RPA vs. AI: Making Informed Choices in Implementation
The dynamic business landscape is demanding enterprise stakeholders and technical leaders adopt cutting-edge technologies like RPA and AI. 79% of tech respondents quote that RPA remains as an optimal technology that has the potential to provide higher ROI. However, when it comes to digitization, many technocrats are often confused about which is the right technology to be implemented.
Though both RPA and AI share connections to some extent, they differ largely in technical specifications and objectives. Artificial intelligence enables systems to learn and function on their own. Whereas, RPA-augmented systems are not just capable of accomplishing predefined tasks; they can plan how to complete new tasks autonomously. To make the most tactical decision, it is crucial to weigh all of the differences, characteristics, and use cases.
4 Implementation Aspects to Consider Before Choosing Between RPA and AI
- Skills Requirement – AI implementation requires dedicated experts skilled in Machine Learning and data science. RPA, instead, can be easily implemented/learned by anyone with elementary computer skills. Thus, RPA is often considered a more accessible option for business leaders looking to streamline their workflows.
- Timespan – Embedding AI within machines involves huge programming and takes longer to get outcomes, while RPA eliminates programming from the equation. RPA also needs minimal maintenance than AI hardware/software. However, AI possesses greater precision than RPA, so if accuracy is leaders’ top priority, AI is the right choice. For obtaining fast automation results, RPA is the suitable option.
- Degree Of Complexity – AI can manage more complicated functionalities than RPA, ensuring adaptability in the long run. Yet again, AI hardware requires additional resources to set up and retain, so it may not be affordable for startups. Also, AI projects are more time-consuming than RPA projects. For these purposes, many specialists recommend startups embark on RPA implementation and then adopt AI when they’re ready. With this approach, leaders can progressively introduce automation into their business without overstretching their assets.
- Cost– In terms of the hardware/software requirements, AI can be quite expensive. For businesses on a close-fitting budget, implementing Robotic Process Automation can be a cost-effective choice. Besides, RPA doesn’t involve the same rate of investment in information annotation and collection as AI tools.
AI is an established technology with huge growth potential. But, it also introduces some risks related to biased outcomes or unanticipated errors during execution. RPA is a slightly risky technology and can automate complex business functions with better flexibility.
AI & RPA Synergy: Maximizing Automation Potential
Automation will become more sophisticated by using RPA platforms augmented with Artificial Intelligence (AI) and Machine Learning capabilities. This will allow RPA bots to more efficiently automate processes and optimize their own performance over time.
RPA is often considered the cornerstone of automation, offering the ability to imitate human activities like data exchanges, entry, and tool interactions. Whereas, AI and Natural Language Processing help RPA bots to perform logical actions like decision-making. By uniting these two technologies, companies can streamline a wider range of practices with higher accurateness and productivity.
Use Cases of how RPA and AI intersection Helps Industries
1). Financial Services
The combination of process automation and data analysis/pattern recognition aids financial firms in experiencing higher efficiency and smart decision-making. These synergies allow new-age FinTech firms to navigate complexities in the regulatory landscape and easily adapt to market conditions. Moreover, delivering tailored services can be done easier with the personalization capabilities of AI algorithms.
- KYC & Mortgage Processing– Financial institutions can delegate the responsibility of preliminary customer data collection, KYC verification, and data entry processes. Subsequently, they can deploy AI algorithms to analyze the gathered data, assess credit risk, and recommend mortgage options. Algorithms consider the customer’s financial history and market trends as the input for mortgage processing. This way, financial administrators can understand risks, make strategic decisions, and ensure compliance.
- Portfolio Management– Processes like transaction execution and rebalancing can be effectively handled by RPA bots. Synonymously, AI algorithms perform the analysis of economic parameters and personal investor preferences. These outputs enable finance firms to deliver insights and exclusive investment strategies for their customers.
Several labor-intensive operations associated with the insurance sector can be transformed by the integration of advanced technologies. The transformative power of AI and RPA helps insurers automate processes, enhance user experience, and strengthen information security.
- Renewal and Management of Policies– The data analysis functionalities of AI algorithms can examine previous claims and policyholder behavior in line with market trends. This offers an opportunity for policyholders to take advantage of discounts and alter their coverage options. Insurers can then use Robotic Process Automation solutions to regulate policy renewal notices and premium evaluations as these are tiresome tasks.
- Fraud Identification and Prevention – RPA in insurance combined with AI can increase the chances of detecting fraud occurrences in the first place. AI algorithms can assess huge volumes of data, including past data, user profiles, and transaction data. This analysis pinpoints suspicious patterns or anomalies in user actions that indicate fraud. RPA bots can refine the analysis process, and discover potential ways for fraud activities.
Let’s look at a use case of RPA & AI in insurance:
One of the accredited insurers in the US & European continent is PZU. Dealing with a range of products and a huge customer base, it was challenging for them to deliver personalized customer service. By partnering with a strategic RPA service provider, they implemented bots for handling user operations like data processing, refunding, and analysis.
“With these bots, we achieved 100% accuracy of data inputting and processing”, says, Szymon, Digital Process Expert.
For retailers, optimizing operations and strengthening decision-making is vital to achieve a competitive edge. The intersection of RPA and AI in retail is mainly directed at modernizing user services and experiences. The below picture shows some of the retail areas that can be highly modernized with AI and RPA.
- Stock Management – One of the effective ways to reduce redundant costs in retail is by automating stock management. By integrating ERP systems with RPA systems, bots can track inventory, process orders, and replenish stocks based on demand. AI algorithms can perform rapid forecasts based on real-time stock availability data. This data can be collected from sources, such as vendor feedback, weather conditions, and demographics.
- Price Optimization– It is highly crucial for retailers to constantly assess their competitors’ pricing updates across various networks. Robotic Process Automation implementation can be helpful in studying competitor’s behavior and performance. Similarly, AI algorithms evaluate market conditions and suggest pricing strategies for retailers that maximize revenue.
The blend of RPA and AI enables industries to experience zero-human intervention operations and launch innovative business models. For digital leaders, it is crucial to stay updated about the potential of RPA and AI and understand how they can be implemented to address business challenges. By embracing the full potential of RPA and AI, leaders can drive their enterprise’s digitization journey forward.