Lately, it seems that we hear about artificial intelligence (AI) every day and see it popping up practically everywhere. AI can do many things humans can - and do them thousands to millions of times faster. This enables us to take humans out of some workflows and instead rely on artificial intelligence to drive automation in many use cases.
Because AI has the potential to redefine multiple technology landscapes, it deserves careful attention. In this case, we have studied AI from an integration lens, exploring how and where it can change the integration landscape. In the context of our research, we have defined integration as, “the use of technologies that enable typically diverse systems within or between organizations to work together to achieve common business goals. This blog summarizes our findings.
First, as a part of our study, we identified eleven classes of AI-related use cases in the integration domain.
In evaluating artificial intelligence in the context of the integration use cases we arrived at a number of observations. Notably, AI indirectly creates demand for integration. This is due to the fact that enabling AI in an enterprise involves collecting, cleaning up, and creating a single representation of data, as well as enforcing decisions and exposing data outside, each of which leads to many integration use cases.
Second, weaknesses in AI decisions can be much more harmful than a weakness in a human decision for three reasons:
Therefore, the teams implementing AI use cases - whether or not they involve integration - should carefully consider the ramifications of those challenges, such as bias and privacy concerns.
Third, challenges, such as a shortage of skilled professionals and data and model interpretability, are limiting the effectiveness of AI adoption. For example, when applied to competitive fields, such as security or stock markets, AI-based systems can end up competing with each other which might lead to worse-than-before situations. One example is stock market flash crashes.
Fourth, multiple forces are driving future AI applications that will be offered as cloud APIs.
In our study, we explored how each class of use case is affected by the opportunities, challenges, and risks identified above. Among the use cases, we have found that four already have systems available and are likely to find more adoption. These are “security,” “data integration,” “extracting useful information from humans,” and “AI support within existing data stores.”
On the other hand, we have identified three use cases that do not face critical challenges but will need 5 to 10 years of development before wide adoption can occur. These include, “increasing usability,” “self-driving operations (DevOps and regulatory compliance),” and “automatic and self-service integration.”
Finally, we have found that the “business automation” use case, while accessible for large corporations, will often be out of reach for small-to-medium enterprises (SMEs), if they are not supported as a cloud API.
In our study, while critically evaluating how AI can affect the integration domain, we identified eleven classes of use cases that can significantly affect integration outcomes. AI has a significant developer community and a rich set of tools. Many real-life AI use cases have demonstrated their effectiveness. Despite the promise, there are obstacles. Risks, such as bias and privacy concerns, along with challenges, such as model interpretability and a shortage of skilled professionals, are limiting the adoption of AI. Our report, How Will AI shape Future Integrations? discusses the opportunities, risks, and challenges in detail, evaluates the feasibility of each use case, and identifies four classes of use cases ready to be exploited.
Learn more about our research here.