Q&A with ZAPTEST on how Generative AI can transform software automation and RPA
Generative AI has exploded onto the scene and captured mainstream attention. While much of the current excitement centers around text and visual content generation, this form of AI has incredible potential to become a multi-purpose tool that can go far beyond current use cases.
One of the most exciting frontiers is the convergence of AI with Software Automation and Robotic Process Automation (RPA). Prompt Engineering is the key to hitting this potential by facilitating a conversational interface with AI that can be part of an exciting hyperautomation future.
We sat down with Alex “Zap” Chernyak, the founder of ZAPTEST, a market-leading enterprise-level Software Test Automation and Robotic Process Automation tool. In this Q&A, Alex shares his thoughts on the potential of Prompt Engineering in the RPA and Software Automation space.
How can prompt engineering help with software automation and RPA?
Prompt engineering is about developing a common language that we can use to interface with AI. It’s about understanding the words, terms, and instructions that we can leverage to get advanced machines to do specific tasks. Learning and thinking about prompt engineering and experimenting with different strategies is essential if we want to get the most from Generative AI.
Where this all becomes more interesting and commercially viable within the enterprise is by using prompt engineering to build software automation. One of the most interesting and so far underutilized elements of automation is Attended Automation. This type of automation is focused on more front-office tasks rather than the traditional back-office duties that RPA was first used for.
Attended automation is like having a personal assistant that you give menial tasks to perform. A lot of automation is set to run based on times or specific triggers, but Attended Automation is more dynamic because it allows the user to stitch it into their workflows.
In an RPA sense, through the use of chatbots, you could trigger a bot to perform a variety of tasks, like sending emails, transferring data, or even logging into a website and buying something. Basically, any task that you can do on a computer.
The big power here is that conversation becomes the new interface between humans and AI. From there, you can use chatbots to trigger RPA workflows for a variety of different purposes.
What do you mean when you say conversation is the new interface?
Currently, users can interface with software automation tools in a few different ways. Traditionally, it was coding, but modern software like ZAPTEST offers codeless drag-and-drop interfaces for users to map human-computer processes.
However, Large Language Models (LLMs) provide a new way to interact with software automation tools. They can break down our instructions into a language their computers understand and use that as the prompt to build software automation workflows.
Obviously, we’ve all seen how this works in terms of Voice Search, where you can, for example, get Alexa to order you a pizza. However, this would be the next step, where you could get an AI to perform a wide range of business tasks on demand.
Who stands to benefit the most from these tools?
Everyone, from startups to enterprises, can get quick wins from software automation. However, nontechnical teams and citizen developers will find a frictionless pathway that allows them to build fairly sophisticated automation without needing to learn a line of code.
The power of software automation lies in its ability to democratize the digital world. You don’t need to study for years or be well-capitalized to achieve great things. While developers will still be crucial, prompt engineering will level the playing field somewhat and make success less about the resources you have and more about the great ideas you have.
In countries where access to education is limited or exorbitantly expensive, prompt engineering can help knock down some of the barriers involved in building products and provide serious support to new founders.
What are some of the most interesting use cases of prompt engineering for RPA?
RPA is a hugely flexible tool that has been adopted across a wide range of industries. Generative AI has a similar malleability, meaning that prompt engineering will be a useful skill across several different disciplines.
Marketing is one area that has already benefited greatly from automation. However, good advertising still needs that spark of creativity. With the right prompt engineering, you could ask a chatbot to, for example, scrape your website for product images, come up with snappy sales copy and descriptions for each product, and then feed them to a Google Performance Max campaign that specifies A/B testing.
Customer service is another discipline that will be greatly enhanced by the combination of Generative AI and RPA. LLMs can converse and understand human conversations and trigger RPA bots based on these interactions. Teams could operate with a wholly automated, 24-7 support that would be able to update CRMs, solve problems, and serve users excellent information, all at a reasonable cost.
Intelligence and reporting are other areas where RPA and prompt engineering can produce great results. RPA produces and outputs lots of data. This data could be fed into an LLM to produce both reports and analyses.
These are just a few use cases that most businesses could take advantage of. The strength of both RPA and AI is their flexibility, which allows them to be bent toward any use.
RPA can mimic human-computer tasks. In some ways, it’s like a digital workhorse that never gets tired and never makes mistakes. However, for years, it still needed a human touch to help with decisions and interpreting specific data. AI helps bridge that gap by adding data-driven decision-making into the mix.
What problems does prompt engineering solve in RPA and software automation?
One of the most commonly cited challenges with implementing RPA solutions is their inability to deal with unstructured data. Bots need clear and well-defined instructions. Without them, they will return exceptions. The big problem here is that about 80% of the data we have around the world is unstructured.
Now, of course, the software automation community has grappled with this problem for quite some time. There are several solutions to these issues in the form of Optical Character Recognition (OCR), Cognitive AI, and Computer Vision Technology (CVT). These sorts of tools can “understand” unstructured data and convert it into a form that bots will find agreeable.
Generative AI will be useful across a wide range of industries. It can process documents, understand and analyze large tracts of data, produce reports, and even facilitate data-driven decision-making. Prompt engineering is the key to unlocking these benefits, and when connected to RPA tools, it will allow businesses to unleash a new era of productivity.
What challenges does prompt engineering pose in the RPA space?
Generative AI, like any emerging technology, is not perfect. The kinks are still being ironed out as we speak. However, the speed of progress at which this technology has improved suggests that some of the teething problems will soon be eliminated. However, there are a few areas where businesses need to stay attentive.
One area is intellectual property rights. If you look at the terms and conditions of Generative AI tools like ChatGPT, there are no assurances that the prompts and words you enter will not become open source at some point in the future. If you’re entering data that contains confidential information or IP, it could become exposed. In short, be very careful about which data you enter when you are prompt engineering.
However, overall, these issues are pretty minor and manageable when compared to the benefits that Generative AI can unlock. All new technology brings an element of risk that teams need to mitigate, and Generative AI is no different.
Any final thoughts?
Generative AI will open up some incredible new possibilities in software automation and RPA. Tools that allow teams to mimic human cognition and decision-making have added a new dimension to software automation, and prompt engineering allows anyone to harness the creativity of AI for a wide range of purposes.
Ultimately, the convergence of AI and RPA is part of an end destination toward hyperautomation, which is a time when everything that can be automated will be automated. Prompt engineering, most likely by voice, will be a big part of that process as humans converse with robots and instruct them to build applications and processes that help us solve a wide range of problems, from esoteric business use cases to universal things like climate change.
About ZAPTEST
ZAPTEST is a full-stack Software Automation testing suite that offers cross-platform, cross-device, and cross-application software testing automation and RPA. With a range of innovative features from 1Script Automation, AI and Computer Vision, and WebDriver Integration, ZAPTEST is the perfect tool for any Test Automation use case. What’s more, Enterprise clients get a dedicated full-time ZAP Expert to ensure implementation and ROI are as straightforward as possible.
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