Why AI’s Breakthrough Use Case for Business is to Help Develop APIs

Article by Vikas Vijendra, Solutions Engineering Team Lead, Kong Asia-Pacific and Japan.

Artificial Intelligence has achieved a major breakthrough in the past six months as the Australian Government created a strategic vision and action plan to develop and adopt reliable, secure and accountable AI systems.

Although there is huge interest in the use of AI – 82% of Australian organizations have some capacity for machine learning, according to a survey – only a fraction of interested parties have an AI use case in production.

That’s changing dramatically as AI improves to help fill technical knowledge gaps, allowing people without years of IT experience to assemble code-based tools that can help them. assist in their daily operations.

AI is already a big time saver for web application developers. Cloud providers offer several advanced AI-enabled application programming interfaces (APIs) for developing cognitive applications. Examples of these services include natural language processing (NLP), facial recognition, and video analytics. Using the APIs of these services significantly reduces development time, as the cloud product does most of the heavy lifting.

In the future, AI could allow users with limited technical skills to discover organizational data sources and create integrations between systems in the form of additional APIs.

APIs are already essential enablers for the operation of a range of large software applications. For example, every time we call an Uber, our app keeps sending us messages based on the driver’s location. Similarly, when we use social networks, we are notified when our relations publish content. Requests and returns of this data are handled by APIs, which act as the connecting “glue” between systems.

APIs will continue to play an important role in technology infrastructures, regardless of how the Internet evolves in the meantime.

Development skills are in demand

Reducing technical barriers to creating new applications and services is desirable for several reasons.

First, Australia has an acknowledged shortage of tech talent.

By one estimate, the country needs about 60,000 new tech workers a year, but universities are only managing to produce a fraction of that amount.

Suppose some of the technical complexity of coding can be abstracted. In this case, Australian organizations may be able to bring in non-technical or technically curious people in the business to digitally innovate on their own, broadening the horizons of their talent pool.

Second, many IT organizations and business leaders want to have “stewards” who advocate for digital change in their respective business units or functions.

Using a decentralized approach to innovation could benefit from improved AI, helping more organizations translate business domain knowledge into APIs and API-enabled products and services.

LCNC summits

What organizations are looking for is an evolution of what we currently have in the market in the form of low-code and no-code development environments – LCNC for short. These are designed to help people in the business experiment and assemble new applications and new ways of consuming or processing data.

A recent survey revealed that 47% of respondents already use LCNC platforms, and more intend to move in this direction. The survey revealed that the top three reasons for using LCNC platforms are to automate workflows, create new applications, and speed up development time. This is important in an agile world, where the time to value is short.

To date, LCNC has been seen as a way to lower the technical barrier to entry for AI implementation itself. As a recent Harvard Business Review article put it, “[LCNC] makes it possible to deploy artificial intelligence – one of the most transformative technologies in a generation – without hiring an army of expensive developers and data scientists.

LCNC arguably becomes even more powerful when it is no longer just a platform for doing AI projects, but when AI is part of the platform itself, helping users of the platform to create all types of digital applications as they see fit.

In addition to helping non-technical users build code-based products and services, AI can also automatically document APIs and other tools created by non-technical users.

Additionally, it may have a role in monitoring security threats and/or optimization opportunities for APIs that have been created.

AI is also for API developers

For more advanced API developers, AI also offers a chance to broaden the skills and ownership of the interfaces they create.

In some contexts, APIOps teams use DevOps and GitOps principles in the context of the API development lifecycle, integrating automation into the API development workflow.

Traditionally, a DevOps team managed infrastructure and code deployment automation while developers focused on building features. As the developer community continues to explore ways where AI/ML can benefit software development in general, an APIOps developer has specific responsibilities: creating the interface and automating deployment.

Here’s an interesting perspective on how the digital transformation of apps, in general, has evolved over the past few years. The next generation of applications built by developers beyond the current shift from legacy to microservices should be intelligent platforms with built-in AIML (API-driven platform foundation) functionality.

AI is still used in much the same way as it is for non-technical users: that is, by removing complexity and allowing an individual to accomplish more in a short time. What it does show, however, is the wide range of improvements made possible by integrating AI into all internal development processes.


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