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Artificial intelligence is an early stage technology and the hype around it is palpable, but IT leaders need to take many challenges into consideration before making major commitments for their enterprises. With AI and data proliferating everywhere in the enterprise, AI and data are no longer centralized assets that IT directly controls.
While the IEEE P802.3dj project is working toward defining 200G per lane for Ethernet by late 2026, the industry is (loudly) asking for 400G per lane yesterday, if not sooner, Jones wrote in a recent Ethernet Alliance blog. Enterprise and campus networks represent a massive market for Ethernet, with over a billion ports shipping annually.
While most provisions of the EU AI Act come into effect at the end of a two-year transition period ending in August 2026, some of them enter force as early as February 2, 2025. It will have to be done by mid-2026, which is a tight timeframe, but Cisco only sees benefits to being part of the AI Pact, Quattrocchi points out.
By 2026, 30% of enterprises will automate more than half of their network activities, according to Gartner. By comparison, less than 10% of enterprises were automating more than half of their network activities in mid-2023. Most network teams choose “all of the above,” says Enterprise Management Associates.
For enterprises investing heavily in AI infrastructure, this development addresses a growing challenge. The L200, coming in 2026, will be available in 32 Tbps and 64Tbps versions. The phased release gives enterprises time to evaluate how optical interconnect technology might fit into their future infrastructure roadmaps.
And some large, cutting-edge enterprises are already beginning to spend money on quantum technology. billion in 2026 though the top use case for the next couple of years will remain research and development in quantum computing. Whats the deal with error correction? Qubits are ridiculously sensitive and unreliable.
HorizonX Consulting and The Quantum Insider, a market intelligence firm, launched the Quantum Innovation Index in February, ranking enterprises on the degree to which theyve adopted quantum computing. Prioritize Because of the complexity of the tasks, ISGs Saylors suggest that enterprises prioritize their efforts.
As AI gets built into every application and service, organizations will find themselves managing hundreds or thousands of discrete agents. This standardization and interoperability will be essential for enterprises to effectively manage and scale their AI initiatives. This third wave of AI promises to transform workflows wholesale.7
The key areas we see are having an enterprise AI strategy, a unified governance model and managing the technology costs associated with genAI to present a compelling business case to the executive team. Our research indicates a scramble to identify and experiment with use cases in most business functions within an enterprise.
While the articles focus is on GenAI, many of the strategies discussed here are broadly applicable to other innovations in IT, as they provide CIOs with a flexible framework for balancing costs and opportunities presented by emerging technologies. million in 2026, covering infrastructure, models, applications, and services.
Vendors are adding gen AI across the board to enterprise software products, and AI developers havent been idle this year either. According to a Bank of America survey of global research analysts and strategists released in September, 2024 was the year of ROI determination, and 2025 will be the year of enterprise AI adoption.
Networking roles are undergoing significant evolution, with a key emphasis on the integration of emerging technologies such as AI and automation, says Joost Heins, head of intelligence at Randstad Enterprise, a global talent solutions provider. Yet network automation lags behind other automation initiatives.
At a rough guess the CIO of an average-size enterprise, following this methodology, would be ready to launch sometime in 2026. It might be an unrationalized applications portfolio. You can certainly do this. The lazy CIOs approach probably makes more sense.
Enterprise resource planning (ERP) software vendor IFS has agreed to acquire Falkonry, the developer of an AI-based time-series data analytics tool, to boost its enterprise asset management (EAM) services portfolio. billion by 2026, from $3.3 billion by 2026, from $3.3 to reach $5.5 to reach $5.5
CMDBs are considered a best practice for IT managers who need to maintain an accurate inventory of enterprise environments. Defined in the Information Technology Infrastructure Library (ITIL), CMDBs include data on the hardware, software, and infrastructure used by the applications and services provided by an IT organization.
14, 2025, many CIOs find themselves at a crossroads, with some considering Copilot+ certified AI PCs as part of their enterprise fleet plans for Windows 11. OEMs that have shipped or are readying AI PCs for 2025 or 2026 include Dell, Acer, Asus, HP, Lenovo, Samsung, and Microsoft.
To help meet demand from enterprises that are shifting asset management methods from legacy applications to cloud-based technology, ERP provider IFS has signed an agreement to acquire Netherlands-based enterprise asset management (EAM) software firm Ultimo. billion by 2026, from $3.3 to reach $5.5 year-on-year.
By 2026, hyperscalers will have spent more on AI-optimized servers than they will have spent on any other server until then, Lovelock predicts. While AI projects will continue beyond 2025, many organizations’ software spending will be driven more by other enterprise needs like CRM and cloud computing, Lovelock says.
Secure Access Service Edge (SASE) is a network architecture that combines software-defined wide area networking (SD-WAN ) and security functionality into a unified cloud service that promises simplified WAN deployments, improved efficiency and security, and application-specific bandwidth policies. billion by 2025.
Oracle is adding new capabilities to its Supply Chain and Manufacturing (SCM) Fusion Cloud to help enterprises manage their logistics. The combination of the data along with machine learning models will aid enterprises in making faster decisions around global logistics, the company said in a statement. billion in 2021.
CDNA 3 is based on the gaming graphics card RDNA architecture but is expressly designed for use in data center applications like generative AI and high-performance computing. And in 2026, the AMD Instinct MI400 series will arrive, based on the AMD CDNA “Next” architecture.
Boeing announced today that its quantum satellite, named Q4S, will launch in 2026 to demonstrate quantum entanglement swapping capabilities on orbit. Europe and Canada have plans to launch satellites in 2025 or 2026. We are leading the way to operationalize and scale quantum technologies for global applications.” billion.
IBM is offering expanded access to Nvidia GPUs on IBM Cloud to help enterprise customers advance their AI implementations, including large language model (LLM) training. IBM Cloud automates the deployment of AI-powered applications, to help address the time and errors that could occur with manual configuration,” Badlaney wrote. “It
SASE , which stands for secure access service edge, is a networking model that combines network and security capabilities into a single, cloud-based service that enterprises can extend to their on-premises employees, remote workers, and branch offices. Check out Network World’s SASE buyer’s guide.)
This next generation processor, dubbed Aurora, is not due until 2026. Aurora offers powerful AI compute capabilities for workloads like RAG and vector databases, but Wittich said it will support all types of enterpriseapplications, not just cloud. “So But don’t be planning to place an order just yet.
Although it can be complex, the right HPC implementation provides your enterprise the computing capabilities necessary for high-intensity applications in many industries, especially those taking advantage of AI. HPC as a service : Another key trend is the emergence of HPC as a fully managed service inside enterprise data centers.
AI applications that threaten citizens’ rights, such as predictive policing or untargeted scraping of internet facial images, are banned. Similar to GDPR, an enterprise with operations in the EU must comply with the EU regulations, which means that the AI Act will impact global enterprises by 2026 at the latest.
Nardecchia adds that widespread adoption will depend on several factors cost, reliability, safety, regulatory compliance, and enterprise integration noting that AI robotics will face the same barriers to adoption as other emerging technologies, but he does not dismiss the possibility.
As large enterprise and hyperscaler networks process increasingly greater AI workloads and other applications that require high-bandwidth performance, the demand for optical connectivity technologies is growing as well. Capacity of these links will need to increase with AI applications, Cisco’s Gartner said.
Salesforce’s reported bid to acquire enterprise data management vendor Informatica could mean consolidation for the integration platform-as-a-service (iPaaS) market and a new revenue stream for Salesforce, according to analysts. This challenge requires tools with capabilities outside of what MuleSoft and Tableau can offer,” Park explained.
This challenge will become more urgent, as IDC predicts the amount of data created will grow to 221 ZB by 2026 3. . For instance, GPUs are more energy-efficient than CPUs because their memory architecture specializes in supporting high-speed data streaming for intensive applications. High-performance computing and supercomputing.
This challenge will become more urgent, as IDC predicts the amount of data created will grow to 221 ZB by 2026 3. . For instance, GPUs are more energy-efficient than CPUs because their memory architecture specializes in supporting high-speed data streaming for intensive applications. High-performance computing and supercomputing.
As enterprises become more data-driven, the old computing adage garbage in, garbage out (GIGO) has never been truer. The application of AI to many business processes will only accelerate the need to ensure the veracity and timeliness of the data used, whether generated internally or sourced externally.
Faced with a long-running shortage of experienced professional developers, enterprise IT leaders have been exploring fresh ways of unlocking software development talent by training up non-IT staff and deploying tools that enable even business users to build or customize applications to suit their needs. What’s expensive is scale.”
Some enterprises “are begging AWS to start certification programs,” Smith said. As IT skills shortages widen and the arrival of new technology accelerates, enterprises must find creative ways to hire, train, upskill, and reskill their employees.” But many enterprises’ training efforts are falling short.
Many developers faced difficulties porting applications developed for a particular computing environment decades ago. Each container leverages a shared operating system kernel and encapsulates everything needed to run an application (application code, dependencies, environment variables, application runtimes, libraries, system tools etc.)
Enterprises are betting big on machine learning (ML). According to IDC , 85% of the world’s largest organizations will be using artificial intelligence (AI) — including machine learning (ML), natural language processing (NLP) and pattern recognition — by 2026. So how can enterprises overcome these challenges?
Not only are enterprises and hyperscalers building or expanding their facilities to accommodate increasing interest in artificial intelligence, but that same AI is gobbling power, and thus creating heat — a lot of it. Data centers are hot, in more ways than one. And that means cooling costs are also growing.
Why risk management is vital Risks in enterprise IT have significantly evolved in the past year, demanding an emphasis on short- and long-term resilience plans spanning multiple areas. And while 99% of packages have updated versions available, 80% of application dependencies remain un-upgraded for over a year.
Oracle is adding a new managed offering to its Cloud@Customer platform that will allow enterprises to run applications on proprietary optimized infrastructure in their own data centers to address data residency and security regulations and solve low-latency requirements. The infrastructure will be managed and operated by Oracle.
As organizations roll out AI applications and AI-enabled smartphones and devices, IT leaders may need to sell the benefits to employees or risk those investments falling short of business expectations. AI-enabled smartphones, those containing chips powerful enough to run AI applications, are already coming to the market.
Business transformation is a journey Great modern enterprises are only as good as their technology, which must keep pace with changing business demands. So, there’s no single approach that works for everyone, and even the best technology will not deliver a great return on investment if your applications and data remain in silos.
It’s following in the footsteps of IBM and Microsoft, which like the German telco have an edge over regular companies contemplating a similar move to Rise in that they have their own clouds in which to host the applications and their own IT services divisions to make the move.
For the past decade, process mining specialist Celonis has been helping enterprises optimize processes around their ERP systems — and more recently has branched out to help them optimize their use of workflow automation platforms, too. Enterprises can save time and money by automating processes — but only if they automate the right ones.
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