Pillar · Intelligent Automation
Intelligent Automation, RPA & Agentic Workflows
The operating-model shift from RPA 1.0 to agentic AI — combining rules-based automation, document understanding, agentic decisioning and BPM orchestration into end-to-end workflows that cover 85–95% of process. Sovereign by default. 6–10 weeks contract-to-production.
RPAIDPAgentic AIBPMSovereignHybrid
Definition
Intelligent Automation, defined.
Intelligent automation is the practice of automating end-to-end business processes by combining multiple technologies: RPA (rules-based UI automation), Intelligent Document Processing (extracting structured data from unstructured documents), AI / agentic workflows (handling judgment-based decisions), workflow orchestration (BPM substrate that coordinates the components), and human-in-the-loop checkpoints (for the exceptions). The shift from pure RPA to intelligent automation is what enables coverage of judgment-based work, not just rules-based work.
The four generations
From RPA 1.0 to agentic — the operating-model shift
Each generation handles a broader share of end-to-end process. The shift isn't about replacing the previous generation; it's about adding capability at the judgment steps RPA couldn't reach.
RPA 1.0 (2015-2020)
Rules-based UI automation. Bots clicking on screens. UiPath, Automation Anywhere, Blue Prism. Worked well for highly structured, repetitive, rules-based tasks. Broke the moment any screen layout changed or any judgment was required.
Coverage
20-30% of process
Rules-bounded, brittle to change
Intelligent Automation (2020-2024)
RPA plus document understanding (IDP), ML-based decisioning, OCR. Broader process coverage. Still rule-bounded at the decision points. Vendors added 'AI' marketing without changing the underlying paradigm.
Coverage
50-65% of process
AI-assisted but rule-bounded
Hyperautomation (2022-2025)
Gartner-coined umbrella term for orchestrated end-to-end automation combining RPA, BPM, low-code, AI, process mining. Better narrative; same underlying components stacked together. The orchestration layer is where the actual value sits.
Coverage
70-85% of process
Orchestrated; still rule-bounded at edges
Agentic AI (2024-)
LLM-driven agents that handle judgment-based tasks the way a human would — read the document, decide on the policy, take the action, escalate when unsure. Bounded autonomy with full audit trail. The first paradigm shift since RPA emerged.
Coverage
85-95%+ of process
Judgment-capable; auditable
Production patterns
Six patterns we deploy
RPA for structured rules
Best fit: highly structured screen workflows, predictable layouts, regulatory mandates to use the legacy UI rather than the API. We still ship it where it's the right answer.
Use where appropriate
Intelligent Document Processing (IDP)
Document classification + extraction + validation. Powered by DocuMage. 94% sustained STP across heterogeneous document types in production.
Default for document-heavy workflows
Process Mining + Discovery
Celonis-style process visibility to find the automation opportunities. We integrate process-mining outputs into the automation roadmap rather than running it as a separate exercise.
Use for prioritisation
Agentic workflow automation
LLM-driven agents handling multi-step processes that require judgment. The Prior Auth Accelerator and AML triage are canonical examples. 80%+ end-to-end automation on workflows that RPA couldn't touch.
Default for judgment workflows
Workflow orchestration (BPM)
Temporal, Camunda, or in-platform BPM as the orchestration substrate. Coordinates RPA bots, IDP, agentic workflows, human review and external systems into a single auditable flow.
Always present in production
Hybrid automation
Most production deployments combine all of the above. RPA for the legacy UI, IDP for the documents, agents for the judgment, BPM for the orchestration. The art is in the choice of which pattern at which step.
Most common production architecture
Production use cases
Where we ship intelligent automation
Invoice processing automation
94% STP on supplier invoices · 10,000+ docs/day at customer sites · €28 FTE → 11 FTE
KYC / Onboarding automation
5-day → 4-hour cycle · 42% → 11% drop-off · OnboardX live at 50,000 banking applications/month
Claims processing (insurance)
FNOL intake + triage in minutes · documents classified + extracted automatically · clinical review pre-populated
Prior authorization (US healthcare)
4.2 days → 4 hours · 71% auto-approval rate on clean submissions · clinical reviewer override rate 6.3%
AML alert triage
False-positive rate down 40% · investigator time per alert down 60% · audit-ready decision trail per alert
HR / Employee onboarding automation
Document collection + verification + provisioning in 24h · status updates pushed to candidate + manager · escalation only on exceptions
FAQ
Intelligent automation — the questions buyers ask
What is intelligent automation?
Intelligent automation is the practice of automating end-to-end business processes by combining multiple technologies — RPA (rules-based UI automation), Intelligent Document Processing (extracting structured data from documents), AI / agentic workflows (handling judgment-based decisions), workflow orchestration (BPM substrate), and human-in-the-loop checkpoints (for the exceptions). The shift from pure RPA to intelligent automation is what enables coverage of judgment-based work, not just rules-based work.
How is intelligent automation different from RPA?
RPA handles rules-based work — predictable, repetitive UI clicks. It breaks the moment any screen layout changes or any judgment is required. Intelligent automation extends RPA with AI components (document understanding, ML decisioning, agentic workflows) that handle judgment-based work alongside rule-based work. The progression from RPA 1.0 → Intelligent Automation → Hyperautomation → Agentic AI is the operating-model shift from rules to judgment, with each generation handling a broader share of end-to-end process.
What is hyperautomation?
Hyperautomation is Gartner's umbrella term for orchestrated end-to-end automation combining RPA, BPM, low-code, AI, and process mining. The narrative is broader than RPA; the underlying technology is the same components stacked together with an orchestration layer on top. The orchestration layer is where the actual value sits — coordinating RPA bots, IDP, agentic workflows, human review and external systems into a single auditable flow.
Is RPA dead now that we have agentic AI?
No — RPA is still the right answer for highly structured rule-based workflows where the legacy system has no API, where regulatory mandates require UI-driven interaction, or where the cost of agentic AI isn't justified by the workflow value. The shift isn't 'RPA dies, agents take over.' It's 'use the right pattern for the right step.' Most production deployments combine RPA, IDP, agentic workflows and BPM into hybrid architectures.
What does intelligent automation cost?
Implementation costs depend on the process complexity, the integration substrate, and the chosen pattern mix. Indicative ranges: simple RPA bot €15-40k per process. IDP deployment for document-heavy workflows €180-320k including the DocuMage platform plus the first 3-4 document types. Agentic workflow for a judgment-heavy process €180-280k. Enterprise hyperautomation platform with multiple coordinated processes €450k-2M depending on scope. ROI is typically 3-9 months on well-scoped processes.
Can intelligent automation work in regulated industries?
Yes, and that's the buyer profile we serve. Every accelerator MindMap ships supports on-premise, air-gapped deployment with full audit substrate — model weights and decision logic on customer infrastructure, full decision trail in customer SIEM, compatible with SAMA, RBI, NHS DSPT and the EU AI Act high-risk system requirements. The sovereign-deployment posture is the default architecture, not an add-on option.
How do you choose between RPA and agentic automation for a specific process?
Three questions cut through. (1) Does the process have judgment steps or only rule-based steps? Agents for judgment, RPA for rules. (2) Does the legacy system have an API? If yes, agents calling structured APIs are better than RPA driving the UI. (3) What's the volume? Below 200 cases / month, simpler tools are usually best. Above 5,000 cases / month, agentic workflows pay back faster despite higher token cost because the labour cost saved per case is so much higher.
Model your automation economics
IDP ROI calculator for document-heavy workflows. Voice ROI for contact centres. Free, no signup.