AILabs designs and operates AI strategy, integration and automation, systems and software builds, and governance and ethics programmes. Where applied AI capability is developed against specific commercial questions rather than acquired through platform enthusiasm.
AILabs is the rebuild of what previously sat as DevLabs. The remit has broadened materially: AI strategy work covering roadmap and use-case selection; integration and automation work covering workflow orchestration through Make.com and equivalent platforms; systems and software builds delivered through the CLG development capability including Fabio as build lead; and governance and ethics work aligned to ISO 42001 and the EU AI Act. Every engagement is anchored against a specific commercial or operational question rather than delivered as generic AI capability development.
AI roadmap design, use-case selection, capability assessment, and strategic prioritisation. Where the organisation decides what to actually build against what to leave to platform maturity.
Explore AI strategy →Workflow automation through Make.com and equivalent platforms. AI tool orchestration, agent integration, and operational automation delivered against specific process questions.
Explore integration →Custom AI systems and software builds. Delivered through the CLG development capability with Fabio leading build. Where a bespoke system produces commercial value that platform tools do not.
Explore systems →AI governance frameworks aligned to ISO 42001 and EU AI Act. Ethics-by-design, risk assessment, and the operational governance discipline enterprise AI deployment increasingly requires.
Explore governance →The AI hype cycle has produced a generation of organisational AI investment that has not delivered commensurate commercial return. The recurring pattern is deployment against enthusiasm rather than against specific questions: platforms adopted because they were prominent rather than because they solved specific operational problems, capability built because AI was strategic rather than because a specific outcome required it, systems commissioned because the technology was interesting rather than because the commercial case survived scrutiny. AILabs is designed against a different discipline.
Every engagement starts with a specific question the client is trying to answer, a specific outcome they are trying to produce, or a specific operational discipline they are trying to embed. The AI capability is designed against that specificity. Delivery integrates with DecisionLabs (for the strategic questions), StandardsLabs (for the ISO 42001 governance frame), and the specific operational lab whose work the AI capability is designed to support (SearchLabs, SalesLabs, LifecycleLabs, ServiceLabs, or others).
The EU AI Act (Regulation 2024/1689) and ISO 42001 underpin the governance work. Every enterprise-scale AI deployment now needs a defensible governance frame; AILabs supplies it.
For organisations of any material scale, applied AI now requires three parallel disciplines: strategic prioritisation (what to build, what to buy, what to leave), operational delivery (how to build it, integrate it, and operate it), and governance discipline (how to deploy it responsibly and how to evidence that responsibility). AILabs is designed to deliver all three as one integrated capability rather than three fragmented workstreams.
Delivery integrates with the specific lab whose operational function the AI capability supports. Where the client is running the full Lifecycle Transformation Programme, AI capability is delivered as part of that programme. Where the client is running SalesLabs Integrated Programmes, AI orchestration supports the sequencer stack and outbound automation.
Unlike the audience-specific sub-services in other labs, AILabs sub-services are largely audience-neutral. The strategic question (what to build), the delivery question (how to build it), and the governance question (how to deploy responsibly) apply to organisations of any commercial orientation. Delivery is tuned to the specific operational context of the client, but the discipline is the same.
Where consumer regulatory context adds specific requirements (Consumer Duty applied to AI-driven consumer decisions, vulnerable customer identification through AI systems, sector-specific consumer AI regulation), coordination with StandardsLabs Consumer Regulatory Readiness delivers the specific framework alignment.
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