PhD Studentship: SCALE-Decode: Unlocking Growth Workflows in UK SMEs University of Exeter in United Kingdom

[Funding of fees plus a stipend of £21,805 per annum for 2026/7.]

PhD Studentship: SCALE-Decode: Unlocking Growth Workflows in UK SMEs at the University of Exeter.

This EPSRC-funded doctoral studentship supports research on how small and medium-sized enterprises (SMEs) can turn early success into sustainable growth. The project examines how responsible AI can help reveal growth workflows, diagnose bottlenecks, and support managerial action in real-world SME settings.

The research is organised into four work packages. The first will map and explain SME scale-up workflows using manager and employee interviews, walkthroughs, task diaries, process documents, and, where appropriate, privacy-preserving event logs. These materials will be analysed using qualitative methods, Business Process Model and Notation, and process mining to distinguish visible symptoms such as delay and rework from underlying causes such as centralised approvals, capability gaps, and poor information transfer. Outputs will include a Scale-up Workflow Framework, anonymised workflow maps, and a bottleneck taxonomy.

The second work package will develop SCALE-Decode, a secure AI system designed to reconstruct workflows, diagnose scaling bottlenecks, and generate feasible action plans. The system will use a knowledge graph connecting activities, dependencies, organisational conditions, evidence, and interventions. It will include tools for gathering information, producing editable maps, diagnosing constraints, and generating and critiquing actions. Each recommendation will be traceable and explain its evidence, prerequisites, expected benefit, cost, effort, risk, and uncertainty.

The third work package will evaluate the system in SMEs through a longitudinal field study. The study will compare AI-assisted diagnosis with existing approaches and assess workflow understanding, decision quality, action completion, and selected operational indicators. It will also investigate when recommendations are accepted, adapted, or rejected, and whether outcomes differ according to workflow maturity, digital readiness, or owner-manager dependence. Employee capability and wellbeing will also be considered, with an emphasis on ensuring that improvements do not depend on work intensification.

The final work package will integrate findings and translate them into practice through academic papers, reusable workflow passports, sector templates, checklists, an adviser toolkit, a policy brief, and public-engagement materials. Practitioner workshops will help refine these outputs for SMEs, advisers, growth hubs, policymakers, and technology providers.

Funding: The studentship provides full fee coverage and a stipend of £21,805 per annum for 2026/7.

Deadline: 8 December 2026.

Institution: University of Exeter, United Kingdom.

Best fit: Applicants with interests in responsible AI, SME growth, workflow analysis, process mining, business process modelling, knowledge graphs, and evaluation of digital decision-support tools.