DEVOPSdigest

Artificial intelligence (AI) has played a critical role in shaping our lives and how we interact with technology since long before generative AI (GenAI) entered the mainstream vocabulary a few years ago. Today, AI is woven into our homes, appliances, cars, devices, applications, and so much more. You name it, AI has likely touched it.

There are no signs of AI's massive growth and adoption slowing down anytime soon. GenAI companies like OpenAI and Perplexity are growing at an astonishing rate, and Bloomberg predicts the GenAI market will soar to a staggering $1.3 trillion by 2032 (up from just $40 billion in 2022).

But none of this AI-driven innovation is possible without application programming interfaces (APIs). APIs have exploded in recent years and are consequently difficult to manage at scale.

APIs act as critical conduits through which AI systems access the real-time data and services that they need to function effectively. They are absolutely essential for supporting the seamless integration and widespread adoption of AI technologies.

What does this look like in action?

Picture a global ecommerce company that uses AI-powered chatbots to address customer support inquiries across countries. The chatbot uses natural language processing (NLP) to understand and respond to customer inquiries based on historical data, but where it really offers value is by providing language-appropriate responses based on real-time, contextual data.

This is where APIs come in. APIs give the chatbot access to real-time information on things like product databases and shipping status, in addition to translating its responses into the customer's preferred language in real time.

As AI proliferates, companies must ensure their APIs are optimized for AI, meaning they are discoverable, documented, and machine-readable. Companies that fail to optimize their APIs in this way will become increasingly invisible in a world where AI is vital for staying competitive.

Here are three strategies for building high-quality APIs at scale to fuel AI success:

1. Think of APIs as Products

APIs are more than just technical enablers. When built strategically, they can be revenue-generating products. In fact, Postman's most recent State of the API report found that 62% of companies have built APIs that create income, signifying a shift from APIs as technical artifacts to products that generate tangible results. And for some companies, APIs are in fact the company's core product(s).

Companies can build API products that enable AI success by applying the same formula behind every great product: Combining technology and design that maps back to real customer problems and meets the needs of the business.

This requires a deep understanding of customers and their pain points.

Why are they facing these problems?

How are they trying to solve for them currently?

What do they ultimately need to accomplish?

This in-depth understanding helps companies build APIs that meet customer needs versus simply building wrappers on top of code.

Great API products are built iteratively. Companies shouldn't aspire to create the perfect API product from the get-go — they should loop customers in on the process and continuously gather their feedback and input to validate their solutions and build trust. Companies can accelerate this process by using API prototypes or mocks to validate their offerings without expending the time and resources to build a full minimum viable product (MVP).

2. Enhance Collaboration

Collaboration is key for building high-quality APIs, but it doesn't come easily. The report also found that 44% of developers need to dig through source code to understand their APIs, 43% rely on their colleagues to explain APIs to them, and 39% say inconsistent documentation is their biggest roadblock to building stellar APIs.

The API development process is fraught with friction for most companies, which will undoubtedly hamper AI initiatives if left unchecked. Now is the time for companies to implement tools that support developer collaboration and productivity, like API documentation solutions for example. These tools serve as a single source of truth for software engineering teams, reducing duplicate work and allowing them to more easily discover internal and external APIs that can potentially be applied to AI use cases.

Companies should also consider adopting solutions that provide a unified space for developers and other teams to collaborate so that all of their API information is in one place. This helps to mitigate some of the pain points mentioned above around communication and chasing down information.

3. Take a Metrics-Driven Approach to Developer Experience

A positive developer experience is a critical differentiator and key enabler of building high-quality APIs. APIs are challenging to build and manage by nature, and taking a metrics-driven approach not only keeps developers happy, but holds conceptual ideas accountable and grounded in reality, enhancing API reliability and efficacy.

Companies can track the following four metrics to gain insights into the developer experience and iterate on their APIs' performance so they can optimize them for use in AI applications.

1. Bounce rate: This refers to the proportion of API requests that fail or return errors instead of successful responses. A high bounce rate can indicate that developers are encountering documentation or accessibility issues, or struggling with a difficult-to-use interface.

2. Time to first call (TTFC): TTFC is the time it takes a developer to make their first successful API call after discovering an API, which is an important indicator of an API's ease of use and adoption potential. If the TTFC is longer than optimal, companies can implement technologies that reduce this time and improve the developer experience.

3. Time to value (TTV): This refers to the time elapsed between when a developer starts working on an API to when they effectively integrate it to achieve its desired functionality/somebody successfully traverses the user journey. Reducing TTV is paramount for adoption and customer satisfaction, which is why it's such a critical metric to track.

4. Time to wow or time to delight: This takes TTV one step further and represents the time between when a developer first interacts with an API to the moment they have a profound realization of its potential/benefits that exceeds their prior expectations.

By implementing the strategies above, companies can avoid API chaos and build and manage a portfolio of APIs optimized for AI. As AI continues to grow and mature, companies who follow this guidance will be well-prepared with quality APIs that accelerate innovation and provide a competitive edge.