Enterprise AI Strategy Drives £30M ARR, Cuts Deployment Lead Time by 50%

Context

A global enterprise leader in risk intelligence and organizational resilience solutions serving Fortune 500 companies faced a turning point. Operating in the Enterprise Software / Risk Management / AI & Data space, the firm had pockets of advanced AI experimentation but no unified direction. The mandate was clear: design and execute the company’s first enterprise-wide AI strategy to move from ad-hoc, siloed AI experiments to a fully governed, secure, and revenue-generating AI program with a target of £30M in new Annual Recurring Revenue (ARR).

Challenges

Without a central AI strategy or governance, nine different business units used a mix of unvetted, high-risk AI tools, creating significant compliance exposure under frameworks such as the EU AI Act and NIST. High-potential ideas were trapped in silos with no clear, repeatable, cross-functional process to advance promising concepts from ideation to funded and technically vetted pilots. The innovation pipeline was slow and lacked a secure, compliant development environment, making deployment lead-time a major blocker to commercialization and causing wasted R&D and missed market opportunities. There was no consistent path to measure or realize ROI from internal AI experimentation.

Implementation

Our Fractional Head of AI established and chaired an enterprise-wide AI Governance Committee to create a single forum for policy, risk assessment, and prioritization across all business units. As part of governance, the team designed a human-centred guardrails framework and a quantitative risk-scoring model to evaluate AI initiatives against regulatory, ethical, and operational criteria. This framework and scoring model were rolled out across the nine business units, enabling transparent approval flows and a 40% reduction in unvetted tool usage.

To address security and deployment bottlenecks, our Fractional Head of AI architected and led the build of a secure FedRAMP/IL5 “safe AI enclave” on AWS and Azure. Paired with a new standardized data classification program, the enclave provided an isolated, compliant environment for model training, evaluation, and secure integration with enterprise systems. This infrastructure removed technical and compliance friction that had previously delayed pilots and productization.

To convert ideas into customer-facing products, our Fractional Head of AI founded and launched the Innovation Discovery Group—an internal “innovation engine” that ran a monthly design sprint framework. These sprints brought product managers, engineers, data scientists, and business unit leaders together to rapidly identify, prototype, and validate the highest-value AI opportunities. The Innovation Discovery Group introduced a repeatable gate process to move candidates from prototype to funded pilot to commercial roadmap, ensuring technical vetting and business alignment at each stage.

Results

The combined governance, secure infrastructure, and innovation operating model enabled rapid, governed commercialization. The Executive Leadership Team (ELT) endorsed the project’s three-year AI strategy and product roadmap, which is projected to deliver £30M in new ARR. The human-centred guardrails and risk-scoring model reduced the use of unvetted AI tools by 40%, substantially lowering compliance exposure. The secure FedRAMP/IL5 safe AI enclave and standardized data classification program cut deployment lead-time for new AI products by 50%, accelerating time-to-market while maintaining regulatory and security controls.

The Innovation Discovery Group created a scalable, enterprise-wide pipeline for AI innovation, fast-tracking multiple high-value prototypes from concept to the commercial roadmap and identifying new subscription revenue streams. Together, these initiatives transformed dispersed experiments into a repeatable, cross-functional machine for secure, compliant, and revenue-oriented AI product development—positioning the company to capture significant market opportunity while managing risk.