Jamie Dimon gets real on AI, sees stocks ‘in some form of bubble territory’ | Fortune
Jamie Dimon on AI Reality: Job Disruption Warnings and Stock Bubble Concerns | AB Panel PC Jamie Dimon Sounds Alarm…
Tag: Artificial intelligence
Walmart And OpenAI Test The Boundaries Of Trust In AI
Walmart And OpenAI Partnership Tests Trust Boundaries in AI Commerce The Dawn of Agentic Commerce Days after global leaders debated…
PepsiCo’s AI Strategy: Owning Core Processes While Leveraging External Technology
PepsiCo’s Chief Strategy Officer reveals how the company buys AI tools while demanding influence over vendor roadmaps. The beverage giant maintains ownership of core AI-augmented processes while leveraging external technology partnerships for scale and innovation.
As enterprises worldwide accelerate their artificial intelligence adoption, PepsiCo has developed a distinctive approach that balances external technology acquisition with internal process ownership. The company’s strategy, articulated by Chief Strategy and Transformation Officer Dr. Athina Kanioura at Salesforce Dreamforce 2025, emphasizes owning core AI-augmented processes while strategically partnering with technology providers.
The Core Philosophy: Own the Process, Influence the Technology
AI Agents: Accelerating Business Efficiency While Amplifying Security Risks
AI agents are revolutionizing business operations with machine-speed execution, but their autonomous nature creates unprecedented security challenges. Traditional human-oriented permission models fail to contain AI-driven actions, requiring fundamental rethinking of authorization frameworks.
As AI agents transition from experimental projects to production environments, organizations are discovering both the tremendous efficiency gains and significant security risks these systems introduce. The fundamental challenge lies in applying human-designed permission models to machine-speed operations, creating a dangerous mismatch between capability and control.
The Acceleration of AI in Enterprise Operations
ChatGPT’s makeover to bring back charm, chat, and a little chaos
ChatGPT Personality Update: Customizable Warmth and Adult Mode Features OpenAI is responding to user feedback by restoring ChatGPT’s expressive, warm…
Walmart goes after Amazon on AI shopping — plus, 3 bits of great Boeing news
Walmart Challenges Amazon in AI Shopping Revolution, Boeing Scores Triple Win Market Volatility and Fed Policy Outlook Wall Street experienced…
Firefox Integrates Perplexity AI Search Engine as Global Default Option
Mozilla has officially integrated Perplexity’s AI answer engine into Firefox as a permanent search option following successful testing. The feature offers conversational search with cited answers and will expand to mobile devices in coming months.
In a significant move that bridges traditional web browsing with artificial intelligence capabilities, Mozilla has officially integrated AI-powered search engine Perplexity as a default option in its Firefox browser. Unlike competitors developing dedicated AI browsers, Mozilla’s approach allows users to enhance their existing browsing experience with advanced question answering capabilities while maintaining Firefox’s core functionality and privacy-focused philosophy.
From Limited Testing to Global Availability
Walmart partners with OpenAI for ChatGPT shopping feature
In a significant move to enhance digital shopping experiences, Walmart has partnered with OpenAI to integrate direct purchasing capabilities into…
Checking the quality of materials just got easier with a new AI tool
AI Tool SpectroGen Revolutionizes Material Quality Verification In a groundbreaking development that promises to transform manufacturing processes across multiple industries,…
Manufacturing AI Deployment Success Hinges on Strategic Data Preparation
Manufacturers possess vast data streams from machines and sensors, yet struggle to extract actionable insights. Learn how structured data preparation through MES enables successful, scalable AI deployment that drives real operational value.
In today’s competitive manufacturing landscape, organizations are sitting on mountains of data generated from every corner of their operations. From sensor networks monitoring equipment performance to production line outputs, the volume of available information continues to grow exponentially. However, the journey from raw data to actionable intelligence remains challenging for many facilities. The promise of artificial intelligence to transform this data into operational excellence is undeniable, but successful implementation requires more than just advanced algorithms.
The critical differentiator between successful and failed AI initiatives lies in the foundational data preparation phase. Without clean, contextualized, and properly structured data, even the most sophisticated AI models will struggle to deliver meaningful insights. This is where Manufacturing Execution Systems (MES) become indispensable, serving as the bridge between disconnected data sources and AI-ready information architectures that support true scalability.