The rapid decarbonisation of the power genera- tion and aviation sectors will require a move away from in- cremental development, exposing designers and researchers to the risk of unexpected results from uncertainty in bound- ary layer state. This problem already exists for parts devel- oped with fully turbulent assumptions, but in novel design spaces the risk increases for both real components, where previous knowledge of similar designs may be inapplica- ble, and particularly in experimental testing of scaled mod- els, where reducing Reynolds number can result in a dras- tic change in flow topology that skews the conclusions of a test. Computational methods struggle to reliably predict boundary layer state so experimental techniques for diag- nosing boundary layer state are needed. Infrared thermography (IR) is a non-invasive technique that offers simple, fast visualisation of boundary layer state with no additional instrumentation. IR is relatively uncom- mon in the literature and there is minimal information avail- able on the best practices for its use. This paper aims to en- courage the adoption of IR as a diagnostic tool by demon- strating routes for optimisation and pointing out pitfalls to avoid. A low-order model is developed and used to predict how the signal-to-noise ratio (SNR) of an IR visualisation changes depending on the thermal design of the test piece. It is shown that in low-speed flows with active heating from the surface the SNR is maximised through a suitable choice of surface insulation, while in high-speed flows, where pas- sive temperature differences are used, there is a crossover between heat transfer and recovery temperature effects that results in an SNR of zero, an effect that can arise in both steady-state and transient experiments. Experimental valida- tion of the 1D model in both flow regimes is shown along- side two case studies on the use of IR in sub-scale testing where uncertainty in boundary layer state results in critical differences from the full-scale flow.