Job Description:

Aioi R&D Lab – Oxford is an AI R&D company based in Oxford, on a mission to harness AI to understand, predict, and manage risk, helping build a safer, more resilient society. 

We sit at the intersection of academia and industry, working with Oxford's professors, researchers, and graduates alongside commercial spinouts and partner companies, to turn frontier research into AI that actually ships rather than just gets published. 

Our work spans applied AI for insurance and adjacent industries, including supply chains, nature, autonomous driving, and the emerging challenges nobody's solved yet, alongside deep research of our own into agentic AI, privacy-preserving technologies, trustworthy AI, complex systems modelling, and quantum computing. 

We build AI products and solutions for insurers, businesses, and public-sector organisations worldwide, and run innovative research projects that push these technologies further, helping people make better decisions in an uncertain world. 

The Strategy Intern – Anomaly Detection Platform will produce a rigorous, evidence-based go-to-market strategy for Aioi R&D Lab's cross-modal anomaly detection technology. 

The Lab has developed anomaly detection technology that surfaces inconsistencies across multiple data modalities — tables, free text, images and more — with particular strength in spotting patterns that link different data types together. Early applications in fraud detection have been highly successful in motor insurance, home insurance and motor warranty claims. The Lab now wishes to test whether the same reusable core technology can power multiple products across different domains, with applications adapted case by case while the underlying engine stays constant. 

The purpose of the role is to ensure: 

• Candidate use cases for cross-modal anomaly detection are identified and assessed beyond insurance fraud, across industries and functions. 

• Each use case is mapped against the technology's reusable platform layers, testing which layers generalise across domains and which would need to be rebuilt case by case. 

• Historical and contemporary platform business models are analysed to identify the factors behind their success or failure. 

• The technology is translated into workflow-specific propositions for each audience, rather than pitched as a single abstract “anomaly detection” capability. 

• Findings are synthesised into a single strategic recommendation, including a proposed go-to-market strategy, blue-ocean opportunities and clearly flagged open questions. 

This is a two-month fixed-term internship hosted at Aioi R&D Lab in Oxford, executing a standing mandate from the Lab's leadership review to map where anomaly detection could be a viable solution across the group. It is not a generic exploratory project.

Responsibilities

Market Research on Use Cases

• Identify and assess potential applications for cross-modal anomaly detection beyond insurance fraud, across industries and functions where spotting inconsistencies across data types (tables, text, images) creates value.
• Map candidate use cases against the technology's reusable platform layers: evidence fusion (combining text, tabular, image, document and audio data), representation (embeddings for claims, entities and relationships), anomaly detection (mismatch, missingness, implausibility, contradictions, unusual network links), change detection (drift, shift, unusual operational patterns) and workflow (alerts, case prioritisation, third-party integrations).
• Test which platform layers generalise across domains and which would need to be rebuilt case by case.
• Assess every candidate use case against both commercial channels: internal MS&AD group companies and external insurance and insurance-adjacent companies, spanning group-company sales and consulting-led work.
• Identify what data could realistically serve as model inputs, and what model outputs would deliver meaningful value in each use case.

Platform Business Model Analysis

• Research historical and contemporary examples of successful platform-based business models.
• Identify the common factors behind their success.
• Analyse examples of platform strategies that failed, and the reasons why.
• Draw out the conditions under which a reusable core technology does, and does not, support a viable platform play.

Synthesis and Strategic Recommendation

• Assess how well the Lab's anomaly detection technology fits a platform business model.
• Prioritise blue-ocean opportunities where the Lab can create distinctive value and sustainable competitive advantage, rather than competing in crowded, red-ocean markets.
• Propose a go-to-market strategy that is ambitious and forward-looking, anticipating how AI-enabled forms of anomaly and fraud are likely to evolve over the coming years.
• Flag open questions, evidence gaps and areas requiring further exploration.

Stakeholder Engagement

• Hold frequent touchpoints with the technical development team to understand the technology's current capabilities, roadmap and constraints.
• Hold frequent touchpoints with the commercial team to ground use-case research in commercial realities and existing customer relationships.
• Conduct user and expert interviews and other primary research with internal and external stakeholders.
• Present findings on the go-to-market strategy to the executive team and seek strategic direction, at the beginning and end of the internship.

Deliverable and Research Quality

• Produce a single extensive report covering: market research findings and candidate use cases mapped to the platform layers; case studies of successful platform strategies with key success factors identified; an assessment of fit between the technology and a platform business model; suggested areas for further exploration; and a proposed go-to-market strategy.
• Make full use of the high-tier AI assistant subscription provided for research and analysis, while ensuring conclusions are driven by original critical thinking and verifiable evidence rather than unexamined generative AI output.
• Maintain a clear audit trail of sources, interviews and assumptions behind each recommendation.

Knowledge, Experience and Qualifications

Qualifications 

Currently studying at Saïd Business School, University of Oxford. 

Essential 

Strong research and analytical skills, with comfort synthesising both qualitative and quantitative information. 

Ability to structure ambiguous, incomplete evidence into a clear, defensible strategic recommendation. 

Demonstrable interest in AI and/or insurtech. 

Ability to work independently and manage own workload to a short, fixed timeframe. 

Confidence engaging multiple stakeholder groups, from technical specialists to senior executives. 

Excellent written communication, capable of producing an executive-quality report. 

Critical judgement in the use of generative AI tools, using them to accelerate research without substituting them for original thinking. 

Desirable 

Prior experience in strategy consulting, product strategy, corporate development or market research. 

Familiarity with platform business models, network effects or two-sided market theory. 

Working understanding of machine learning concepts, sufficient to hold a productive conversation with engineers and researchers. (The role does not require hands-on model development or software engineering experience.) 

Exposure to insurance, financial services or claims operations. 

Experience designing and conducting structured user or expert interviews