Data-driven AI systems are data-driven AI systems, not by intuition or personal experience. The character of digital companies is clearly different from those that are not. Where traditional organizations use technology mainly in their operational core, digital organizations are applying it to its outer limits with the ultimate goal of creating customer satisfaction. It does this by maximizing the potential of its data by discovering new opportunities, hidden risks, emerging customer expectations and competitive moves. These insights are gathered with agility and ease, and in context, enabling companies to respond quickly and to the point. As digital organizations are increasingly driven by data, so are their decisions and actions.


If data is the lifeblood of a digital company, Artificial Intelligence (AI) technology is at its heart. AI, especially its subsets including machine learning, deep learning, and advanced analytics, can automate much of the insight-gathering and decision-making in data-driven enterprises, and amplify data of value many times over.


But simply using the latest AI solutions doesn't make a data-driven company. Most incumbents need to take several steps before they are ready to transform. Unfortunately, they face many obstacles, including, but not limited to, an inflexible core trapped in old technology, outdated processes, and a lack of digital skills.


However, with the right approach and partners, a successful transformation is within the grasp of every organization. Infosys has helped many clients through this journey using the following strategies:


With the goal of moving clients away from conventional use cases or point solution-based approaches to enable them to monetize data on an industrial scale, we started by crafting a blueprint opportunity to create value through data. From there, we devised a roadmap for building the capabilities needed to realize the opportunities outlined in the blueprint. Broadly speaking, the roadmap requires:


Modernize the core of enterprise legacy systems so they can undergo digital transformation
Build intelligent intelligent systems that find hidden or unknown signals and correlations in data Use Artificial Intelligence technologies to create learning, adaptable organizations that evolve at breakneck speed


Here is a brief explanation of each:

Modernizing the enterprise core: There is a lot of data and insight trapped in silos within the legacy organizational core that must be released to create a flexible underlying set of services. This foundation can be broken down into components, dynamically managed, and automated to convey evolving contexts. But to do so, legacy systems at the core of the enterprise must first be modernized or divested. This is what we do at retail mortgage banks to enable them to generate credit scores for prospects, and process applications in real-time. Specifically, we redesigned the credit bank acquisition decision engine and tweaked their old mainframe to build agility, after which they were able to generate credit applicant scores in less than 50 milliseconds.


Build intelligent cognitive systems: Once data is released from the legacy core, it digitizes the data supply chain for cognitive interpretation and for making data-driven decisions across the organization. We've helped retail businesses understand a broad spectrum of structured and unstructured data related to consumer actions, market response to campaigns, customer needs, pricing considerations, and more. Here, machine learning models are the key to developing recommendation logic to promote products in real-time.


Using AI technology: This step involves using a number of AI models to almost completely automate the solving of business problems. This allows continuous learning and continuous improvement to be incorporated into validated models and new models. A financial services client experienced this in real life. Organizations use AI techniques to detect inconsistencies and other issues in data values and transaction volumes that may be indicative of suspicious activity, and troubleshoot decision makers if necessary. It also studies these data patterns to predict and prevent unfavorable events.


The material above was delivered by a presenter from Ukraine in an international visiting lecturer held by STEKOM University in collaboration with Universities from Ukraine. The presenter's name is Oleksii Panasenko. He is a Lecturer at the Vinnitsia State Pedagogical University
(Ukraine), Ph.D. in Mathematics. He is also a data scientist at NestLogic Inc. His interest in science is in mathematics: fractal analysis. Meanwhile, his interest in teaching is working with mathematically gifted pupils; mathematical olympiads. In addition he has an interest in all things related to machine learning, data science, AI. The time for the visiting lecture to be held is on May 12 2023 with initial remarks by Dr. Joseph Teguh Santoso who is the Chancellor of STEKOM University and guided by Mrs. Novita.


This international webinar activity is part of the implementation of STEKOM University's commitment to increase various international activities. This was done in order to realize the vision to become an international-class university. Various international activities carried out by STEKOM University continue from year to year. There are international activities that are sustainable and there are also some international activities that are not sustainable. All types of international activities are accommodated and regulated by the International department of STEKOM University.