AI & Automation
Where should a company start with AI?
With a process that is high-volume, rule-bound and low-consequence if wrong. That produces a measurable return quickly and builds the internal confidence needed for harder use cases. Starting with a flagship customer-facing system is the most common way to stall, because the risk arrives before the experience does.
Before tooling, three decisions gate everything downstream: who reviews AI-assisted output before it ships, what claims require a source, and what the organisation is willing to be accountable for. Software cannot supply a decision nobody has made.
A readiness assessment is usually cheaper than a failed pilot. It establishes data quality, process maturity and risk appetite, then ranks use cases by return and feasibility — including the ones to avoid.
Commercial · Updated 2026-08-10
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