AI AND STRATEGIC FORESIGHT - 2026/7

Module Overview

This module introduces students to the principles and practices of artificial intelligence (AI) and strategic foresight. As AI technologies continue to reshape industries, organisations, and societies, decision-makers increasingly face complex uncertainty regarding technological development, governance, and societal impacts. Strategic foresight provides tools and frameworks that help organisations anticipate change, explore alternative futures, and make more informed decisions under uncertainty.

The module explores how AI influences the ways in which organisations and societies understand and prepare for the future. Students will examine key concepts related to AI development, emerging technological trends, and the broader social, economic, and ethical implications of AI systems. At the same time, the module introduces core foresight methods, such as identifying drivers of change, analysing uncertainty, and constructing future scenarios.

Through lectures and workshops, students will learn how to apply foresight thinking to analyse complex technological developments and explore plausible future outcomes. The module emphasises critical thinking, structured analysis, and responsible engagement with AI technologies.

By the end of the module, students will be able to apply strategic foresight tools to examine how AI may shape future developments across different domains, evaluate opportunities and risks associated with emerging technologies, and communicate foresight insights in both analytical and professional formats.

Module provider

Surrey Hospitality & Tourism Management

Module Leader

LING Erin (Hosp & Tour)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): 100

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

• Artificial intelligence (AI) fundamentals 
• AI applications in businesses
• AI Ethics 
• Introduction to futures and strategic foresight: definitions, importance, and applications.
• The synergy between AI and strategic foresight: enhancing predictive analytics, data interpretation, and scenario planning
• Understanding changes and uncertainty 
• Foresight in organisations: methods and tools 
• Incorporating foresight into the innovation process
• Introduction to scenario planning, backcasting, and other foresight tools
• Utilising generative AI for scenario creation: tools, techniques, and practical exercises
• Developing a responsible approach to AI-enhanced strategic foresight

Assessment pattern

Alternative Assessment

The assessment will be replaced with an Individual report of 1,500 words.

Assessment Strategy

The assessment strategy for this module is designed to evaluate students' ability to apply strategic foresight thinking to the evolving landscape of artificial intelligence, while also encouraging responsible and critical engagement with AI tools. Given the rapid development of generative AI technologies, the assessments are structured to prioritise analytical reasoning, structured foresight methods, and reflective judgement. The strategy is designed to equip students with a holistic set of skills and knowledge, from the theoretical underpinnings of AI and strategic foresight to practical application and communication in a professional context. Together, these assessments will prepare students to effectively navigate and influence future business landscapes. The module therefore uses a combination of applied foresight analysis and professional communication tasks, allowing students to demonstrate both conceptual understanding and practical foresight capability. 

The first assessment aims to develop students' ability to apply foresight frameworks to explore how AI may shape the future of a specific issue or domain. Students will complete an AI Strategic Foresight Canvas. The canvas format encourages structured analytical thinking and visual reasoning, which are core competencies in strategic foresight practice. 

The second assessment aims to develop students' ability to communicate complex ideas clearly and professionally to a broad audience. This task challenges students to be innovative and creative in how they present information, utilising different formats (articles, videos, infographics) to convey their insights effectively, and encourages them to stay up to date with industry trends and debates, fostering a habit of lifelong learning. Creating content for LinkedIn allows students to begin building a professional online presence and personal brand, essential in today¿s digital world.

Thus, the summative assessment for this module consists of:

• Individual AI Strategic Foresight Canvas (one page),  addresses learning outcomes 1, 2, 3, 4, 6, 7

Students will complete an AI Strategic Foresight Canvas (one page, visual), with explanation text (400-600 words), applying foresight frameworks to explore how AI may shape the future of a specific issue or domain. Each student will formulate a specific foresight question related to AI, identify key drivers of change and critical uncertainties, construct a scenario matrix, describe plausible future scenarios, and consider strategic and responsible AI implications. Students are expected to incorporate AI tools and foresight techniques in their projects. The assessment will focus on the innovative use of these tools to create plausible and vivid future scenarios, the strategic roadmap developed through backcasting (or similar tools), and the feasibility and creativity of the recommended actions. 

• Individual LinkedIn Content Creation (250 words and an image/video),  addresses learning outcomes 1, 4, 5, 7

Each student will create and post an original piece of content on LinkedIn that discusses the role of AI in strategic foresight, focusing on a specific case study, trend, or theoretical concept, with an emphasis on ethics and responsible use of AI in strategic foresight. The content should engage with current discussions in the field and include visual or simulated elements created with AI tools (e.g. an article with video and/or infographic).

Formative assessment and Feedback

Students receive verbal and written feedback, including verbal feedback from instructors and peers during workshops, consultation on assessments, comments on the Canvas and LinkedIn post, and a formal feedback sheet outlining students¿ strengths, areas for improvement, and suggestions on how to improve students¿ performance on the summative assessments.

Module aims

• Familiarise students with the fundamentals of artificial intelligence (AI) and strategic foresight, and explore how AI can play a pivotal role in enhancing foresight activities.
• Provide a solid theoretical foundation in managerial cognition and prospective sense-making to support the practical application of foresight in the visitor economy and general business and management.
• Integrate foresight methods, drawing from strategic management concepts, to enhance decision-making processes. This includes understanding how foresight contributes to strategic planning and the implementation of strategic initiatives to achieve sustainability and resilience.
• Guide students in incorporating strategic foresight into innovation, enhancing their ability to anticipate changes and innovate accordingly. This includes understanding the dynamics of innovation within the context of foresight.
• Equip students with techniques and tools that support tasks in strategic foresight, preparing them to address complex future scenarios in various contexts. Train students in the application of strategic foresight methods, such as scenario planning and backcasting.
• Enable students to apply backcasting using AI tools to map out steps towards achieving a desired future state.
• Train students how to use generative AI to develop rich, detailed scenarios that aid in envisioning and planning for future possibilities.
• Address the ethical implications of using AI in strategic foresight, ensuring students are aware of the ethical considerations in their future foresight activities.
• Equip students with tools for effective communication of foresight insights, using storytelling and visualisation techniques to engage relevant stakeholders.

Learning outcomes

Methods of Teaching / Learning

The teaching and learning methods include:

Lectures and guest lectures designed to provide a framework of knowledge.

Hands-on workshops on using AI models to help in understanding complex future dynamics, practical sessions with detailed exploration of foresight techniques, with a focus on how to utilise AI to analyse vast datasets for trend spotting, uncertainty mapping, and scenario development.

Tasks to engage students in creating multiple, divergent future scenarios to understand the range of possible outcomes.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MAN3262

Other information

n/a

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.