Intelligent Automation
Redesign work across people, systems, robots and AI agents.
Combine process intelligence, workflow, RPA, document AI and decision automation — while keeping people in control of exceptions.
The problems this solves
What usually brings people here
If one of these sounds like your situation, this is the right page.
Our teams spend their days moving data between systems.
High-volume, rules-based work that exists only because two systems do not talk to each other. It is expensive, error-prone and demoralizing.
Invoice, claim or document processing is a manual bottleneck.
Unstructured documents arrive in volume, get keyed by hand, and the backlog grows faster in peak periods exactly when speed matters most.
We automated some processes and the benefit did not last.
Bots were built on top of a broken process, then broke whenever an underlying system changed. Maintenance cost overtook the saving.
We do not know which processes are worth automating.
No objective view of volume, variation, exception rate or cycle time, so candidate selection is driven by whoever asks loudest.
Capabilities
What we actually do
Process and task mining
Establish what the process actually does — volume, variants, exception rate, cycle time — before automating it.
Process redesign
Remove the steps that should not exist. Automating waste makes waste faster.
Robotic process automation
Attended and unattended automation for rules-based work across systems without APIs.
Intelligent document processing
Extraction, classification and validation from unstructured documents, with confidence thresholds and human review.
Workflow orchestration
Long-running processes with approvals, retries, escalation and full execution history.
Conversational automation
Assistants for service and internal support, scoped to what they can genuinely resolve.
Agentic workflows
AI agents with defined tools, explicit approval gates, observability and human oversight.
Decision automation
Business rules externalized so policy can change without a code release.
Automation Center of Excellence
Pipeline, standards, reuse, bot operations and the governance that stops sprawl.
The pipeline
How automation gets past the pilot
The order matters more than the tooling. Automating a process nobody has measured reproduces the existing mess faster.
01
Process
The work as it is actually done, not as documented.
02
Discovery
Volume, variation, exceptions and who handles them.
03gate
Process mining
Evidence for which path is worth automating.
04
Workflow
The path made explicit, with owners and states.
05
RPA
For systems with no API worth integrating against.
06
AI
Document understanding, classification, extraction.
07
Agents
Scoped to a task, with tools and observability.
08gate
Human approval
Proportionate to consequence, never skipped for state changes.
09
Action
Executed and recorded, so it can be audited later.
Comes out the other end: Work removed from people who were only moving data, with the exceptions still reaching someone accountable.
How we approach it
Principles we hold to
Measure the process first
Process mining before automation design. Without volume, variant and exception data, automation candidates are selected on anecdote.
Fix, then automate
If a step exists only because of a system gap, the right answer is often an integration rather than a bot. We say so.
Design for the exception
The exception path determines whether an automation is trusted. It gets designed first, not last.
Outcomes
What you should end up with
- A governed automation portfolio improving speed, accuracy and visibility.
- Exception handling that routes to people with the context they need.
- Automations that survive system change rather than breaking on it.
- A candidate pipeline prioritized on measured effort rather than on advocacy.
Where this usually starts
A short working session on your situation — what you are trying to change, what is in the way, and whether this is the capability that addresses it.
Evidence
What changes, and how you would know
Two different things belong here. One is the structural change this capability makes, which we can describe today. The other is a client outcome, which publishes only when a client has approved it in writing with a timeframe and a baseline.
Automating a process nobody has measured reproduces the existing mess faster. The order below is the part that matters.
- Before · How the work runs
- People re-keying between systems that do not talk
- Exceptions handled by whoever notices them
- No record of how long a case actually takes
- Process knowledge held by three people
- After · How it runs afterwards
- Straight-through handling for the cases that qualify
- Exceptions routed to a named owner with the context attached
- Every step timed, so the next bottleneck is visible
- The process written down in the system that runs it
What moved: Work stops being invisible. The gain is not only the removed keystrokes — it is that the next constraint can be found instead of guessed at.
A structural model of the change, not a record of an engagement. No client, figure or timeframe is implied — those publish only against an approved claim.
Client outcomes for Intelligent Automation
Proof pending approvalA published outcome here will carry the client's written approval, the timeframe it was measured over and the baseline it was measured against. Until all three exist, this section says so rather than showing a figure you could not check.
Where this capability leads
Every connection below is recorded in the content itself, and each one says why it is here.
Capabilities
Technologies
- UiPathIntelligent Automation works with this technology.
- Automation AnywhereIntelligent Automation works with this technology.
- Microsoft Power PlatformIntelligent Automation works with this technology.
- Agentic AIIntelligent Automation works with this technology.
- Generative AIIntelligent Automation works with this technology.
- HyperautomationIntelligent Automation works with this technology.
Sectors
- InsuranceIntelligent Automation is applied in this sector.
- Banking & Financial ServicesIntelligent Automation is applied in this sector.
- Healthcare & Life SciencesIntelligent Automation is applied in this sector.
- Logistics & TransportationIntelligent Automation is applied in this sector.
- TelecommunicationsIntelligent Automation is applied in this sector.
Explore where this capability fits
Answer four questions about your goal, sector, environment and constraint, and see how this capability sits alongside the others in a path — with the reasoning shown.
Start from a goal
Questions
Frequently asked
- What is the difference between RPA and intelligent automation?
- RPA automates rules-based interactions with existing system interfaces. Intelligent automation combines that with process mining to choose the right targets, document AI to handle unstructured input, decision engines for policy, workflow orchestration for long-running processes, and AI agents for tasks requiring judgement. RPA is one component rather than the category.
- Why do automation programs stall after the first few processes?
- Usually because bots were built on top of processes that were never redesigned, with no shared components and no operational ownership. Each automation is bespoke, each breaks independently when an underlying system changes, and maintenance eventually consumes the capacity that would have built the next one. A Center of Excellence with reuse standards and bot operations is what prevents it.
- How should AI agents be governed in an automation program?
- With the same controls applied to any actor that can change enterprise data: a defined scope of tools, explicit approval gates for consequential actions, complete execution logging, and permission-awareness so the agent cannot reach data the requesting user could not. An agent without an audit trail is not deployable in a regulated process.
Discuss an automation opportunity
Tell us what you are trying to change. Someone who does this work reads every request and connects you with the right capability team.