Assistant to automatically standardise test case formatting, and GitLab to run and test scripts automatically in the CI/CD pipeline. QA is one of the most impactful applications of AI tools in the SDLC and can commonly slash the resources required by up to 60%, while increasing test coverage. Deployment When a product is deployed to end users, AI can be added to the CI/CD to forecast use patterns and improve caching strategies, as well as automatically prioritise and schedule tasks for parallel execution. With AI oversight, the number of repetitive tasks is automatically reduced and resource allocation anticipated, improving latency and product release cycles without added manual effort. And AI-driven caching accelerates and simplifies rollbacks (reverting a newly deployed system to a more stable version of itself) by analysing previous deployments and predicting the necessary steps, reducing further manual effort by DevOps teams, for instance. My team uses Dytrance during deployment, which monitors and analyses system status, and sends self-healing recommendations in real time. Maintenance and Monitoring At this stage, teams work to fix bugs, keep the system secure and functioning well, and make improvements based on user feedback, performance data and unmet user needs. AI can automatically perform root cause analysis for error monitoring, and suggest solutions for maintenance and debugging. Tools my team uses include AWS Cloud Watch and Azure Monitor with AIOps, which automatically collect, analyse, and suggest responses based on monitoring data, accelerating issue response and system updates by 10x. The big picture The acceleration of the individual stages of software development is incentive enough for some teams to add tools and GenAI models to their workflows; especially at stages like QA and coding, where use cases are various and results potent. But by taking a step back and considering AI's impacts on the SDLC holistically, the argument in favour of AI implementation can be turned into a real business case. A business case that can be used to accelerate AI transformation across an organisation: Backed by a strong framework, organisations implementing AI across their SDLC see a 30%+ acceleration across projects in the first 6 months. The keyword being 'strong.' Organisations need a framework that guides leadership to select tools and govern their use, measures outcomes to understand the amount of value different tools offer, and encourages adoption in teams' workflows. Without it, teams are unable to measurably extract the full potential from new tools and efforts, and risk breaching internal and third-party governance in areas such as data privacy. Keeping my word count and your patience in mind, I split my deep dive into a framework for AI governance, measurement and adoption into a separate article: Here is Inside an AI-assisted software development framework: using tools is not enough Part 2.
