RPA and Generative AI are two popular tools in the digital transformation landscape
These two tools are widely used because of their wide-ranging capabilities. RPA’s handling of repetitive tasks enables employees to focus on higher value work; generative AI pushes the boundaries of automation through creation of original content.
Therefore, business leaders shouldn’t view these tools as fleeting trends, but as transformative technologies capable of reshaping their companies companies’ operational efficiency.
In this article, we will focus on:
- The characteristics of RPA and Generative AI
- Their differences
- Top 15 joint use cases
- To inform business leaders before they invest in these tools.
How can generative AI speed up RPA developers?
Generative AI can speed up RPA bots’ programming through helping them overcome the “blank canvas” problem. Similar to writers facing a “writer’s block,” citizen developers can suffer from the “blank canvas” problem– not knowing where to start designing a program from scratch, especially one dealing with complex logic or handling error requirements.
With generative AI, the user can provide a high-level description of what they want to do, and the AI model translating the demands into functional codes. Python, specifically, can be essential for delivering AI and automation because of its being open-sourced and widely available.
Learn more about Python RPA, and explore the top open-sourced RPA tools under $2,000 annually:
How can generative AI be used in RPA bots?
1. Customer service
RPA bots can create automated workflows in customer service for:
- Collecting customer information
- Updating databases
- Scheduling follow-ups
Simultaneously, customer service centers can use Generative AI models in their workflows to create personalized responses to customer queries, based on each customer’s history and situational context.
The combination will allow a highly personalized, efficient, and scalable customer service operation.
2. Marketing & advertising
RPA can automate some of the marketing operations, like collecting customer data or scheduling marketing campaigns.
Concurrently, generative artificial intelligence tools can create personalized content, like custom-tailored ads or personalized product recommendations based on the collected data. And copywriters can use generative writing tools, like ChatGPT, to create tags or headlines.
In Brazil, for instance, Burger King and McDonalds ran advertising campaigns, where ChatGPT wrote the slogans.
Learn more about Generative AI use cases in marketing.
3. Product development & designs
RPA can create automated workflows to handle:
Generative AI, on the other hand, could create new product designs or features based on existing data, enabling companies to rapidly prototype and innovate.
4. Data analytics & management
RPA can gather and pre-process data, while generative AI could generate synthetic data to augment existing datasets, fill in the missing values, or create data for testing purposes.
This conjunction can streamline the entire process of data analytics and data management, leading to more robust and reliable data analytics outcomes.
RPA in healthcare can automate administrative tasks, like scheduling appointments, maintaining patient records, or processing insurance claims.
And an intelligent automation technology like generative AI can create synthetic patient data for research without violating privacy laws, as well as generating possible patient outcomes based on their health data.
The health clinic Phoenix Children’s3, for example, used RPA and generative AI for complex tasks like predicting patient malnutrition, reducing appointment no-shows, and projecting emergency room visits based on seasonal data.
Learn more about how generative AI is being used in healthcare systems.
6. Financial services
RPA in banking and finance can automate data entry, compliance reporting, due diligence, or loan processing.
Generative AI, meanwhile, can generate potential financial scenarios for asset management and risk modeling, improve fraud detection, or provide personalized financial advice to customers.
For example, Deutsche Bank4 used AI and RPA to automate its Adverse Media Screening, lowering the number of false positives and improving compliance.
Explore 10+ use cases of generative AI in finance.
7. Human resources
RPA can automate HR tasks like granting PTOs, scheduling interviews, gathering employee data, or administrating the onboarding process.
Generative AI can assist HR staff by:
- Creating personalized training material
- Predicting employee performance based on historical data
- Simulating responses to various HR policies
8. Retail & ecommerce
RPA can automate tasks related to inventory management, order processing, or CRM management.
In parallel, generative AI can be used to create:
- Personalized product recommendations
- Virtual shopping experiences
- Dynamic pricing models based on real-time market conditions
For example, Chinese researchers used5 a PPGAN (Personalized Pointer Generative Adversarial Network) model to create short product titles. Their model outperformed conventional models by a click through rate of 5.18% compared to 3.53%.
9. Supply chain management
RPA can create an automation platform where users can track shipments, update inventory data, monitor freight conditions, and generate invoices.
Incorporating generative AI in supply chain management can help create predictive models for demand forecasting, optimize routes for logistics, or provide valuable insights about disruptions by simulating scenarios.
RPA and generative AI in supply chain management can minimize supply delays and optimize responses to unforeseen circumstances.
10. Real estate
RPA can be used in real estate to automate property data collection, updating listings, or handling lease agreements.
Generative AI can create virtual property tours, predict property values, or even create architectural designs with respect to cost, environmental, and spatial criteria.
For example, users have been able to create different designs and furnishing layout of bedrooms using Stable Diffusion.
Tasks like monitoring network traffic, identifying suspicious activities, or updating security patches can be automated with RPA.
Generative AI can, at the same time, simulate different attack scenarios, generate synthetic datasets for training the security models, or predict future security leaks based on patterns.
The use of RPA in education could entail student registration, financial aid calculation, class scheduling, or grade reporting.
Generative AI can, in extension, create practice questions, develop personalized learning materials, give real-time feedback, and more.
Explore how generative AI is transforming the education sector in more detail.
13. Legal services
Robotic process automation can automate document review, contract analysis, or legal billing.
Generative AI can create legal briefs, simulate different legal scenarios and mock trials for training, or even provide legal advice based on similar previous cases.
Learn more about how generative AI is being used in the legal field.
RPA in agriculture can automate tasks like data collection from crops, irrigation, or manure addition.
Generative AI can improve forecasting and productivity by creating predictive models for crop yield, optimizing farm layout for efficient planting, or simulating the effects of dry seasons or different farming techniques on supply.
RPA in manufacturing could include inventory tracking, quality control, or order processing. Generative AI, on the other hand, can design product prototypes, optimize production processes, or create stress testing scenarios.
Explore the top use cases of AI in manufacturing.
We kicked off the article without explaining what these technologies are, assuming they are widely known. If you haven’t come across them before, here are their definitions:
What is RPA (robotic process automation)?
RPA is one of the automation technologies that uses software bots to automate established business processes that are rule-based and repetitive.
RPA’s programming can be code-based, no-code, or hybrid, with each approach having its own characteristics.
RPA is flexible to automate more than 100 business tasks, including, but not limited to:
What is generative AI?
Generative AI is a subset of artificial intelligence (AI), focused on creating new content.
It uses algorithms like generative adversarial networks (GANs) and LLMs (large language models) to generate data that’s similar to the input data it’s been trained on. Generative AI has been used in content creation to make images and music, while it’s recently made strides in areas like drug discovery and design engineering.
Explore the use cases of generative AI in more detail.