For the past few years, automation technologies like Robotic Process Automation (RPA) and Artificial Intelligence (AI) have been transforming businesses to reach unprecedented levels of operational efficiency and cost savings.
While having undeniable similarities, RPA and AI tend to differ in terms of many features, applications, and methods of influencing business operations and processes. This article is a comprehensive comparison of AI and RPA and explores their differences, benefits, pros and cons, and the transformative potential their powerful combination brings.
What Is Robotic Process Automation?
Robotic process automation (RPA) mimics human interactions with computers, especially repetitive tasks based on rules. These include data collection, user interface operation, and many others that bots do quicker and more precisely, increasing a company’s operational efficiency. Unsurprisingly, these happen to be tasks that people would rather ignore in order to concentrate on work that bots struggle with, as it requires critical thinking.
Companies across various industry domains leverage RPA to streamline operations. Besides data entry, RPA is invaluable for invoice processing and accounts payable, customer and employee onboarding, account management, report generation… The list goes on and on.
RPA Features
RPA tools automate structured and predictable processes based on explicit rule sets.
You can find some of their key features below:
- Rule-based execution means RPA bots’ operation is impossible without predefined instructions and decision trees.
- Bots interact with graphical user interfaces, enter data, and derive information from various applications.
- RPA bots are easy to scale up and down to tackle increasing or reducing workloads and effortlessly operate across multiple processes.
- RPA implementation is non-invasive, therefore bots have no problems adapting to existing IT infrastructure, and changes are rarely necessary.
RPA Benefits
Implementing robotic process automation brings numerous advantages. RPA bots boost company efficiency, as they work 24/7 nonstop and accelerate processing times. RPA eliminates human errors to improve data accuracy, quality, structure, and consistency.
Automation of routine activities is also very cost-efficient, as qualified human resources can turn their attention to more creative and value-added work. Companies can typically deploy RPA in a quick manner thus benefiting from accelerated ROI.
What Is Artificial Intelligence?
AI refers to various technologies and approaches dedicated to simulating human intelligence. Unlike RPA bots, AI solutions learn from data, adapt to changing scenarios, and occasionally make autonomous decisions. From healthcare to retail and from finance to manufacturing, AI has revolutionized numerous industry domains, and its impact is only going to increase.
AI Features
AI solutions rely on a set of key technologies that enhance their capabilities.
You can find the most significant ones below:
- Machine Learning (ML) Algorithms that, unlike bots, learn and adapt, which allows them to improve with experience.
- Natural Language Processing empowers machines to recognize, comprehend, and generate human speech and texts.
- Computer Vision enables AI systems to identify, understand, and interpret visual information about objects and people to mimic human capabilities.
- Expert Systems are AI programs that solve complex challenges by emulating human experts’ decision-making abilities.
AI Benefits
AI’s importance for transforming business operations cannot be overstated. AI revolutionizes decision-making by accessing and analyzing large data sets to derive actionable insights and recommendations based on numerical evidence. It also empowers companies to tailor and personalize products and services according to trends and customer feedback.
Unlike RPA, AI can automate complex cognitive tasks that were once deemed impossible to handle without human expert intervention. Its predictive capabilities also facilitate business analysts and strategists when detecting trends, patterns, and opportunities that may remain unnoticed even for experienced experts.
RPA vs AI: Key Differences
Businesses leverage both RPA and AI for automation, but there are quite a few differences in methodology, capabilities, and applications.
1. Task Complexity and Cognitive Capabilities
RPA is applicable when automating fairly straightforward repetitive tasks. Bots need explicit rules, structured data, and predefined workflows. Unlike them, AI has the capacity to tackle much more complex tasks with higher cognitive requirements and adapt to changing scenarios.
2. Data Requirements and Decision-Making
RPA thrives on structured data and pre-programmed rules but cannot learn or develop over time without human assistance. Unlike bots, AI can handle unstructured data as its machine learning algorithms detect patterns, make predictions, and finetune decision-making.
3. Flexibility and Adaptability
RPA bots need reprogramming if they are to adjust to changing processes or user interfaces. AI and ML are much more flexible as they can adapt to dynamic environments and enhance performance whenever they access new data and feedback.
4. Execution and Expertise
RPA implementation rarely requires too advanced expertise, so it is not uncommon to have business analysts or process experts in charge of it. With AI and ML implementations, a superior level of software engineering and data science expertise is a must.
5. Scalability and Value
It is true that RPA is easily scalable when the volume of repetitive and straightforward tasks grows. However, if processes change dynamically or become more complicated, the quality of bot performance deteriorates. AI solutions can learn and adapt to enhance performance over time and automate tasks and processes that are truly sophisticated, which increases their value proposition in the long term.
RPA vs AI – Competition or Collaboration?
Rather than juxtapose robotic process automation and artificial intelligence, many companies have chosen to utilize both approaches and leverage their combined powers to streamline operations and enjoy more flexibility.
1. Intelligent Automation of Processes
AI integration into RPA solutions results in upgraded bots that tackle more complex and dynamic processes. RPA bots can also leverage natural language processing and computer vision to process unstructured texts and derive information from visual data, such as scanned documents. Furthermore, ML models help RPA systems adapt to dynamically changing processes in scenarios they would otherwise struggle with.
2. Decision Support
AI decision support can complement RPA’s capabilities to handle predefined processes, as AI analysis of historical data and current conditions gradually optimizes RPA workflows. ML-detected patterns and anomalies in RPA-generated data and predictive analytics that foresee future trends are also welcome additions that serve as major upgrades to RPA bots.
3. Continuous Optimization of Processes
The combination of RPA and AI helps optimize business processes, for example, AI-powered mining tools can detect potential obstructions or opportunities by analyzing RPA logs. ML algorithms also utilize identified patterns and outcomes to suggest upgrades, while Natural language capabilities generate accurate performance reports and constructive recommendations.
RPA vs AI: Which One to Choose?
Should you go with robotic process automation or artificial intelligence implementation? Or should you combine them? Here is what you need to consider:
Companies typically use RPA for defined and structured rule-based processes, while AI can handle challenges that involve unstructured data, sophisticated decision-making, learning, and adaptation to dynamic scenarios. RPA relies on less data and solely operates on existing and unchanging systems. Conversely, AI training requires sizeable data sets, which results in more significant infrastructure investments.
While RPA rapidly boosts efficiency and reduces expenses for clearly defined processes, AI’s transformative impact is more profound and lasting and spreads across a wider range of business applications. Furthermore, RPA is easier to implement in-house as it does not need such advanced levels of technical expertise, while AI projects might require external tech vendors with access to a broader talent pool.
Last but not least, RPA’s adherence to clearly defined rules facilitates regulatory compliance. To address compliance considerations and grant transparent decision-making, AI often requires extra effort and vigilance.
Conclusion
RPA and AI have reshaped the face of modern business, and no wonder. Within a very short time frame, they have brought innovation to unprecedented levels and revolutionized all business processes and operations while boosting quality, accelerating ROI, and reaching almost unthinkable efficiency. Their differences only underscore the benefits of leveraging their combined capabilities and using RPA’s reliability and lower maintenance as a solid base to build upon with AI’s human-like cognitive prowess.
Scalefocus’ expertise in RPA and AI has already made us a trusted technology vendor for numerous companies worldwide. Of course, we would be delighted to discuss your own intelligent automation projects and challenges – check for more details here, and do not hesitate to get in touch!
