In today’s evolving technological landscape, businesses are increasingly turning to automation to enhance efficiency and reduce operational costs. Two prominent technologies in this domain are Robotic Process Automation (RPA) and Artificial Intelligence (AI). While both aim to streamline business processes, they differ significantly in their capabilities and applications. This article explores the distinctions between RPA and AI, and explains why AI offers more substantial benefits to businesses.
Understanding Robotic Process Automation (RPA)
Robotic Process Automation, or RPA, is a software technology that uses “robots” (i.e., specialized computer programs) to standardize and automate repeatable business tasks. Users configure scripts that activate keystrokes in a specific order and the software robots mimic human actions, such as understanding what’s on a screen, navigating systems, and identifying and extracting data. RPA can be used to:
- Send out generic response emails
- Enter data into multiple fields, reenter data, and copy and paste data between systems
- Generate reports based on data
- Read and verify invoices, transcripts, etc.
- Monitor ad performance and automatically adjust bids
- Gather patient/client details and schedule appointments
- Retrieve customer profiles, support, and order information
Learn more from our guide on how RPA impacts the audit process.
Understanding Artificial Intelligence (AI)
Artificial Intelligence, or AI, is the simulation of human intelligence processes by computer systems, or “machines,” that allows them to learn, reason, and act like humans. AI processes include learning (acquiring information and contextual rules for using the information), reasoning (using context and rules to produce output), and self-correction (learning from failures and updating the process). AI uses math and logic to simulate human reasoning and learns from data by identifying patterns and relationships. It can make predictions, recommendations, or decisions based on patterns in data as well as learn from its errors to improve its accuracy. Some examples of AI include:
- Chatbots on websites
- Voice-assistance on smartphones
- Virtual assistance for customer support
- Self-driving vehicles
- Facial recognition and handwriting recognition
- Music recommendations
- Robot vacuum cleaners
- Drafting emails, newsletters, and other content for marketing purposes
- Image recognition and CAPTCHA
Artificial Intelligence is a broad spectrum of technologies, including machine learning, natural language processing (NLP), deep learning, robotics, neural networks, generative AI, reactive AI, reinforcement learning, expert systems, narrow AI, artificial general intelligence (AGI), etc. While we won’t dive into the nuances and many classifications of AI in this article, keep in mind that many of these technologies fall under the category of AI and are often subsets of one another:
Key Differences Between RPA & AI
- Scope of Automation: RPA is designed for automating specific, repetitive tasks within existing workflows, operating strictly under set rules. In contrast, AI aims to simulate human intelligence, enabling it to handle complex tasks that involve decision-making, problem-solving, and learning.
- Adaptability: RPA systems are static and require manual updates when processes change. They cannot adapt to new inputs or learn from data. AI systems, however, continuously learn and evolve, improving their performance over time without explicit reprogramming.
- Data Handling: RPA works efficiently with structured data and predefined inputs. AI excels in processing unstructured data, such as natural language text and images, making it invaluable for tasks like customer service automation and predictive analytics.
- Implementation Complexity: Implementing RPA can be straightforward for simple tasks but becomes complex and less effective as processes require more decision-making and variability. AI implementations are inherently more complex but offer greater flexibility and scalability in automating intricate processes.
Why is AI Superior To RPA When Delivering Business Benefits?
The evolution of automation technologies is steering toward more intelligent and adaptable solutions, leading to discussions about the potential decline of traditional Robotic Process Automation (RPA) in favor of Artificial Intelligence (AI)-driven automation. Here are a few reasons AI is considered superior to RPA.
AI is Dynamic & RPA is Static
RPA is rule-based, meaning it follows a predefined set of instructions. If a process changes even slightly, the RPA bot must be reprogrammed. AI continuously learns and adapts to new patterns. For instance, an AI-powered chatbot learns from past conversations, improving its responses without human intervention.
For example, an RPA bot for invoice processing will fail if a vendor slightly changes the format of their invoice. An AI-based system, on the other hand, can recognize the new format and adapt without requiring manual reconfiguration. In another instance, a customer service automation system using RPA can only execute pre-scripted responses. AI-powered customer service systems (like chatbots using Natural Language Processing) can understand customer intent, extract meaning, and generate human-like responses.
AI Enables Decision-Making, but RPA Just Executes Tasks
RPA is purely a task automation tool, executing routine processes without making any decisions. AI can analyze large datasets, extract insights, and make complex decisions based on context. In financial fraud detection, RPA can only flag transactions based on fixed rules (e.g., “flag transactions over $10,000”). AI, however, can analyze past transactions, detect anomalies, and predict fraud based on behavioral patterns.
AI Works Across Multiple Processes, RPA is Limited to One Task
RPA works best for single, repetitive tasks. AI, on the other hand, can operate across multiple workflows, making it a more scalable and flexible solution. A bank using RPA can automate customer KYC (Know Your Customer) form processing but still requires manual verification for complex cases. AI-powered document analysis can handle complex cases, extract insights, and cross-verify data across different sources.
AI Automates Higher-Value, Strategic Work
RPA mainly replaces low-value, repetitive tasks. AI enables automation in high-value areas like strategic decision-making, demand forecasting, and customer behavior analysis. An e-commerce company using AI can analyze customer purchase history and predict future buying behavior, enabling personalized marketing campaigns. RPA, in contrast, can only automate manual tasks like sending confirmation emails.
AI Delivers Long-Term Cost Savings, RPA Requires Constant Maintenance
RPA is cheaper to implement initially but requires frequent updates and maintenance as processes evolve. AI may have a higher upfront cost but provides long-term cost savings by reducing human intervention and continuously improving efficiency. A retail company using AI-powered inventory management can predict stock levels and optimize orders dynamically, reducing storage costs and waste. RPA, in contrast, would require separate rule-based bots for different inventory tasks and frequent updates when product demand changes.
Why is AI Superior To RPA When Auditing Business Processes?
As an auditor, one of the things I find fascinating about the continually transforming world of automation and artificial intelligence is how these technologies have been (and will be) used to improve our ability to perform audits. Artificial intelligence isn’t just superseding RPA in terms of how it streamlines a company’s day-to-day operations, but also in how it improves the quality of audits. Compared to Robotic Process Automation, AI offers a more advanced approach to auditing by enhancing accuracy, detecting fraud, reducing costs, and improving efficiency. Here’s how AI outperforms RPA in the auditing process.
Automated Data Extraction & Processing
AI-powered optical character recognition (OCR) and natural language processing (NLP) can extract data from invoices, contracts, and financial statements. While robotic process automation also has some capacity to perform these functions, if the set of documents being inspected isn’t uniform (say a set of differently formatted invoices from different clients), the bot may not be able to locate the correct data fields.
Fraud Detection & Risk Assessment
While RPA can extract data, AI goes the extra mile by analyzing massive datasets and flagging unusual transactions and inconsistencies. Machine learning algorithms detect anomalies and suspicious patterns in financial transactions, improving fraud prevention.
Predictive Analytics for Risk Management
AI can predict potential compliance risks by analyzing historical data and external market trends. An example of this would be AI warning auditors if a company’s financial patterns suggest bankruptcy risk or hidden liabilities. Note that RPA can not perform such predictive auditing functions.
Automated Compliance Audits
AI can be used to scan documents and check for compliance against regulations, reducing the risk of human oversight. An example of this would be AI determining SOX compliance by verifying financial statements and reporting irregularities. While RPA can perform simple comparisons of values against pre-set criteria or thresholds, it can not evaluate values against complex guidance.
Continuous Auditing & Real-Time Monitoring
AI allows for continuous auditing by drawing conclusions from previous evaluations. While software bots can be programmed to continually perform certain tasks, RPA does not have the capability to draw conclusions based on trends and deficiencies noted in prior audits.
The Future of Business Automation: AI’s Triumph Over RPA
In summary, AI is not causing the outright “death” of RPA but is rather transforming the automation landscape. By addressing the limitations of traditional RPA, AI-driven solutions are paving the way for more intelligent, adaptable, and efficient automation strategies. Although RPA is a useful tool for automating simple, rule-based tasks, AI is the key to business transformation. AI-driven automation helps businesses scale, adapt, and make intelligent decisions, leading to increased efficiency, cost savings, and a competitive advantage in the long run. AI can also assist businesses to effectively audit their own processes to prevent fraud.
Looking to modernize your automation strategy and ensure compliance in the AI era? Linford and Company can help you evaluate your current RPA implementations and guide your transition to AI-powered solutions. Contact us today to learn about our audit services, including SOC 1 audits and SOC 2 audits for AI-enabled processes.
For more related content on AI, check out these blogs:
- Risk Management in the Era of Large Language Models and Generative AI
- SOC 2 Audit Considerations for AI & ML Subservice Organizations
Helen has 10 years of experience in audit, cybersecurity, and data privacy and has worked in public accounting as well as industry. She started out her career at Deloitte managing audit readiness assessments, Sarbanes-Oxley 404 audits, and SOC 1 & 2 audits. More recently she worked as a Director of Cybersecurity Compliance at an Ed-tech start-up, Guild, and specialized in building out and maturing their audit, risk, and partner support programs. Between 2015 and 2023, Helen sat on the ISACA Denver chapter board of directors and taught CISA prep courses. She is a certified information systems auditor (CISA), a certified information privacy technologist (CIPT), and a certified risk & information systems control (CRISC) professional.