Latest Pervinder Johar News
Aug 16, 2019
How AI Can Reach Its Potential By Being Domain-Specific | Paid Program Pervinder Johar Pervinder Johar is the CEO of Blume Global , a pioneer in global logistics and digital supply chain solutions for every move, mode and mile. Share to facebook Getty Most business-focused conversations around the future of artificial intelligence focus on the immense potential of its capabilities -- often described in terms that leap quickly from optimistic to incredibly speculative. Predictions compare artificial intelligence (AI)’s trajectory with that of the steam engine and predict the potential productivity gains from AI to be anywhere from $13.7 trillion to $15.7 trillion by 2030, according to the McKinsey Global Institute and PricewaterhouseCooper, respectively. Even once you strip away any misconceptions and ratchet back the hype , it’s clear that AI’s potential to deliver value, even in the near term -- particularly across vertical industries and specific functions -- will be significant. It will require a highly focused approach for AI to reach that potential, one that leverages comprehensive, industry-specific data and domain expertise -- real, human expertise that only comes from experience -- alongside cutting-edge technology. AI’s great promise comes from its initial premise: It gets smarter as it learns from more data, similar to the human ability to learn from experience. And tech giants like Google, Amazon and Microsoft have used this to get a leg up in developing AI capabilities; quite simply, they have more data and engineering resources than most corporations. They’ve made a push that can broadly be called “AI for all” -- horizontally focused solutions looking for a problem to solve. But while these “AI for all” solutions can have broad applications, they lack the industry- and function-specific capabilities to solve real business problems. Their creators don’t necessarily understand the core problems of companies in specific industries like automotive, retail or healthcare and business processes like supply chain execution, logistics and finance. Instead, they have created a solution that is going to need significant adaptation and modification, either by the customer or a team of high-priced consultants, to deliver results. Yet businesses aren’t deploying AI because it’s an amazing new technology. They are betting on AI to ultimately help them do their jobs better, making the business case that AI can help them increase sales, decrease costs and mitigate risk. The Importance Of Domain Expertise The next phase of AI success happens when technical capabilities are matched with industry-specific expertise. Even the most advanced AI requires human input, but it doesn’t replace human judgment -- not by any stretch. There are things that we are taught and things we learn through experience, and AI needs to be created from both, built on data from the things we are taught and rules based on specific expertise. For example, manufacturers who want to increase the uptime of their assembly lines shouldn’t choose a horizontal AI provider; rather, they should be looking for a maintenance, repair, operate (MRO) solution with expertise and demonstrable value in their particular industry. Or let’s take my field -- supply chain. It is, as I wrote earlier this year , an area that is ready for AI disruption. But it will take much more than slapping a machine learning overlay atop a transportation management system (TMS). An effective AI solution for the supply chain would need to be fine-tuned with vast amounts of highly specific data and highly technical, specific human insights. With this combination, AI and machine learning can deliver recommendations to reduce delays, predict demand, plan capacity and, ultimately, reduce costs. These are all goals that companies strive to meet on a daily basis. So why is a domain-specific approach necessary for the next iteration of AI? • Depending on your industry or your job function, success can look very different: There is truly no one-size-fits-all approach. How you measure success is largely dependent on the goals you have set. An e-commerce provider may want AI-powered incremental increases in per-order sales, while a trucking company could need predictive capabilities to optimize fleet utilization. • Your data is as unique as your business: At a more granular level, data is the lifeblood of every industry, and every industry has different data. AI platforms need to be adjusted to these domain-specific variables in order to deliver real value, quickly. • Your time-to-value is shorter: Domain-specific platforms reduce implementation pains and associated lost productivity because they have a better understanding of the task at hand. An AI initiative that requires significant input and customization in order to comprehend the specifics of your industry means a longer pathway to success -- and less enthusiasm for investing in similar future technologies. We are at a significant inflection point in the adoption of AI-enabled solutions. Linking domain expertise and data with technical innovation is necessary for technology to reach its full potential to deliver measurable, effective results to the companies that implement them.