What Are We Automating? On the Need for Vision and Expertise When Deploying AI Systems

Implementing in-house AI in the modern business is a classic example of digital transformation, often appearing simple and attractive, particularly given the emergence and availability of powerful, easy-to-use frameworks like TensorFlow or PyTorch. Such AIs are commonly considered for replacing cumbersome manual or physical systems, where neural networks may appear to be almost a panacea automation solution to solve scalability or diversification concerns. However, such systems have subtle and sometimes very surprising behaviours that require considerable domain expertise, in order to implement a functional system without expending more effort than the system ultimately gains. Fundamentally, they need to be deployed with a clear sense of what the AI system is going to achieve. Careful attention must be paid at the outset to drafting a clear and concrete design specification that indicates the intended function and, equally, draws a line under capabilities that are out of scope. Likewise, an effort needs to be made either to identify in-house people with the required skill sets to develop the system or alternatively to enter into close working partnerships with external providers who can identify the needs and clearly articulate an appropriate solution. Most challenging of all, especially at large scale, is the emerging ‘data gap’—the need to have access to or generate enormous volumes of labelled data—which often comes only at costs outside the budget of all but the largest companies. A case study in design collaboration between an emerging company transitioning from a physical to a virtual technology and a university research group with substantial expertise in AI systems is presented, both as an illustration of the complex design considerations and a model for how to build in-house expertise. The collaboration is ongoing, and outcomes are still preliminary, but the company is now starting to gain an appreciation for the complexity of real-world AI deployments and has developed a strategic plan that enables future growth. The emerging overall message is that modern AI is more an exercise in data automation than process automation.