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Apptio: Why scaling intelligent automation requires financial rigour

adoption, ai, AI (Artificial Intelligence), AI Business Strategy, Artificial Intelligence, automation, enterprise, Features, Inside AI, Interviews, investment, strategy, TechEx Events

Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour. The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide […]

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FedEx tests how far AI can go in tracking and returns management

ai, AI (Artificial Intelligence), AI Business Strategy, AI in Action, Artificial Intelligence, enterprise, Featured, Featured News, Features, Retail, Retail & Logistics AI

FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays. That pressure is pushing

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Klarna backs Google UCP to power AI agent payments

agentic ai, agents, ai, AI (Artificial Intelligence), AI Business Strategy, AI in Action, Artificial Intelligence, commerce, Features, Finance, Finance AI, Fintech, Google, Inside AI, klarna, Retail & Logistics AI, ucp

Klarna aims to address the lack of interoperability between conversational AI agents and backend payment systems by backing Google’s Universal Commerce Protocol (UCP), an open standard designed to unify how AI agents discover products and execute transactions. The partnership, which also sees Klarna supporting Google’s Agent Payments Protocol (AP2), places the Swedish fintech firm among

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PepsiCo is using AI to rethink how factories are designed and updated

ai, AI (Artificial Intelligence), AI Business Strategy, AI in Action, Artificial Intelligence, digital twins, Features, manufacturing, Manufacturing & Engineering AI, supply chain

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

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China’s hyperscalers bet billions on agentic AI as commerce becomes the new battleground

ai, AI (Artificial Intelligence), AI and Us, AI in Action, AI Market Trends, Artificial Intelligence, Deep Dives, Features, Human-AI Relationships, Inside AI, Retail & Logistics AI

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

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Does Anthropic believe its AI is conscious, or is that just what it wants Claude to think?

ai, AI (Artificial Intelligence), AI alignment, AI anthropomorphism, AI behavior, AI consciousness, AI Ethics, AI psychosis, AI sycophancy, AI welfare, Amanda Askell, anthropic, Anthropic Claude, Artificial Intelligence, Biz & IT, chatbots, claude, Constitutional AI, Dario Amodei, Features, Large Language Models, Machine Learning, rlhf

Anthropic’s secret to building a better AI assistant might be treating Claude like it has a soul—whether or not anyone actually believes that’s true. But Anthropic isn’t saying exactly what it believes either way. Last week, Anthropic released what it calls Claude’s Constitution, a 30,000-word document outlining the company’s vision for how its AI assistant

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Franny Hsiao, Salesforce: Scaling enterprise AI

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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

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Databricks: Enterprise AI adoption shifts to agentic systems

adoption, agentic ai, agents, ai, AI (Artificial Intelligence), AI and Us, AI Business Strategy, AI Market Trends, Artificial Intelligence, automation, databricks, enterprise, Features, governance, Governance, Regulation & Policy, infrastructure, Inside AI, Special Reports & Series, strategy, World of Work

According to Databricks, enterprise AI adoption is shifting to agentic systems as organisations embrace intelligent workflows. Generative AI’s first wave promised business transformation but often delivered little more than isolated chatbots and stalled pilot programmes. Technology leaders found themselves managing high expectations with limited operational utility. However, new telemetry from Databricks suggests the market has

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Lowering the barriers databases place in the way of strategy, with RavenDB

ai, AI (Artificial Intelligence), AI in Action, ai tooling, Artificial Intelligence, Data Engineering & MLOps, Databases, Features, nosql, Sponsored Content, TechEx Events

If database technologies offered performance, flexibility and security, most professionals would be happy to get two of the three, and they might have to expect to accept some compromises, too. Systems optimised for speed demand manual tuning, while flexible platforms can impose costs when early designs become constraints. Security is, sadly, sometimes, a bolt-on, with

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Controlling AI agent sprawl: The CIO’s guide to governance

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Corporate networks are filling up with AI agents, creating a governance blind spot for leaders managing multi-cloud infrastructures. As distinct business units race to adopt generative technologies, CIOs especially find their ecosystems populated by fragmented and unmonitored assets. This mirrors the shadow IT challenges of the cloud era, but involves autonomous actors capable of executing

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