I recently got briefed by Spencer Askew, the CEO of Teknowlogi, about the SaaS-based AI Platform his company has introduced. In preparation for the call, I spent some time looking at their web site and then became worried that the call would be a waste of time. I’ve been coming across experts talking about how artificial intelligence (AI), machine learning, and predictive analytics are set to transform industry. This technology is being overhyped. It is as if all you must do is wave your magic AI wand and all sorts of wonderful things will happen.
But I was most pleasantly surprised by the call. Mr. Askew admitted that there are many solution providers that sell AI and then these projects turn into long and costly consulting projects. “A tremendous number of hours can be spent writing business rules and then telling a machine what it should be thinking about,” Mr. Askew said. In contrast, his firm has long and deep domain knowledge of the 3PL industry. Their solution – Tai – has prebuilt analyzers that can look at the data in companies’ various software systems to uncover improvement opportunities without a long consulting engagement.
One class of machine learning solutions looks at data inputs and the desired outcomes with the goal of learning a general rule that can then be applied to improve a process. This is the form of machine learning that Teknowlogi is pursuing. Mr. Askew gave some specific examples.
3PLs, like most companies, like to be paid in a timely manner. At one large 3PL with multiple divisions, prebuilt rules are being used to look at why some divisions are being paid on time by a customer, and that same customer is not paying other divisions in a timely manner. In this case, the desired outcome is to be paid on time. The inputs are how timely the divisions are being paid by their customers and differences in the way the different division’s accounting departments are interacting with those customers. For example, the contract may say that the 3PL should be paid in 60 days. One division is consistently paid more promptly. The division that is paid quicker may send out two email reminders in the week before the bill is due, and then if not paid on time, contact the customer two times by phone per week until paid. A division being paid much more slowly, does not send out an email reminder before the bill is due and, when not paid on time, contacts the customer only one time per month by email and phone until paid. In this case, Tai uncovers the best practice – email reminder in the week prior and then two phone calls per week until paid.
But beyond uncovering the best practice, the Tai system automates the process. The system can automatically send out reminder and dunning emails to the customer, and send reminders to designated accounting personnel to follow up by phone. Mr. Askew estimated that it required about 100 prebuilt rules to discover this accounts receivable best practice and then automate it.
Mr. Askew claimed that they have “millions” of prebuilt rules. That is probably an exaggeration. But clearly, they have predefined many, many improvement scenarios and, for these scenarios, can hit the ground running. Other accounting, IT, density pricing, and cross selling scenarios were described. Patents are pending for many of these scenarios. And while Teknowlogi has domain expertise in logistics, clearly the kinds of cross selling and accounting scenarios they are seeking to improve exist in many industries.
One final point is worth making. What they are NOT doing is pointing their analyzers at data in a single system like a transportation management system (TMS). This is good, TMS and warehouse management systems are already very good at optimizing and executing the processes under their control. Instead, the Tai platform is being pointed at a variety of systems and focused on making improvements in “grey spaces” that are currently not getting enough attention.
This is not the first AI solution I’ve been introduced to. But it is the AI solution which seems most practical.