Robotic Process Automation (RPA) and Artificial Intelligence (AI) are two technologies that are often talked about in the same breath. However, while there is some overlap between RPA and AI, they are fundamentally different technologies with distinct use cases.
Defining RPA and AI
RPA refers to software bots that are programmed to automate repetitive, rules based digital tasks. RPA bots follow pre-defined business rules and mimic human actions like clicking, typing, copying and pasting data between applications.
AI, on the other hand, is intelligence demonstrated by machines to simulate human cognition. AI systems learn from data patterns and experiences to make decisions and predictions, or take actions towards a set goal.
Key Differences Between RPA and AI
| Parameter | RPA | AI |
|---|---|---|
| Functionality | Automates routine tasks by following rules | Mimics human intelligence for decision making |
| Approach | Rules based | Self learning based on data |
| Ease of implementation | Fast and simple | Complex, requires huge data sets |
| Error handling | No intelligence to detect or recover from errors | Can self correct errors using learning |
| Evolution | Requires updates for new processes | Continuously evolves and improves on its own |
RPA Functionality vs AI Functionality
The core functionality of RPA revolves around the automation of high volume, repetitive tasks such as data entry, extraction and processing. RPA bots have no inherent intelligence and cannot make decisions outside of the rules defined for them.
In contrast, AI systems are designed to make judgments and decisions much like a human would. Unlike RPA, AI gets better at making decisions over time as its algorithms process more data.
RPA is Rule Based, AI is Self Learning
RPA bots follow strictly defined rules and lack the ability to handle exceptions. Any changes to the business process would require reconfiguring the RPA bot.
AI systems are trained on large volumes of data, identifying patterns and correlations rather than following strict rules. They continue to learn from new data and refine their algorithms to become smarter over time.
Implementation Complexity
RPA solutions are relatively fast and simple to implement, with limited coding required. The RPA development lifecycle focuses on understanding existing processes and configuring bots to automate them.
AI implementation requires significant upfront effort in data preparation, model development and training before going live. Specialized data science skills are essential for the sucessful development and implementation of AI solutions.
Suitability of RPA and AI By Process Type
Structured vs Unstructured Processes
RPA is ideal for automating processes that have clearly defined structured inputs, rules, and outputs. For example, customer onboarding, claims processing, HR paperwork. AI excels at making sense of loosely structured and unstructured processes like interpreting images, audio data, or written text. Common use cases include chatbots, recommendation engines, predictive maintenance.
Rules-Based vs JudgementDecisions
RPA bots perform best when the required decisions are rules based with predefined logic. For example, calculating insurance premiums, approving loans, processing orders. The self-learning capability of AI enables judgment based decisions requiring perception and evaluation. Applications like identifying fraud, predicting customer churn, personalized recommendations.
RPA and AI Integration
While RPA and AI serve different automation purposes, combining them unlocks new possibilities:
- RPA can be used to collect and prepare data required for training AI algorithms.
- AI can enhance RPA bots by providing image recognition, natural language processing, predictive analytics capabilities.
- RPA can help scale the usage of AI solutions across the enterprise by automating repetitive tasks.
Gartner predicts that by 2024, organizations will lower operational costs by 30% by combining RPA and AI.
RPA vs AI: When to Use Which?
Use RPA for:
- Structured, repetitive processes with clear rules like forms processing, data transactions.
- Quick wins through rapid automation of legacy processes.
- Reducing costs by eliminating inefficiencies.
Use AI for:
- Decision making requiring perception, judgement and predictive insights.
- Uncovering hidden insights from unstructured data like images, text, audio.
- Frequent process changes or exceptions requiring adaptability.
The Future of RPA and AI
While RPA will continue to thrive at automating rigid business processes, AI adoption is expected to see massive growth:
- Narrow AI focused on specific tasks will become commonplace. Human in the loop AI like chatbots will evolve with advances in natural language processing.
- Artificial general intelligence (AGI): AI matching overall human cognitive abilities predicted to be achieved by 2050. However lack of explainability remains a barrier to mainstream adoption of AGI.
- RPA AI hybrid platforms that combine robotic process automation with AI like machine learning, NLP and computer vision will gain traction.
With rapid technological innovation, the lines between RPA and AI may get blurred. But they will continue to play complementary roles enabling organizations to achieve unique automation goals aligned to their needs. RPA will remain the gateway drug to AI adoption. But AI will be the holy grail enabling enterprises to re-imagine business processes.
Conclusion
RPA and AI offer complementary technology capabilities to drive digital transformation and human machine collaboration. RPA provides flexible automation of repetitive tasks, while AI adds intelligence to unlock deeper insights and improvements.
While RPA focuses on enlisting an army of bots to eliminate inefficiencies, AI develops expertise to enhance decision making. An integrated strategy leveraging the strengths of both can enable organizations to achieve more holistic automation goals. With hyperautomation emerging as an enterprise priority, RPA and AI will converge to deliver intelligent automation. Though the technologies have evolved separately so far, their symbiotic partnership is set to grow as part of accelerating automation roadmaps.
FAQs
Can RPA and AI work together?
Yes, RPA and AI offer complementary capabilities that can work symbiotically when combined. RPA provides the plumbing for structured data to flow into AI systems. And AI adds brainpower to handle unstructured data and unpredictable use cases.
Will RPA become obsolete with advancing AI?
While AI will take on more sophisticated automation challenges, RPA will continue to thrive in automating repetitive, rules based processes. RPA solves different problems compared to AI. As enterprises adopt a hyperautomation strategy, RPA will be an essential part of the automation fabric powering straight through processing.
What processes are best suited for RPA vs AI?
RPA is ideal for automating highly repetitive, high volume tasks with fixed rules and structured data. For example, processing invoices or insurance claims. AI excels at perceptual and judgement based processes like fraud detection, personalized recommendations, predictive insights.
Does RPA require coding skills?
One of the biggest advantages of RPA is it can be adopted without extensive technical skills. Leading RPA platforms use graphic design interfaces minimizing the need for coding expertise. Subject matter experts can build RPA bots with minimal guidance.
How long does it take to implement RPA and AI?
A typical RPA implementation takes anywhere between 6 to 12 weeks from planning to deployment. AI implementations are more complex due to dependencies on sourcing, cleaning and tagging training data sets. So an AI Proof of Value would span 12 to 16 weeks, while enterprise wide adoption occurs over months and years.
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