About the Project

Project Details

Enterprise adoption of Generative Artificial Intelligence (Gen AI) has accelerated across industries; however, enterprise-wide AI scaling remains fragile and uneven. While firms frequently execute AI pilots, they struggle to transition from experimentation to sustained cross-functional infusion and measurable enterprise-level value. Recent evidence indicates that more than 80% of AI initiatives fail to deliver material business impact, a phenomenon described as the “proof-of-concept trap”. This trap reflects a structural mismatch between pilot- and scaling environments. Pilots are characterised by limited scope, curated data, and experimental flexibility, while scaling environments require enterprise-grade data architecture, system integration, infrastructure investment, compliance,and governance controls.

In this sense, de-risking AI scaling is not about restricting risk, but about developing organisational conditions that allow AI scaling without systemic fragility or cascading failure. Beyond technical challenges, scaling failures are amplified by behavioural and organisational risks. Employees frequently rely on AI outputs without adequate evaluation, deploy unauthorised tools (shadow AI), expose sensitive data to public systems,or fail to disclose AI use in decision processes. Such practices generate compliance exposure, reputational risk, and decision distortions at functional and strategic levels. This results in AI scaling becoming a high-risk transformation process rather than a natural extension of technological adoption. Systemic vulnerabilities such as Gen AI technical debt, interoperability constraints, data sovereignty pressures, and skills erosion further undermine long-term enterprise viability.

This project conceptualises AI scaling as a socio-technical transition process that requires systematic de-risking across strategic, operational, governance, behavioural, and infrastructural dimensions. It aims to identify the critical enablers and dynamic configurations that allow organisations to transition from AI pilots to enterprise-wide infusion while mitigating scaling-induced risks.

AI scaling is a socio-technical transformation process that requires multi-level risk orchestration rather than isolated governance interventions. This involves:

Strategic De-risking - Align AI scaling with enterprise strategy, value realisation, and long-term architectural governance to prevent pilot proliferation and systemic fragility.
Operational De-risking - Embed AI safely into workflows through robust data infrastructure,integration mechanisms, monitoring systems, and conformity governance.
Tactical De-risking - Manage day-to-day human–AI interaction and decision making risks by strengthening AI explainability, oversight, responsible use, and workforce capability.

Synthesising industry challenges in AI scaling with theory, the project shall identify AI scaling enablers, governance mechanisms and acceptable risk outcomes using a mixed methods design. This project reframes “AI scaling” as (i) post adoption infusion - breadth across functions and depth of workflow embedding and (ii) “de risking” as the reduction of scaling failure modes and adverse incidents while increasing durable value capture. The research scope will include Gen AI and agentic AI systems and develop road maps that enables organisations to scale AI responsibly, sustainably, and at enterprise level, positioning them to move beyond pilots toward durable, risk-adjusted value creation.

Person Specification

The successful applicant should hold, or expect to achieve:

A First or Upper Second Class Honours undergraduate degree, and a Masters degree with Merit or Distinction, both in relevant subjects.

Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.

Desirable / Essential Skills or Experience

Required characteristics

An understanding of the process of AI of digital technology adoption Should be interested in doing applied research and have the ability to engage with industrial stakeholders (e.g. Barclays, Capgemini, TÜV SÜD), translate practical problems into research questions, and work on real-world case studies. Strong communication skills, intellectual curiosity, self-motivation, and the ability to work both independently and collaboratively in interdisciplinary environments are essential. Evidence of capability in mixed-methods research design. This includes the ability to conduct systematic literature reviews and analyse qualitative data from semi-structured interviews.

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:

English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
A Curriculum Vitae (Resume) which details your education and work history.
Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

This position will be based on the Aston Campus in Birmingham, UK. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Apply for this position here

Funding Notes

This project covers all tuition fees and includes an annual stipend.

Please note that the successful candidate will be responsible for any costs relating to moving to Birmingham and/or visiting the Aston campus. International students must meet the financial requirements for the visa, flights, and NHS Surcharge. Applicants should be confident that they can meet these costs before applying.

Further information can be found here: Financial Requirements | Aston University

Beyond Pilots: A Socio-Technical Framework for Scaling and De-Risking Enterprise AI

Aston University

Aston St, Birmingham B4 7ET, UK