An MBA dissertation on AI and banking given a defensible mixed methods design
Two phase mixed methods design approved, dissertation completed to submission
As told to Inkspire by Layla Kholoud, with their consent to share this account. See all case studies.
The Challenge
Layla’s MBA dissertation asked how UK banking and financial services organisations are managing the workforce consequences of AI adoption, a question important enough to matter and broad enough to be unanswerable without real boundaries. Existing industry reports covered AI’s operational benefits well; the empirical evidence on strategic workforce management during that transformation was thin, and a dissertation trying to cover all of it risked saying very little about any of it.
The Outcome
Working through Stage 02 and Stage 04, the study was bounded to UK regulated banking and financial institutions across a defined 2020 to 2025 window, and the mixed methods design was built as a genuine two phase sequence rather than two disconnected methods run side by side: interviews with VP level financial services professionals, using a semi structured format and thematically analysed, feeding directly into a validated survey scale deployed to a wider sample. Each choice behind that sequence, the interview protocol, the sample size, the specific AI governance maturity scale used, was documented against why it served the research objectives. The dissertation was completed to the University of Bath’s submission standard, with the findings on workforce impact framed as its central empirical contribution.
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