Robotic process automation (RPA) has transformed how organizations streamline business operations by automating repetitive, rules-based tasks. However, RPA tools have traditionally struggled with processes involving computer vision, such as reading text in images or verifying document authenticity. This is where integrating RPA with artificial intelligence (AI) capabilities, specifically computer vision, unlocks new intelligent automation possibilities.
In this comprehensive guide, we will explore how RPA and computer vision can work synergistically to create end-to-end intelligent process automation.
Overview of RPA and Computer Vision
What is RPA?
RPA software automates repetitive, manual tasks by mimicking human actions. Bots can interact with user interfaces to copy-paste data, fill forms, extract information, trigger responses, and complete workflows. RPA adoption has grown exponentially with the COVID-19 pandemic accelerating digital transformation. The RPA market is forecast to reach $13.74 billion by 2028 as more functions and departments implement automation.
However, most RPA tools have limited capabilities in working with unstructured data like scanned documents or handwritten text. They also cannot perform visual tasks like facial recognition or damage assessment. This is where integrating computer vision becomes vital.
What is Computer Vision?
Computer vision is a field of AI focused on enabling machines to identify, process, and analyze visual data like images, videos, and 3D models. It powers use cases like facial recognition, self-driving vehicles, medical imaging analytics, and quality inspection.
Deep learning techniques have drastically improved computer vision capabilities in recent years. The computer vision market is estimated to grow from $10.10 billion in 2022 to $19.21 billion by 2027 as these AI models are deployed across industries.
Why Combine RPA and Computer Vision?
While RPA excels at automating structured digital tasks, computer vision AI models can handle unstructured data and visual perception challenges robots struggle with. Together, they enable end-to-end intelligent automation of processes spanning both digital and physical domains.
Benefits of integrating RPA with computer vision include:
- Higher automation rates by incorporating computer vision tasks
- More adaptable solutions that can understand changes in application UIs
- Cost and time savings from process efficiency at scale
- Augmented quality through reduced human errors
Next, let‘s explore some real-world applications combining computer vision and RPA for business impact.
Use Cases for RPA and Computer Vision
1. Automating Data Extraction from Scanned Documents
Many organizations still receive high volumes of paper documents that need to be digitized and processed. Whether medical records, invoices, submitted claims or application forms, managing the intake and data extraction remains largely manual.
RPA bots have no visibility into the content within scanned documents. However, natural language processing and computer vision tools like optical character recognition (OCR) can automatically:
- Classify and sort documents
- Extract handwritten and printed text
- Read checkboxes, tables, and key-value pairs
- Validate document signatories
The structured extracted data can then be ingested into databases and workflow systems rapidly using RPA automation. This eliminates slow and expensive manual document processing while retaining high accuracy.
One example is an insurance firm using AI-enabled RPA to process vehicle damage claims faster. Computer vision spots dents, cracks, and paint damage on uploaded images while also reading policy documents. This speeds up claim assessments and linking covered incidents to the correct policies.
2. Mining Process Data for Automation Opportunities
Organizations often don‘t know what to automate or struggle with constant process changes. Advanced Process Mining techniques can map processes automatically and identify the best scenarios for RPA and computer vision automation.
Process mining algorithms analyze system logs, desktop recordings, enterprise data sets, and application trails. Computer vision models additionally capture and interpret on-screen user actions more contextually through:
- Activity clustering to build a process taxonomy
- Optical character and shape recognition
- Interpreting changes in workflow steps
These insights allow creating a fluid automation roadmap attuned to process variability – instead of systems architects manually evaluating processes. Projects can scale faster with quick wins and maximum return on investment.
As an example, leading banks use advanced process mining to accelerate automation of lending operations. By benchmarking processes across branches and surfaces, they deploy targeted RPA augmented with AI to boost productivity.
3. Verifying Document Authenticity to Prevent Fraud
Financial institutions lose billions annually to fraud schemes like identity theft, fraudulent documentation, and inflated insurance claims. Verifying document authenticity thus becomes key, especially in remote customer onboarding processes.
While RPA tools alone cannot reliably confirm document credibility, computer vision algorithms spot manipulated images and fonts. By comparing documents to verified templates and data patterns, they can:
- Assess tampering through noise filters, motion detectors and masking analyzers
- Match submitted photos to ID portrait images via facial recognition
- Validate header/footer continuity across document pages
This allows flagging potentially fraudulent cases for human review – vastly reducing false negatives. One major US bank tapped this capability to cut false matches in background checks by 92% using intelligent document processing.
Combining RPA and computer vision unlocks instant, accurate verification as documents get uploaded into databases via robotic automation. This cuts fraud leakage while smoothing customer experience with rapid approvals for legitimate applicants.
The Benefits of Integrating RPA and Computer Vision
As the examples illustrate, RPA and computer vision working in tandem facilitate end-to-end process automation with multiple advantages:
1. Higher Automation Rates Across More Processes
RPA tools can automate almost all rule-based digital tasks but hit limitations with physical documents or human perception steps. Infusing computer vision expands this scope to business processes dependent on scanned files, field data, sensors, or decision intelligence.
2. Better Adaptability to Process Changes
Most RPA scripts break easily when application interfaces or data patterns change – causing failure rates over 30% during bot runtime. However, computer vision AI models can read displays, pop-ups and industry-standard forms accurately despite minor changes. This results in reliable automation.
3. Multifold Efficiency Gains and Cost Savings
By working in concert, RPA and computer vision sharply accelerate process speed, throughput, compliance, and service levels for complex workflows. Organizations can save 50-70% in operational costs through staff time savings and error reduction. The rapid ROI unlocks funding for scaling automation across departments.
According to Gartner, Hyperautomation strategies leveraging integrated tools like RPA and AI could enable 60% cost reduction over 3 years[1].
4. Higher Quality Through Objective Assessments
Computer vision techniques apply consistent logic for inspections and classifications where human reviewers disagree or make emotional decisions. This data-driven objectivity augments quality for many document validation, risk analysis and evaluation tasks – especially useful in financial underwriting and insurance claims.
Overcoming Challenges in Adopting RPA and Computer Vision
However, practically implementing RPA and computer vision poses some technological and organizational challenges:
1. Significant Investments Needed for Custom AI Models
While powerful out-of-the-box computer vision APIs exist, bespoke models matching specialized use cases often perform better. But developing accurate AI requires huge volumes of labeled real-world data covering process variability. Data preparation and model building needs sizable data science teams and infrastructure.
Organizations should thus validate ROI by starting with pre-built solutions and tactical deployments. Cloud-hosted AutoML tools also ease model creation today without deep expertise.
2. Integrating Diverse Technologies and Skill Sets
RPA tools, workflow platforms, data pipelines, computer vision, NLP and other components must synchronize for end-end automation. Ensuring interoperability, access management and monitoring across tech layers grows complex fast.
Firms should architect systems holistically and invest in integration early through enterprise service layers and APIs. Cross-skilled teams combining business analysts, RPA developers, data engineers and AI/ML experts further enable managing the technical debt.
3. Navigating Regulations for AI Implementation
Computer vision applications like facial recognition and surveillance attract ethical debates regarding privacy infringement and bias issues. Governments are developing clearer guidelines regarding transparency and accountable AI principles. However, legal uncertainty persists, especially for global institutions.
Organizations can mitigate risks by rigorously auditing data use, allowing user opt-outs in consumer products, open-sourcing key algorithms and involving community review boards[2]. Adhering to frameworks such as the Trustworthy AI principles from the EU also builds reliability.
The Outlook for Intelligent Process Automation
As more processes get disrupted by digitalization, RPA and AI integration will accelerate to unlock efficiency for unstructured tasks. Let‘s examine some key developments and adoption trends ahead:
Financial Services Lead RPA-AI Model Adoption
Banks, insurers and capital markets were early RPA adopters to improve customer onboarding, underwriting and regulatory compliance. High document volumes, rising fraud levels post-COVID and thin margins also drive interest in embedded computer vision for better operational resilience[3].
Insurers especially need to speed up claims processing and verification. A 2021 EY survey found 87% of insurance executives already implementing some form of AI like computer vision, RPA, predictive analytics or chatbots[4].
Healthcare Looks to Automation for Cost and Outcome Gains
Healthcare processes like claims management, patient intake, medical coding handle endless forms, bills and prescriptions touching multiple legacy systems. Computer vision tools accurately extracting and linking data across sources offers significant potential.
Over 75% of health systems now prioritize optimizing back-office functions through AI tools according to a Becker‘s Hospital Review[5]. Revolutionizing clinical functionality also hinges on advanced image recognition techniques for detecting various conditions.
Public Sector Drives Automation Initiatives Globally
Many governments struggle with rapidly validating citizen documentation for tax payments, financial aid, licenses, immigration and law enforcement scenarios. Intelligent data extraction and screening systems can securely speed up these volumes while improving compliance and experience.
The UK government has an automation task team driving RPA adoption since 2016. Multiple agencies from transport to defense now utilize hundreds of software bots[6]. The General Services Administration (GSA) in the US runs an emerging tech program encompassing RPA, AI and blockchain applications.
Concerns on AI Bias and Job Losses Persist
However, governments and society continue contending with risks of large-scale AI tech displacement of jobs and potential discrimination. One UK survey revealed 71% of citizens worried about automation-driven unemployment in 2022[7]. Civil rights groups have presented reports to US Congress on demographic biases coded into AI tools.
Responsible technology development and change management remains vital for public confidence in automation transformation.
RPA scales task automation across enterprises but needs computer vision‘s perceptive capabilities for managing unstructured data touches in processes. Together, they future-proof processes with data-driven intelligent automation. This guide covered real-world applications, benefits and adoption trends showcasing their promise.
Hopefully this provided comprehensive insight into unlocking intelligent process automation with RPA and computer vision. Do share any other use cases or questions in the comments!
References:
- Gartner Identifies Top Seven Hype Cycles for 2022. Gartner (2022)
- Building Trust in Public Sector AI. FutureGov (2021)
- 2022 RPA Benchmarking Study. Ernst & Young (2022)
- Global Insurance Outlook. Ernst & Young (2021)
- 75% of hospitals & health systems plan to invest in AI in next 5 years. Becker Hospital Review (2021)
- Using RPA to improve public services. National Audit Office (2020)
- Public divided on whether robots should be used to provide social care. Ipsos MORI (2022)