IT provider Tata Consultancy Services (TCS) teamed with Anthropic to accelerate enterprise AI adoption, with a focus on highly regulated sectors where accuracy, auditability and governance requirements have historically slowed deployments.

As a Global Premier Partner in the Claude Partner Network, TCS will establish a dedicated business unit focused on delivering industry services and AI expertise built on Anthropic’s Claude family of models, with early access to new releases.

It will also provide Anthropic’s Claude AI assistant to more than 50,000 employees across engineering, finance, legal, marketing, and sales.

The two companies will jointly go to market with AI capabilities across financial services, public services, life sciences, healthcare, aviation, telecom, and medtech.

The partnership also spans several TCS businesses and platforms. Diligenta, TCS’s UK-based life and pensions business with over 22 million customers, will use Claude to improve customer experience through agentic process transformation.

TCS iON, which conducts more than 75 million annual assessments across 1,500 cities in India, will deliver learning and certification programmes on Claude models to help build an AI-certified workforce.

K Krithivasan, CEO and managing director of TCS, stated enterprise AI value comes from understanding business context, orchestrating complex systems and applying deep AI engineering talent.

“By combining Claude with our industry expertise, engineering rigour and large-scale transformation capabilities, we will help customers move faster to production, especially in industries where trust, resilience and regulatory discipline are critical,” he said.

The partnership comes as Anthropic, now valued at $965 billion, looks to extend the momentum which has pushed its valuation above rival OpenAI, with both startups moving towards public listings.

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Ericsson unveiled a software subscription designed to bring telco-grade AI models directly into basebands and radios to enable operators to boost 5G performance, automation and energy efficiency without adding new hardware.

According to the vendor, the software uses AI models designed to operate in real time within the RAN, supported by continuous learning and agentic AI capabilities for more advanced network operations. The company stated its telco-grade models are built for ultra-low latency inference, with a focus on reliability across diverse RAN environments.

The service works with Ericsson 5G Advanced across purpose-built and Cloud RAN platforms. Ericsson stated the software uses its silicon in radios and the latest generation of RAN compute to employ “the right AI model in the right part of the radio network”, while its AI capabilities can also be deployed across partner platforms.

The first features are now available, with more to follow later in the year. Initial functions include AI-powered scheduler for link adaptation, beamforming and multi-layer coordination.

Ericsson claimed the technology has been deployed in more than 15 trials worldwide, delivering up to 20% higher downlink throughput and up to 10% better spectral efficiency, alongside support for up to twice as many high-traffic users. It also said AI in RAN could achieve up to 95% coverage prediction accuracy.

Marten Lerner, head of networks strategy and product management at Ericsson, said the company is “redefining what’s possible in mobile networks by bringing powerful AI capabilities to service providers”, adding “we are taking a major step toward AI-native networks”.

Further comments from Ericsson partners SoftBank, Bell, SK Telecom and Rogers focused on the software’s potential benefits around real-time optimisation, network performance, energy savings, automation and support for emerging AI-driven services.

Joe Madden, principal analyst at Mobile Experts, added: “With a software upgrade, operators can squeeze more capacity, better observability, and more accurate location-based services out of the 5G network they bought years ago.”

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AI platform disruptions rose sharply in early 2026 as growing enterprise adoption and heavier workloads exposed reliability issues across the full infrastructure stack, according to research from Ookla.

Analysing 471 days of US Downdetector data from 1 January 2025 to 16 April 2026 across ChatGPT, Claude, Gemini, Microsoft Copilot, AWS and Microsoft Azure, Ookla recorded 3.7 million user-reported problems.

High-signal disruption days, defined as when a service recorded more than 10 times its own median daily report volume, rose from six across four major AI apps in Q1 2025 to 51 in Q1 2026, according to the report by Ookla analyst Luke Kehoe.

Anthropic’s Claude model accounted for 39 of those 51 disruption days, making it the clearest example of scale-up volatility in the period. Gemini accounted for seven, Copilot three and ChatGPT two.

Claude recorded near-zero Downdetector reports in early 2025 before moving into a sustained report baseline from mid-July as adoption grew.

In Q1 2026, Claude generated 314,996 reports, while March volume alone was nearly three times February’s level. Ookla noted the pattern cannot be attributed to a single outage, with disruption clustered around demand surges, model-release windows and platform instability as Claude Code and Cowork usage scaled rapidly.

OpenAI’s ChatGPT produced the largest individual disruption signals in absolute terms, including roughly 68,000 reports on 2 December 2025, but its underlying reliability trend has improved.

Its monthly median daily report volume fell from a peak of 2,157 in April 2025 to 1,166 in April 2026, even as OpenAI reported more than 900 million weekly active users and rapid growth in Codex usage.

Google’s Gemini and Microsoft’s Copilot showed smaller but distinct patterns. Gemini’s high-signal disruption days rose from zero in Q1 2025 to seven in Q1 2026, consistent with rapid user growth.

Copilot’s outage pattern reflected its position inside Microsoft’s broader enterprise range, with far fewer reports on weekends, reflecting enterprise-aligned use.

Cloud infrastructure also featured prominently in the reliability picture. AWS’s 20 October 2025 DynamoDB DNS event generated more than 315,000 US reports, while Microsoft’s Azure Front Door incident on 29 October produced nearly 96,000, illustrating how failures in cloud control planes can cascade into AI platform disruptions.

Ookla concluded AI reliability has moved well beyond model serving, with failure points now spanning feature gates, GPU fleets, developer APIs, login systems and demand-management policies, all of which can appear to the end user as a single outage.

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