AI, Data & Automation

An AI readiness assessment is a structured review of whether an organization can actually deliver and run AI in production, rather than whether it wants to. It normally covers four things: the state of the data (where it lives, its quality, lineage, and whether it can be accessed at the speed a model needs); the platform and infrastructure available to train, host, and monitor models; governance, including security, privacy, model risk, and the approvals a use case would have to clear; and the operating model, meaning who owns a model once it is live and how it gets retrained. The output is a prioritized set of use cases with the specific blockers named against each, so the ones that can move now are separated from the ones that need foundations first.

RPA automates a task; intelligent automation automates a decision. Robotic process automation follows deterministic rules against structured inputs, it will move data between two systems reliably, and it breaks the moment the input changes shape. Intelligent automation combines that execution layer with the ability to handle unstructured input and judgement: extracting fields from a document, classifying an exception, deciding which of several paths a case should take. The practical distinction for an infrastructure operator is scope. RPA is a good fit for a stable back-office process. Where the work involves reading engineering documentation, validating circuit or billing records against messy source data, or triaging alarms, rules alone will not hold, and the process needs a model in the loop.

Most AI programs stall on data rather than on models. The minimum is reliable ingestion from the operational systems that matter, a consolidated store the business agrees is the source of truth, documented lineage so an output can be traced back to its inputs, and quality remediation on the fields the use case actually depends on. In infrastructure, that last point is usually the hard one: asset registers, circuit inventories, and field records tend to be incomplete, duplicated across systems, and maintained by hand, and no model will compensate for that. The sequence that works is to fix the data behind one high-value use case and prove it, rather than attempting an full cleanup before anything ships.

FinOps, or financial operations, is the practice of managing cloud spend as an engineering discipline rather than a finance one. The goal should be to make costs visible to the teams who generate them, attributing them to services and owners, and treating optimization as continuous work rather than an annual review. It usually becomes urgent at one of two moments: after a migration, when the bill turns out to be materially higher than the business case assumed, or when AI and GPU workloads arrive and cost per workload becomes volatile enough to matter month to month. The work itself involves right-sizing, commitment coverage, storage tiering, killing idle resources, tagging discipline, and depends far more on engineers changing behaviour than on a dashboard.

Most start with a readiness assessment or a single scoped use case rather than a platform program. The assessment route suits an organization that has several candidate use cases and no agreed order; the single use case route suits one that already knows its problem and wants proof before committing further. Either way, the first engagement is deliberately small and time-boxed, and it is scoped to produce something operable rather than a recommendation document. Where the work then scales, it typically does so along one of two lines, either deeper into the same domain or wider across similar processes. That's the point at which the delivery model is usually revisited.