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APA as the Foundation of Artificial Intelligence

Artificial Intelligence (AI) is expected to change how businesses operate and while the potential of AI automation is slowly being explored, its current hype is around how it is taking over routine operational work and creating opportunities for people to focus on more decision-oriented tasks.   

Why AI is still a developing concept

The problem with industries wanting to implement AI is the absence of organized, ready-to-use data. Data as an important business asset is still a relatively new concept and most data across enterprises if available is dispersed and inconsistent because of their complex IT structure. For AI to operate in real-time, a foundation of advanced analytics is important. In order to achieve this, legacy systems must be transformed, and their data management simplified.

For implementing AI in an enterprise, a single source of unified data and rules that assimilates knowledge about and experience with various mathematical methods and tools including statistical, time series, and graph analysis, as well as machine and deep learning is required. That single data source must also be able to feed real-time data across all business applications and analytics, support all departmental information needs, and should have the flexibility to be used in diverse analytic techniques that need different data structures. Additionally, the process for developing analytics also just like the process for developing applications must be automated and kept consistent with the pre-set business’s unified data and rules.

Moving towards AI

For AI to be integrated into an enterprise, it needs a holistic view of data and information, the absence of which leads to operational silos. In terms of its usage of knowledge representation, reasoning, and language processing, AI is in a sense, an extension of advanced analytics used in diagnostics and prescriptive analytics, automation, and machine and deep learning.

A logical step of moving towards AI would be to develop capabilities with advanced analytics. Using advanced analytics and automating process actions using a trust partner’s unified data, rules, management, governance, and control to process streaming data in real-time is key to creating a foundation for adding future AI capabilities.

Conclusion

The fastest and best way to become an AI-enabled enterprise of the future is to transform, accelerate and unify the development of business analytics. iOPEX can help you replace your legacy analytics with a unified data and rules platform that will simplify and manage your data for analytics, gaining your human workforce with ample time and resources to focus on business outcomes. The analytics can be reused, managed, shared, and controlled to add rigor in operational business process actions that are much needed for effective operational and automated analytics.



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