AIHA Office Agentic AI & Hyperautomation Implementation Office · demo

Delivery

Complexity recommendations & cost estimator

Answer six questions to get the recommended approach, autonomy, deployment model and AI engine, with indicative platform, implementation and run costs, and how an experienced, dedicated professional can reduce cost, rework and operational risk.

Questions

  • Process variability
  • Data sensitivity
  • Integrations
  • Decision impact
  • Volume
  • Regulation

Savings

  • Up to 45%, based on PIR2-IT implementation experience; indicative, not a guarantee.
  • Rework avoided (requirements, risk tier and guardrails right first time) (15–20%)
  • Reuse of patterns, guardrails, evaluation sets and templates (8–12%)
  • Right-sizing (no air-gapped or GPU where not needed) (5–8%)
  • Security and compliance first time (no late redesign) (4–6%)
  • Procurement of models, GPU and licences (2–4%)

Risk benefits

  • Operational risk: Fewer incidents and processing errors: guardrails, validators and HITL designed in, not added after the first failure.
  • Reputational risk (image): No public AI failures, wrong customer answers or biased decisions; tone filters and four-eyes on customer-facing output.
  • Stability: Pinned versions, regression gates, tested rollback and capacity planning: fewer outages and emergency changes.
  • Regulatory exposure: EU AI Act, GDPR and DORA evidence built along the way; fewer findings and remediation programmes.
  • Security: Prompt and data poisoning, raw-data corruption and exfiltration addressed in design (see AI security threats).
  • Time-to-value: Fewer failed pilots and stalled scale-ups; benefits tracked in the rBMS from G1.

Public demo with fictional data (Example Group). © 2025–2026 Russo-Got Marius Mihail & PIR2-IT. All rights reserved.

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