RPA Vs. IDP: Which Automation System is Better?

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Automation is transforming how organizations operate, with Robotic Process Automation (RPA) and Intelligent Document Processing (IDP) emerging as key enablers of this change. RPA and IDP are two prominent technologies driving efficiency and digital transformation across industries. Adoption of automation is accelerating – a Deloitte global survey found 78% of companies have already implemented RPA (with another 16% planning to in the next few years). This widespread uptake highlights RPA’s growing role in streamlining business processes. At the same time, enterprises face a lot of unstructured data, such as emails, PDFs, scanned forms, and other documents that don’t fit neatly into databases. Traditional automation solutions struggle with this unstructured content, creating demand for IDP solutions that intelligently extract and interpret data from documents. 

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Source: turian's production, based on Delloite's data

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What is RPA (Robotic Process Automation)?

Robotic Process Automation (RPA) is a software technology that uses “bots” or software robots to mimic human actions within digital systems. In essence, RPA bots act like virtual workers performing repetitive, rule-based tasks across applications. These bots can be configured to click buttons, navigate screens, copy and paste data, fill forms, or perform calculations just as a person would, but with greater speed and consistency. RPA operates at the user interface level or via APIs, integrating with existing software without requiring changes to those systems. Because bots follow predefined scripts, RPA excels at tasks that have clear rules and structured inputs (for example, transferring data from a spreadsheet to an ERP system, generating routine reports, or reconciling records across databases). Setting up an RPA workflow does not always require deep programming skills; many RPA platforms offer low-code drag-and-drop tools, allowing business users to record actions and quickly deploy bots to replicate those steps. It’s important to note that RPA on its own is not “intelligent”: it does not learn or make judgments; it strictly follows the rules it’s given. This makes RPA highly reliable for repetitive tasks, but it also means RPA by itself cannot interpret free-form content or adapt to changes unless reprogrammed.

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What is IDP (Intelligent Document Processing)?

Intelligent Document Processing (IDP) is a technology that automates the intake, understanding, and extraction of data from documents, especially semi-structured or unstructured documents that traditionally required manual reading. In contrast to standard data entry or basic optical character recognition (OCR), IDP combines OCR with artificial intelligence (AI) and machine learning (ML) to not only convert images or PDFs into text, but also to interpret that text and organize it into structured, usable information. In essence, IDP attempts to read documents like a human would, identifying what type of document it is, pulling out the key fields or details, and validating the information.

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How IDP Works

Under the hood, an IDP system uses a pipeline of intelligent technologies to process documents. The process often starts with OCR, which serves as the “eyes” of the system: OCR software scans PDFs or scanned images and converts printed or handwritten text into machine-readable text. On its own, OCR doesn’t understand meaning; it simply digitizes the characters on the page. IDP goes further by applying machine learning models and natural language processing (NLP) to interpret the content. ML algorithms (the “brain” of IDP) are trained on examples to recognize patterns. For instance, an IDP solution can be trained to recognize the layout of an invoice and learn where to find the vendor name, invoice number, date, line items, totals, etc. Using classification models, the IDP first determines the document type (invoice, purchase order, contract, onboarding form, etc.) so it knows what data to expect. Then extraction models locate and pull out the relevant data fields. Advanced IDP systems employ NLP to understand context (for example, realizing that “Amount Due” on a bill is equivalent to “Total Payable” on another vendor’s invoice). They may also cross-validate extracted data against known formats or business rules (e.g. invoice date must be before due date) to improve accuracy. 

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IDP produces structured data (like JSON or XML or a database record) that contains the key information from the original documents, ready to be used in other systems or processes. This end-to-end document handling (classify, extract, validate, and route the data) is what makes IDP “intelligent” compared to basic scanning. Modern IDP solutions can process a variety of document formats (PDFs, images, emails with attachments, etc.) and even handle varying layouts using AI that generalizes beyond fixed templates.

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Source: turian

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RPA vs. IDP: Key Differences in Automation

RPA and IDP are complementary technologies, but they differ fundamentally in their approach and ideal applications. Below we compare RPA and IDP across several dimensions:

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Source: turian

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Type of Input Data

Perhaps the biggest difference is the nature of data each handles. RPA works best with structured data: information that is already organized in databases, spreadsheets, or standard digital formats. If the input is uniform and computer-readable (for example, fields in a web form or entries in a CSV file), RPA can readily use it to execute tasks. RPA does not natively understand unstructured content like free-form text in documents or emails. By contrast, IDP is specifically designed to handle unstructured or semi-structured data found in documents. IDP can extract data from invoices, PDFs, images, emails, and other content that doesn’t have a predefined structure. In summary, RPA is ideal for structured digital inputs, whereas IDP is purpose-built to make sense of unstructured documents.

Automation Method (Rules-based vs. AI-driven)

RPA follows explicit, pre-defined rules to execute processes: it’s a deterministic rules-based automation. A bot will do exactly what it’s configured to do, no more no less. This makes RPA very predictable, but it lacks flexibility or “judgment.” IDP, on the other hand, uses AI/ML algorithms to drive automation, meaning it is probabilistic and data-driven. IDP systems actually learn from examples and improve over time at recognizing documents and data patterns. The key distinction is that RPA bots do not learn or adapt on their own (they require reprogramming when conditions change), whereas IDP’s machine learning models can be retrained or updated to handle new document formats or slight variations. In short, RPA is the “if X, do Y” automation, while IDP is the “figure out what this document says, then process it” automation.

Capabilities and Scope

RPA’s capabilities lie in executing actions in software systems: clicking, typing, moving files, triggering transactions, etc. It orchestrates processes across multiple applications but does not inherently include data understanding. It might be seen as the “digital workforce” carrying out tasks. IDP’s capability is understanding content: it’s focused on reading data from documents and making it usable. If RPA is the muscle (the “brawn”), then IDP is the brain added to give understanding. RPA is typically not equipped with natural language processing or computer vision out of the box. IDP fills that gap by providing language and vision intelligence (through OCR/NLP) to interpret documents. However, IDP by itself doesn’t execute end-to-end processes, it usually provides extracted data that RPA or other systems then take action on. In practice, RPA and IDP often work together: IDP extracts and structures the data, and RPA plugs that data into IT systems or workflows. For example, IDP might read an invoice and capture all fields, then an RPA bot logs into an accounting system to enter the invoice for payment approval. Without IDP, the RPA bot would be “blind” when faced with a PDF invoice; without RPA, the IDP output might still require a person to key it into a system. This interplay highlights their distinct but complementary roles.

Accuracy and Efficiency

On highly structured, repetitive tasks, RPA bots can achieve near-perfect accuracy and very high efficiency as long as the inputs conform to expected rules. However, when faced with variability or unstructured inputs, traditional RPA tends to fall short. For instance, if an input screen’s layout changes or an invoice from a new vendor has a different format, a standard RPA process might fail or produce errors because it wasn’t programmed for that case. IDP, with its AI basis, is more tolerant of variability in input. It can handle a range of document formats and still accurately extract data, especially after learning from examples. In terms of raw accuracy, sources indicate RPA alone might accurately process ~70–85% of cases when dealing with documents, whereas IDP can reach a close to 98% accuracy on document data extraction after training. Additionally, IDP can greatly reduce the need for human verification: a well-trained IDP system may only require manual review on exception cases, whereas a pure RPA+OCR approach without IDP intelligence might require humans to regularly fix data or handle format exceptions. In comparison, IDP achieves in general higher accuracy and can handle more complex document types than RPA. Processing speed is another factor to consider: once configured, RPA performs individual tasks very quickly (e.g. updating a record in milliseconds), but if a process involves reading a document, a basic RPA approach might rely on slower template-based OCR. IDP solutions often use optimized AI models and can process documents in parallel, achieving faster throughput per document. To put this into an example, an IDP system might parse an invoice in under a minute, end-to-end, whereas a human doing the same takes several minutes. Overall, IDP tends to win on efficiency in any scenario involving document interpretation, while RPA is extremely efficient at repetitive keystrokes/clicks on structured data.

Scalability and Maintenance

Both RPA and IDP are scalable in their own ways, but the effort to scale and maintain them differs. Scaling RPA usually means deploying more bot licenses or instances to handle a higher volume of transactions in parallel. This is straightforward if processes are well-defined, but it can become complex to manage hundreds of bots and ensure they don’t conflict or overload systems. Additionally, maintaining RPA scripts can be challenging if underlying applications change frequently (each time an application UI or workflow changes, the RPA script may need updating). IDP scales by processing more documents, which often means ensuring the ML models can handle new document variations as volume increases. Training and maintenance of IDP models is an ongoing consideration: as new document layouts or types appear, the models may need additional training data or tuning to maintain high accuracy. However, modern IDP platforms are improving in automatically learning from corrections (continuous learning). In terms of infrastructure, both RPA and IDP can be deployed on-premise or scaled via cloud services. Many IDP solutions today offer cloud-based APIs that automatically scale to volume, whereas RPA scaling may be limited by the number of virtual machines or bots you have running. Another difference is resilience to change: RPA is brittle with unstructured changes but stable with structured processes; IDP is adaptable to unstructured input changes but has its own learning curve. It’s often noted that the combination of RPA and IDP can improve overall scalability: IDP makes the automation more resilient to input variability, and RPA provides the throughput and integration to systems, yielding end-to-end scalability with fewer human touchpoints.

Cost and ROI

Cost considerations for RPA vs IDP can differ. RPA costs primarily involve software licenses for RPA platforms (usually priced per bot or process) and the development effort to configure bots. Deploying RPA for simple tasks can be cost-effective and quick. In fact, on average RPA implementations result in ~59% cost reduction in the targeted processes. IDP solutions often involve license or subscription costs based on document volume or users, and initial setup includes training the AI models which might require machine learning expertise or support from the vendor. Thus, the upfront cost and effort for IDP can be higher, especially if dealing with a wide variety of document types. However, the ROI from IDP can be very high for document-intensive processes: a well-chosen IDP use case like invoice processing or insurance claims can pay back the investment quickly by enabling a team to handle 5× or 10× the volume without adding headcount. It’s also worth noting that IDP and RPA are not either-or in budgeting: many companies invest in both as part of an intelligent automation toolset. Some RPA vendors bundle document processing capabilities or offer IDP modules, which can make the combined approach more cost-effective. Ultimately, RPA tends to have lower complexity, lower upfront cost per process and is ideal for quick wins on simple tasks, while IDP involves higher sophistication and investment that pays off for complex, data-intensive tasks. Organizations should evaluate ROI on a case-by-case basis: often the highest ROI comes from using RPA and IDP together to fully automate a process.

In summary, RPA and IDP differ in focus: RPA automates structured, rules-driven tasks, whereas IDP automates the interpretation of unstructured data. RPA is the workhorse for moving data between systems, and IDP is the intelligence for understanding content. Both improve efficiency but in different domains of work. Rather than competitors, they are complementary tools that often work best in tandem, which is why leading automation strategies typically incorporate both technologies.

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RPA or IDP: Choosing the Right Automation Strategy

Given the strengths of RPA and IDP, a frequent question is whether to implement one or the other. In reality, as we mentioned before, the best automation strategy is not an either/or choice but a smart combination of both, aligned to your specific processes. In this section you can find some recommendations to help you decide based on process complexity, scalability needs, and budget considerations.

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1. Assess Process Characteristics

Start by analyzing the processes you want to automate in terms of their tasks and inputs. If the process primarily involves moving data between existing systems, clicking through software, or performing calculations based on defined rules (and the inputs are already digitized and structured) then RPA is likely the right tool for that job. For example, automating user account creation across IT systems or generating routine finance reports each morning from various databases are classic RPA-only scenarios. RPA shines for high-volume, repetitive workflows that don’t require interpreting content. On the other hand, if the process involves handling many documents, emails, or text-rich inputs that need to be read and understood, then IDP (potentially combined with RPA) is the appropriate choice. A process to automate employee onboarding that involves reading offer letters, IDs, and forms would benefit from IDP to extract data from those onboarding documents, alongside RPA to enter the data into HR systems. In many cases, you will find that a given business process has both structured and unstructured elements. 

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2. Consider Complexity and Exception Rates

Simpler processes with low variability are well-served by RPA. If a task rarely deviates from the standard path, RPA can be implemented quickly and will be robust. However, if a process has a lot of exceptions, edge cases, or decision points that depend on reading data, you may need the flexibility of IDP/AI. A good strategy is to start by automating the most rule-based subset of a process with RPA, and introduce IDP for the parts that involve complex data extraction or classification. Over time, you can expand the IDP’s training to cover more cases, thus shrinking the exception pool. If a process is extremely complex (many decision branches, unstructured inputs, and/or intricate rules), you might consider re-engineering the process first or breaking it into smaller components that can be automated separately. In some situations, a business rules engine or decision model combined with RPA can handle complexity, but when those decisions require interpreting text or images, IDP/AI is needed. As a rule of thumb: use RPA for complexity in workflow logic, use IDP/AI for complexity in data interpretation. High exception rates also mean you should plan for human-in-the-loop involvement initially, and choose technologies that support that.

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3. Scalability and Volume Considerations

If your process volume is low or moderate, a lightweight RPA deployment might suffice and be very cost-effective, even if some manual document handling remains. For instance, if you only process 50 invoices a month, investing in a full IDP solution may not have a justifiable ROI. However, for high-volume processes (e.g., when there are hundreds or thousands of documents to process per month), IDP becomes increasingly attractive because it removes the document processing bottleneck. Similarly, consider how your needs might grow. A solution that works for 10 transactions a day might not for 1000 a day. RPA can scale by adding bots, but if those 1000 transactions involve documents, you’ll need IDP to avoid requiring a small army of bots each doing OCR with templates. It’s often more scalable to have one well-trained IDP model handle many variations than to maintain many RPA bot versions for each variation. Generally, RPA is easier to scale in linear fashion (each new bot adds capacity), whereas IDP can scale in a more exponential way (one model can handle many more documents once trained). For enterprise scalability, a combination where IDP provides a flexible data ingestion layer and RPA provides a transactional execution layer is a robust design, and many large organizations are adopting this pattern.

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There may be further issues to consider, but the message we want to send is: hybrid approach — the best of both. In many scenarios, the best strategy is a hybrid approach: leverage RPA and IDP together to cover the full spectrum of the process. This isn’t just a theoretical ideal; it’s increasingly the norm in successful automation programs. It’s often not about choosing one technology over the other, but about identifying which parts of your workflow should be handled by RPA vs. IDP. Use each for what they are best at: RPA for tasks that don’t require “thinking”; and IDP for reading and understanding content.

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The key is process orchestration: ensuring all the components (RPA bots, IDP models, humans) work together. Investing in a platform or framework that allows you to integrate these components will pay off. This is exactly turian’s approach: rather than silo RPA and IDP, turian provides an integrated AI agent that handles unstructured data and structured actions in one flow. turian’s AI agents can interpret documents using advanced AI and then perform relevant actions based on business requirements, such as updating an ERP or sending a response. This unified approach means businesses don’t have to choose one or the other; they deploy an automation solution that embodies both. When selecting vendors or solutions, look for this kind of integration. Does the RPA tool easily connect with an IDP tool? Does the IDP output feed smoothly into your workflow tool or RPA bots? turian’s solution emphasizes seamless integration into existing systems (ERP, CRM, email), which is crucial for minimizing disruption and maximizing value quickly.

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To choose the right strategy, consider the following steps:

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1. Identify Your Goals and Pain Points: What are you trying to achieve – cost reduction, faster processing, better accuracy, compliance? And where are the current bottlenecks – too much manual data entry, too many errors, not enough capacity? This will clarify whether RPA, IDP, or both are needed. For example, if compliance errors are a pain, focus on IDP to improve accuracy of data extraction and RPA to enforce checks.

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2. Inventory Your Processes: Break down the candidate processes into steps and categorize those steps (structured vs unstructured input, rule-based vs judgment-based). This mapping often naturally segments which technology fits where.‍

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3. Start Small, Then Scale: It’s wise to start with a pilot on a contained process to build experience. If new to automation, perhaps pick a purely RPA use case first to get quick success (e.g. automated report generation). If you already have RPA in place, the next step could be adding IDP for a document-heavy process to extend automation. Ensure you measure results to build a business case for scaling up.

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4. Evaluate Technology Compatibility: Ensure that whichever RPA or IDP solutions you choose can integrate well with your IT infrastructure. Compatibility with your existing systems (ERP, CRM, databases) is crucial. Also, the RPA and IDP tools should work together, either via native integration or through APIs. For instance, turian’s AI agent can plug into your email and ERP without you having to replace those systems.

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5. Consider Long-Term Maintenance: Choose solutions that your team (or partners) can maintain and update. If you have AI expertise in-house, you might be comfortable training IDP models; if not, look for IDP solutions with pre-trained capabilities or vendor support. Similarly, ensure you have or develop RPA development skills or use a provider that offers support. The total cost of ownership includes this maintenance aspect.

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6. Plan for Governance: As automation expands, treat your bots and AI models as part of the workforce that need oversight. Define who will monitor bot performance, handle exceptions, and update the systems for regulatory changes or process changes. Building a Center of Excellence (CoE) for automation is a best practice for larger programs, providing standards and central expertise.

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Ultimately, the right automation strategy is one tailored to your business needs. Keeping a holistic view will ensure you invest in the right areas. 

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Use Cases: When to Use What

To help you determine when to use RPA versus IDP, it’s useful to look at real-world use cases across various business functions. Below we explore examples in areas where automation is delivering significant benefits, and discuss which technology is suited for each scenario.

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Procurement

Procurement processes involve a mix of structured transactions and document-heavy communications with suppliers, making them ripe for both RPA and IDP. RPA is very effective for operational procurement tasks that are rule-based (for example, automatically creating purchase orders in an ERP system, updating vendor records, or cross-checking prices against a database). If a company receives purchase requisitions or uses an e-procurement portal, an RPA bot can take the structured requisition data and generate a PO, or match received goods to orders in the system. IDP becomes crucial when procurement workflows involve unstructured documents like supplier quotes, contracts, or emailed order forms. 

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Consider a common scenario: a buyer sends out a request for quote to multiple vendors and receives quotes or proposals back as PDF documents or email attachments. Instead of an employee reading each quote and copying details into a comparison spreadsheet, an IDP solution can automatically parse the key fields from each vendor quote (prices, terms, etc.). Then RPA can compile those into a summary or input them into a procurement system for analysis. 

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Another high-impact use case is purchase order processing via email: many suppliers still email order confirmations or updates. AI (with IDP under the hood) can read incoming emails and attachments from suppliers and extract order details, and RPA logic then updates the ERP system with those details or triggers the next steps. This hybrid approach can drastically reduce cycle times. Overall, RPA can be used in procurement for transactional, repetitive tasks (creating orders, updating systems) and use IDP when data from documents (quotations, invoices, delivery notes) needs to be captured accurately. Most procurement automation successes combine the two. 

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Logistics 

The logistics sector deals with a large volume of documents and data spanning orders, shipments, deliveries, and customs, making it a prime candidate for intelligent automation. RPA is effective in logistics for coordinating data across different systems. For example, an RPA bot can take a daily spreadsheet of shipments and automatically input updates into a transportation management system. IDP adds value by handling the paper and PDF documents inherent in logistics: bills of lading, packing lists, customs declarations, freight invoices, etc. IDP can automatically parse a bill of lading (extract shipper, consignee, item descriptions, quantities), or read a customs declaration form to capture tariff codes and values. Once extracted, RPA can use that data to, say, populate a customs clearance system or generate shipping labels. 

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Another example: when a supplier emails a delivery update, IDP interprets the new delivery date and quantity, and the RPA component automatically updates the purchase order in SAP and sends back a confirmation – all in near real-time. This kind of end-to-end handling improves supply chain agility and accuracy. 

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In logistics use cases, RPA is best for transaction processing and system integration tasks, while IDP is best for extracting data from shipping documents and communications. Together, they enable what we might call “digital logistics operators”. Companies in retail and manufacturing have managed to automate large chunks of their order fulfillment and shipping processes using this combo. The result is faster throughput (shipments processed faster, with fewer delays waiting on human data entry) and improved accuracy in supply chain data (fewer errors in addresses, quantities, etc).

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Conclusion

Automation is no longer a luxury: it has become a necessity for organizations looking to improve efficiency, accuracy, and responsiveness in today’s digital world. RPA and IDP each play a critical role in this automation landscape, and understanding their differences is key to leveraging them effectively. RPA is ideal for automating structured, rule-based tasks: it brings speed, consistency, and reliability to high-volume repetitive work, yielding benefits like reduced errors, cost savings, and freed-up human capacity. IDP is the solution for automating document processing and other unstructured data tasks: it enables machines to read and interpret documents with a level of accuracy and context that approaches human understanding, thereby eliminating manual data entry and accelerating processes that were once bottlenecked by paperwork.

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We saw that RPA and IDP differ in focus – one is process-driven and the other data-driven – yet they are complementary. RPA handles the “doing,” while IDP handles the “reading.” The most powerful automations often arise from combining the two: using IDP’s AI intelligence to extract and comprehend information, then using RPA’s procedural rigor to take action on that information. This synergy allows end-to-end processes to be automated in a way that neither technology could achieve alone. Businesses should not view RPA vs IDP as a rivalry or an either/or choice, but rather as building blocks of an integrated intelligent automation strategy.

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Looking ahead, the line between RPA and IDP is blurring as part of the broader trend of hyperautomation. Future automation solutions are increasingly integrating RPA with IDP, allowing organizations to automate more complex tasks than ever before. The evolving landscape will likely bring more unified platforms where a single “digital worker” or AI agent can handle both clicking buttons and reading documents. This promises exciting gains in efficiency but also calls for careful planning around governance and compliance (especially in Europe’s regulatory environment). Businesses will need to ensure their automation initiatives align with data privacy laws and ethical AI practices, incorporating human oversight for transparency and control.

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turian’s approach exemplifies the integration of RPA and IDP for optimized automation. By combining an AI brain with robotic arms, turian’s AI agents can intelligently process emails, attachments, and documents, and then execute updates in enterprise systems in real-time. This fusion of capabilities (understanding unstructured input and performing structured tasks) represents the future of automation. It delivers true intelligent automation that not only removes manual effort but also adapts to complexity and provides end-to-end solutions. turian’s solution shows how a thoughtfully integrated RPA+IDP strategy can eliminate 80% or more of manual admin work in processes like procurement and order management. The result is a scalable, fast, and accurate workflow that grows with your business and maintains a human-in-the-loop for oversight when needed.

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In conclusion, RPA and IDP are both indispensable in the modern automation toolkit. Organizations should leverage each where it fits best and aim for a harmonious integration to fully realize the benefits. The key takeaways are: use RPA to automate the routine and IDP to automate the interpretation. 

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As you plan your automation strategy, remember that the ultimate goal is not just automation, but intelligent automation. With the right mix of RPA and IDP, you can transform operations, empower your workforce to focus on what matters most, and set your organization up for long-term agility and success in the digital era.

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