AI AND DECISION INTELLIGENCE - 2025/6

Module Overview

This module introduces MBA students to the core principles, tools, and applications of Artificial Intelligence (AI) and Decision Intelligence and their role in digital transformation and reshaping business strategy. It explores how organisations create value, drive innovation, and build competitive advantage through AI- and data-driven business models, strategies, decision-making, and operational excellence. The module examines the transformative impact of AI and data across three levels: at the macro level, influencing labour markets and industry dynamics; at the micro level, shaping firm strategy, innovation, performance, and process optimisation; and at the individual level, informing managerial judgment and decision-making. Students will also gain practical experience in applying descriptive analytics, data visualisation and storytelling, and decision-making and basic optimisation tools using relevant software packages. The module equips future business leaders with the understanding of the evolving business landscape shaped by AI and data and the knowledge and confidence to translate data into actionable insight and turn insight into impact within digitally enabled, data-driven organisations.

Module provider

Surrey Business School

Module Leader

TAVALAEI Mahdi (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

Indicative content includes:
¿ AI, Data and Digital Transformation in Business
¿ AI- and Data-Driven Business Models and Strategy
¿ Economics of AI and Digital Transformation
¿ AI, Markets and Industry Dynamics
¿ From Data to Insight and Business Value
¿ AI- and Data-Driven Decision-Making
¿ Descriptive Analysis and Storytelling with Data
¿ Prescriptive Analysis and Decision Intelligence Tools
¿ Decision-making Practice through Business Simulation

Assessment pattern

Alternative Assessment

Individual assignment instead of a group presentation

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they understand the core concepts of AI, data, and digital transformation in business and can apply them in practical, decision-focused contexts. It allows students to show their ability to interpret data, communicate insights effectively, and critically engage with the implications of AI and data-driven approaches across organisational and market settings. Assessments will emphasise both analytical thinking and the ability to translate technical understanding into valuable and actionable business insights.

Thus, the summative assessment for this module consists of:
¿ Group Presentation
¿ Individual Project

Formative assessment:
Formative assessment in this module takes place throughout its delivery, with students receiving regular feedback through class participation, case study discussions, working with analytics tools/software, and guided learning components.

Feedback:
¿ Oral Feedback: Students will receive regular in-class feedback throughout the module. They will also receive oral feedback after the group presentation.
¿ Written feedback: Students will receive written feedback on both assessments (group work/presentation and individual assignment). This will be provided by a first marker and the moderator in the context of the marking rubric uploaded on SurreyLearn.

Module aims

• Explain key concepts and principles of AI, data analytics, and digital transformation and assess their relevance to the contemporary business landscape.
• Examine the impact of AI and digital transformation at individual, micro, and macro levels in business contexts.
• Explore how organisations create, capture, and sustain value through data-enabled business models, strategies, and operations.
• Examine how AI and data influence and support managerial judgment and strategic decision-making.
• Build practical skills and confidence in applying analytics tools to generate insights, support decision-making, and drive value in organisational practice

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to introduce students to the foundational concepts of AI and data and digital transformation through a mix of interactive lectures, practical exercises, group-based activities, and real-world case study analysis. It aims to build both conceptual understanding and applied skills by engaging students in hands-on activities, tool demonstrations, and structured discussions and decision-making tasks. Learning is supported by guided independent study, curated readings, online resources, and multimedia content to deepen understanding of the topics. The teaching strategy emphasises critical thinking, peer learning, and reflective practice to support the development of confident, data-informed business professionals. Students are expected to prepare for each session through assigned readings and are encouraged to explore current developments in AI, digital transformation, and decision intelligence beyond the classroom

The learning and teaching methods include:

¿ Interactive lectures introducing key concepts, frameworks, and real-world examples.
¿ Case study analysis of form and consequences of AI and digital transformations in the business context.
¿ Hands-on workshops using industry-standard software to develop practical skills.
¿ Group activities and structured discussions, encouraging critical reflection and peer-to-peer learning.
¿ Guided independent study, including reading materials, online resources, and practice exercises to support self-directed learning.
¿ Guest lectures or industry speakers (where possible) to provide insight into current practices and trends.

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: MANM604

Other information

Surrey Business School, MBA programme, is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: Digital capabilities: This module introduces students to foundations of AI and digital transformation concepts from a business perspective, helping students understand how emerging digital technologies can be applied to create value and drive value and competitive advantage. The module also enables students to develop confidence and proficiency in using digital tools and techniques to support business decision-making. Students will work hands-on with data and analytics tools to explore and interpret business challenges, create compelling data visualisations, and extract insights from structured decision frameworks. Employability: Group work in this module provides students with the opportunity to build professional networks, apply theoretical knowledge to practical challenges, and develop decision-making skills through real-world scenarios. Students are required to exercise critical thinking and work collaboratively to analyse business models, evaluate strategies, and interpret data. These activities help them refine their ability to assess strategic choices and respond to complex business cases¿capabilities that are essential for career readiness and leadership roles. Global and cultural capabilities: The module fosters global and cultural awareness by engaging students in collaborative learning within a diverse, international cohort. Through group activities, case study analysis, and discussions, students examine how AI and data are shaping markets, industries, and decision-making across different global contexts. Collaborative activities provide exposure to diverse perspectives, helping students build the confidence and awareness needed to navigate AI and data-driven decision-making in global business environments. Resourcefulness and resilience: The module builds students¿ resourcefulness and resilience through a dynamic, team-based learning environment. Students reflect on their contributions, learn from peers, and develop critical thinking and problem-solving capabilities in collaborative settings. These capabilities are reinforced through active participation in discussions and case study analysis. Through their individual assessments and learning journeys, students also develop greater independence and proactiveness. The module encourages them to think clearly in complex situations, adapt to changing environments, and approach decision-making with confidence in data-rich business contexts. The module is designed to build confidence, curiosity, and competence in working with AI and data as essential components of modern business practice. Sustainability: By embracing AI and digital transformation trends and their impacts on various levels and in different contexts, the module encourages students to think beyond short-term gains and consider long-term sustainability. It fosters a broader perspective on how emerging technologies can shape more resilient, efficient, and forward-thinking business practices in an evolving global landscape.

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 2025/6 academic year.