Latest news with #businessmodels
Finextra
4 days ago
- Business
- Finextra
The Future of Open Finance: Impacts of New Regulation, Use Cases, & Business Models
Simos Eacott, Head of Payments, NatWest, speaks at EBAday 2025 in Paris about the the future of Open Finance and the impacts of new regulation, use cases, and business models. We hear how much the switch to an expanded open finance ecosystem will enable growth, and whether the industry is prepared for the potential changes.
Trade Arabia
03-06-2025
- Business
- Trade Arabia
Integrating technologies can reshape value chains: WEF
World Economic Forum (WEF) has unveiled a framework showing how integrating technologies at varying maturity stages can reshape value chains and create new business models. A new WEF report examining how emerging technology combinations are reshaping industries and how business leaders can harness these insights to guide investment, build capabilities and position ecosystems strategically. It also offers valuable insights to policy-makers by highlighting how emerging technologies intersect and amplify one another, which can help break down regulatory silos and anticipate wider, system-level impacts. The Technology Convergence Report, developed in collaboration with Capgemini, introduces the 3C Framework – combination, convergence and compounding – to help decision-makers identify where emerging technologies intersect to spark new business models. 'Rapid advances across multiple technology domains are creating an undeniable shift in industries. The Technology Convergence report gives leaders a clear model to harness what is coming next,' says Jeremy Jurgens, Managing Director at World Economic Forum. By considering both technological maturity and deployment context, the report identifies 23 high-potential combinations drawn from over 230 subcomponents across eight critical domains: artificial intelligence, omni computing, engineering biology, spatial intelligence, robotics, advanced materials, next-generation energy and quantum technologies. While the framework focuses on the impact of technology combinations over individual breakthroughs, it highlights AI as a key enabler, making many of these synergies commercially viable. Several standout combinations demonstrate the transformative potential of technology convergence across sectors like infrastructure, healthcare, energy and transportation, including: Cognitive robotics: Agentic AI, spatial intelligence and robotic advancements in manipulation and adaptive control are enabling autonomous systems to make intelligent decisions in complex environments, driving progress in automotive and smart manufacturing. Digital twin ecosystems: Advances in sensor networks and AI simulation systems are enhancing digital twins, enabling more integrated, end-to-end impact, and expanding their efficiency and applicability across sectors from aerospace to healthcare. Hybrid quantum-classical computing: By combining quantum capabilities with the reliability of classical systems, this approach is already unlocking quantum's potential in finance, molecular simulation and complex optimization. Materials informatics: Predictive modelling and transformers are accelerating R&D in advanced materials by enabling virtual testing of combinations and structures before synthesizing them in a lab, driving more efficient development cycles in manufacturing, chemicals and beyond. 'The question is not about whether technology convergence will reshape industries. That journey has already begun. The real challenge is how companies can position themselves to be champions of convergence,' says Aiman Ezzat, Chief Executive Officer of Capgemini. This report serves as a practical guide for leaders seeking to turn insight into action, calling for a systems-thinking approach, balanced investments across tech maturity stages, value chain repositioning and ecosystem readiness. It combines qualitative and quantitative insights from the World Economic Forum's Technology Convergence Community, composed of subject matter experts from the Forum's global network of business, academic, civil society, public sector leaders, who provided domain expertise and case studies. This was complemented by Capgemini's global survey of 2,000 senior executives across 18 countries, 10 industries and five continents. - TradeArabia News Service
Arabian Business
14-05-2025
- Business
- Arabian Business
AI won't fix your business if your systems are still broken
With landmark AI advancements announced seemingly every week, it's easy to get swept up in the frenzy — one moment, the focus is on a new GenAI model, the next, on agentic AI or AIOps. Tempting as it may be to see AI as the next frontier, let's be clear — this isn't the beginning. We're well into the AI era, and for most businesses, the question is how to proceed most effectively. Becoming AI-native is no longer a differentiator; it's a hygiene factor. On the face of it, this will undoubtedly seem like a tall order. But rest assured that this is not uncharted territory. Many organisations have successfully navigated similar transformations, albeit with different objectives. So rather than give in to FOMO, we can look to the past for perspective. Think of the digital-first pivot: success didn't happen overnight, and it wasn't about being first. It was about getting it right. Lessons from the digital-first decade Think back to when organisations first went digital. There was resistance, confusion, and, for some, the illusion that simply launching a website or setting up an e-commerce portal was enough. But true digital-first businesses did more than just bolt on new tools. They reimagined entire business models, streamlined operations and rewired customer interactions. Banks moved services online, retailers embraced omnichannel experiences, and governments digitised citizen services. The same principle applies to AI. It's not about which AI tool to use — it's about rethinking workflows, augmenting human intelligence, and embedding AI into everyday operations. The winners in this new era will be those that don't just use AI but integrate it into the very fabric of how they work. Business leaders must, therefore, understand how to embed an AI-native culture into their organisation in the most natural way, just as they did when becoming digital-first. Overcoming the fear factor With every major shift, there's fear, and today, it's AI that sparks anxiety. But just as digital transformation didn't eliminate the need for people, neither will AI. The key is understanding that AI doesn't replace human expertise — it enhances it. Take finance teams. Could an AI system send out contracts and invoices without oversight? Could a chatbot handle a complex, high-stakes customer negotiation? Not likely. AI excels at speeding up processes, eliminating repetitive tasks and surfacing insights — but human judgment remains irreplaceable. What sets AI-native organisations apart is their approach to upleveling talent. When businesses transitioned to being digital-first, they didn't just introduce new tools and platforms; they invested heavily in upskilling their workforce at all levels. Large banks trained their entire workforce — not just IT teams — to work with digital platforms, while logistics companies retrained warehouse staff to work alongside automated systems rather than be replaced by them. AI demands the same investment in people. The most advanced AI systems in the world won't help a business if employees don't know how to work alongside them. Training and reskilling will determine whether an organisation truly becomes AI-native. Forward-thinking businesses aren't just hiring AI specialists; they're embedding AI literacy into their entire workforce. Laying the foundation: IT comes first If AI is the next great shift, IT is where it begins. Just as a weak digital infrastructure made digital transformation difficult, legacy systems will prevent businesses from becoming AI-native. Many organisations still operate on such systems, patched together over decades. These weren't built for AI, and layering AI tools on top of them often leads to breakages rather than breakthroughs. The first step therefore is to be open to the idea that even the most complex legacy systems can now be modernised with the help of AI. Historically, people have said, 'No, it's too complicated.' However, today's AI capabilities make it possible to untangle even the most complex legacy systems. Embedding AI into everyday workflows In the early days of digital transformation, businesses didn't overhaul their entire operations overnight. Instead, they started with areas that were low risk but high impact — rolling out cloud-based collaboration tools, digitising paper-based processes, and automating routine administrative tasks. Email replaced fax machines. Cloud storage eliminated filing cabinets. Employees didn't just accept these changes; they embraced them because they made work faster, easier, and more efficient. AI adoption follows the same trajectory. Once the IT foundation is solid, AI should be woven into daily operations in a way that enhances — not disrupts — the employee experience. HR processes, for example, are a natural starting point, just as payroll digitisation and automated expense reporting were during the digital era. AI-powered self-service tools can streamline leave requests, retrieve company policies instantly, and simplify benefits management. These small yet meaningful improvements set the stage for broader AI adoption. When employees see first-hand how AI simplifies their work, resistance fades. When approached in this way, the transformation isn't forced — it's organic. AI becomes something employees actively want, not something they fear. AI-native: The new imperative If the last 20 years have taught us anything, it's that businesses don't succeed by merely adopting new technology — they succeed when they redefine themselves through it. Look at Mashreq Bank, which embraced digital-first banking, shifting nearly all customer interactions to mobile apps, eliminating the need for physical branches. Or Aramex, which integrated AI to optimise delivery routes and predict demand, setting a new benchmark for efficiency. Now, businesses are at a new crossroads. Becoming AI-first isn't a futuristic ambition — it's an immediate imperative. The question isn't whether AI will be part of an organisation's strategy, but whether AI will define its very DNA. Those who wait risk being left behind, while those who embrace AI-first thinking will lead the future of business.
