AI will boost your Advantage: The question is no longer if a company should use AI, but where it brings the greatest competitive advantage. There are three areas where AI has now shifted from a “nice-to-have” to a “must-have” technology. Companies that push the boundaries of AI to sharpen predictions, boost efficiencies, and optimize the real-time pricing or stock control of their products are moving faster and further than rivals still conservatively wavering over the wisdom of using AI for these purposes.
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As more companies embed artificial intelligence in their products, services, processes, and decision-making, the definition of what AI is and where it can be most effectively applied is evolving as rapidly as the techniques themselves. What started out as algorithms used to determine loans, select new hires, and empower chatbots (with mixed success), is now deeply embedded and used in everything from predicting climate risks to picking sales leads. The question is no longer if a company should use AI — but where it brings the greatest competitive advantage.
In our work with companies, we see three areas where AI has now shifted from a “nice-to-have” to a “must-have” technology. Companies that push the boundaries of AI to sharpen predictions, boost efficiencies, and optimize the real-time pricing or stock control of their products are moving faster and further than rivals still conservatively wavering over the wisdom of using AI for these purposes.
Over the past few years, AI has migrated from a technology that finds relationships in data and predicts existing trends more accurately to a technology that spots future shifts in everything from leisure spending and travel patterns to company credit worthiness by analyzing preferences and sentiments in vast quantities of data including text, voice, images, digital news feeds, and social media.
AI can now recognize disruptors on the horizon by making connections between embedded characteristics, allowing companies to prepare more effectively for disruptive events. Early AI warning systems for fraud can now detect bots, making them increasingly essential to get ahead of evolving tactics of hackers, nation-state actors, malware, and ransomware. Market shock-adaptive machine learning algorithms help leading banks predict not just the performance of their investments, but also potential vulnerabilities caused by disruptors such as Covid-19.
This helps banks and larger companies mitigate the impact and potential bankruptcies in their investment portfolios. For example, one bank was able to predict in weeks, instead of months, which loans would be unlikely to be paid off and reduced their number by 70 percent, boosting the returns on its overall loan portfolio by tens of millions of dollars. Similarly, AI enabled an aerospace parts distributor that suffered from excess inventory and cash flow shortages during industry downturns to forecast more accurately how much demand for its parts would fall when Covid-19 hit. As a result, the company was able to reduce its working capital by hundreds of millions of dollars and double its on-time deliveries.
In areas such as insurance, human resources, and conduct surveillance, machine learning reads through forms and reviews voice and video recordings to highlight where the reviewer’s attention should be focused, how a call should be routed, or simply if an attachment has been forgotten. The development of so-called “attention” approaches that learn which parts of the input are more critical, has accelerated the use of natural language processing, allowing AI to more reliably link seemingly unrelated concepts and work faster. […]
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