Agentic AI is shedding light on leadership deficiencies that have long hindered business growth, offering fewer excuses and more accountability for effective management.
After years of observing corporate AI adoption, it’s clear that selecting which AI model to use—Copilot, GPT, Gemini, Claude, Grok, or even Chinese models like Deepseek and Qwen—is often the first step companies ponder. However, this choice might not be as crucial as it seems. Think of the AI model like a microprocessor: important, but just one part of a larger system. Companies don't always need the most powerful model for every task; simpler models can handle routine queries cost-effectively, while more complex tasks are assigned to advanced models. This layered approach is already being adopted, with firms like Deepseek focusing on competitive pricing and Microsoft pushing smaller, specialized models for specific industries and tasks. The real edge lies beyond the model itself—in how companies leverage their unique institutional knowledge, workflows, documents, and feedback loops to build smarter, tailored AI systems. This creates a profound competitive advantage, as institutions with rich, context-specific learning outperform those relying solely on generic, large models. Ultimately, it’s about institutional sovereignty: owning your data and learning determines how valuable your AI implementation truly is, far beyond just picking the latest model.
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.
The creator economy is projected to reach nearly $500 billion next year, yet many brands still struggle to connect effectively with creators. Despite the booming market size, the true value lies in how well creators fit with brand identity rather than just their follower counts.
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.
Anthropic is gearing up for an IPO that could rival or surpass SpaceX’s historic $75 billion fundraising milestone. The company, despite posting a net loss of nearly $42 billion in 2025—five times its loss the previous year—is optimistic about its stock market debut, potentially filing for the public offering by the end of this month. This move aims to secure one of the largest IPOs in history.
Patrick Collison of Stripe emphasized that the true economic impact of AI hinges on the efficient use of limited computing power. The company has confirmed it has acquired OpenRouter, although the financial and contractual specifics have not been made public.
Enterprise AI teams have shifted from relying on a single orchestration platform to running multiple — typically three — to avoid dependency on one vendor and address security and control concerns. Microsoft leads current usage, with Anthropic gaining interest as a next step, but challenges remain in cost visibility and controlling token usage. A survey of 107 enterprises shows 85% use two or more orchestration tools, and 64% use three. Hybrid control planes are expected to grow, with many firms planning platform changes within a year. Enterprises prioritize flexibility, security, reliability, and agent execution control over factors like model alignment or latency. Spending focuses on monitoring, security, and workflow tooling to ensure multi-step task completion rather than just user experience. Control issues persist, with 20% unable to stop excessive AI agent spending in real time, relying on a mix of platform controls, custom middleware, and reactive monitoring. Despite progress, most AI systems are still evolving from basic chatbots to true multi-step autonomous agents, with only a small fraction widely deployed at scale. Enterprises are building infrastructures for future agentic capabilities but are still early in realizing their full potential.
