AI-Mediated Strategizing – PhD Studentship

RFCSR; Cranfield; On-site; GBP 18,000 - 20,000; Full time

Job summary

Cranfield University invites applications for an AI-mediated strategizing PhD studentship focused on how artificial intelligence influences strategic decision-making within organizations. The successful candidate will work within Cranfield School of Management, benefiting from rigorous training, supervision, and an international research community.

The three-year full-time programme requires a detailed proposal, evidence of strong research ability, and a good command of English, with funding and

Qualifications

• Eligibility: UK first-class or upper second-class undergraduate degree in management, business, information systems, AI, or related fields.
• Submit a detailed research proposal outlining a clear research question, literature context, methodology, expected contribution, and timeline.
• English proficiency as per university requirements.

Responsibilities

• This PhD studentship supports rigorous academic research with structured doctoral training, supervision, and access to international research communities.

Skills

Strategy research

AI in management

Independent research

Academic writing

Interdisciplinary research

Communication

Education

UK first-class or upper second-class undergraduate degree

Job description

AI-Mediated Strategizing – PhD Studentship

Cranfield University
Cranfield, United Kingdom

General Description
This PhD studentship at Cranfield University focuses on the emerging field of AI-mediated strategizing, exploring how artificial intelligence influences and transforms strategic decision-making processes within organizations. The research aims to investigate the interaction between human strategists and AI systems, examining how AI tools shape cognition, collaboration, and strategic outcomes in complex environments.

The project is situated within Cranfield School of Management, a globally recognised institution known for its applied research, strong industry links, and emphasis on real-world impact. The successful candidate will engage in rigorous academic research supported by structured doctoral training, supervision, and access to a vibrant international research community.

The PhD programme typically runs for three years full-time and includes an induction phase, ongoing research training, and regular progress reviews. Candidates will be expected to produce an original doctoral thesis that contributes significantly to knowledge in the field of strategy, management, and AI integration. Opportunities are provided for conference participation, international exposure, and interdisciplinary collaboration.

The research will contribute to advancing understanding of how AI technologies reshape strategic practices, decision-making frameworks, and organizational dynamics, with implications for academia, industry, and policy.

[Eligibility Criteria]
Applicants should hold a UK first-class or upper second-class undergraduate degree with honours, or an equivalent international qualification, in a relevant discipline such as management, business, information systems, artificial intelligence, or related fields.

Candidates are required to submit a detailed research proposal outlining a clear research question, literature context, methodology, expected contribution, and timeline.

Applicants must demonstrate proficiency in English and meet the university’s recognised English language requirements.

[Required expertise/skills]
Strong interest in strategy, management, and artificial intelligence
Ability to conduct independent, high-quality academic research
Knowledge of research methods and academic writing
Analytical and critical thinking skills
Interest in interdisciplinary research involving technology and organizational studies
Strong communication and presentation abilities

Salary details
Funding details not explicitly specified for this studentship. Standard PhD tuition fees apply:
Home students: approximately £5,238 per annum
Overseas students: approximately £23,525 per annum (non-technical programmes), excluding additional costs

Application Deadline
Not explicitly specified