Locally advanced non-small cell lung cancer (LA-NSCLC) patients could benefit in overall and progression-free survival from regular dosimetric treatment plan adaptations during radiotherapy. This is known as adaptive radiotherapy (ART). However, implementing an adaptive radiotherapy workflow presents a highly cumbersome process. First, repeated planning-CT imaging during treatment is required, which results in additional radiation dose for patients. Second, an ART workflow includes the repetition of various manual and semi-automated tasks such as target and organ-at-risk contouring on the images and dosimetric treatment planning. These obstacles hinder widespread ART implementation. To avoid repeated planning-CT imaging, position-verification imaging can be utilized. Modern cone-beam CT (CBCT) imaging, integrated into the treatment unit, assists radiation therapists (RTTs) in administering the dose. Recent improvements in CBCT imaging sources and detectors have enhanced image quality. Moreover, it may be possible to calculate radiation dose directly on these CBCTs. Utilizing CBCT imaging for plan adaptation could also eliminate the need for an additional CT procedure, thereby increasing patient comfort. To address the labor-intensive contouring and treatment planning steps, CE-marked and validated commercial AI applications are already being used to support organ contouring and accelerate the treatment-planning process. These tools are currently applied to pre-treatment planning CTs. The time efficiency of these contemporary tools in a prospective ART workflow has yet to be studied, as has the feasibility of applying these applications within a CBCT-based ART workflow.