January 2026

For many large companies, the most useful form of AI right now has little to do with writing emails or answering questions. At PepsiCo, AI is being tested in places where mistakes are costly and changes are hard to undo — factory layouts, production lines, and physical operations. That shift is visible in how PepsiCo […]

Mid-January saw insurance company, Travelers, announce a new deal that empowers 10,000 engineers and data scientists with AI assistants. However, less than two weeks on, Travelers’ leadership explained that the company’s true competitive advantage lies in expertise, not AIs alone, believing this is what will drive longer-term profit growth. According to Travelers’ chief executive officer

The artificial intelligence industry’s pivot toward agentic AI—systems capable of autonomously executing multi-step tasks—has dominated technology discussions in recent months. But while Western firms focus on foundational models and cross-platform interoperability, China’s technology giants are racing to dominate through commerce integration, a strategic divergence that could reshape how enterprises deploy autonomous systems globally. Alibaba, Tencent

New research from Accenture has discovered insurance executives are planning on increased investment into AI during 2026 despite a widening skills gap in insurance organisations. Surveying 3,650 C-suite leaders over 20 industries and 20 countries, the Pulse of Change poll revealed 90% of the 218 senior insurance executives intend to spend more on AI over

A new report from Deloitte has warned that businesses are deploying AI agents faster than their safety protocols and safeguards can keep up. Therefore, serious concerns around security, data privacy, and accountability are spreading. According to the survey, agentic systems are moving from pilot to production so quickly that traditional risk controls, which were designed

Scaling enterprise AI requires overcoming architectural oversights that often stall pilots before production, a challenge that goes far beyond model selection. While generative AI prototypes are easy to spin up, turning them into reliable business assets involves solving the difficult problems of data engineering and governance. Ahead of AI & Big Data Global 2026 in

A White House paper titled “Artificial Intelligence and the Great Divergence” sets out parallels between the effects of the industrial revolution in the 18th and 19th centuries and the current times, with artificial intelligence positioned as guiding the way the world’s economies will be shaped. Artificial intelligence now sits at the centre of US economic

2026 will see forward-thinking organisations building out their squads of AI agents across roles and functions. But amid the rush, there is another aspect to consider. One of IDC’s enterprise technology predictions for the coming five years, published in October, was fascinating. “By 2030, up to 20% of [global 1000] organisations will have faced lawsuits,

Artificial intelligence has moved into the US workplace, but its adoption remains uneven, fragmented, and tied to role, industry, and organisation. Findings from a Gallup Workforce survey covering the period to the end of December 2025 show how employees use AI, who benefits most from it, and where areas of uncertainty remain. The findings draw

For banks trying to put AI into real use, the hardest questions often come before any model is trained. Can the data be used at all? Where is it allowed to be stored? Who is responsible once the system goes live? At Standard Chartered, these privacy-driven questions now shape how AI systems are built, and