Start a project →

// Insights Latest from
Vertex Agility

May 29, 2026

The New EU Cloud Rules Only Bind Governments. The Reason Behind Them Binds You Too.

On 27 May 2026 the European Commission presented its Tech Sovereignty Package, including the Cloud and AI Development Act, restricting sensitive public-sector data from US hyperscalers. The restrictions stop at the public sector, but the underlying issue, namely US CLOUD Act jurisdiction over American-incorporated providers regardless of where data is stored, applies to every regulated business. This article explains the legal mechanism, the Dutch precedent blocking the Kyndryl and Solvinity acquisition, the trap of overcorrecting toward immature European providers, and the practical steps CIOs should take around data classification, portability, hybrid and multi-cloud design, and encryption key custody.
May 22, 2026

AI Is Writing More of Your Code Than Ever. Your Process Hasn’t Caught Up.

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.
May 20, 2026

The Agentic AI Advantage: How Token Economics Separates the Programmes That Scale From the Ones That Stall

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.
May 15, 2026

The $2 Trillion Cloud Mirage: Why Your Vendor’s AI Dependency Is a Systemic Enterprise Risk

A report from The Information revealed this week that Anthropic and OpenAI together account for roughly half of the combined $2 trillion revenue backlog held across Amazon, Microsoft, Google, and Oracle. This article examines the structural concentration risk this creates, the implications for enterprise cloud pricing and resource availability, and the practical steps CIOs can take to insulate their infrastructure strategies.
May 13, 2026

The Data-First Reversal: Why 2026 Is the Year Cloud-First Strategies Hit the Wall

A techUK report has confirmed what CIOs have felt in their cost reviews for two years: cloud-first strategies are hitting sovereignty and cost walls. This article examines the egress tax problem, the implications of the UK Data Use and Access Act 2025, and what a genuine data-first architecture – one where compute moves to data, not the other way around – actually requires organisations to do differently.
May 6, 2026

You’re Budgeting for Infinite AI Compute. The Grid Has Other Plans.

AI data centres are projected to consume more energy than Germany and France combined by 2030, yet most enterprise AI strategies are still built on the assumption that compute is cheap and limitless. This article examines the energy and infrastructure constraints that are closing the brute-force compute era, and sets out the practical architecture shifts – task decomposition, context discipline, evaluation-driven model selection – that organisations need to make now.
Apr 29, 2026

The Stack You Built for Your Analysts Won’t Work for Your Agents

While nearly two-thirds of enterprises have experimented with AI agents, fewer than 10% have successfully scaled them, with 80% citing data limitations as the core obstacle. This article explores why the modern pipeline-centric data stack, designed for human analysts, struggles with autonomous AI systems, and examines the critical shift toward semantic metadata, upstream quality enforcement, and machine-readable context.
Apr 24, 2026

Your Data Team Is Shipping Faster Than It Can Be Trusted. That’s a Problem.

dbt Labs’ 2026 State of Analytics Engineering Report found trust in data has surged to 83% as the top organisational priority — the steepest single-year rise ever recorded — driven by 71% of data professionals concerned about incorrect AI outputs reaching stakeholders. With only 7% of enterprises saying their data is completely AI-ready and Gartner finding organisations that see returns invest four times more in data foundations, this article examines the acceleration-to-governance gap and what agent-ready data infrastructure actually requires.