Generating ROI in AI / ML and IoT / IIoT projects with Hybrid Public Cloud and Kalki.IO

Companies have been debating about IoT, AI / ML and digital programs which are exclusively hosted in their data center or fully on Public Cloud like AWS / Azure or Google Cloud. However, the Return on Investment (ROI) for many of these programs are not very clear mainly because of the way they are envisaged and rolled out. Many AI / ML or Analytic uses cases fail because of lack of clear ROI or Use Case or a well defined strategy to migrate from a Proof of Concept (POC) to production.

IoT and IIoT are critical to the digital transformation journey for many industries. While AI / ML and Analytics do have a of hype associated with it, customers are slowly but steadily trying to strategize about investing in compelling use cases with clear ROI. While data in itself does not bring valuable insights or savings, so does Analytics or AI or ML by itself generate valuable insights or savings unless applied to the right problem with the right set of data with a realistic or practical set of objectives in mind.

The existing data centers hold massive amounts of data. However many of the Use Cases that can potentially generate savings or ROI or create competitive advantage are not available because of lack of IoT or IIoT or a digital transformation strategy in place. A stand alone program to address this would not be ideal as unless the problem is well defined, the gains are very difficult to be quantified and hence integrating existing data with a clear digital strategy, with AI / ML for IoT will be a critical driver for success

A Hybrid architecture with Public Cloud Platforms like AWS / Azure or Google Cloud with dedicated IoT Infrastructure for Distributed Energy Resources and Energy & Utilities like kalki.io and SyncConnect that integrates to the IT infrastructure is ideal for such AI/ML use cases to generate clear ROI's. The approach is to integrate and deploy specific Use Cases with a Platform like kalki.io and SyncConnect that can collect field data from IoT or IIoT devices, sensors or gateways, Integrate with existing IT / OT systems within the enterprise, create a digital twin that feeds the AI/ML Cloud infrastructure with realtime data for training and ongoing analytics. With the interfaces and training designed to be ongoing with improvements fed back as Insights into On Premise applications it will create a symbiotic positive spiral and benefits.

With the right use cases, right processes to build the POC to seamlessly transition into a Production System , with a Hybrid approach to data collection and integration, with an ongoing effort at training and improving the data set and weights, Companies can create real ROI and bottom line improvement with AI / ML and IoT / IIoT.