There is a desire within many organisations to decrease the product development cycle time, getting more products to market quicker. This is, however, often expected to occur against a backdrop of decreased resources, expectations of products with increased functionality, and an ever-changing and increasingly-constrained regulatory environment. Formulations are complicated designs and, although understanding can be developed through the creation of simple, model formulations, a final ink can still present surprising and unpredictable results. Many of the measurements made during development are labor-intensive and time-consuming, thus reducing the time skilled scientists have to investigate innovative materials and approaches. High-throughput techniques can allow experimenters to move away from a more traditional, one variable at a time, approach toward more multi-variate experiments, utilising statistically-designed approaches. It can be imagined that the use of such techniques could increase the operational effectiveness of a development process, thereby decreasing both the time to market, and associated product development costs, without compromising the quality of the designs proposed. The inclusion of automated formulation and measurement capability can also be predicted to reduce the time spent by our scientists on routine tasks, allowing them more free time to innovate. This presentation will give an overview of the opportunities we see for an automated development process, and the benefits (and challenges) that this will bring. It will also explore the opportunities that artificial intelligence and machine learning present and how the "internet of chemical things" could play a role in future research.