THANK YOU FOR SUBSCRIBING
Abhinav Singhal, Chief Strategy Officer, Asia Pacific, thyssenkrupp
Companies looking to adopt AI today are bombarded with technology companies and start-ups selling advanced machine learning-based solutions built on exciting use cases. However, before kickstarting newer pilots and investing in these advanced solutions it is useful to step back and reflect on the overall intent of using AI for the organization and the traditional suite of analytical techniques and resources available.A recent study by McKinsey & Company compiling hundreds of AI use cases and applications across multiple industries found out that just in 16% of use cases advanced machine learning-based AI solutions were only applicable and traditional analytical methods were not effective. In 69% of the use, cases advanced AI methods such as deep neural networks helped in improving performance of already established methods and for remaining 15% could provide only limited benefit. This is also consistent with our experience that the greatest potential for AI is to create value in use cases where already established analytical techniques (such as regression or classification) can be used, but where neural network techniques can generate additional insights or broaden the application base. It is a common pitfall for companies to get caught up in all the hype surrounding the advanced AI developments and miss the rationale of adopting AI in the first place. Oneway, CIOs can assess the suitability of an AI solution is it to break it down into simpler elements and ask five basic questions. 1. What is the core business problem to be solved? Over two-third of the expected value from using AI is in either revenue generation use cases (e.g., product recommendation, customer service management, pricing & promotion) or operational improvement (e.g., predictive maintenance, yield optimization, supply chain). While consumer-led industries such as retail and high tech tend to see more potential from marketing and sales-related AI applications, manufacturing , and other heavy industries see more benefit in using AI for operational excellence. The remaining value pool is distributed across the support functions, for example, task automation, people analytics, risk assessment, etc. Given the wide range of applicability of AI techniques, it is important to pin down the source of value creation for the company and then determine the pain points or opportunities where it makes most sense to investin AI deployment. 2. Which analytical method or technique is best applicable? Most of the business problems can be classified into few standard types and have a corresponding set of established techniques to solve them.