Manual handling of mixed inputs
Teams re-key information from emails, PDFs, scans and forms because no single rule set can read them all.
02 / Automation
Intelligent Process Automation
Intelligent process automation takes on the processes that defeat simple bots: the ones with unstructured inputs, judgement calls and frequent exceptions. We combine workflow orchestration, machine learning and language models, and we design the human checkpoints from the start.
At a glance
- Service
- Intelligent Process Automation
- Pillar
- Automation
- Typical starting point
- Process discovery and automation assessment
- Typical first phase
- 3 to 6 weeks for assessment and a first pilot design
- Key technologies
- Intelligent Automation, Machine Learning, Natural Language Processing
Durations depend on scope, data readiness and integration needs.
What is Intelligent Process Automation?
Short answer
Intelligent process automation (IPA) is the combination of workflow automation, robotic process automation and AI techniques such as machine learning, document understanding and language models, used to automate end-to-end processes that involve unstructured data and decisions rather than only fixed rules.
Traditional automation works well when every input looks the same and every rule can be written down. Real processes rarely behave that way. Invoices arrive in dozens of formats, customer emails mix several requests, and approvals depend on context that sits in someone's head.
IPA adds a layer of perception and judgement to the workflow. A model reads the document or message, classifies it, extracts what matters and estimates how confident it is. Rules and policies decide what happens next. Items the system is unsure about go to a person, and the corrections they make become training data for the next version.
The result is not a process without people. It is a process where people spend their time on the cases that need them.
Business problems
What problems does Intelligent Process Automation solve?
These are the situations where organisations most often find this service worthwhile.
Bots that break on exceptions
Existing RPA scripts fail whenever a field moves or a case falls outside the happy path, so staff end up babysitting them.
Slow turnaround on routine decisions
Simple approvals wait in queues because each one still needs someone to read, check and route it.
Inconsistent outcomes
Different people handle the same case differently, which creates rework, complaints and audit findings.
Little visibility into the process
Nobody can say reliably how long cases take, where they stall or why they come back.
Scaling means hiring
Volume growth is met by adding headcount because the process cannot absorb more work.
Use cases
Where can Intelligent Process Automation be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Finance operations | Invoice intake, three-way matching, expense checks and vendor onboarding with exception queues. |
| Customer service | Reading, classifying and routing inbound emails, then drafting responses for agent review. |
| Insurance and lending | Claims or application triage, document verification and completeness checks. |
| HR and shared services | Employee requests, onboarding paperwork and policy queries across multiple systems. |
| Procurement | Purchase request validation, supplier document checks and contract data extraction. |
| Healthcare administration | Referral intake, prior authorisation paperwork and coding support with clinical review. |
Who should consider it
- Operations leaders running high-volume, document-heavy processes
- Shared service centres with existing RPA that is costly to maintain
- Organisations where turnaround time or error rates affect customers
- Teams that need automation with an audit trail for compliance
When it may not be the right fit
If a process is low volume, changes every month or is not yet documented, it is usually better to stabilise and simplify it first. We will say so during assessment.
How AARHIT approaches it
How we deliver Intelligent Process Automation
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Process discovery
We map the process as it really runs, including exceptions, using interviews, system logs and sample cases.
Output: a process map, volume and effort baseline, and a list of pain points ranked by impact.
Stage 2 of 6
Opportunity assessment
Each step is scored for automation potential, data availability, risk and value, so the first release targets the best candidates.
Output: a prioritised automation backlog and a recommended pilot scope.
Stage 3 of 6
Solution design
We design the target workflow: which steps use rules, which use models, where confidence thresholds sit and how exceptions are handled.
Output: target process, architecture, human review design and success criteria.
Stage 4 of 6
Pilot build
A working pilot runs on real or representative cases, often in shadow mode alongside the current process so results can be compared safely.
Output: a measured pilot with accuracy, throughput and exception rates.
Stage 5 of 6
Production rollout
The pilot is hardened, integrated with source and target systems, secured and released in stages with monitoring in place.
Output: a production workflow with runbooks, dashboards and access controls.
Stage 6 of 6
Continuous improvement
Corrections from reviewers feed model retraining, and thresholds are adjusted as confidence improves.
Output: regular performance reviews and a roadmap for extending automation.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Orchestration | Workflow engines such as Camunda or Temporal, or low-code platforms like Power Automate |
| Task automation | RPA tools such as UiPath or Automation Anywhere where no API exists |
| Document and language AI | OCR, layout-aware document models, classifiers and large language models |
| Decisioning | Business rules engines and policy tables maintained by the business |
| Integration | REST APIs, message queues and connectors to ERP, CRM and document systems |
| Monitoring | Process mining, dashboards and audit logs for every automated decision |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Shorter cycle times for routine cases
- Lower manual effort per transaction, with staff focused on exceptions
- More consistent decisions and fewer errors
- Full traceability of what was automated and why
- Capacity to absorb volume growth without matching headcount growth
How long does implementation generally take?
Typical timeline
An assessment and pilot design usually takes 3 to 6 weeks. A focused pilot on one process often runs 6 to 12 weeks. Production rollout across several processes is typically planned in releases over 3 to 9 months, depending on integrations and approvals.
What determines complexity?
- Number and variety of input formats
- How many systems the process touches
- Availability of historical cases for training and testing
- Regulatory and audit requirements
- How often the process and its rules change
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Fix the process before automating it
Automating a broken process makes the same mistakes faster. Simple redesign often removes steps before any technology is added.
Design the exception path first
The share of cases that need a person defines staffing, service levels and user experience. It deserves as much design effort as the automated path.
Plan for model drift
Document formats, customer language and business rules change. Accuracy has to be measured continuously, not only at launch.
Bring operations staff in early
The people who handle cases today know the exceptions. Their involvement improves the design and makes adoption far easier.
| Risk to consider | How we manage it |
|---|---|
| Over-automation of judgement calls | Confidence thresholds, sampling of automated decisions and clear rules for what always goes to a person. |
| Poor data quality in source systems | Data profiling during assessment and validation rules at intake. |
| Fragile screen-level automation | Prefer APIs; where RPA is unavoidable, isolate it and monitor failures closely. |
| Unclear ownership after launch | A named process owner, runbooks and a support model agreed before go-live. |
| Sensitive data exposure | Data minimisation, masking, role-based access and hosting choices that meet your policies. |
FAQ
Questions about Intelligent Process Automation
What is the difference between RPA and intelligent process automation?
RPA follows fixed rules to operate software the way a person would, and works best on structured, repetitive tasks. Intelligent process automation adds AI so the workflow can read unstructured documents, classify requests and make bounded decisions, with people handling the uncertain cases.
Do we need to replace our existing RPA bots?
Usually not. Many IPA programmes keep working bots and add AI steps around them, for example a document model that feeds clean data to an existing bot. We replace bots only when maintaining them costs more than rebuilding.
How do you decide what is automated and what goes to a person?
Each automated decision carries a confidence score. We set thresholds with your process owners using test results, start conservatively, and relax them only when sampled accuracy supports it.
How much historical data do we need?
It depends on the task. Document classification can start with a few hundred labelled examples per type, and modern language models can work with fewer. The assessment tells you what exists and whether labelling effort is needed.
Can intelligent process automation work with legacy systems?
Yes. Where systems have APIs we use them. Where they do not, we use RPA or database integration carefully and isolate those parts so changes in the legacy system are easy to detect and fix.
How is performance measured after launch?
We track straight-through processing rate, exception rate, accuracy on sampled cases, cycle time and cost per case against the baseline measured during discovery.
Next step
Considering Intelligent Process Automation for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.