AI AND ANALYTICS TOOLS FOR BUSINESS - 2026/7

Module Overview

Students will develop a solid understanding of Artificial Intelligence (AI) and gain practical skills in applying Analytics tools for business. It explores how data-driven technologies - from traditional machine learning models to emerging generative AI tools- are reshaping organisational decision-making, strategy, and innovation. Students will develop skills in data preparation, exploratory data analysis, machine learning and predictive modelling techniques, including clustering, classification, decision trees, and neural networks. The module also introduces cutting-edge developments in Large Language Models (LLMs), generative AI, and prompt engineering, enabling students to understand, interact with, and critically assess these technologies in a business context. A strong emphasis is placed on practical application through hands-on experience with leading analytics software packages and LLM tools.  Students will engage with real-world business scenarios to apply AI and analytics tools for problem-solving, customer insight, operational efficiency, and strategic value creation.

Module provider

Surrey Business School

Module Leader

SOTUNDE Deji (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: 

• Overview of Artificial Intelligence (AI), machine learning, and analytics in business 
• AI-Assisted Data Understanding, Collection, and Preparation
• AI-Assisted Exploratory Data Analysis and Visualisation
• Predictive Modelling, including Supervised and Unsupervised Methods 
• Generative AI, Large Language Models (LLMs) and Prompt Engineering in Business 
• AI for Business Process Automation and Operational Efficiency
• Integrating AI and Analytics into Business Cases (such as Marketing, Finance, and Supply Chain)

Assessment pattern

Alternative Assessment

Group assignment - An individual assignment

Assessment Strategy

The assessment strategy is designed to develop both individual analytical skills and problem-solving abilities in applying AI and analytics tools to business challenges. It assesses students and allows them to show their ability in applying data preparation, exploratory analysis, and predictive modelling techniques using software tools. This enables students to demonstrate technical proficiency and critical analysis of business data. The assessment strategy also encourages teamwork and strategic thinking, requiring students to develop an AI and analytics strategy to address a real-world business problem. This integrates the practical application of tools with strategic recommendations and communication skills. Together, the assessments ensure that students gain practical experience and strategic insight into the use of AI and analytics for business decision-making.Thus, the summative assessment for this module consists of:

• Group Assignment (addresses learning outcomes 2, 3, 4 and 5)
• Individual project (addresses learning outcomes 1, 2 and 5)

• Oral Feedback: Students will receive regular in-class feedback throughout the module.
• Written Feedback: Students will receive written feedback on both assessments. Feedback will be provided by the first marker and moderated in accordance with the marking rubric available on SurreyLearn.

Module aims

• Explain key concepts and distinctions between traditional AI, machine learning and generative AI in business contexts. Interpret and prepare business data for analysis.
• Apply AI and analytics techniques to support evidence-based decision-making
• Critically evaluate the potential and limitations of AI tools in business settings.
• Build hands-on skills using tools and LLM platforms for business analysis.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy for this module adopts a blended and interactive teaching approach, combining theoretical concepts with practical application to equip students with the skills required to use AI and analytics tools in business. This mix of teaching methods is designed to adopt both technical competence and strategic thinking, preparing students to apply AI and analytics tools confidently in business environments.The learning and teaching methods include:

• Interactive lectures to introduce key concepts, frameworks, and emerging trends in AI and analytics.
• Hands-on workshops using industry-standard software and generative AI tools to develop practical skills.
• Case study discussions to explore real-world business applications of AI and analytics.
• Group activities and problem-solving exercises to encourage collaborative learning and critical thinking.
• 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 challenges in AI and analytics adoption.

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: MANM607

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 enables students to develop confidence and proficiency in using digital tools and techniques to support business decision-making. Students will work hands-on with AI and analytics platforms to explore and interpret business challenges, create compelling data visualisations, and extract insights from structured decision frameworks. The module also introduces students to artificial intelligence and analytics concepts from a business perspective, helping them understand how these technologies can be applied to create value and drive digital transformation in organisations.

Employability: Group work in this module provides students with the opportunity to build professional networks, apply theoretical knowledge to practical challenges, and develop analytical skills through real-world scenarios and hands-on practices. Students are required to exercise critical thinking and work collaboratively to analyse data, apply and evaluate models, and interpret data. These activities help them refine their ability to comprehend the AI-driven and analytical frameworks, assess strategic decisions 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 and discussions, students examine how AI and data are applied to support decision-making and business practices across different global contexts. Collaborative activities provide exposure to diverse perspectives, helping students build the confidence and awareness needed to navigate AI and analytical tools 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 practical projects 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 analytical tools as essential components of modern business practice.

Sustainability: This module encourages students to consider how AI and analytics tools can be applied not only for performance and efficiency but also for long-term, sustainable business practices. By working with data-driven methods, students learn to identify opportunities that support smarter resource use, reduce waste, and inform decisions aligned with sustainable growth.

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.