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Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it. BotMan AI uses an AI-powered migration pipeline that maps an application’s dependencies, business logic and data models in a knowledge graph.
The semiconductor industry is entering a new era of hardware engineering as artificial intelligence accelerates demand for more powerful chips, interconnected systems and faster product development.
AI is helping companies interpret biological data for drug discovery and more personalized care. Healthcare technology company Flahy Inc. is applying that approach to clinical decision support, bringing together information that could affect a person’s next step in prevention or treatment,…
The artificial intelligence infrastructure race is no longer just about building faster chips. AI infrastructure bottlenecks are shifting attention to a bigger challenge: keeping those chips supplied with data, connected through high-speed networks and operating within increasingly demanding power…
Professional services firms are rethinking how they deliver client value as artificial intelligence takes on more routine work, shifting attention toward human judgment, trusted outcomes and new operating models.
Enterprise IT teams are deploying applications faster than ever, but their ability to detect and resolve production problems isn’t always keeping pace. The growing complexity of hybrid infrastructure, cloud-native applications and interconnected digital services is putting pressure on traditional…
Enterprise AI orchestration must account for agents working across the data, applications and infrastructure businesses already use. As organizations move beyond experiments, running those systems consistently brings questions of access, oversight and operational control into sharper focus.
A control plane for distributed AI could help applications decide which tasks to run on a device, at the edge or in the cloud. Seismora Inc. is developing networking technology to coordinate AI workloads across different providers and computing environments, according to Vito Palermo (pictured),…
The knowledge graph can give enterprise AI applications context by connecting data with the business relationships needed to support decisions. Knowledge graphs can connect enterprise data with the meaning AI applications need.
AI agents have made identity the front line of enterprise security. Agents now number in the thousands. They pick up access nobody meant to give them, and when they’re blocked partway through a task, they look for another way in.
AI inference is fast becoming the workload that decides the economics of the AI boom. Training built the first wave of GPU clouds, but serving models faster and cheaper will define the next.
Enterprises racing to put agents into production are learning that the AI improvement loop never really ends. Agents must be run, observed, evaluated and improved continuously. CoreWeave Inc. has been assembling a full-stack platform aimed at agentic AI.
The full-stack AI cloud market is about more than access to graphics processing units: it also encompasses the tools needed to improve models and agents in production. Providers are seeking to distinguish themselves through the tooling, partners and expertise they combine with hardware.
On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are rethinking architectures designed around elastic cloud capacity.
AI model training can leave startups managing infrastructure alongside the products they are trying to build. Willow Care Inc., the company behind AI dictation app Willow Voice, chose CoreWeave Inc. for its forward-deployed support rather than compute alone.
Back-end AI gives manufacturers opportunities to improve operations, but choosing where to apply it requires attention to business value. For Aston Martin Lagonda Ltd., that means evaluating operational use cases while preserving its personalized customer experience.
AI-powered process automation requires software that understands the industry and workflows it serves. Moving from a promising prototype to practical use also requires implementation expertise. Infor (US) LLC develops software around the specific processes its customers use in their industries.
Demand for new data centers, power plants and highways is fueling a construction boom, even as the industry wrestles with declining productivity and a shortage of skilled machine operators. Physical AI, which lets machines perceive their surroundings and act on them, is emerging as one answer.
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