We are witnessing a quantum leap towards AI-powered automation and the democratization of AI. What do they mean for the world of developers?
The launch of ChatGPT late last year has given developers a foundational model with enormous potential to revolutionize industries and businesses. Over the last few months, we witnessed an AI gold rush, with the much-awaited releases of ChatGPT-4 and Google Bard, both of which promise advanced language processing capabilities that can improve the efficiency of automated workflows.
Today, over 90% of organizations in Australia, Singapore, Japan, South Korea, and China have already made Intelligent Process Automation (IPA) a key focus, going beyond Robotic Process Automation (RPA) to drive comprehensive end-to-end process automation at scale.
Moving forward, we can expect generative AI tools such as ChatGPT to further accelerate IPA projects in the region, given that advancements in generative AI are already helping automation developers with tasks like generating test code.
Accelerating the development of test scripts
With some languages better suited for certain frameworks, libraries, and platforms, it has become necessary to employ multiple languages in automation projects to integrate different components seamlessly.
Modern automation projects may require integration with existing codebases or systems written in different languages to maintain compatibility and extend functionality. However, this task may be particularly challenging for developers who lack proficiency in programming languages used in existing systems.
Generative AI tools such as ChatGPT can help plug this gap, generating test scripts across languages such as Python, JSON, C and XAML based on the requirements of the user – and even converting code sequences from one programming language to another. Besides improving developers’ work efficiency and saving significant time in automation projects, generative AI is democratizing digital skills education and lowering the barriers to entry into tech roles.
Empowering citizen developers to create automation workflows
A challenge in scaling automation projects is a lack of technical skills, but AI tools such as ChatGPT are plugging this gap by enabling non-technical users to generate automation workflows from simple language descriptions. Non-technical users can input a simple prompt explaining what they wish to achieve, and the generative model is able to create a code as an output, for a true no code experience.
Generative AI is a game-changer that enables non-technical users to easily achieve automation goals and unlock new possibilities in their day-to-day work. It not only acts as a catalyst for citizen development, but also helps businesses save time and resources by empowering non-technical users with the ability to generate automation workflows independently. The natural language processing capabilities of generative AI tools are making coding accessible to all and democratizing the automation development process further.
Streamlining the user manual creation process and automating updates
Creating user documentation can be a tedious and time-consuming task for developers. Using AI generation tools to create user documentation for new automation processes or systems not only provides time savings, but also delivers better consistency and accuracy.
For example, our team has developed a series of connectors to OpenAI ChatGPT to send prompts and receive responses, enabling businesses to quickly create bespoke content for user manuals.
Advanced versions of ChatGPT or similar AI generation tools can also potentially help with updates in user manuals. Developers only need to communicate the latest changes to the system, and the model will automatically incorporate changes into a new version.
Generating testing data
Newly created automation processes often require a specific test dataset, such as marketing contact lists, supplier lists, and invoice data. With a single prompt, advanced AI tools can quickly generate randomized sets of dummy data that respect logical integrity rules – accelerating the creation of test data.
Developers can also easily customize the output by making a few tweaks to the prompt. With the ability to fast-track the creation of test datasets, developers can build automation projects more efficiently and with greater accuracy.
The way forward
Generative AI is well on its way towards becoming a mainstay in the automation development process. This means developers need to quickly build new skills to stay ahead of the game.
The value of good communication skills cannot be overstated, as developers need to work closely with different stakeholders, including end users, to ensure that automation processes fully address stakeholders’ needs and requirements. Beyond technical chops, developers need to solidify their understanding of business operations to be able to identify opportunities for automation.
Advancements in large language models may have the potential to improve automation workflows and revolutionize automation development, but the road to mainstream adoption and responsible usage is still paved with challenges.
For generative AI tools such as ChatGPT to serve as a force for good, we need to address data privacy concerns and develop guardrails to better protect against new threats and vulnerabilities.
Additionally, developers must be aware of the biases that can unsuspectingly creep into AI algorithms and work towards reducing or mitigating this risk.
This necessitates developers to work closely with human subject matter experts to teach AI models the intricacies of business functions to better validate, update, maintain, and increase the accuracy of AI systems. Human-in-the-loop capabilities reduce the risk of poor decision-making from AI algorithms by capitalizing on employees’ abilities to correct errors and provide feedback to AI systems.
Ultimately, it is critical to balance innovation and the responsible usage of AI tools as developers capitalize on their potential to democratize the automation development process to drive sustainable growth and success.
