AI-driven connected enterprises enhance data integration and streamline workflows using APIs and AI tools.


Copyright: – “The AI-Driven Connected Enterprise” – APIs, RPA, Automation


SwissCognitive_Logo_RGBSuccessful and innovative enterprises are well connected. They are notably good at preparing and harnessing external data. Artificial intelligence (AI) can enhance sources, processes and workflows, making a well-run enterprise stronger, quicker and more competitive.

Being able to access and use data from customers, suppliers and other stakeholders is a good indicator of an organization’s capacity to make the right decisions. An externally informed mindset, according to the authors of a McKinsey report on innovative companies, is less vulnerable to biases and internal politics and enables rapid course-correction of strategies, R&D priorities and other initiatives.

Applied smartly, information can improve decision-making and erode inefficiencies. The right kind of data infrastructure is what enables a company “to break down (or at least perforate) silos,” as McKinsey puts it. What you need are integrated data connections, more structured data, and a platform or fabric that can unify workflows, tasks and analytics. All can benefit from AI.

Connectors And APIs

Data integration is a complex equation. To start with, enterprises use myriad application programming interfaces (APIs), typically paired with connectors, to link with data sources. Managing these sets is a challenge. One way we do so is through crowdsourcing, enabling the reuse and adaptation of capabilities.

Many of our clients are already familiar with the task-mining capabilities of robotic process automation (RPA) and AI/machine learning (ML) algorithms. But you also can use AI to build and manage your API infrastructure.

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An emerging use case for generative AI (GenAI) is developing, optimizing and protecting APIs. (See, for instance, this Google Cloud session.) These kinds of deployments can, in turn, trigger a virtuous cycle: simplifying existing stacks of APIs, which make it easier to adopt more AI. The other prerequisite to using data is making sure that it’s in good order.[…]

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