While some companies — most large banks, Ford and GM, Pfizer, and virtually all tech firms — are aggressively adopting artificial intelligence (AI), many are not. Instead they are waiting for the technology to mature and for expertise in AI to become more widely available. They are planning to be “fast followers” — a strategy that has worked with most information technologies.
We think this is a bad idea. It’s true that some technologies need further development, but some (like traditional machine learning) are quite mature and have been available in some form for decades. Even more recent technologies like deep learning are based on research that took place in the 1980s. New research is being conducted all the time, but the mathematical and statistical foundations of current AI are well established.
System Development Time
Beyond the technical maturity issue, there are several other problems with the idea that companies will be able to adopt quickly once technologies are more capable. First, there is the time required to develop artificial intelligence systems. Such systems will probably add little value to your business if they are completely generic, so time is required to tailor and configure them to your business and the specific knowledge domain within it. If the AI you are adopting employs machine learning, you will have to round up a substantial amount of training data. If it manipulates language — as in natural language processing applications — it can be even more difficult to get systems up and running. There is a lot of taxonomy and local knowledge that needs to be incorporated into the AI system —similar to the old “knowledge engineering” activity for expert systems. Artificial intelligence of this type is not just a software coding problem; it is a knowledge coding problem. It takes time to discover, disambiguate, and deploy knowledge.
Particularly if your knowledge domain has not already been modeled by your vendor or consultant, it will typically require many months to architect. This is particularly true for complex knowledge domains. For example, Memorial Sloan Kettering Cancer Center has been working with IBM to use Watson to treat certain forms of cancer for over six years, and the system still isn’t ready for broad use despite availability of high-quality talent in cancer care and artificial intelligence. There are several domains and business problems for which the requisite knowledge engineering is available. However, it still needs to be manipulated to a company’s specific business context. [Read More]