Scott Logic

For the past two years, almost every conversation about AI has focused on the rapidly evolving technical side – models, copilots and increasingly, agents.

Time and again, the biggest concern we hear from clients about agentic AI is a loss of control.

The promised benefits of AI-powered data democratisation are highly compelling.

The conversation around AI is often dominated by models, tooling and the latest advances in generative and agentic AI.

Consider the best work you’ve done recently.

Often, leadership and agility are discussed as separate concerns.

Agentic AI is impacting every part of an organisation, from customer journeys to back office, supply chains and software engineering.

Migrating a legacy codebase of any reasonable complexity has always been a challenging activity, but the economics of this migration have changed with the introduction of AI tools that can help with that process.

Everyone is talking about agentic AI.

Ask most people what ‘organisational readiness’ means and you’ll get a fairly consistent answer: governance in place, funding secured, the right team structure, a delivery methodology everyone’s signed up to.

Artificial Intelligence (AI) has dominated the technology and mainstream media headlines for years.

In coding agents, the trio of: models, runtimes (e.g.

A couple of weeks ago we organised an event, Adopting Agentic: Software Engineering for the AI Age, where we held talks and discussions around the future of our industry in the face of massive AI disruption.

Setting yourself up to try out open-weight models for agentic development isn’t difficult, but it isn’t as straightforward as downloading a coding agent from one of the handful of well-known AI vendors.

The Rise of Agentic Orchestration Throughout 2025 we observed a shift in the way that AI was used.

THE INSTITUTE FOR SYNTHETIC URBANISM & COMPUTATIONAL DECAY White Paper: Toward a Taxonomy of Agent-Driven Displacement Agentrification (noun) /ə-dʒɛn-trɪ-fɪ-ˈkeɪ-ʃən/ The sociotechnical phenomenon in which the “original character” of a human-centric space,

The traditional Software Development Life Cycle (SDLC) treats requirements, design, implementation, verification, and maintenance as separate phases coordinated by humans.

After months working with GitHub Copilot in VS Code and recently switching to Claude Code, the transition turned out to be more involved than expected.

Ponytail is a trending Skill that “makes your AI agent think like the laziest senior dev in the room”.

In my recent blog post on Rethinking ‘Transformation’: From projects to long-term capability, I asserted that how we define transformation shapes how it’s funded and delivered.