AI SaaS and RPA solve different layers of automation. RPA excels at deterministic UI/API task execution (“clicks and keystrokes”), while AI SaaS adds cognition: understanding unstructured inputs, making policy‑safe decisions, and emitting typed, auditable actions. The modern pattern combines them: AI handles classification, extraction, reasoning, and approvals; RPA executes repeatable steps where APIs are missing. Orchestrate both under clear SLOs, policy gates, and rollback, and measure cost per successful action—not scripts run.
Where each fits best
- RPA strengths
- UI automation for legacy systems without APIs; repetitive, stable workflows; high‑volume data entry; screen‑scrape moves; deterministic rules.
- AI SaaS strengths
- Unstructured inputs and reasoning (emails, PDFs, chats, images); retrieval‑grounded answers with citations; policy‑aware decisions; typed tool‑calls to modern APIs; summarization and next‑best‑action.
High‑value joint use cases
- Invoice/AP processing
- AI: classify, extract line items, detect duplicates, apply policy; produce a validated JSON voucher and exception reasons.
- RPA: post to legacy ERP screens where APIs don’t exist; attach documents; reconcile IDs.
- Claims and forms
- AI: read forms, photos, evidence; apply rules; draft decisions and letters with citations.
- RPA: update core admin systems via UI, trigger checks, archive artifacts.
- Customer support changes
- AI: resolve L1 with policy‑safe actions (refund within caps, plan edits) and approvals.
- RPA: push changes into older billing/CRM UIs that lack endpoints.
- HR onboarding/offboarding
- AI: generate checklists, validate entitlements, draft comms; decide exceptions.
- RPA: provision/deprovision in legacy apps with fragile UI flows.
- Data remediation and reconciliations
- AI: detect anomalies and propose compensations with rationale.
- RPA: execute bulk fixes across multiple old systems reliably overnight.
Reference architecture (AI × RPA, governed and safe)
- Grounding and reasoning (AI plane)
- Permissioned retrieval over docs/policies/logs; citations and timestamps; refusal on low evidence.
- Small‑first routing for classify/extract; escalate to synthesis for briefs; cache embeddings/snippets.
- Typed actions and orchestration
- Tool registry with JSON Schemas mapped to APIs; policy‑as‑code (eligibility, limits, maker‑checker, change windows), idempotency, simulation, and rollback.
- RPA adapter tool for “ui_task.run(payload)”: passes a compact, schema‑validated spec (selectors, steps, data) to bots.
- RPA execution plane
- Bots run scripts with retries/backoff, screenshot evidence, and checkpoints; report granular results and artifacts; respect maintenance windows.
- Decision logs and audit
- Immutable logs linking input → evidence → decision → API/RPA actions → outcomes; store artifacts (citations, screenshots, diffs).
- Observability and SLOs
- Dashboards for p95/p99 per surface, groundedness/citation coverage, JSON/action validity, RPA success rate, retries, reversal/rollback rate, and cost per successful action.
Design patterns that work
- API‑first, RPA‑fallback
- Prefer typed API tool‑calls; route to RPA only when APIs are missing or insufficient; keep parity tests to migrate off RPA as APIs arrive.
- Suggest → simulate → apply → undo
- Always preview impact and rollback; require approvals for high‑risk moves; keep instant undo where feasible (compensations if not).
- Contracts over keystrokes
- Describe RPA jobs with declarative specs (targets, fields, invariants, evidence capture), not brittle free‑text steps.
- Drift defense
- Contract tests for APIs; visual/selector monitors for UI drift; auto‑open PRs to update mappings or bot selectors; canary runs before scale.
- Progressive autonomy
- Start with suggestions and one‑click applies; graduate to unattended for low‑risk, reversible flows with low reversal history.
Governance, safety, and privacy
- Policy‑as‑code gates (refund/discount caps, SoD/maker‑checker, change windows, quiet hours).
- Tenant isolation, SSO/RBAC/ABAC, PII/PHI redaction; residency/VPC options; “no training on customer data.”
- Prompt‑injection and egress guards for AI; least‑privilege credentials and secrets rotation for bots.
Metrics that matter (treat like SLOs)
- Quality/trust
- Groundedness/citation coverage, refusal correctness, JSON/action validity, RPA step pass rate, reversal/rollback rate.
- Reliability and performance
- p95/p99 latency (AI hints, decisions, end‑to‑end with RPA), retry rates, DLQ depth, drift incidents.
- Economics
- Cost per successful action; router mix (tiny/small vs large models), cache hit; RPA minutes per action, bot utilization; API vs RPA cost share.
- Outcomes
- Tickets resolved, invoices matched, claims approved, onboarding completed; exception rate and time‑to‑resolution.
90‑day rollout plan
- Weeks 1–2: Map and fence
- Pick two workflows requiring both reasoning and legacy execution; define policies, approvals, rollback; catalog APIs vs RPA gaps.
- Weeks 3–4: Grounded drafts + contracts
- Ship AI classification/extraction and cited briefs; define JSON Schemas for actions and an RPA job spec; instrument groundedness and JSON validity.
- Weeks 5–6: Safe actions + RPA bridge
- Implement API actions and RPA adapter; add simulation and evidence capture; track completion and reversal rate.
- Weeks 7–8: Routing + cost control
- Add small‑first routing, caches, and per‑workflow budgets; separate interactive vs batch lanes; publish CPSA and router/bot utilization dashboards.
- Weeks 9–12: Hardening + drift defense
- Contract tests, bot selector monitors, canary runs; autonomy sliders; audit exports; weekly “what changed” with outcomes and CPSA trend.
Common pitfalls (and how to avoid them)
- “Chat over bots”
- Bind AI to typed actions and RPA job specs; measure successful actions and reversals, not messages.
- Free‑text to production UIs
- Never let models emit unconstrained RPA instructions; enforce schemas and simulation; fail closed on unknowns.
- Over‑reliance on RPA
- Prefer APIs; sunset bots as endpoints appear; keep parity tests and migration plans.
- No rollback for UI changes
- Design compensations for non‑idempotent UI steps; keep change windows and maker‑checker for risky flows.
- Cost and latency creep
- Route small‑first; cache aggressively; batch RPA where possible; cap variants; prioritize interactive lanes.
Bottom line: AI SaaS and RPA are complementary. Let AI understand, decide, and govern; let RPA execute where APIs don’t exist—under typed contracts, approvals, and rollback. Operate to SLOs and budgets, track cost per successful action, and progressively replace brittle UI bots with API calls as the stack modernizes.