Hero question: A customer buys three items at 08:02. How can that tiny event become a smarter stock decision, a better customer experience—and a more sustainable business?
From data to insight. From insight to action. From action to sustainable value.
Imagine a customer buys milk (£1.45), bread (£1.10) and strawberries (£2.00). The total is £4.55. The transaction is small; the information system behind it is not. At checkout, a transaction processing system records the sale. Inventory is updated. A supplier signal may be generated. A dashboard may detect unusual demand. Analytics or AI may forecast tomorrow. A manager decides what to order, where to place stock and how much risk to accept.
Diagnostic poll
Which element creates the most business value?
Why this matters
The Week 1 slides repeatedly make one connected argument: strong decisions come from the right data, effective information systems, intelligent analytics or AI, and responsible human judgement. Sustainable value is the outcome—not the starting point.
Source basis: supplied Week 1 slide deck, especially the introductory data → information → IS → AI insight → human judgement → decision → value sequence.
Key takeaway: Great decisions are socio-technical. Technology can accelerate decisions, but people, processes, data quality and controls determine whether the result is useful and responsible.
Now that the business problem is visible, the next step is to make the learning goals explicit.
Learning outcomes
Your Week 1 Learning Outcomes
By successfully completing this week, you will be able to:
LO1
Explain the fundamental concepts and components of information systems (IS) and artificial intelligence (AI) in modern business.
LO2
Differentiate how business information systems and AI support organisational operations, managerial decision-making and digital transformation.
LO3
Analyse how data is transformed into business intelligence to improve organisational performance, innovation and sustainable business value.
LO4
Evaluate the opportunities, risks and ethical responsibilities of using information systems and AI to support effective, evidence-based and sustainable business decisions.
How to use these outcomes: Each major section below shows which outcomes it develops. By the end, you should be able not only to define IS and AI, but to connect them to evidence, decisions, risk, responsibility and sustainable value.
LO1 begins with the foundation: what an information system actually is.
LO1 · Essential → Applied → Advanced
1. What Is an Information System?
Definition: An Information System (IS) is an organised combination of people, processes, data, technology and controls that collects, processes, stores and shares information to support operations, management decisions and strategic business success.
Information systems are socio-technical systems: business outcomes depend on the interaction of all five components, not technology alone.
People
Users, employees, managers, customers, suppliers and IT specialists use and create information.
Processes
Business activities and workflows transform inputs into valuable information and services.
Data
Facts about transactions, customers, products, employees and the environment must be accurate, timely and relevant.
Technology
Hardware, software, databases, networks and cloud platforms capture, process, store and deliver data.
Controls
Policies, security, access rights, quality standards and audits protect information and ensure reliability.
Traditional IS
Mainframes and local systems focused heavily on recording and reporting transactions.
Digital IS
Networks, databases, internet and mobile technologies support integration, access and sharing.
Intelligent IS
Analytics, AI, machine learning and automation support insight, prediction and optimisation.
Strategic IS
Digital platforms and ecosystems focus on innovation, sustainability and long-term value.
What information systems enable
Faster, more accurate business decisions.
Operational efficiency and lower cost.
Innovation and new business models.
Risk management, security and compliance.
Sustainable business performance and social responsibility.
Critical connection: IS are not “the IT department.” They are organisational systems in which technology is one component. A technically excellent tool can still fail if people do not trust it, processes conflict, data is poor or controls are weak.
Key takeaway: Information systems connect people, processes, data, technology and controls to turn information into intelligent action, competitive advantage and sustainable value.
Once an information system is defined, the next question is how raw data actually becomes something a manager can act on.
LO1 · LO3
2. How Information Becomes Business Value
The slide deck’s central value chain moves through six stages. Data is captured; information adds structure; knowledge explains patterns; business intelligence explores what may happen; managers decide; actions create measurable value.
Decision questions: What happened? What is happening? Why? What may happen? What should we do? What value did we create?
Retail example from the slides
Data: a sandwich sale is recorded at 08:02.
Information: morning sandwich sales are above normal.
Knowledge: warm weather and local events may help explain demand.
Business intelligence: a forecast estimates higher demand tomorrow.
Decision: managers decide whether and where to increase production or stock.
Important evidence rule: the slide’s numerical example (+28% demand; 1,500 extra sandwiches) is an instructional scenario. No authoritative source was supplied for those exact figures, so this webpage does not present them as verified real-world facts.
Five enablers of value
Quality data
Accurate, complete, timely and relevant data underpin good decisions.
Integrated systems
ERP, CRM, SCM and BI connect data across functions.
Analytics & AI
Models can uncover patterns, forecast trends and recommend actions.
Human expertise
Context, experience, ethics and judgement remain essential.
Sustainability focus
Information can support lower waste, lower energy use and long-term stakeholder value.
Key takeaway: Data is not automatically valuable. Value emerges when the right data meets the right systems, analytics and human judgement to support the right decision.
Different decisions require different kinds of systems. The next section shows how IS support operational, managerial, analytical and strategic levels.
LO2
3. From Transactions to Strategy
Information systems support decisions at every level. A useful hierarchy moves from high-volume operational transactions through managerial reporting and analytical decision support to strategic executive intelligence.
As information moves upward, it becomes more aggregated, analytical and strategic.
Four decision levels
System
Primary role
Typical questions
Examples
TPS
Capture routine transactions accurately and efficiently.
What happened?
Sales, orders, payments, inventory, payroll.
MIS
Provide regular reports and summaries to monitor performance.
Older systems often focused on recording transactions, siloed databases, historical reporting, manual analysis and security as a specialist IT concern.
Now: integrated and intelligent
Modern digital systems increasingly emphasise integrated platforms, real-time information, AI-assisted analytics, privacy and security by design, and sustainability embedded in operations and strategy.
When these decision layers share data rather than operate in silos, the organisation becomes an integrated digital enterprise.
LO2 · LO3
4. The Integrated Digital Enterprise
Modern organisations connect customers, employees, suppliers and partners through shared data and coordinated platforms. Integration improves visibility and can reduce duplication, slow hand-offs and conflicting reports.
ERP
Integrates core resources such as finance, procurement, inventory, HR and assets.
CRM
Connects customer data, sales, service, loyalty and retention.
SCM
Coordinates suppliers, inventory, logistics and demand planning.
BI / Analytics
Provides dashboards, KPI monitoring, predictive insights and what-if analysis.
E-commerce & digital channels
Connects online stores, apps, marketplaces and click-and-collect experiences.
Technology foundation
Cloud, databases, APIs, security and governance enable reliable integration.
People and processes remain at the centre
Shared platforms do not create value by themselves. Culture, collaboration, skills, change management and governance determine whether integration improves decisions or simply spreads poor-quality data faster.
Business value created
Customer value
Personalised experiences, faster service and higher satisfaction.
Operational excellence
Integrated processes, better quality and lower cost.
Better decisions
Real-time information and analytics can speed action.
Risk & resilience
End-to-end visibility supports control and compliance.
Sustainability impact
Better resource visibility can help reduce waste and emissions.
Key takeaway: The integrated digital enterprise transforms data into intelligence, connects the wider ecosystem and can create sustainable value for customers, business and society.
Integration is operationally useful—but can it also become a source of competitive advantage?
LO2 · LO3
5. IS as a Strategic Business Resource
The slides organise strategic value into three moves: automate existing work, inform better decisions, and transform the business by creating new products, experiences, capabilities or ecosystems.
1. Automate
Do existing work better and faster: efficiency, standardisation, visibility and fewer errors.
2. Inform
Create insight: data visibility, performance measurement, forecasting and decision support.
3. Transform
Redesign the business: new models, innovation, customer experience and ecosystems.
Resource-Based View (RBV) lens
The Week 1 material uses the RBV logic to ask whether information-system capabilities are valuable, rare, hard to imitate and well organised. The strategic implication is important: buying the same software as a competitor rarely creates durable advantage by itself. Advantage comes from the complementary bundle—data assets, process know-how, culture, integration, governance and human capability.
Competitive positioning
Differentiation: superior digital experience or personalisation.
Cost leadership: automation and optimisation lower cost-to-serve.
Focus & agility: real-time data supports rapid response.
Ecosystem advantage: platforms and data sharing create networked value.
Manager questions
Are our systems aligned with business strategy?
Are our data assets well managed, secure and trustworthy?
Are we using IS and AI to create future value for customers and society?
Can a competitor easily copy the capability, or is the advantage embedded in our organisation?
Academic foundation: RBV is used in the supplied slides as a strategic lens. The reading list provides core IS and business analytics texts; see References.
To make these ideas concrete, apply them to one of the most data-rich environments students encounter every week: grocery retail.
Activity 1 · UK + international retail
6. Activity Lab: Mapping the Intelligent Retail Information System
Every purchase creates data. Your role is to act as a Business Information Systems consultant and trace how a retailer turns one basket into operational action and strategic intelligence.
Customer purchase
Product selected and scanned.
Checkout / TPS
Transaction captured accurately and quickly.
Inventory update
Stock levels update automatically.
Supplier replenishment
Orders or signals coordinate supply.
MIS / DSS dashboard
Managers see metrics, alerts and scenarios.
BI / strategic decision
Pricing, promotion, assortment, stock and sustainability choices.
Access controls, fraud detection, risk and compliance.
Policies, audits, incidents, privacy controls.
Task 2 — Recommend three improvements
AI demand forecasting
Use demand signals to improve availability while measuring forecast error and waste.
Smart shelf / IoT sensors
Improve real-time visibility of stock, shelf life or temperature—while assessing privacy, security and maintenance risk.
Personalised offers
Use loyalty data to tailor promotions, but test fairness, consent, value and customer trust.
Verified case anchors
Tesco: its 2025 annual report describes digital capability and Clubcard as sources of personalised customer insight; it reported more than 23 million Clubcard households in the UK. Official Tesco Annual Report 2025
Walmart: its 2025 Form 10-K states that the company invests in AI and generative AI to improve customer and associate experiences and efficiencies in supply chain, operations and management, while also recognising privacy, compliance and legal risks. Official Walmart annual reports
M&S: official reporting describes Digital, Data and Technology as a strategic investment area supporting transformation and customer experience. Official M&S investor reports
Source correction: the Week 1 activity slide includes 2024 grocery market-size, market-share, loyalty, waste and AI-investment figures. Because the slide does not provide full source definitions/URLs for every number and no raw dataset was supplied, this webpage preserves the activity structure but does not repeat those figures as verified evidence. Where such figures are needed for assessment, use ONS, company reports, Kantar/NIQ, WRAP or another clearly documented source and record the indicator, unit, geography, period and update date.
The retail system is now mapped. The next step is to distinguish AI from ordinary automation and analytics.
LO1 · LO2
7. What Is Artificial Intelligence?
Artificial Intelligence (AI) is the capability of computer systems to perform tasks that typically require human intelligence—such as learning, understanding, reasoning, recognising patterns, making predictions, generating content and supporting decisions.
Analyses data to understand what happened and why. Example: sales dashboards.
Artificial intelligence
Learns from data, finds patterns, predicts or generates recommendations and actions. Example: demand forecasting or recommendations.
Key AI capabilities
Machine learning
Learns patterns from data and improves model performance over time.
Natural language processing
Understands, interprets and generates human language.
Computer vision
Interprets and analyses images and video.
Predictive analytics
Uses historical and contextual data to estimate future outcomes.
Generative AI
Creates new content such as text, images, code, summaries and synthetic data.
AI in business: from problem to learning loop
Problem
Define the business objective and success criteria.
Collect
Capture relevant data.
Prepare
Clean, integrate and organise data.
Build
Select methods and train/configure models.
Predict
Generate insights, forecasts or recommendations.
Decide
Humans review and act; systems may execute within controls.
Learn
Measure outcomes and improve continuously.
Key takeaway: AI is not about replacing people; it is about augmenting human intelligence to make better decisions, create value and build a more sustainable future.
AI capability is not static. The slides show a progression from rules to learning, prediction, generation and emerging agentic action.
LO2 · LO4
8. From Automation to Agentic AI
As systems move from rule-based automation toward higher autonomy, potential business impact increases—but so do governance, control and accountability requirements.
Key shifts highlighted in the slides
Rule-based → learning systems.
Siloed data → integrated, real-time data.
Descriptive analytics → predictive and prescriptive AI.
Human in control → human + AI collaboration.
Task automation → workflow and decision automation.
Static processes → adaptive systems.
Cost centre → strategic growth enabler.
IT-led → business + IT partnership.
Risk increases with autonomy
Data privacy and security.
Bias, fairness and ethics.
Transparency and explainability.
Skills and AI literacy gaps.
Legacy-system integration.
Dependency on data quality.
Over-automation and loss of human judgement.
Key takeaway: The journey from automation to agentic AI is not only technological—it changes how organisations use data, distribute decision rights and create accountability.
The safest way to adopt advanced AI is not to begin with a fashionable tool. Begin with a business problem and trace the decision pipeline.
LO3
9. How AI Learns Business Value from Data
Critical principle: start with the business problem—not with the AI tool. This connects the slides to data-analytic thinking: analytics and AI should support better, evidence-based business decisions.
Business problem
What outcome are we trying to improve?
Data
What evidence is relevant and lawful to use?
Model / algorithm
Which method fits the task and risk?
Pattern
What relationships are detected?
Prediction / recommendation
What action is suggested?
Human review
Does context support acting?
Action + feedback
What happened, and what should change?
AI across the business
Marketing
Personalisation and segmentation.
Finance
Fraud detection, credit and risk assessment.
Operations
Forecasting and optimisation.
Supply chain
Inventory, routing and supplier management.
HR
Workforce analytics—requiring careful fairness and privacy governance.
Customer service
Chatbots and service support.
Management
Scenario analysis and decision support.
Decision boundary question: Which decisions should AI recommend, which may be automated under controls, and which should humans retain? The answer depends on consequence, reversibility, uncertainty, legal requirements, fairness, explainability and stakeholder impact.
That boundary leads directly to one of the most important themes in Week 1: augmentation rather than blind automation.
LO3 · LO4
10. Human + AI: Augmentation, Not Blind Automation
The slides frame the strongest partnership as AI providing speed and scale while humans provide context, values and accountability. Better decisions happen when AI provides insight and humans provide wisdom.
A forecasting model predicts a sharp rise in demand for fresh sandwiches tomorrow. Choose how you would respond.
Select an option.
Critical thinking + AI
Question assumptions and AI outputs.
Validate evidence and check data quality.
Consider multiple perspectives and scenarios.
Understand bias, uncertainty and limitations.
Make decisions aligned with values and strategy.
Golden rule: Trust, but verify. Collaborate, but remain accountable.
Human oversight matters because intelligent systems can produce unintelligent outcomes when data, design or governance are weak.
LO4
11. When Intelligent Systems Make Unintelligent Decisions
AI is only as reliable as its data, design, context and governance. Errors at machine speed can scale quickly, so critical thinking and oversight are not optional.
“Garbage in” is not just a technical problem: errors can move quickly from data into decisions and stakeholder outcomes.
Bias
Historical or unrepresentative data can reproduce unfair patterns.
Hallucination
Generative systems can produce plausible but false information; high-stakes outputs require verification.
Opacity
Complex models may be hard to understand, limiting accountability and trust.
Privacy
Over-collection or misuse of personal data can violate rights, law and trust.
Automation bias
People may trust system outputs too readily and ignore contradictory evidence.
Real-world cautionary examples from the Week 1 slides
The slides reference well-known cases including Amazon’s discontinued recruiting tool, credit-scoring fairness concerns, Zoom privacy concerns, a chatbot refund-information error, and a fatal autonomous-vehicle incident. These examples are retained as teaching prompts. Students should verify the original case evidence before using them in assessed work.
Mitigations
Better, representative, accurate and ethical data.
Explainability appropriate to risk and audience.
Human-in-the-loop or human-on-the-loop oversight where consequential.
Governance, documented accountability and audit.
Continuous monitoring, learning and improvement.
AI literacy gap
The slide deck treats AI literacy as the new digital literacy: users need to understand AI capability, ask the right questions, evaluate outputs, identify limitations and keep learning.
Bottom line: Good data + good design + good governance + human judgement = more trustworthy decisions.
The next step is to turn those risk principles into an operational governance framework.
LO4 · Governance
12. Responsible AI: From Innovation to Trust
Responsible innovation asks three questions: Can we?Should we?How should we? Trust is not a barrier to innovation; it is infrastructure for sustainable innovation.
GOVERN — Who is accountable?
Establish roles, responsibilities, policies and organisational values.
MAP — What is the context?
Understand the use case, data, systems, stakeholders and possible impacts.
MEASURE — How do we test?
Evaluate performance, bias, safety, security, robustness and lifecycle risk.
MANAGE — What should change?
Prioritise and implement responses; improve, control, communicate or stop.
Verified framework: NIST AI RMF 1.0 uses the four functions Govern, Map, Measure and Manage; NIST describes risk management as continuous across the AI lifecycle. NIST AI Risk Management Framework
Characteristics of trustworthy AI
Valid
Achieves intended outcomes for the intended purpose.
Reliable
Performs consistently and accurately.
Safe
Minimises risk to people, property and environment.
Secure
Resilient to attacks and protects systems, data and models.
Transparent
Processes and limitations are communicated clearly.
Explainable
Decisions and outputs can be understood at an appropriate level.
Privacy-enhanced
Respects privacy and supports data protection.
Fair
Avoids unjustified bias and discrimination.
UK connection
The UK’s 2025 AI Opportunities Action Plan set out 50 recommendations to grow the AI sector, drive adoption and improve products and services. A January 2026 progress report stated that commitments had been met against 38 of the 50 actions. This makes skills, adoption, infrastructure and responsible use current business issues rather than distant futures. AI Opportunities Action Plan · 2026 progress report
Responsible AI principles in practice
Microsoft publicly describes six responsible AI principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability. These provide a useful comparison with the trust and governance themes in the slides. Microsoft Responsible AI principles
Governance explains how to use AI responsibly today. Strategy also requires thinking about what may change next—and being explicit about uncertainty.
LO2 · LO3 · LO4
13. Evidence, Trends and the Next 10 Years
No longitudinal CSV dataset was supplied with the Week 1 materials. Rather than fabricate a 20-year numerical series, this page uses a transparent 20-year evidence timeline and separates observed developments from future scenarios.
Observed era
c. 2006–2014
Enterprise digitisation, cloud services, mobile access and integrated databases expand the reach of information systems. Rule-based automation and business intelligence remain prominent.
Observed era
c. 2015–2021
Machine learning, deep learning, real-time analytics, IoT and platform ecosystems expand predictive and optimisation use cases.
Observed era
2022–2026
Generative AI and emerging agentic workflows accelerate AI adoption. Organisations also increase attention to governance, privacy, security, data quality and workforce skills.
Scenario horizon
2027–2036
Potential developments include more AI agents, hyperconnectivity, digital twins, composable enterprises, autonomous workflows and human-centred transformation. These are scenarios, not certainties.
Evidence status: the historical periods are teaching syntheses aligned with the Week 1 slide evolution diagrams and current official/company reporting. They are not a quantitative time series. If you add historical indicators later, record definition, unit, geography, frequency, period, update date and source URL.
10-year scenario controls
These controls do not predict financial returns. They illustrate causal trade-offs in a modelled teaching scenario.
Baseline scenario
Three transparent 2027–2036 scenarios
Scenario
Assumptions
Potential opportunities
Potential risks
Baseline
Steady adoption; governance and skills improve but unevenly.
Future principle: technological capability is not destiny. Outcomes depend on organisational design, data quality, human capability, governance and stakeholder choices.
The most useful way to finish is to make students design, justify and challenge a responsible digital business strategy themselves.
Active learning
14. Apply, Discuss and Design
Individual: AI opportunity–risk canvas
Choose one organisation. Identify one business problem, relevant data, an appropriate information system, a possible AI contribution, the human decision owner, intended value and three risks.
Group: Responsible AI strategy
Build a five-principle framework: (1) business value and outcomes, (2) customer trust and privacy, (3) ethical governance and transparency, (4) data quality and cybersecurity, (5) sustainability and continuous improvement.
Innovation challenge
Design an AI-enabled retail service that creates customer and business value while reducing waste. Your pitch must include: the data required; the IS components; AI role; human oversight; stakeholders; risks; controls; sustainability impact; one KPI for value and one KPI for harm. Explain what would make you stop or redesign the system.
Microsoft Learn challenge from the Week 1 recap
The slides encourage students to build digital and AI literacy through Microsoft Learn, including AI fundamentals, data fundamentals, Power BI, cloud computing, productivity and security learning paths. Verify current course names and certificate availability directly on Microsoft Learn before presenting any credential as guaranteed.
Before leaving Week 1, test whether you can distinguish systems, AI, value creation and responsibility.
Formative assessment
15. Knowledge Check
Choose the best answer. Feedback explains why, so this is a learning activity rather than a score-only test.
1. Which definition best captures an information system?
2. Which sequence best represents value creation?
3. Which system is mainly associated with what-if analysis and scenario planning?
4. What is the strongest starting point for an AI project?
5. Which statement best reflects responsible human–AI collaboration?
6. What are the four NIST AI RMF core functions highlighted in the slides?
Week 1 synthesis
16. Integrated Recap
1. Information systems
Collect, process, store and distribute information through people, processes, data, technology and controls.
2. Digital enterprise
Integrated systems connect functions, customers, suppliers and partners to improve visibility and coordination.
3. Strategic resource
IS capabilities can improve efficiency, differentiation, agility and sustainable value when supported by organisational capabilities.
4. AI in business
AI learns from data to find patterns, predict, generate and recommend—but differs from automation and analytics.
5. Human + AI
AI augments human intelligence; people provide purpose, values, critical thinking, creativity and accountability.
6. Risks & responsible AI
Bias, hallucination, opacity, privacy and automation bias require governance, testing, security and oversight.
7. Future of IS & AI
Real-time data, AI agents, IoT, cloud and digital twins may reshape enterprises, but future outcomes remain uncertain and choice-dependent.
Six principles to remember
Start with the business problem—not the technology.
Data quality determines AI quality.
AI learns from data; humans set direction and judgement.
Critical thinking + ethics + governance = more trustworthy AI.
Information systems are the organisational foundation around AI.
Responsible use should create value for business, people and planet.
Final Week 1 message: Information Systems enable. AI accelerates. Humans decide. Together—when designed responsibly—they can create better decisions, better businesses and a better future.
Revision aid
Glossary
AI (Artificial Intelligence)
Computer systems capable of tasks associated with human intelligence, such as learning, reasoning, recognising patterns, predicting, generating content and supporting decisions.
Analytics
Systematic analysis of data to understand what happened, why it happened, what may happen next and what action may be useful.
BI (Business Intelligence)
Processes and tools that turn organisational data into reports, dashboards, patterns, trends and decision-relevant insights.
CRM
Customer Relationship Management: systems that organise customer data, interactions, service, sales, loyalty and retention.
DSS
Decision Support System: an analytical system that helps managers explore alternatives, scenarios and what-if questions.
ERP
Enterprise Resource Planning: an integrated system connecting core functions such as finance, procurement, inventory, operations and HR.
ESS
Executive Support System: high-level information and intelligence for strategic decisions, risks, opportunities and long-term direction.
Generative AI
AI that creates new content such as text, images, code, summaries or designs in response to instructions and context.
Information
Data that has been organised, structured and given context so that it has meaning.
Information System (IS)
An organised combination of people, processes, data, technology and controls that collects, processes, stores and shares information to support operations, management decisions and strategic success.
Machine Learning
Methods that enable systems to learn patterns from data and improve predictions or classifications without every rule being explicitly programmed.
MIS
Management Information System: systems that provide regular reports, summaries and performance measures for managerial control.
NLP
Natural Language Processing: AI methods used to understand, interpret and generate human language.
SCM
Supply Chain Management: systems for coordinating suppliers, inventory, logistics, demand planning and delivery.
TPS
Transaction Processing System: systems that capture routine, high-volume operational transactions such as sales, orders, payments, inventory movements and payroll.
Academic integrity
References & Further Learning
The first four references reproduce the reading list supplied in the Week 1 slide deck. The additional official sources were used to verify contemporary UK policy, responsible-AI frameworks and selected retailer examples.
Laudon, K.C., Laudon, J.P. and Traver, C.G. (2025) Management Information Systems: Managing the Digital Firm. 18th edn., Global edn. Harlow: Pearson. Chapters 1–3.
Albright, S.C. and Winston, W.L. (2020) Business Analytics: Data Analysis & Decision Making. 7th edn. Boston, MA: Cengage. Chapters 1–2.
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., Cochran, J.J., Fry, M.J. and Ohlmann, J.W. (2020) Statistics for Business & Economics. 14th edn. Boston, MA: Cengage. Chapters 1–2.
Provost, F. and Fawcett, T. (2013) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Sebastopol, CA: O’Reilly Media. Chapters 1–3.
Tesco PLC (2025) Annual Report and Financial Statements 2025. Tesco PLC. Official source
Walmart Inc. (2025) Annual Report / Form 10-K for the year ended 31 January 2025. Walmart Inc. Official source
Marks and Spencer Group plc (2025) Annual Report and Financial Statements 2025. Marks and Spencer Group plc. Official source
National Institute of Standards and Technology (2023) Artificial Intelligence Risk Management Framework (AI RMF 1.0). Gaithersburg, MD: NIST. Official source
Department for Science, Innovation and Technology (2025) AI Opportunities Action Plan. London: UK Government. Official source
Department for Science, Innovation and Technology (2026) AI Opportunities Action Plan: One Year On. London: UK Government. Official source
Microsoft (2026) Responsible AI: Principles and Approach. Microsoft. Official source
Verification note: statistics or examples displayed visually in the source slides but lacking a complete authoritative source in the supplied materials are treated as teaching prompts, not as verified evidence. Do not copy them into assessed work without checking the original source.