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In one month, the enterprise software industry bought its way toward agents that act rather than advise. The controls to govern them are running behind.
The Model Context Protocol has shifted from a technical curiosity to a default question in enterprise software evaluation. Following CircleCI’s June 2026 MCP server release and Databricks’ move to govern MCP services in Unity Catalog, this article explains what MCP changes for integration cost, vendor lock-in, and AI governance, and sets out the practical procurement questions technology leaders should write into their evaluations.
At WWDC 2026, Apple unveiled a rebuilt Siri powered by a custom Google Gemini model reported to carry 1.2 trillion parameters, at a reported cost of roughly $1 billion a year. This article argues the deal is a template rather than a surrender: Apple rented the frontier model but kept the customer relationship, the data path, the context layer, and the interface. It sets out the four-part playbook enterprises should copy – task-level model bake-offs, gateway architecture that keeps providers swappable, owned small models for routing economics, and treating context as the proprietary asset – alongside the dependency and concentration risks of renting without an exit.
More than $40 billion has gone into enterprise AI, and 56% of CEOs say they have nothing to show for it – the gap has surprisingly little to do with the models
A CloudBees survey of more than 200 enterprise technology leaders found that 81% reported an increase in production issues linked to AI-generated code, even as 92% remained confident their code was production-ready before shipping. This article examines the verification gap created when AI generates code faster than teams can validate it, the rising and largely untracked costs that follow, the absence of clear governance ownership, and the practical steps engineering leaders can take to close the gap.
Google now processes 3.2 quadrillion tokens a month, up from 480 trillion a year ago. While vendors compete on per-token prices, the organisations pulling ahead on AI are the ones tracking a different metric entirely: tokens per business outcome. This article explains why per-token price is the wrong number to optimise, introduces tokens per resolved outcome as the unit that makes agentic AI economics legible, and sets out the three architectural patterns – task decomposition, context discipline, and evaluation-driven model selection – that create durable cost and performance advantages in agentic AI programmes.
UiPath’s 2026 report found 78% of executives believe operating model reinvention is essential for agentic AI. McKinsey found 80% are seeing no bottom-line impact. This article examines the structural, foundational, and governance barriers creating that gap — and what the organisations pulling ahead are doing differently.
A practical series exploring how AI and automation improve visibility, governance, and optimisation across modern technology estates. Learn how to reduce cloud waste, prevent cost overruns, and implement intelligent controls at scale.
An in-depth look at why many agentic AI initiatives struggle, when AI agents add real value, and how organisations can design workflows where humans and agents work effectively together.
