AI and machine learning are becoming more commonplace, but the people using such systems may not be qualified to operate them. A study by Dun & Bradstreet showed that 83% of finance teams at leading finance and credit lending companies in the U.K. and U.S. are automating at least one part of their processes.
AI is being built into more systems and software as organizations attempt to compete in the algorithmic age. With the level of machine intelligence reaching new heights, the number of experts is not growing proportionally. To compensate, AI libraries, APIs, systems and software are becoming easier to use so more people can take advantage of them. However, ease of use does not necessarily diminish risks.
At present, there’s no minimum competence level one must have to operate an AI system, except perhaps data scientists with graduate degrees in math, statistics or computer science who use the most sophisticated tools. While there are AI-related nano degrees and certificates for technologists and business leaders, there’s no central licensing or certification entity that everyone trusts, at least yet.
Time to market isn’t everything
Earlier this year, Gartner reported that 37% of the 3,000 CIOs surveyed were either implementing AI or would be doing so soon. A newer study by Dun & Bradstreet showed that 83% of finance teams at leading finance and credit lending companies in the U.K. and U.S. are automating at least one part of their processes.
Granted, not all AI systems are alike. Some of them are relatively “dumb,” because they use pre-determined inputs and outputs. However, even simple systems need to be monitored and updated. For example, a company building a customer service chatbot will typically want to expand the list of questions the bot is capable of answering.
More sophisticated systems use machine learning or deep learning to unearth patterns or signals in data. Those systems also require ongoing attention, albeit on more levels. For example, the data used for machine learning training tends not to be static and data quality is important. As new data comes in, the model must be tuned to ensure its ongoing accuracy. Or, to meet a different business goal, an organization might use different data, algorithms, and models.
“Where we’ve had success is where we’ve been able to bring machine learning or AI to [clients] and basically ingest the data that relates to their accounts receivables with the data we have and give them quick results they can act on,” said Andrew Hausman, general manager, Financial Solutions at Dun & Bradstreet. “We go back to clients several times a year and fine-tune the results based on what they want to see: For example, if somebody says we want to grow more sales, we want to extend more credit to clients or we want to be more risk averse because a recession is coming.”