Using AI and behavioural science to support individuals at risk of alcohol addiction

Alcohol addiction continues to create significant health, social and economic challenges. Many individuals experience difficulties in recognising early risk patterns, accessing timely support, or receiving personalised guidance before alcohol-related behaviours become more harmful.

Although digital health technologies are rapidly advancing, many existing tools remain generic and static. There is a clear opportunity to combine artificial intelligence, behavioural science and clinical guidance to deliver more personalised, preventative and scalable support.

Through an Innovate UK Accelerated Knowledge Transfer project, Stallions Technologies Ltd partnered with the University of Wolverhampton to explore how AI-enabled behavioural modelling could support individuals experiencing alcohol-related addictive behaviours. The project aimed to develop a proof-of-concept platform capable of identifying behavioural risk patterns and providing timely, evidence-informed support.

Project Objectives

The project set out to develop a proof-of-concept digital tool that could support individuals through AI-driven behavioural insights.

The partnership brought together academic expertise in AI, behavioural modelling, cybersecurity, digital health and clinical-informed design with the business partner’s experience in software development and digital product delivery.

Key objectives included:

• developing a behavioural monitoring framework;
• creating an AI prediction engine to identify high-risk situations;
• aligning personalised support with CBT-informed principles;
• building a functional mobile application prototype;
• establishing a scalable technical architecture for future development and commercialisation.

Project Overview

Combining Artificial Intelligence and Behavioural Insight

The project developed a mobile application that uses behavioural data and AI to support individuals managing alcohol-related addictive behaviours.

Users can record mood, stress, cravings and consumption-related patterns. These inputs are analysed to identify trends, highlight potential risk situations and provide personalised behavioural insight. The system acts as a digital behavioural model, helping users better understand their own patterns and possible triggers.

Embedding Evidence-Based Support

The design of the platform was informed by CBT principles, behavioural science and clinical guidance. Academic input helped ensure that the system remained supportive, evidence-informed and appropriate for a non-diagnostic digital support tool.

The focus was on increasing awareness, encouraging behaviour change and supporting early risk identification, while signposting users to professional support where required.

Building a Practical Digital Health Solution

Close collaboration between Stallions Technologies Ltd and the University of Wolverhampton enabled the project team to align clinical-informed requirements with practical software development.

The iterative development process supported refinement of the behavioural framework, AI model and mobile app design. This resulted in a working proof-of-concept that demonstrated the feasibility of AI-driven behavioural support in the alcohol addiction and wellbeing space.

The project successfully delivered a functional mobile application prototype to support individuals experiencing alcohol-related addictive behaviours.

The platform enables users to log behavioural and wellbeing-related data, which is processed by an AI model to identify trends and predict potential risk situations. The outputs support self-awareness, coping strategies and early risk recognition.

Alongside the mobile app, the project produced:

• a behavioural monitoring framework;
• an AI-based risk prediction model;
• a scalable system architecture;
• technical documentation for future development;
• a foundation for further feasibility testing, product refinement and commercialisation.

The prototype demonstrated the practical feasibility of combining behavioural science and AI within a digital support tool for addiction-related behaviours.

The project established a strong foundation for innovation in digital health solutions for addiction support and behavioural wellbeing.

It demonstrated how AI and behavioural science can help individuals recognise and respond to risk patterns earlier, supporting more personalised, preventative and accessible approaches to recovery support.

For Stallions Technologies Ltd, the project accelerated early-stage product development and created a pathway towards future commercialisation in the digital health market. For the University of Wolverhampton, it enabled academic expertise in AI, cybersecurity, behavioural modelling and digital health innovation to be translated into a practical real-world application with societal value.

The collaboration also strengthened the relationship between the business and the university, creating opportunities for future joint innovation, follow-on funding, clinical evaluation, and digital health product development.