AI is reshaping how organisations operate across nearly every sector. Let’s explore specific applications and their impact.

Healthcare: Faster Diagnosis and Treatment

AI is changing healthcare delivery while supporting (not replacing) medical professionals.

Current applications:

Medical imaging analysis: AI reviews X-rays, MRIs, and CT scans to spot potential issues. Example: Detecting early signs of diabetic retinopathy in eye scans, catching cases before vision loss occurs.

Drug discovery: AI analyses molecular structures to identify promising treatments faster. Impact: Reducing development time from years to months for some drugs.

Treatment recommendations: AI suggests personalised treatment options based on patient data. Important: Doctors make final decisions. AI provides supporting information.

Administrative automation: AI handles appointment scheduling, insurance verification, and billing. Impact: More time for medical staff to focus on patient care.

Reality check: AI cannot replace clinical judgment. It’s a tool that helps medical professionals work more efficiently and catch issues they might miss. Human expertise remains essential.

Financial Services: Security and Speed

Banks and financial institutions use AI to protect customers and streamline operations.

Current applications:

Fraud detection: AI analyses transaction patterns in real-time to spot suspicious activity. Example: Flagging unusual purchases before fraudulent charges complete.

Credit assessment: AI evaluates loan applications considering hundreds of factors. Impact: Faster decisions, but raises fairness concerns about algorithmic bias.

Trading algorithms: AI executes trades based on market conditions. Risk: Can contribute to market volatility during unusual events.

Customer service chatbots: AI handles routine banking queries 24/7. Example: Balance checks, transaction history, basic troubleshooting.

Reality check: While AI speeds up many processes, it can perpetuate existing biases in lending if training data reflects historical discrimination. Careful monitoring is essential.

Retail: Personalised Shopping

Online and physical retailers use AI to understand customer preferences and optimise operations.

Current applications:

Product recommendations: AI suggests items based on browsing and purchase history. Example: Amazon’s recommendation engine drives 35% of purchases.

Inventory management: AI predicts demand to reduce overstock and stockouts. Impact: Less waste, better availability.

Dynamic pricing: AI adjusts prices based on demand, competition, and inventory levels. Concern: Can lead to price discrimination if not carefully managed.

Visual search: AI lets customers find products by uploading photos. Example: “Find shoes that look like these.”

Reality check: Recommendation systems create “filter bubbles” where you only see products similar to past purchases, potentially limiting discovery of new items.

Transportation: Smarter Movement

AI optimises traffic, plans routes, and powers varying levels of vehicle automation.

Current applications:

Traffic prediction: AI forecasts congestion and suggests alternative routes. Example: Google Maps using real-time data to avoid delays.

Ride-sharing matching: AI connects riders with drivers efficiently. Impact: Reduced wait times, optimised driver routes.

Predictive maintenance: AI identifies vehicles needing service before breakdowns. Benefit: Fewer unexpected failures, improved safety.

Autonomous vehicles: AI handles varying levels of driving tasks. Status: Still developing. Full autonomy remains years away in most conditions.

Reality check: Self-driving technology faces significant challenges with unusual weather, unexpected obstacles, and ethical dilemmas. Human oversight remains critical.

Manufacturing: Quality and Efficiency

Factories use AI for defect detection, predictive maintenance, and process optimisation.

Current applications:

Quality control: AI-powered cameras spot defects invisible to human inspectors. Impact: Higher product quality, reduced waste.

Predictive maintenance: AI predicts equipment failures before they happen. Benefit: Less downtime, lower repair costs.

Production scheduling: AI optimises manufacturing sequences. Result: Increased efficiency, reduced bottlenecks.

Reality check: High implementation costs mean smaller manufacturers may struggle to access these technologies, potentially widening the gap between large and small producers.

Customer Service: Always Available

Businesses use AI to handle routine customer enquiries and route complex issues to humans.

Current applications:

Chatbots: AI handles common questions instantly. Example: “What’s my account balance?” “Where’s my order?”

Email sorting: AI categorises and prioritises customer messages. Impact: Faster response to urgent issues.

Sentiment analysis: AI detects frustrated customers for priority handling. Benefit: Intervene before complaints escalate.

Reality check: AI chatbots frustrate customers when they can’t handle nuanced problems or understand context. Clear escalation paths to human agents are essential.

You interact with AI more than you probably realise. Here are common applications you likely use regularly.

Recommender Systems

What they do: Suggest content, products, or connections based on your behaviour.

Where you see them:

• Netflix suggesting films and shows
• Spotify creating personalised playlists
• LinkedIn recommending connections
• YouTube proposing next videos

How they work: Analyse your past choices and compare to similar users’ preferences.

The trade-off: Great for discovery but can create “filter bubbles” where you only see familiar content.

Virtual Assistants

What they do: Understand voice commands and complete tasks.

Where you see them:

• Siri, Alexa, Google Assistant
• Voice typing on smartphones
• Smart home device control

How they work: Speech recognition converts voice to text, natural language processing understands intent, task execution follows through.

The limitation: Work well for simple commands but struggle with complex, multi-step requests or ambiguous phrasing.

Predictive Text and Autocomplete

What they do: Suggest next words as you type.

Where you see them:

• Smartphone keyboards
• Email composition
• Search engines

How they work: Statistical language models predict likely next words based on context.

Why it matters: Speeds up communication but occasionally suggests embarrassing or incorrect words.

Navigation and Maps

What they do: Calculate optimal routes considering real-time conditions.

Where you see them:

• Google Maps traffic predictions
• Uber arrival estimates
• Delivery time calculations

How they work: Process real-time traffic data, historical patterns, and current events.

The benefit: Saves time and fuel by avoiding congestion.

Content Moderation

What they do: Filter harmful content on social platforms.

Where you see them:

• Email spam filters
• Social media content flagging
• Comment moderation

How they work: Classification models identify problematic material for review or removal.

The challenge: Balancing free speech with safety. AI makes mistakes in both directions.

Face Recognition

What they do: Identify individuals from facial features.

Where you see them:

• Phone unlocking
• Photo organisation and tagging
• Airport security

How they work: Map facial features and compare to stored profiles.

Privacy concern: Raises questions about surveillance and consent, particularly in public spaces.

Data-driven insights: Patterns invisible to manual analysis. Example: Customer segmentation revealing unexpected market opportunities.

Predictive capabilities: Anticipating future events. Example: Equipment failure prediction enabling proactive maintenance.

Optimisation: Finding best solutions from countless options. Example: Supply chain routing considering hundreds of variables.

Accessibility and Inclusion

Assistive technologies: Speech recognition for mobility limitations, image description for visual impairment, real-time translation breaking language barriers. Impact: Technology access for more people.

Cost reduction: Services accessible to broader populations. Example: AI-powered financial advice available beyond wealthy clients.

Personalisation: Adapted to individual needs. Example: Learning apps adjusting to each student’s pace.

Every technology brings drawbacks. Understanding AI’s risks helps you deploy it responsibly.

Bias and Fairness

How bias enters AI:

• Historical data reflects past discrimination
• Underrepresentation of certain groups
• Biased labelling by human annotators

Real-world examples:

Hiring AI favouring male candidates: Trained on male-dominated historical hires.

Facial recognition less accurate for darker skin: Training data predominantly light-skinned faces.

Credit scoring disadvantaging minorities: Proxy variables like postcode correlate with race.

Impact:

• Perpetuates societal inequalities
• Discriminatory outcomes presented as objective
• Harm to already marginalised groups

Mitigation:

• Diverse training data
• Regular bias testing
• Human oversight of decisions affecting people

Privacy and Surveillance

Data collection concerns: AI requires extensive personal data, often collected without full awareness. Example: Behaviour tracking across websites building comprehensive profiles.

Surveillance capabilities: Facial recognition in public spaces, activity monitoring and analysis, predictive profiling. Concern: Normalising constant surveillance.

Data breach risks: Concentrations of valuable personal data create single points of failure. Impact: Identity theft, financial fraud when breaches occur.

Protection measures:

• Data minimisation (collect only what’s needed)
• Strong security measures
• User control over personal data
• Transparent data practices

Job Displacement

Automation impact: Routine cognitive and physical tasks at risk. Example: Data entry, basic customer service, some driving jobs.

Uneven effects: Some sectors affected more than others. Lower-paid workers often more vulnerable. Geographic concentration of impact.

The transition challenge: New AI-related jobs created but require different skills. Workers need retraining. Education systems lag behind. Risk: Widening inequality between skilled and displaced workers.

Positive perspective: Many jobs will be augmented, not replaced. AI handles routine tasks while humans focus on judgment and creativity. Example: Radiologist using AI to flag issues but making diagnosis.

Security and Misuse

Deepfakes: Realistic fake audio and video. Example: Fake celebrity endorsements, political manipulation. Impact: Erosion of trust in media.

Cybersecurity threats: AI enhancing attack sophistication. Example: AI-generated convincing phishing emails. Result: Arms race between AI attack and AI defence.

Social manipulation: Micro-targeted persuasion at scale, behaviour modification. Concern: Democratic processes undermined.

Lack of Transparency

Black box problem: Cannot explain AI decisions, especially in deep learning. Example: Loan denied but bank cannot explain specific reasons. Impact: Cannot challenge or appeal automated decisions.

Accountability gaps: Who is responsible when AI causes harm? Developer, deployer, user, or AI itself? Challenge: Legal frameworks not designed for AI.

Mitigation:

• Explainable AI research
• Regulatory transparency requirements
• Clear accountability frameworks

Responsible AI deployment requires careful attention to ethical principles.

Key Ethical Principles

Fairness: AI should treat all people equitably. Outcomes should not discriminate based on protected characteristics. Regular bias audits and fairness assessments needed.

Transparency: People should know when interacting with AI. Decisions should be explainable. Data usage should be clear.

Privacy: Respect for personal data. Collect only what’s needed. User control and consent.

Accountability: Clear responsibility for AI outcomes. Recourse when AI causes harm. Human oversight for high-stakes decisions.

Safety: Robust testing before deployment. Monitoring for failures. Fail-safe mechanisms.

Beneficence: AI should benefit humanity. Consider societal impact, not just profit. Address potential harms proactively.

Ethical Dilemmas

The trolley problem in autonomous vehicles
Imagine a self-driving car facing an unavoidable accident. Should its systems prioritise the safety of passengers or pedestrians, and who gets to define the rules for making those trade offs? In reality, there is no universally accepted answer, and every choice raises moral and legal questions.

Resource allocation in healthcare
AI may be used to help decide how limited medical resources are distributed. Should the aim be to maximise the number of lives saved, or to maximise quality adjusted life years? How much weight should be given to factors such as age, lifestyle or perceived social value? Each approach reflects a different ethical stance and none is straightforward.

Predictive policing
Tools that try to predict who might commit crimes could allow earlier intervention, yet they also risk reinforcing discrimination. The challenge is finding a balance between public safety and the protection of civil liberties, especially when the data used may reflect existing social biases.

Hiring and employee evaluation
AI systems can assess job candidates or monitor performance, offering efficiency and consistency. However, they can also introduce or amplify bias, so questions arise about fairness, transparency and the individual’s right to an explanation or appeal.

Responsible AI Development

Ethics by design
Ethical considerations should be built in from the very start of a project rather than treated as an afterthought or a simple compliance requirement. Bringing together diverse teams helps ensure a wider range of perspectives and reduces the risk of blind spots.

Engaging stakeholders
People who will be affected by an AI system should have a voice in shaping it. This means involving more than just technical experts and actively seeking input from users, communities and other relevant groups. Their feedback can highlight concerns that might otherwise be missed.

Carrying out impact assessments
Before an AI system is deployed, it is important to examine the potential harms and unintended consequences it could create. This assessment should not be a one off exercise, regular monitoring after launch helps to identify new risks and ensures the system continues to behave as intended.

AI will change jobs without eliminating them entirely. Understanding this helps you prepare.

Jobs Most Affected

Higher automation risk:

• Routine cognitive tasks (data entry, basic analysis)
• Predictable physical tasks (assembly line work)
• Simple customer service

Medium risk (augmentation likely):

• Professional services (law, accounting)
• Healthcare support roles
• Teaching and training

Lower automation risk:

• Complex problem-solving
• Leadership and management
• Creative and strategic thinking
• Emotional labour (counselling, care work)

The Augmentation Model

Human-AI collaboration: AI handles routine, data-heavy tasks. Humans focus on judgment, creativity, and empathy. Example: Lawyer using AI for document review, focusing on strategy.

Skills that complement AI:

• Critical thinking and judgment
• Emotional intelligence
• Complex communication
• Creativity and innovation
• Ethical reasoning
• Adaptability

Continuous learning: Learning to work with AI tools. Focusing on uniquely human capabilities. Both technical and soft skills matter.

Societal Considerations

Income inequality: Those working with AI may earn significantly more. Displaced workers may struggle. Concentration of wealth in AI-owning companies.

Work identity: Many find meaning through work. Questions arise when AI does much of what we did. Potential for more time in fulfilling pursuits. Challenge of social structures built around employment.

You’ve completed your exploration of AI in the real world. Here’s what you now understand:

AI Applications Across Industries

• Healthcare uses AI for imaging analysis and drug discovery while keeping doctors in control
• Financial services deploy AI for fraud detection and credit assessment
• Retail leverages AI for recommendations and inventory management
• Transportation uses AI for navigation and developing autonomous vehicles
• Manufacturing applies AI for quality control and predictive maintenance

AI in Daily Life

• You encounter AI in recommender systems, virtual assistants, predictive text, navigation, content moderation, and face recognition
• These applications provide convenience but also raise privacy questions
• Understanding where AI operates helps you make informed choices about its use

Benefits of AI

• Efficiency through automation of repetitive tasks and 24/7 operation
• Enhanced decision-making through data insights and predictions
• Accessibility improvements through assistive technologies and cost reduction
• Consistency in quality and reduced human error

Risks to Manage

• Bias and fairness issues from historical data and underrepresentation
• Privacy concerns from data collection and surveillance capabilities
• Job displacement particularly for routine tasks
• Security threats including deepfakes and sophisticated cyberattacks
• Transparency challenges with black box decision-making

Ethical Principles

• Fairness, transparency, privacy, accountability, safety, and beneficence guide responsible AI
• Ethical dilemmas have no simple answers (trolley problem, resource allocation)
• Responsible development requires ethics by design and stakeholder engagement

Impact on Work

• AI will augment most jobs rather than eliminate them entirely
• Skills complementing AI (creativity, judgment, empathy) become more valuable
• Continuous learning and adaptability are essential for career success