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Artificial intelligence (AI) over the past decade has shifted from research theories to actual practices in corporate offices. But it isn’t within the reach of many smaller companies, yet, the Wall Street Journal reported. The high upfront costs of AI tools, scarcity of people who can implement the technology at individual operations, and more pressing IT expenses have widened the gap in AI implementation. But a range of players, from large technology vendors to startups, are coming up with tools that allow small businesses to use the technology without a data scientist on staff. Still, even with such tools, it can take time for any company, large or small, to implement a new technology into its business processes. The operational efficiency of an AI system, while desirable, is still far from a priority for many companies. Many small firms looking to use AI have to build everything from scratch, said Brad Fisher, KPMG’s U.S. leader for data analytics and artificial intelligence. Even then, the firms may lack the breadth of data to train and test these systems before they are deployed. “Deploying AI technologies is a lot more complex than tech vendors would lead them to believe,” Fisher said. “Many smaller organizations’ IT departments are understaffed, with IT professionals struggling to deal with more pressing concerns, such as updating aging hardware, securing corporate networks and supporting growing tech requests from end users,” said Peter Tsai, senior technology analyst at Spiceworks, a professional network for IT workers.

Edward Altman, who created the Z-score method for predicting bankruptcies 50 years ago, has built on the model to assess creditworthiness of small- and medium-sized enterprises, Bloomberg News reported. Together with Gabriele Sabato, previously head of risk appetite decisioning at Royal Bank of Scotland Group Plc, Altman founded London-based Wiserfunding Ltd. to calculate the 12-month default probability for businesses with less than 150 million euros ($168 million) of revenue. Their methods cover 19 European countries and have correctly forecast whether a company would default or stay solvent in about 90 percent of cases, Altman said in a phone interview. Altman and Sabato are seeking to tap into private-debt markets, which have exploded as traditional lenders retreat from smaller businesses in the aftermath of the financial crisis. They use artificial intelligence to analyze corporate governance and management capacity along with financial and macroeconomic metrics to help lenders identify companies ahead of a downturn that he sees being characterized by more and bigger corporate failures. “We are increasingly talking to investors in these lenders who are sensing a change in the economy and want to see the credit quality of the companies the lenders they invest in are lending to,” Altman said. “In the next two to three years, there will be a shake up in the credit industry.” Non-bank lending has grown in recent years, with 151.8 billion euros raised for a range of private-debt investment strategies in Europe from 2014 to 2018, according to Preqin. That compares with 49 billion euros raised from 2009 to 2013.