Introduction
Intelligent automation examples are easier to find in marketing decks than in production systems. Below are five that are actually running today, one per business function — maintenance, finance, customer service, insurance claims, and manufacturing — drawn from organizations that have put the technology to work, not pilot decks.
Intelligent automation combines robotic process automation (RPA), artificial intelligence, and business process management to handle end-to-end work rather than a single repetitive step. Where older automation followed a fixed script, intelligent automation reads unstructured input, makes a judgment call within defined limits, and routes anything it can’t resolve to a person — the same design principle behind most production AI agents deployed in the enterprise today.
Each example below includes what the organization was trying to solve, what they built, and what changed — with a distinction between figures the company has published and figures that are still vendor claims requiring independent verification.
What Makes These Intelligent Automation Examples Work
The intelligent automation examples in this article share three ingredients: the system reads or senses something (a document, a sensor feed, a customer message), it makes a decision using AI rather than a fixed rule, and it acts on that decision inside a real business system — posting an entry, scheduling maintenance, answering a customer, or flagging a claim for review. Pure RPA, by contrast, only replays a scripted sequence of clicks and breaks the moment the input changes shape. For a deeper walkthrough of the underlying systems, see our complete guide to autonomous AI systems.
In short: intelligent automation is RPA plus AI plus orchestration, applied to a full process rather than one task, with an audit trail a human can review. The five cases below each apply that pattern to a different function.
1. Predictive Maintenance: Siemens’ Senseye Platform
The problem: unplanned downtime on industrial equipment — turbines, motors, production lines — is expensive and hard to predict with fixed maintenance schedules, which either intervene too late or service healthy equipment unnecessarily.
The automation: Siemens’ Senseye Predictive Maintenance platform ingests sensor and operational data — vibration, temperature, pressure — from industrial assets and uses machine-learning models to flag developing failures before they happen, without requiring extra hardware on-site. Siemens has since layered generative AI onto the platform through a conversational interface, letting maintenance staff ask about asset health in plain language rather than reading raw dashboards.
What changed
Siemens frames the shift as moving maintenance teams from reactive, breakdown-driven work to risk-prioritized planning — early warnings ahead of failure, servicing scheduled by risk rather than a fixed calendar, and spare-parts planning done proactively. Note: specific downtime-reduction percentages circulating for Siemens vary widely across third-party marketing blogs (from roughly 20% to 50%) and are not consistently sourced to one Siemens disclosure — treat any single number you see quoted elsewhere with caution and check Siemens’ own press materials for the current figure.
2. Document Processing in Finance: Bradesco’s Accounts Payable Overhaul
The problem: Bradesco, one of Brazil’s largest banks, had roughly 60 internal departments manually receiving, checking, and routing paper invoices and payment documents under Brazil’s tax and compliance rules — a slow, storage-heavy process.
The automation: working with Xerox, Bradesco built a platform that captures documents, applies the correct processing rules automatically, and routes payments without paper handling at any step — the same intake-to-approval pattern covered in our guide to open-source workflow automation tools for teams building something similar in-house.
What changed
According to the published case study, a process that previously took up to 10 days now runs in roughly four minutes, with more than 400,000 payments now processed automatically and paper consumption cut sharply. Company claim, vendor-published case study — Xerox and Bradesco produced this account jointly; it demonstrates the pattern (intelligent document processing collapsing a multi-day approval chain into minutes) but isn’t independently audited.
3. AI Customer Service: Bank of America’s Erica
The problem: high volumes of routine banking questions — balances, transactions, subscription charges — were consuming call-center and branch capacity that could otherwise go to more complex client needs.
The automation: Erica, Bank of America’s AI-driven virtual financial assistant launched in 2018, handles voice, text, and in-app questions, and proactively surfaces insights like unexpected recurring charges. The same underlying technology now powers separate tools for business clients (CashPro Chat), wealth-management staff (ask Merrill, ask Private Bank), and internal employees (Erica for Employees).
What changed
Per Bank of America’s own newsroom disclosures: Erica passed 3 billion client interactions and roughly 50 million users by August 2025, with CashPro Chat used by 65% of corporate clients and Erica handling more than 40% of those interactions. Separately, the bank reports Erica for Employees is used by over 90% of its roughly 213,000 staff and has cut calls into the internal IT service desk by more than 50%. These are official, company-published figures, not independent audits, but they come from Bank of America’s own investor and press communications rather than a third-party vendor blog.
4. Insurance Claims Automation: Aviva’s AI-Assisted Claims Journey
The problem: settling an insurance claim involves many interdependent decisions — damage severity, injury involvement, fraud risk, repair-versus-replace, which repair shop to use — and small delays or inconsistencies at any step frustrate customers and add cost.
The automation: Aviva, the UK’s largest general insurer, rebuilt its claims journey (documented in a McKinsey case study) around AI tools that support claims professionals at each decision point, while deliberately keeping a “double helix” design that can switch a case between AI-assisted and fully human handling — for example, defaulting to human handling for personal-injury claims. That kind of decision boundary is exactly the sort of control regulators are starting to expect; see our coverage of the EU AI Act’s Article 50 transparency rules for how this is being formalized.
What changed
McKinsey’s account emphasizes that Aviva treated the effort as more than a technology rollout — it invested more than 40,000 training hours to build a digital-first culture alongside the AI tooling, on the premise that better accuracy and better customer experience needed to move together rather than trading off. This is an independent, third-party (McKinsey) account of a named insurer’s approach — useful as evidence of design principles, though it is not a peer-reviewed evaluation.
5. Collaborative Robotics: Volkswagen’s Engine Assembly
The problem: certain steps in engine assembly are physically demanding and injury-prone for human workers, while still requiring precision that fully autonomous robotics couldn’t handle alone.
The automation: according to IBM’s own documentation on intelligent automation, a Volkswagen engine-production plant in Germany uses collaborative robots (“cobots”) that work alongside production staff to take on the physically demanding step in the assembly process, rather than replacing the human role entirely. Organizations building similar agent-plus-human setups closer to the edge, rather than in the cloud, can see our notes on local AI agent deployment.
What changed
IBM describes the outcome as fewer workplace injuries, faster processing, and improved accuracy — a human-plus-machine division of labor rather than full automation. This is a company-published example (via IBM’s own materials) rather than an independently verified study, but it illustrates a distinct pattern from the other four: automation applied to physical assembly work, not information processing.
How the Five Examples Compare
| Organization | Business function | Core technology | Evidence type |
|---|---|---|---|
| Siemens | Industrial maintenance | Sensor AI + generative AI interface | Company-published |
| Bradesco (via Xerox) | Accounts payable | Intelligent document processing | Vendor case study |
| Bank of America | Customer & employee service | Conversational AI / NLP | Official investor/press disclosure |
| Aviva | Insurance claims | AI-assisted decision support | Independent consulting case study |
| Volkswagen | Manufacturing assembly | Collaborative robotics | Company-published |
The pattern across these intelligent automation examples: each one is scoped to one complete workflow — invoice intake to payment, sensor reading to work order, question to resolved answer — rather than spread thin across many disconnected tasks.
Where to Start If You’re Evaluating This
- Pick one complete workflow, not a department. “Invoice intake to approval” is scopeable; “finance” is not.
- Check whether the trigger data is already structured or messy. Sensor feeds and invoices are relatively structured; open-ended customer messages need stronger NLP.
- Decide the human-in-the-loop rule before you build anything. Aviva’s default-to-human rule for personal-injury claims is a useful model: define upfront which cases never get full automation.
- Confirm someone owns the exception queue. Every one of the five examples above still routes unresolved cases to a person — automation with no owner for exceptions tends to stall, which is the single biggest reason pilots fail to reach production; see our ROI framework on why AI agents fail in the enterprise.
Limitations to Plan Around
None of the five examples above ships as pure autonomy. Aviva explicitly designed a path back to human handling for sensitive claims. Bank of America keeps a “Mobile Servicing Chat” hand-off for anything Erica can’t resolve. Volkswagen’s robots work alongside people rather than replacing the role. That consistency is worth noting: the organizations getting real value from intelligent automation are the ones that treated human oversight as part of the design, not a fallback bolted on afterward.
It’s also worth separating what’s independently verifiable from what isn’t. Bank of America’s figures come from its own newsroom and investor communications; Aviva’s case is told through McKinsey, a third party that worked on the engagement; Bradesco’s numbers come from a joint case study with its vendor, Xerox. None of these is a peer-reviewed audit — a reasonable caveat before citing any of these numbers as a benchmark for your own project.
FAQ
What is a simple example of intelligent automation?
A simple example is a system that reads an incoming invoice, extracts the vendor and amount using AI rather than a fixed template, checks it against a purchase order, and either posts it automatically or routes it to a person when something doesn’t match — as Bradesco did with its accounts payable process.
How is intelligent automation different from RPA?
RPA follows a fixed script and breaks when the input changes format. Intelligent automation adds AI and orchestration on top of RPA, so the system can handle unstructured input, make a judgment call within limits, and manage a process end to end rather than one scripted step.
Which industries use intelligent automation the most?
Based on the intelligent automation examples documented above and the wider industry reporting reviewed for this article, banking, insurance, and manufacturing show some of the most mature production deployments — largely because they combine high document or sensor volume with clear rules for when a case needs human review.
Does intelligent automation replace human jobs?
In the examples here, no — each one keeps a defined human-in-the-loop role: Volkswagen’s cobots work alongside assembly staff, Aviva routes sensitive claims to people by default, and both Bank of America and Bradesco route exceptions to human review rather than forcing full automation.
Sources
- Siemens Press Release — Senseye Predictive Maintenance — official description of the platform and its generative AI update
- Xerox — Bradesco Accounts Payable Case Study — vendor-published account of the invoice automation deployment
- Bank of America Newsroom — Erica Surpasses 3 Billion Interactions — official adoption and usage figures
- Bank of America Newsroom — Erica for Employees — internal IT service-desk impact figures
- McKinsey & Company — Aviva’s AI-Assisted Claims Journey — independent case study of design approach and training investment
- IBM — What Is Intelligent Automation? — official definition and the Volkswagen collaborative-robotics example
This article synthesizes official company disclosures, one vendor-published case study, and one independent consulting case study, current as of August 20, 2026. Figures for Siemens, Bradesco, and Volkswagen come from company or vendor sources and are not independently audited; Bank of America’s figures are drawn directly from its own newsroom disclosures; the Aviva account is McKinsey’s independent description of a client engagement. Adoption numbers and internal metrics for any of these programs may change — recheck primary sources before reuse in downstream reporting.
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