THIS WEEK: Is your infrastructure ready for AI implementation? Our pre-deployment checklist

TECH

Over 85% of AI initiatives stall before reaching their full potential. In our experience, it's the infrastructure, data quality, and organisational readiness that determine whether a pilot becomes a production system or an expensive proof of concept. Here's the checklist we run through with clients before any AI deployment.

Data readiness

AI is only as good as the data it runs on. Before implementation, you need to understand where your data lives, how clean it is, and whether it's accessible in the format the AI system needs. This means auditing data sources for completeness, identifying where data is siloed across systems, and establishing governance for how data is collected and maintained going forward.

A question we always ask: can you point to the data that would train or inform this AI system? If the answer is 'it's probably in the CRM and maybe in some spreadsheets', the implementation will struggle. Structured, accessible, well-labelled data is the foundation.

Infrastructure and compute

Modern AI workloads have specific infrastructure requirements. Cloud-native, elastic compute handles variable demand without over-provisioning for peak load. API architecture needs to be sound if the AI system will integrate with existing tools and workflows. Companies that redesign their workflows and modernise infrastructure are twice as likely to report EBIT gains from AI adoption, according to McKinsey.

We evaluate whether a client's current infrastructure can support the proposed AI workload, or whether infrastructure modernisation needs to happen first. Getting the sequence right saves significant rework costs. Building an AI system on legacy infrastructure often creates a ceiling that limits what the system can ultimately do.

Security and compliance

AI systems create new security surface areas. If the system accesses sensitive data, processes personal information, or makes decisions that affect individuals, you need to establish controls before deployment. This includes access controls and authentication, data encryption in transit and at rest, audit logging for AI decisions, and a process for handling requests to explain or challenge AI-generated outputs.

For organisations subject to GDPR, whether AI-generated decisions constitute automated processing with legal effects is a particularly important question. If they do, specific rights apply and you need infrastructure to support them before go-live.

People and process readiness

Successful AI deployment is as much a people and process challenge as a technical one. Teams need to understand what the AI system does, why its outputs can be trusted (and when they can't), and how their workflows change as a result. More than half of employees report they haven't been adequately trained to work with AI, which helps explain why technically sound systems often see low adoption rates in practice.

We include change management planning in every AI project scoping conversation. This means identifying who the system affects, designing clear handoff points between AI decisions and human oversight, and creating feedback mechanisms so the system can be corrected when it's wrong.

Testing and evaluation before you go live

AI systems need to be tested against real-world scenarios, not just synthetic benchmarks. This means defining what good looks like, testing edge cases and adversarial inputs, establishing baseline performance metrics, and creating a monitoring plan that catches degradation over time. A system that performs well in testing and drifts in production without anyone noticing has failed its governance requirements.

We also recommend defining rollback procedures before go-live. If the system produces unexpected outputs at scale, you need a clear plan for how to revert to the previous process while issues are investigated.

What this means

Successful AI deployment is a systems challenge, not just a technology selection. The organisations that get it right treat infrastructure, data, security, and people readiness with the same rigour they apply to the AI system itself. Getting these foundations right before you build is significantly cheaper than retrofitting them after the fact.

If you're planning an AI implementation and want to run through this checklist in the context of your specific situation, we're happy to help.