Four ways AI and integration will come together in 2020

Artificial intelligence (AI) and integration increasingly play symbiotic roles in shaping modern application development, and these roles will only continue to expand during this decade. On the one hand, AI is proving to add strong value to the software that developers rely on to run, connect and protect their applications. On the other, we also see a steady rise in integration, particularly as more apps rely on APIs to combine services and data from diverse systems.

To understand how integration and AI are combining to impact enterprises, my colleagues and I recently conducted an extensive emerging technology analysis examining the opportunities, challenges, and usage patterns. All told, we found eleven use case types. But adoption for many of them will be limited by risks, such as bias and privacy concerns, and challenges, such as the shortage of skilled professionals and the difficulty of interpreting AI models.

Still, four scenarios are minimally affected by the risks and challenges we have identified, and consequently, we expect them to have a stronger presence in 2020. Let’s explore our key findings in these four areas.

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Cloud APIs will democratize AI

To date, custom AI model building has been limited to large organizations with the resources to tackle the complexity of AI deployment and management, not to mention the scarcity of experts and data. Instead, we need to build a few models and allow them to be shared by many. Cloud APIs make it possible for a few organizations to concentrate on providing the expertise and collecting data required to solve a given problem, and then share or market the AI models they build. In this way, cloud APIs hold the promise to solve many AI use cases in 2020 by letting organizations of all sizes gain access to AI models provided by data experts.

AI for API security will become mainstream

The wide availability of APIs is increasing both the attack surface and potential risks of systems. Hackers have turned their attention to APIs in order to compromise and control apps, services, data, networks, and devices. And increasingly, they are applying tactics that circumvent static security policies. In the last couple of years, companies have begun implementing AI-powered API security, which closes the gaps in traditional rule-based API protections by automatically discovering anomalous API traffic behavior. In 2020, this adoption will accelerate as AI-powered security moves from a competitive differentiator to a critical must-have for many businesses.

AI will become a standard tool for data integration

To date, organizations have spent extensive resources on reconciling data from many sources—often with different formats and semantics—into meaningful records. AI can streamline this process by automatically finding data with similar meanings but different representations and then reconciling them. Together, AI and big data technologies have exponentially increased the value of data. And the significant advantage gained by making full use of available data in an agile manner will further drive the use of AI for data integration in 2020.

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AI will drive automatic and self-service integration

AI can be used to simplify the process of creating integrations by automatically generating code based on higher-level input from users or by providing suggestions (e.g. code completing) and automating tedious steps, such as data mapping. With low unemployment expected to continue in 2020, organizations will remain hard-pressed to find developers. Therefore, 2020 will see a growing demand for AI-powered tools, which facilitate the delivery of integrated services fundamental to serverless apps and microservices by lowering the expertise required from developers while increasing their efficiency and productivity.

Artificial intelligence is complex, and integration continues to evolve as more enterprise development revolves around microservices and serverless applications. However, enterprises can begin to gain the combined benefits of the technologies in 2020 and beyond.

You can learn more about the four use case types described here, as well as the other seven we have identified on GitHub.

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