Apple Pay, a leading mobile payment method worldwide, has been widely accepted at most contactless terminals—except at Walmart for over a decade. Now, Walmart is changing course. When Apple Pay debuted in 2014, it revolutionized mobile payments, allowing users to pay easily with their smartphones. However, Walmart resisted adopting Apple Pay, partly because Apple Pay's strong privacy protections limited Walmart's ability to collect customer data. Instead, Walmart initially backed CurrentC, a competitor payment system that ultimately failed. Walmart then launched its own Walmart Pay in 2016, allowing it to track customer spending more effectively. Despite this, Walmart held off on adopting NFC-based tap-to-pay solutions like Apple Pay and Google Pay—until now.

Recently, Walmart announced it will begin supporting tap-to-pay, including Apple Pay, Google Pay, and Samsung Pay, starting in select stores and expanding to all Walmart and Sam’s Club locations by the end of 2026. Walmart will continue to offer Walmart Pay alongside these new options, aiming to give customers more payment choices and make shopping more convenient. A major factor driving this change appears to be customer demand, as users increasingly expect to pay using mobile wallets integrated into their smartphones.

Slack, owned by Salesforce, has launched Slack Code, a new feature that integrates AI coding agents like Anthropic's Claude Code, Cognition's Devin, GitHub Copilot, and Vercel's agent directly into dedicated Slack channels. This allows entire teams to collaboratively watch, guide, review, and ship software, moving AI coding out of individual terminals into a transparent, multiplayer workflow. Slack Code creates project-specific channels where all work, including code diffs and live previews, is visible and archived for audit. The approach shifts the bottleneck from coding itself to judgment and creativity, expanding participation across roles such as engineers, product managers, and designers. Slack emphasizes security by restricting agent permissions to those of the invoking user, ensuring no cross-team data leaks. This launch positions Slack as a key platform for collaborative AI-driven development amid Salesforce's strategic pivot toward AI-powered enterprise solutions. Leaders in the space see a future where multiplayer channels and single-user terminals coexist, with a growing preference for collaborative workflows fostering higher quality and efficiency in software development.

OpenAI, which had earlier opposed California's AI safety bill SB 53, is now advocating for the state to enhance the legislation to better address AI safety concerns.

HR's role is evolving beyond simply managing tasks; it is now actively reshaping how work is designed and carried out.

The recent breach at Apollo serves as a stark reminder of the dangers posed by social engineering tactics, which allowed attackers to infiltrate key financial systems without relying on malware.

For the past two years, the prevailing belief in enterprise AI was that greater autonomy meant better performance. The idea was to build agents that could independently plan, decide, and act across multi-step workflows with minimal restrictions. However, in real-world production settings, this approach is proving problematic. Successful companies are now focusing on creating AI agents with clearly defined responsibilities that operate within strict rules.

Recent forecasts reveal that over 40% of agentic AI projects may fail by 2028, not due to AI capability but because of rising costs, unclear business value, and insufficient risk controls. Governance maturity in responsible AI remains low, with only about 30% of organizations achieving advanced control measures. This gap between AI capability and governance is reshaping the competitive landscape: the priority has shifted from deploying the most autonomous agent to building trustworthy systems that satisfy risk, compliance, and legal teams.

Full autonomy often breaks down in production because autonomous decisions are hard to trace and audit, especially in regulated environments like finance or healthcare. Integration complexity arises when legacy workflows must be rebuilt to accommodate AI agents acting without human input. Enterprises that dive into this without a comprehensive governance strategy tend to stall or cancel projects.

Leading enterprises adopt four key governance patterns: narrow-scope agents instead of broad ones, human checkpoints before critical decisions, built-in decision traceability, and active data sovereignty to limit risk exposure. This approach balances autonomy with accountability and reduces the risk of costly errors or compliance breaches.

A practical framework for evaluating AI agents involves asking whether action decisions can be reconstructed, agents have bounded responsibilities, checkpoints exist before decisions execute, and data access is properly contained. This framework helps businesses scale AI with calibrated control rather than unchecked autonomy.

Ultimately, the companies that win with agentic AI by 2027 will be those that earn the trust of legal and compliance teams through disciplined governance embedded from the start. It’s not about maximum autonomy but smart orchestration of agents with clear oversight and accountability.

Google has launched a new option for publishers that allows readers to mark them as preferred sources across Google Search, Discover, and Google News. This feature aims to help publishers maintain or increase their web traffic at a time when AI-driven search technologies are reducing the number of clicks sent to traditional websites.

Since the introduction of ChatGPT, a significant portion of new web content is being created or edited by AI technologies. These models are increasingly influential in shaping online information and digital communication.

Ramp has introduced Router, a new service that allows users and companies to access and switch between different large language models through a single API. This innovation simplifies the integration and use of various AI models, enhancing flexibility and efficiency for developers and businesses alike.

France continued to stand out as a premier destination for tech investment in Europe during the first half of 2026. Funding was predominantly directed towards sectors such as artificial intelligence, space exploration, fintech, and healthcare. Key deals, including significant contributions to companies like Eutelsat, played a crucial role in this robust investment landscape.