Latest news with #enterprises
Yahoo
3 days ago
- Business
- Yahoo
Expanding External Opening-Up and Deepening Exchange and Cooperation
CHENGDU, China, May 31, 2025 /PRNewswire/ -- The 20th Western China International Fair was held in Chengdu from May 25 to 29 under the theme "Deepen Reform for More Momentum, Expand Openness for Greater Growth." This year marks the 25th anniversary of the Fair's establishment, attracting over 3,000 enterprises from 62 countries and regions, with Bolivia, Cuba, and Nicaragua participating for the first time. Representatives from the guest countries of honor, Hungary and Laos, indicated that through the Fair, they have reached cooperation intentions with several Chinese enterprises and see broad collaboration potential with Western China in culture, tourism, technology, and culinary sectors. The next-generation artificial sun "China's HL-3" and other new and pioneering technologies and products were showcased at this Fair, speaking volumes about Western China's advanced manufacturing capabilities and scientific innovation potential. Currently, 13 of China's 80 national-level advanced manufacturing clusters are located in Western provinces. During this Fair, various parties from Western China signed 416 investment cooperation projects with domestic and international investors, totaling 354.3 billion yuan. Economic and trade matchmaking events such as the Multinational Enterprises "Invest in Sichuan" Symposium (Italy Session) and the 15th Western China International Sourcing Conference have provided broader platforms for exchange and cooperation between Chinese and foreign enterprises. View original content to download multimedia: SOURCE WCIF
Forbes
22-05-2025
- Business
- Forbes
Four Key Enablers Of M&A Opportunities In 2025
Rusty Wiley is CEO of Datasite, a leading SaaS platform used by enterprises globally to execute complex, strategic projects. Heightened tensions between major economies are translating into greater scrutiny of cross-border transactions, particularly in the technology, energy and infrastructure sectors. Meanwhile, shifting U.S. trade policies are altering company market valuations and disrupting intricate global supply chains. Some of this activity was foretold. In 2024, global dealmakers said trade tensions and global interdependent supply chains were the top factors most likely to disrupt mergers and acquisitions (M&A) activity in 2025, according to my company Datasite's reach. Let's take a deeper look at how geopolitical friction and other factors are impacting M&A activity this year. Some of the challenges predicted last year are becoming evident, as global deal hold rates on Datasite increased three percentage points in the first quarter of this year compared to the same time last year. Some bankers, for example, are telling their clients to postpone M&A until U.S. trade policy becomes clearer. Buyers are now also conducting more thorough due diligence, increasing their use of question-and-answer tools in virtual data rooms to interrogate deal information. Additionally, tariff risk analysis is in just about every valuation model. Yet, amid the caution, there are signs of resilience. New global deal kickoffs, asset sales and mergers on Datasite are up 4% year to date (January through April) compared to the same period in 2024. Since these are deals at inception, rather than announced, it can provide a good indication of what's to come. To navigate M&A in this cautious market, dealmakers must stay strategic and flexible, focusing on strong due diligence and clear value. Deals must align with core goals. Dealmakers need to expect longer timelines and closer review. They must also plan to manage risks and adjust quickly as the environment changes. Thanks to an assertive combination of creative dealmaking strategies, innovative tools and keen insight into their specialist sectors, dealmakers are continuing to rise to the occasion. Creative dealmaking has emerged as a vital skill for navigating uncertain markets, offering the adaptability needed to push M&A through when conventional strategies stall. Inventive approaches, including private investment in public equity (PIPEs) and special purpose acquisition companies (SPACs), are gaining traction, though not reaching the activity levels of 2021. With these approaches, it's clear that dealmakers are thinking outside the box, looking for alternative transaction models to overcome structural and regulatory barriers, allowing deals to proceed where traditional methods might falter. In Europe, the Middle East and Africa (EMEA) region, for example, dealmaking is increasingly focused on bolt-on acquisitions and divestitures. These strategies are effective in helping companies navigate economic headwinds and sector-specific disruptions. Businesses can sharpen their strategic focus by shedding non-core operations through divestitures and carve-outs. In particular, bolt-on acquisitions—which provide private equity and corporate buyers with a targeted, cost-effective path to growth by integrating smaller, complementary businesses—were identified as a top 2025 EMEA M&A opportunity, according to Datasite's research. Of course, these strategies also come with risks. Bolt-ons can lead to missed opportunities for synergies. Also, because there are different management structures, decision making and resource allocation conflicts can occur. Additionally, managing several independent subsidiaries can weaken the acquirer's focus and limit the value gained from the acquisition. Still, there are other areas spurring interest in M&A. Global technology, media and telecommunications (TMT) kickoffs on Datasite rose 10% year over year in January and February of this year, driven by increased demand for AI. For example, the need for extensive data center capacity requires substantial energy resources and investment. With major AI innovation announcements from the U.S., China and the U.K. since the start of the year, deal activity in this sector has accelerated. AI investment activity is also gaining momentum across a wide range of industries as organizations seek to integrate AI into their processes. In environments where developing in-house solutions is limited by expenses or talent shortages, M&A can offer a cost-efficient alternative for enhancing technological capacity through the integration of existing technologies. Beyond immediate technical gains, AI acquisitions can also future-proof operations. As AI becomes more deeply embedded in everything from customer service to supply chain optimization, organizations are looking to build resilient, scalable infrastructures that can adapt to changing market and client demands. This is the reason why acquiring AI-focused firms with proven capabilities can support long-term transformation agendas. To be sure, while every transaction involving AI is unique, there are some core questions dealmakers must ask during due diligence to ensure a positive outcome. Among the factors buyers must consider are data provenance, quality and security; alignment to human ways of working; intellectual property; infrastructure requirements; technology evaluation; and legal and regulatory compliance requirements. Another area attracting M&A attention is the consumer sector. New global consumer kickoffs on Datasite rose 19% year over year in the first quarter. One reason for the uptick is that consumer companies are streamlining their portfolios by divesting non-core assets that don't align with long-term strategies. For example, Shell began divesting 1,000 of its convenience stores last year to focus on its commercial clients over retail. Ultimately, there are reasons to be optimistic about the current and future state of dealmaking. Dealmakers have become used to navigating choppy waters over the last few years, adapting to economic volatility, geopolitical tensions and shifting market dynamics. This has inspired a more nimble approach to M&A transactions, which will result in new opportunities rising to the surface. Additionally, despite volatility linked to tariff announcements, M&A professionals are thinking long term, adopting creative approaches and focusing on industries that demonstrate long-term value. This will define M&A transactions in 2025, particularly as dealmakers look to release pent-up demand. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Fast Company
13-05-2025
- Business
- Fast Company
Anaconda launches an AI platform to become the GitHub of enterprise open-source development
AI integration remains a top priority across enterprises worldwide, yet success remains elusive despite widespread enthusiasm and significant investment. An October 2024 study by Boston Consulting Group found that only 26% of companies have derived measurable business value from their AI initiatives. As a result, CEOs face mounting pressure to deliver tangible ROI, shifting focus from experimentation to real-world outcomes. Modern AI development increasingly relies on open-source foundations, enabling rapid iteration and innovation. Many transformative breakthroughs have emerged from community-driven development—primarily in Python, the dominant language in data science. However, as enterprises attempt to operationalize these advances, foundational cracks are becoming harder to ignore. Fragmented toolchains, limited oversight, and inconsistent practices introduce significant vulnerabilities at scale. Security, in particular, is a growing concern. Over half (58%) of organizations use open-source components in at least half of their AI and ML projects, yet nearly a third (29%) cite security risks as their biggest challenge with open-source tools. The final deadline for Fast Company's Brands That Matter Awards is Friday, May 30, at 11:59 p.m. PT. Apply today.
Forbes
12-05-2025
- Business
- Forbes
The Top 3 Challenges Enterprises Face On Their GenAI Journey
Manosiz Bhattacharyya, Chief Technology Officer, Nutanix . getty It's been about two years since generative AI (GenAI) exploded on the scene and garnered widespread interest with the launch of OpenAI's ChatGPT in late 2022. Since then, enterprises everywhere have been in a race to harness the power of GenAI within their own organizations to boost productivity, improve decision making, optimize customer experience and fuel innovation. According to a survey conducted by Bain & Company, 87% of enterprises have deployed or are piloting the technology in their organizations. GenAI has officially transitioned from its "hype" phase to a more practical implementation phase in which enterprises are applying the technology to a variety of use cases and seeing real value. But there's still a long way to go, and as far as GenAI's potential is concerned, this is just the tip of the iceberg. As enterprises continue to adopt and experiment with the technology, they will inevitably encounter roadblocks related to complexity, cost and control. Let's explore what each of these challenges entails and how organizations can overcome them to get the most out of their GenAI investments. As GenAI proliferates, enterprises should abide by the mantra, "keep it simple." This is much easier said than done, of course. By nature, building, training and running GenAI models requires incredibly complex infrastructure. In addition to its substantial resource requirements from a compute, storage and bandwidth perspective, GenAI also demands specialized hardware such as graphics processing units (GPUs). Furthermore, while training often takes place in data centers with GPU capacity, inferencing may need to occur at the edge, further compounding this complexity. If enterprises can't effectively manage their GenAI infrastructure, it will quickly become cumbersome—especially at scale—potentially canceling out some of the technology's game-changing benefits. For many enterprises, the best way to manage this complexity is by leveraging cloud platforms that offer pre-configured GenAI environments to keep infrastructure management to a minimum. For enterprises that want to keep GenAI on-premises or within hybrid environments, adopting tools that automatically optimize resource utilization and simplify data management can greatly reduce the burden of infrastructure management. Another way enterprises can streamline GenAI infrastructure is by establishing an internal AI committee dedicated to reducing the fragmented use of the technology across the organization. In most enterprises, every line of business is trying to leverage GenAI their way, which can create operational silos and complicate infrastructure. Having centralized oversight in this area can dictate things like which types of models are permitted and which GenAI vendors can be used within the organization. This not only further simplifies infrastructure management; it keeps costs in check and bolsters security. Based on GenAI's robust resource and infrastructure requirements, it should come as no surprise that costs can balloon if not carefully controlled. This is especially true for GenAI applications that run in the public cloud using a token-based pricing model. As organizations expand their use of GenAI and continue to refine their existing models, they'll inevitably require more tokens, leading to a dramatic spike in costs. Whenever possible, enterprises should reduce their exposure to these token-based platforms and instead opt for solutions that allow them to use a model as many times as needed without being charged based on usage. By only incurring infrastructure-based costs, enterprises can build, train and run models in a much more cost-effective manner. This is especially critical as GenAI evolves from human-driven to, eventually, self-driven agentic AI systems. Every application will be infused with GenAI and utilize more models, and token-based pricing will quickly become cost-prohibitive. To avoid this predicament and better support innovation, enterprises should consider adopting platforms with an infrastructure-based cost model sooner rather than later. 3. Control While running GenAI in the public cloud certainly has its perks, it has its downsides as well—one being that it can create data control challenges from both a training and inferencing perspective. Once a model has "seen" data, there's no going back. This is of particular concern in highly regulated industries like finance and healthcare, where data privacy is of the utmost importance. To reap the benefits of GenAI safely, enterprises need to apply strict security controls to their models. The most effective way to do this is by ensuring that the security controls the organization already has around its training data are also reflected in its models. In other words, if someone doesn't have access to the data, they should not have access to the model either. Where this becomes difficult is in environments where model systems and data systems are disparate: It can be challenging to ensure the data systems' security controls map back to the models since they're separate entities. In these cases, enterprises should rely on infrastructure providers that offer a model inference system with built-in rules as code (RaC) policies so their security controls are consistently applied to their models. This gives enterprises access control of every model so that any application or person who uses the model can be given the right permission. Conclusion Organizations are just beginning to scratch the surface of what's possible with GenAI. As with any newer technology, there will undoubtedly be challenges along the way. However, by minimizing infrastructure management, controlling costs and implementing security controls, enterprises can set themselves up for success and more GenAI-powered breakthroughs in the near future. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
07-05-2025
- Forbes
The Realistic Path To Quantum Computing: Separating Hype From Reality
The Realistic Path to Quantum Computing: Separating Hype from Reality getty If there's one technology that has captured the imagination of futurists and tech enthusiasts as much as generative AI, it's quantum computing. The buzz is deafening – promises of breakthroughs in encryption, pharmaceuticals, and financial modeling fill headlines. We're told that quantum will change everything, making today's supercomputers look like abacuses. But before enterprises start reshaping their strategies around an imminent quantum revolution, let's take a hard look at where this technology actually stands today – and what it will take to make it truly transformative. Quantum computing isn't just a faster version of what we already have – it's a complete paradigm shift. Unlike classical computers that process bits as either 0s or 1s, quantum computers use qubits, which can exist in multiple states simultaneously. This property, known as superposition, theoretically allows quantum computers to perform complex calculations at speeds that classical systems simply can't match. For IT service providers, the implications are massive. Quantum computing has the potential to crack problems that were previously considered impossible – think real-time risk modeling, hyper-efficient supply chains, and unbreakable cryptographic security. But before enterprises rush to invest, they need a realistic understanding of where we are on the quantum timeline and what's actually achievable in the next few years. Despite the hype, quantum computing is not an overnight sensation. Major technical challenges still stand in the way of widespread enterprise adoption. Let's break down the most significant hurdles: 1. Qubit stability: The fragility problem Qubits are incredibly fragile. Even the slightest environmental disturbance – like a tiny fluctuation in temperature – can cause them to lose coherence, leading to computational errors. Researchers are working on topologically protected qubits to improve stability, but we're still five to seven years away from reliable, large-scale systems. 2. Error correction: The Achilles heel In classical computing, error correction is straightforward. In quantum computing, it's exponentially more complex. Right now, quantum error rates are significantly higher than classical ones, making large-scale computation impractical. Advances in error correction are progressing, but we likely won't see scalable, reliable systems for at least another five years. 3. Scalability: More qubits, more problems Scaling quantum computers isn't as simple as adding more qubits. Unlike classical chips that can be stacked and scaled efficiently, quantum systems require significant improvements in architecture and quantum interconnects. We may be a decade away from quantum systems that can reliably tackle enterprise-scale problems. Even with these obstacles, quantum computing isn't just an academic exercise—it's starting to show real promise. Several industries are already experimenting with quantum-enhanced solutions: Cybersecurity and Cryptography – Quantum Key Distribution (QKD) is showing potential in secure communications, with companies like ID Quantique leading the charge. – Quantum Key Distribution (QKD) is showing potential in secure communications, with companies like ID Quantique leading the charge. Pharmaceuticals – Firms like Biogen are leveraging quantum algorithms to accelerate drug discovery, particularly for diseases like Alzheimer's. – Firms like Biogen are leveraging quantum algorithms to accelerate drug discovery, particularly for diseases like Alzheimer's. Automotive and Mobility – Volkswagen and D-Wave are exploring quantum computing to optimize EV battery materials and improve traffic flow modeling. – Volkswagen and D-Wave are exploring quantum computing to optimize EV battery materials and improve traffic flow modeling. Financial Services – JPMorgan Chase and Goldman Sachs are developing quantum models for portfolio optimization and risk analysis. These use cases demonstrate that while large-scale quantum adoption is still years away, selective applications are already proving valuable in highly specialized domains. Where is quantum headed? The race toward quantum supremacy – the point at which quantum computers outperform classical computers for specific tasks – is in full swing. But what will determine when (and how) enterprises can start integrating quantum into their operations? 1. The infrastructure battle Quantum computing requires an entirely new infrastructure – something only a handful of companies, such as IBM, Google, and Rigetti, are actively developing. This raises concerns about monopolization. Will quantum computing power be centralized in the hands of a few dominant players, limiting enterprise access and innovation? 2. Hybrid computing is the future Quantum computing won't replace classical systems overnight. Instead, we'll see hybrid environments where quantum and classical computers work together, with quantum handling complex computations while classical systems manage everything else. Enterprises should prepare for this hybrid approach rather than betting on a full quantum transition in the near future. 3. Government and private investment will be key Quantum computing requires significant investment, and governments are stepping up. The U.S. National Quantum Initiative, along with similar efforts in Europe and China, is pouring billions into quantum R&D. Meanwhile, tech giants and venture capitalists continue to fund startups tackling quantum hardware and algorithms. Enterprises should watch where this investment flows – because it will shape when and how they can leverage quantum technology. 4. The workforce challenge Quantum computing expertise is scarce. Organizations that begin investing in a quantum-ready talent pipeline now – through upskilling, partnerships, and research collaborations – will have a competitive edge once quantum computing becomes mainstream. What should enterprises do today? Given the challenges and the long road ahead, what should businesses be doing now to prepare for quantum computing's future impact? Here are some strategic steps: Develop a quantum roadmap – Companies should assess how quantum computing could impact their industry and start building a roadmap for adoption. This doesn't mean overhauling everything, but identifying key areas where quantum could create a competitive advantage in the next decade. – Companies should assess how quantum computing could impact their industry and start building a roadmap for adoption. This doesn't mean overhauling everything, but identifying key areas where quantum could create a competitive advantage in the next decade. Invest in research and partnerships – Collaboration with academic institutions, quantum startups, and industry groups can provide early exposure to quantum capabilities. – Collaboration with academic institutions, quantum startups, and industry groups can provide early exposure to quantum capabilities. Monitor quantum readiness in cybersecurity – Quantum will eventually disrupt encryption standards. Enterprises should start preparing for quantum-resistant cryptographic solutions now. – Quantum will eventually disrupt encryption standards. Enterprises should start preparing for quantum-resistant cryptographic solutions now. Experiment in a low-risk environment – Companies can begin running quantum simulations and proof-of-concept projects through cloud-based quantum services like IBM Quantum and AWS Braket. This allows them to gain familiarity with the technology without heavy upfront investment. – Companies can begin running quantum simulations and proof-of-concept projects through cloud-based quantum services like IBM Quantum and AWS Braket. This allows them to gain familiarity with the technology without heavy upfront investment. Build a quantum-skilled workforce – Hiring quantum talent may be difficult now, but organizations can start by upskilling existing teams in quantum-related areas like linear algebra, probability, and quantum algorithms. Final thought: The future belongs to the quantum-prepared Quantum computing isn't a passing trend – it's an inevitable evolution of computational technology. But broad adoption is still several years away. Enterprises that wait for quantum to reach full maturity before taking action will find themselves playing catch-up in a radically transformed digital economy.
