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How separating logic and search boosts AI agent scalability

agentic ai, agents, ai, AI (Artificial Intelligence), Artificial Intelligence, Deep Dives, enterprise, Features, How It Works, Inside AI, scaling

Separating logic from inference improves AI agent scalability by decoupling core workflows from execution strategies. The transition from generative AI prototypes to production-grade agents introduces a specific engineering hurdle: reliability. LLMs are stochastic by nature. A prompt that works once may fail on the second attempt. To mitigate this, development teams often wrap core business […]

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AI Expo 2026 Day 2: Moving experimental pilots to AI production

ai, AI (Artificial Intelligence), AI and Us, AI Business Strategy, ai expo, AI in Action, AI Market Trends, Artificial Intelligence, data, Data Engineering & MLOps, enterprise, Features, governance, Governance, Regulation & Policy, How It Works, Inside AI, Security, techex, TechEx Events, workforce, World of Work

The second day of the co-located AI & Big Data Expo and Digital Transformation Week in London showed a market in a clear transition. Early excitement over generative models is fading. Enterprise leaders now face the friction of fitting these tools into current stacks. Day two sessions focused less on large language models and more

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AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise

adoption, ai, AI (Artificial Intelligence), AI Business Strategy, ai expo, AI Market Trends, Artificial Intelligence, data, enterprise, Features, governance, Governance, Regulation & Policy, How It Works, infrastructure, Infrastructure & Hardware, Inside AI, TechEx Events

While the prospect of AI acting as a digital co-worker dominated the day one agenda at the co-located AI & Big Data Expo and Intelligent Automation Conference, the technical sessions focused on the infrastructure to make it work. A primary topic on the exhibition floor was the progression from passive automation to “agentic” systems. These

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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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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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How Formula E uses Google Cloud AI to meet net zero targets

agentic ai, agents, ai, AI (Artificial Intelligence), AI and Us, AI Business Strategy, AI in Action, Artificial Intelligence, cloud, digital twins, enterprise, Entertainment & Media, Environment & Sustainability, formula e, google cloud, Inside AI, net zero, strategy, Sustainability

Formula E is using Google Cloud AI to meet its net zero targets by driving efficiency across its global logistics and commercial operations. As part of an expanded multi-year agreement, the electric racing series will integrate Gemini models into its ecosystem to support performance analysis, back-office workflows, and event logistics. The collaboration demonstrates how sports

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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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Balancing AI cost efficiency with data sovereignty

ai, AI (Artificial Intelligence), AI Business Strategy, Artificial Intelligence, data, enterprise, Features, governance, Governance, Regulation & Policy, Inside AI, Opinion, Privacy, Security, sovereignty, strategy

AI cost efficiency and data sovereignty are at odds, forcing a rethink of enterprise risk frameworks for global organisations. For over a year, the generative AI narrative focused on a race for capability, often measuring success by parameter counts and flawed benchmark scores. Boardroom conversations, however, are undergoing a necessary correction. While the allure of

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Scaling AI value beyond pilot phase purgatory

adoption, ai, AI (Artificial Intelligence), AI Business Strategy, Artificial Intelligence, enterprise, Features, governance, Governance, Regulation & Policy, ibm, Inside AI, strategy

Scaling AI value from isolated pilots to enterprise-wide adoption remains a primary hurdle for many organisations. While experimentation with generative models has become ubiquitous, industrialising these tools (i.e. wrapping them in necessary governance, security, and integration layers) often stalls. Addressing the gap between investment and operational return, IBM has introduced a new service model designed

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