Artificial intelligence (AI) has led to a seismic shift in the business landscape, largely due to the surge in popularity of large language models like ChatGPT. From predictive models that foster better decision-making to generative AI code tools that enable teams to build applications faster, AI offers incredible benefits to organizations. Businesses need to embrace this technology or risk falling behind their competitors.
Low-code platforms, which allow software engineers and employees with minimal coding experience to automate processes and build apps using drag-and-drop interfaces, have been blazing a trail for years. These gains will fortify the areas where AI alone currently falls short. This blog post covers five key benefits of low-code AI tools that unlock the speed and efficiency gains of AI while minimizing the risks and side effects.
[Did you know that you don’t have to choose between using AI and keeping your data private? Learn more with Implementing Private AI: A Practical Guide.]
Low-code AI tools make it easy to build custom AI models. For example, with Appian AI Skills, you don’t have to hire an extensive team of data scientists and data engineers or invest heavily in new infrastructure. If you want to create a high-quality model to classify and extract information from incoming documents, you simply upload a batch of documents and have Appian perform the model training process on your behalf. Once you deploy this to production, you have your own model—trained on your own data—to save you time while achieving incredible accuracy.
Corporate boards and organizational leaders alike are mandating the use of AI in business. It’s not really a question of “will you use AI” but rather, “How will you use AI?” But keep in mind—even if there’s a mandate to use AI in the business, you still need to get a tangible return on your investment.
AI process platforms that include low-code are purpose-built to help organizations design, automate, and optimize end-to-end processes. This ensures that you can place AI into a wider process so that it immediately generates real value. For instance, you might already have mapped out a customer service process involving multiple apps. With a platform approach, you can easily integrate generative AI tools that incorporate natural language processing into your customer service applications to help reps craft effective response emails fast.
AI may grab much of the spotlight, but it’s not the only musician in the band. Mastering end-to-end process automation requires multiple tools, including robotic process automation (RPA), API integrations, and workflow design. AI isn’t a skeleton key—sometimes, an RPA bot is what’s needed to do the trick. For example, procedural, step-by-step tasks like entering data into a system lacking an API would be better served by RPA, which can be more precise than AI (especially considering the possibility of AI hallucinations). Remember that AI is one tool among many. Don’t lean on it to the exclusion of other foundational technologies.
[To truly impact the full enterprise, it’s critical to take a wider view of automation. Learn how by reading What Is Process Automation?]
AI depends on data. Whether you’re using AI to route emails, generate interfaces, study metrics to make predictions, or produce data visualizations, everything relies on your underlying data infrastructure. Yet, too often, data remains locked in disparate data silos, making it challenging to operationalize AI.
Some low-code platforms offer data fabric architectures, which help unify enterprise data and provide a 360-degree view of data across systems. Instead of migrating data, a data fabric allows you to work with your disparate data sources in a virtual data layer—no need to move or refactor data to make it available for use by AI models.
Many AI tools available on the market come with privacy risks. While some companies choose to take on this risk, heavily regulated industries like healthcare, pharmaceuticals, or public sector organizations can’t take chances. Accidentally leaking protected health information will lead to heavy fines. Look for low-code AI tools that are part of a platform that has already invested greatly in security and compliance.
Beyond that, low-code AI tools include governance frameworks that ensure you don’t unknowingly introduce security concerns. Consider the example of writing code with generative AI. While generative AI will generate code when asked, there’s no promise that code will be performant or secure. You still need expertise to ensure you aren’t introducing vulnerabilities into software or releasing bugs based on incompatible code versions. With a low-code platform, most of the coding has been completed by teams of developers and reviewed by security experts. You won’t have to worry about introducing vulnerabilities due to hallucinations or AI training issues—the low-code platform development team has done the work on your behalf.
In the AI era, all organizations face incredible pressure to innovate fast. But you’ll lead the pack if you can leverage AI in a way that generates solid value for your business while protecting it from the inherent risks.
Combining AI with low-code can help you harness the power of AI without getting bogged down by these risks. These tools can help streamline development and offer a seamless way of integrating AI into your operations. They also come with the added bonus of built-in security and compliance features to help protect your reputation (and reduce your risk of hefty fines). In a nutshell, low-code offers a no-nonsense way to quickly and safely get your business ahead in the AI game.
This blog post mentioned how AI has to be part of a wider automation effort. If you want to learn what else should be in your automation portfolio and how best to approach these efforts, learn more by downloading the report, Gartner® Emerging Tech Impact Radar: Hyperautomation.