Runpod, a cloud-based startup specializing in AI computing rentals, has secured $100 million in funding, boosting its valuation to $1 billion—an impressive tenfold increase in less than two years. The company has reportedly declined acquisition offers exceeding $500 million, positioning itself as a key player in the current AI computing supply crunch.

Google has integrated direct screen interaction capabilities into Gemini 3.5 Flash, its fastest AI model introduced at I/O 2026. This upgrade allows AI agents within the model to visually access screens and perform actions such as clicking, typing, and scrolling on various devices and browsers. Previously, this functionality was only available through a separate AI model, but now it comes built into Gemini 3.5 Flash, streamlining user experience and expanding practical applications. The move also reflects Google's push to build confidence among enterprise users in the model’s reliability and security.

New information has come to light regarding Valor Equity Partners' latest fundraising initiative. Last year, the firm announced it was seeking capital for a new fund but did not specify the amount. Recent reports indicate that they are targeting $2.5 billion for Fund VII, highlighting significant growth ambitions.

The brief phase of token maxing out is over. Now, companies are entering a phase of token rationing to better manage and control AI usage within their budgets.

Starbucks is partnering with TikTok to boost employee-generated content, recognizing that Gen Z often discovers new products through these personalized social media posts. This initiative aims to deepen engagement and enhance brand awareness by leveraging authentic voices from within the company.

After years of experimenting with AI, many companies still wrestle with understanding the real returns on their AI investments. Executives often find AI ROI ambiguous and industry-specific, with limited guidance on measuring actual impact versus encouraging investment. Our interviews with over 30 CEOs reveal that AI ROI measurement is far from standardized — identical investments can yield entirely different success definitions. Return on AI investments varies by the nature of the AI technology used: analytical AI typically offers measurable financial gains in specific areas, while generative AI improves speed, quality, or volume requiring more effort to translate into financial terms. Industry context further shapes how ROI is defined, for example, consumer goods focus on supply chain efficiency, while B2B marketing values creative output and lead conversions.

We’ve identified three practical pathways organizations take to manage AI ROI:

1. Function-focused approach: Concentrating on a single function like customer service or HR, applying tailored AI tools and measuring function-specific KPIs such as response times or error rates. This approach helps build credible proof points and organizational confidence before broader rollout.

2. Coordinated approach: Managing multiple AI initiatives across functions with shared platforms and capabilities, using a blend of broad operational and function-specific metrics. Coordination enhances learning, comparison, and scaling but requires standardization to avoid fragmented efforts.

3. Enterprise portfolio approach: Applying rigorous financial governance at the portfolio level, treating AI investments like capital projects with metrics such as net present value (NPV) and internal rate of return (IRR). This stage supports enterprise-wide decision-making and scaling while balancing discipline and strategic flexibility.

Successful AI ROI management demands prioritizing high-impact use cases, committed leadership, and adaptable measurement as both AI technology and business contexts evolve. Organizations typically progress through these approaches, continually refining how they translate AI activity into measurable business value.

Anthropic has introduced Claude Tag, an innovative AI integration for Slack that acts as a continuous, shared teammate within Slack channels. Unlike traditional chatbots, Claude Tag functions collaboratively across team members, learning over time, taking initiative, and autonomously handling complex tasks. Currently in beta for Claude Enterprise and Team plans, it replaces Anthropic's previous Slack app and represents a significant leap toward embedding AI deep into enterprise workflows. Administrators control access, tools, and budgets, ensuring secure and tailored deployment across channels. Claude Tag leverages the advanced Claude Opus 4.8 model, enabling multitasking, proactive monitoring, and asynchronous project management. This release marks Anthropic’s strategic push into the collaboration layer where critical work coordination happens, positioning the AI as a persistent, insightful team member while raising new considerations around vendor dependency, security governance, and cost.

Oracle is committing $70 billion this year to artificial intelligence, focusing on building AI-capable servers and data centers. However, this investment has led to significant workforce reductions, with Oracle cutting 21,000 jobs over the past year—a 13% decrease from the previous year. The company attributes these layoffs directly to the adoption of AI technologies, noting potential risks such as reduced productivity and loss of skilled employees. Oracle is not alone; other tech giants like Meta and Microsoft have also reduced staff to fund AI initiatives, collectively planning to spend $700 billion on AI infrastructure in 2026. Despite the AI push, Oracle's stock has declined over 11% this year, raising concerns about the sustainability of such heavy investments amid fears of an AI-driven market bubble.

Data is plentiful in today’s business world — from market research to financial metrics — but should every decision rely solely on data? We asked 20 leaders from the Fast Company Impact Council how they integrate data-driven approaches with their intuition. Their perspectives reveal it’s less about choosing one over the other and more about blending both to make smarter decisions. They emphasize using all information available, setting clear timelines, recognizing patterns through experience, and knowing when to trust instinct over numbers. Many highlight how data can validate insights but often falls short in predicting future possibilities or emotional impact. Leaders also discuss the rising role of AI in decision-making, the importance of feedback loops, and caution against using data merely to justify pre-made decisions. Ultimately, they agree that data and gut instinct are complementary forces that, when balanced well, enhance leadership and business outcomes.

Recent survey findings show that 79% of companies using agentic AI experience expensive mistakes. Moreover, the usual IT governance strategies are failing to address these issues effectively.