How is this different from RPA?

RPA replays deterministic UI steps. AI automation reads unstructured content, reasons about it and handles variation — the two are often combined.

Where should we start?

A single high-volume, document-heavy workflow with a measurable baseline and a clean system-of-record integration.

Use-case inventory, risk tier, evaluation gates and production monitoring — the same controls as any other enterprise AI workload.

First-party figures from Pronix research. Each links to the report or playbook that publishes it.

40–60%: Moving from 2–5% sampled human QA to 100% automated scoring cuts QA delivery cost by 40% to 60% — margin that flows straight to the delivery centre.Source: BPO AI Automation Benchmarks 2026 →
25–40%: FNOL automation, imagery-based damage assessment and severity triage cut cycle time 25% to 40% on the impacted claim segments.Source: Insurance AI Benchmarks 2026 →
31%: Retrieval, integration and evaluation infrastructure is now the single largest line in the enterprise AI budget at roughly 31% of spend.Source: State of Agentic AI in the Enterprise 2026 →

NIST — AI Risk Management Framework (AI RMF 1.0) (2023)The govern / map / measure / manage structure Pronix uses to organise AI controls.

Counting automated tasks flatters the programme; the share of transactions completed with no human touch is what changes unit cost.

Every workflow defines what escalates, to whom, with what context — that design is what keeps quality stable as volume grows.

Automation writes into the existing ERP, policy-admin, EHR or CRM rather than creating a parallel record.