What is intelligent automation? Definition, components and examples
For operations and transformation leaders: a plain definition of intelligent automation (also called intelligent process automation), how it differs from RPA, real examples, and how to start.
Key takeaways
- Intelligent automation (IA), also called intelligent process automation (IPA), combines artificial intelligence with automation tools such as RPA and workflow software to automate whole business processes, not single tasks.
- RPA follows fixed rules on structured data. Intelligent automation adds the ability to read documents, understand language and make routine decisions, so it can handle work RPA can't.
- Its building blocks are AI and machine learning, document AI, language models, workflow, RPA and integrations, with people reviewing the exceptions.
- Common examples include invoice processing, loan application intake, insurance submissions and claims, customer onboarding and support email triage.
- Start with one document-heavy, rule-based process, measure a baseline, and track the straight-through processing rate as the main measure of success.
On this page
Intelligent automation (IA), also called intelligent process automation (IPA), combines artificial intelligence with automation tools such as robotic process automation (RPA) and workflow software to run whole business processes, not just single tasks. AI handles the parts that need understanding, like reading a document or classifying a request; automation carries out the steps; people handle the exceptions.
This guide covers the components, how intelligent automation differs from RPA, examples and how to get started.
What is intelligent automation?#
IBM defines it as using AI, business process management and RPA "to streamline and scale decision-making across organizations." Many processes mix two kinds of work: an invoice or loan packet arrives as a PDF, someone reads it (judgment), then enters and routes it (rules). Traditional automation covers only the rules; intelligent automation covers the whole chain.
- Invoices
- Loan packets
- Emails
- 01AI reads and classifies
- 02Rules decide
- 03RPA or API enters the data
- 04A person handles exceptions
The components of intelligent automation#
AI and machine learning
Predicts and classifies from data, such as which support requests are urgent.Document AI
Classifies documents and extracts checked data from invoices, bank statements or ACORD forms. Also called intelligent document processing.Language models
Read and write text, such as pulling the request out of an email or drafting a reply.Workflow and BPM
Runs the steps, approvals and hand-offs, such as sending an invoice to the right approver.RPA
Software bots that operate applications with no API, such as keying data into a legacy claims system.Integrations and APIs
Move data between systems, such as posting an approved invoice to the ERP.Human in the loop
People review exceptions and low-confidence results, such as a flagged field on a loan application.
Intelligent automation vs RPA#
| RPA | Intelligent automation | |
|---|---|---|
| Inputs | Structured data on screens and in files | Structured and unstructured: PDFs, scans, emails, images |
| Decisions | Fixed rules only | Rules plus predictions and classification |
| Change | Breaks when screens or formats change | Adapts to new layouts and formats more easily |
| Scope | Individual tasks | End-to-end processes |
| Best for | Repetitive steps in systems without APIs | Processes that start with documents or need routine judgment |
RPA isn't obsolete: in intelligent automation it's one tool among several, used where a system has no API. For how bots and document AI split the work, see RPA vs AI for document data extraction.
Intelligent process automation examples#
Accounts payable
Read invoices, match them to POs and receipts, route them for approval and post to the ERP. See accounts payable automation.Lending
Classify an application packet, extract income from bank statements, pay stubs and tax returns, cross-check them and prepare the file for an underwriter. See IDP for lending.Insurance
Triage broker submissions, extract ACORD forms and loss runs, and route each to the right underwriter. See insurance automation.Customer onboarding and KYC
Read ID documents and proof of address, check them against the application and open the account.Customer service
Classify incoming emails, pull out the request, and route it or answer it.
Benefits of intelligent automation#
Manual work or RPA alone
- A person reads each PDF and email before anything happens
- Work waits in inboxes between steps
- Values are keyed by hand, and errors surface later
- More volume means more people
Intelligent automation
- Documents and emails are read and classified as they arrive
- Each step starts as soon as the last one finishes
- Data is captured once, checked and passed on, and every step is logged
- People handle the exceptions, so volume can grow faster than the team
Adoption is broad but shallow: McKinsey's 2026 State of AI survey found 89% of organizations regularly use AI in at least one business function, but only 44% say AI is scaling across their enterprise. Automating one whole process, rather than piloting a tool, is a practical way to close that gap.
How to get started#
- Pick one processHigh volume, document-heavy and rule-based, with visible pain today.
- Measure a baselineCycle time, cost per item and error rate, before anything changes.
- Automate the document step firstIt's usually the bottleneck. Docsumo's document AI reaches 99% field-level accuracy on 250+ document types and 95%+ straight-through processing.
- Connect the rest of the workflowSend data to the system of record through API and webhooks. In Docsumo, checks of your own can be set up as workflow steps: an AI step or your own Python code.
- Design the exception pathDecide who reviews what, with the source document in view.
- Track the straight-through rateThe share of items no one touches. Expand once it's stable. See straight-through processing.
The bottom line#
Intelligent automation is automation that can read, understand and decide, not just repeat. It combines AI, document processing, workflow tools and RPA to automate whole processes, with people handling the exceptions. Start with one document-heavy process, measure it, and grow from there.
Book a demo with a few of your own documents, or start a free trial.
Frequently asked questions#
What is intelligent automation in simple terms?
It's software that can both do tasks and make routine decisions. AI reads documents, understands text and predicts outcomes; automation tools then carry out the steps; people handle the cases the system isn't sure about.
What is the difference between intelligent automation and RPA?
RPA uses bots to repeat fixed steps on structured data, such as copying values between screens. Intelligent automation adds AI, so the process can handle unstructured inputs like PDFs and emails and make rule-based decisions.
Is intelligent process automation the same as intelligent automation?
The terms are used interchangeably. Both describe combining AI with process automation to automate end-to-end workflows.
What is the difference between intelligent automation and hyperautomation?
Hyperautomation is the broader idea: automating as many business and IT processes as possible with a coordinated set of tools. Intelligent automation is one part of it, the combination of AI, workflow and RPA that automates a single process from start to finish.
What is an intelligent automation platform?
Software that brings the parts of intelligent automation together (AI models, document processing, workflow, connectors or bots, and a review queue) so a whole process can be built, run and monitored in one place. Platforms differ mainly in where they started: RPA, workflow or document AI.
What are examples of intelligent automation?
Reading and approving invoices, verifying income from bank statements and pay stubs, triaging insurance submissions and claims, onboarding customers with ID checks, and routing support emails. See accounts payable automation for one example.
Sources
- IBM: What is intelligent automation?
- McKinsey: The state of AI in 2026: On the road to ROI (August 2026)
First published . Last updated .