Chapter 14: Digital Management, AI and the Future of Work

1. Introduction

Digital technologies have changed how organisations create value, coordinate activities, serve customers and manage employees. Digital management is not simply the purchase of computers or the use of social media. It is the deliberate integration of digital technologies, information, people, processes and organisational strategy. It requires managers to understand how data is produced, how technology changes work, how decisions can be improved through analytics and how innovation can be governed responsibly.

Artificial intelligence (AI), automation, cloud computing, digital platforms and remote-working systems are increasingly present in organisations of every size. A small business may use a cloud accounting system and an online marketplace, while a large organisation may use predictive analytics, robotic process automation and AI-supported recruitment. These technologies can improve speed, quality and flexibility, but they can also create risks involving privacy, cybersecurity, exclusion, algorithmic bias and job displacement.

The future of work will therefore depend not only on technological capability but also on managerial judgement. Effective managers must connect digital investments with organisational objectives, redesign jobs thoughtfully, develop employee capabilities and establish ethical controls. Digital transformation is ultimately a management and organisational change challenge, not merely an information-technology project.

2. Learning Objectives

By the end of this chapter, students should be able to:

1. Explain digital management, digital transformation, AI, automation and the future of work.

2. Distinguish between management information systems, decision-support systems and business analytics.

3. Evaluate digital platforms, remote-work practices and technology-enabled collaboration.

4. Apply a structured framework for planning and managing digital transformation.

5. Analyse the benefits and limitations of automation and AI in organisations.

6. Identify cybersecurity, privacy, algorithmic-bias and governance risks.

7. Explain how technology changes job design, skills and managerial responsibilities.

8. Recommend responsible and strategically appropriate digital-management practices.

3. Meaning and Concept

Digital management is the planning, organising, leading and controlling of organisational resources through the effective use of digital technologies and data. It includes the management of digital operations, information systems, virtual teams, technology projects, digital customer relationships and technology-related risks.

Digital transformation is the significant organisational change created by the adoption and integration of digital technologies. It may involve replacing paper processes, developing online services, redesigning supply chains or changing the organisation’s business model. Digitisation refers to converting analogue information into digital form, such as scanning paper records. Digitalisation refers to using digital technology to improve an existing process, such as using an online approval workflow. Digital transformation is broader: it changes processes, structures, capabilities and sometimes the entire basis of competition.

Artificial intelligence is the capability of computer systems to perform tasks associated with human intelligence, including recognising patterns, interpreting language, making predictions and generating content. Machine learning is a major approach to AI in which systems identify patterns from data rather than relying solely on explicit rules. Generative AI produces new text, images, code, audio or other content in response to instructions.

Automation is the use of technology to perform tasks with limited human intervention. It may be rule-based, such as automatically sending an invoice, or intelligent, such as using machine learning to classify customer complaints. Automation does not always eliminate a whole job. More commonly, it changes the combination of tasks within a job and increases the importance of judgement, communication and problem-solving.

The future of work refers to changing patterns of employment, work organisation, skills, technology and workplace relationships. It includes remote and hybrid work, platform work, flexible employment, human–machine collaboration and continuous reskilling.

4. Fundamental Principles

Strategic alignment

Technology should support organisational purpose and measurable objectives. A firm should not adopt AI merely because competitors are doing so. Managers should first identify the business problem, the expected value and the risks.

Human-centred design

Digital systems should be designed around the needs, capabilities and rights of users. Employees and customers should be consulted, accessibility should be considered and systems should not make work unnecessarily confusing or intrusive.

Data quality and evidence

The quality of decisions depends on the quality of data. Data should be accurate, relevant, timely, complete and appropriately structured. Managers must also understand that data reflects the processes and assumptions through which it was collected.

Security and resilience

Digital systems must be protected against unauthorised access, disruption, manipulation and loss. Resilience also requires backup arrangements, recovery plans and the ability to continue critical operations during an outage.

Accountability and transparency

A person or group must be responsible for each important digital system. Users should understand, at an appropriate level, how decisions are made and how they can challenge errors.

Inclusion and fairness

Digital transformation should benefit diverse users and should not systematically disadvantage people because of gender, ethnicity, disability, age, socioeconomic status or other characteristics. Accessibility and fairness must be considered from the design stage.

Continuous learning

Technology and work practices change rapidly. Organisations must provide training, experimentation and opportunities for employees to acquire new skills. Learning is a continuing managerial responsibility rather than a one-time project.

5. Major Components / Elements

Digital infrastructure

Infrastructure includes devices, networks, servers, databases, cloud services and cybersecurity controls. Cloud computing allows organisations to access computing resources and software through the internet rather than owning all physical equipment. It can increase flexibility and reduce capital expenditure, but it also creates dependency on service providers and connectivity.

Data and information resources

Data may concern customers, employees, transactions, assets, suppliers or market conditions. Data governance establishes rules for ownership, quality, access, retention, security and responsible use. Information is processed data that is meaningful for a decision or activity.

Management information systems

A management information system (MIS) collects, processes, stores and distributes information to support organisational operations and management. Transaction-processing systems record routine activities such as sales and payroll. Management reporting systems produce regular summaries. Decision-support systems assist with analysis of semi-structured problems. Executive information systems present selected indicators to senior managers.

Digital platforms

A digital platform connects different groups, such as buyers and sellers, drivers and passengers, or creators and audiences. Platforms create value through interaction and network effects: the service may become more valuable as more participants join. However, platform organisations must manage trust, quality, pricing, data ownership and the interests of different user groups.

People and digital capabilities

Digital capability includes technical skills, data literacy, communication, critical thinking, adaptability and ethical judgement. Managers require sufficient understanding to question technical recommendations and to connect digital initiatives with business performance.

Processes and governance

Technology must be integrated into workflows, policies, decision rights and performance systems. Governance defines who may approve systems, access data, monitor performance and respond to incidents.

6. Process / Framework / Methodology

A practical digital-transformation process can be organised into eight stages.

Stage 1: Diagnose the organisation. Managers assess current processes, customer expectations, systems, skills, costs and risks. Process mapping can reveal duplication, delays and unnecessary manual work.

Stage 2: Define the business problem. The organisation should state the problem in operational terms, such as reducing order errors, improving response time or increasing access to a service. A technology solution should not be selected before the problem is understood.

Stage 3: Establish objectives and measures. Objectives should be specific and linked to indicators such as processing time, customer satisfaction, productivity, error rates, revenue or employee experience. Both benefits and risks should be measured.

Stage 4: Assess data and feasibility. Managers examine whether suitable data exists, whether it is lawful to use, whether systems can integrate and whether employees have the required skills. Cost, suppliers, infrastructure and cybersecurity should be assessed.

Stage 5: Design with stakeholders. Employees, customers, technical specialists and affected communities should contribute to system design. Prototypes and user testing can identify problems before full implementation.

Stage 6: Pilot and evaluate. A limited pilot allows the organisation to test technical performance, usability, fairness and financial assumptions. The pilot should include clear success criteria and a method for reporting unexpected effects.

Stage 7: Implement and manage change. Implementation requires communication, training, revised procedures, support and leadership. Employees are more likely to adopt a system when they understand its purpose and can influence how it affects their work.

Stage 8: Monitor, improve and govern. After launch, managers monitor performance, security, user feedback, unintended consequences and changing regulations. Digital systems require continuing maintenance and periodic review.

7. Important Theories and Models

Technology–organisation–environment framework

The technology–organisation–environment framework explains technology adoption through three dimensions. The technology dimension concerns availability, compatibility, complexity and relative advantage. The organisation dimension includes leadership support, resources, culture and employee capability. The environment dimension includes competition, regulation, suppliers and customer expectations. A technically attractive system may fail if organisational readiness is weak or the external environment is unsuitable.

Technology Acceptance Model

The Technology Acceptance Model proposes that perceived usefulness and perceived ease of use influence attitudes and intentions to use a system. In practice, employees are more likely to adopt a digital tool when it helps them perform meaningful work and is not unnecessarily difficult to operate. Training, trust and managerial support also influence acceptance.

Diffusion of innovations

Diffusion theory describes how innovations spread among members of a social system. People may be innovators, early adopters, early majority, late majority or laggards. Managers can use respected early adopters as champions, demonstrate observable benefits and reduce perceived complexity.

Socio-technical systems theory

Socio-technical theory argues that organisational performance depends on jointly designing social and technical elements. A new software system cannot compensate for unclear responsibilities, poor communication or unsuitable incentives. Technical efficiency should be balanced with employee autonomy, cooperation and wellbeing.

Task-based view of automation

The task-based view distinguishes between tasks within jobs rather than treating jobs as indivisible units. Technology may substitute for routine tasks, complement professional judgement or create new tasks such as system monitoring and data interpretation. This model supports job redesign and reskilling rather than assuming that automation always means complete job elimination.

Data-information-knowledge-wisdom hierarchy

This hierarchy distinguishes raw data from interpreted information, applied knowledge and informed judgement. Large quantities of data do not automatically create wisdom. Managers must add context, experience, values and critical evaluation.

8. Practical Applications

Digital management can improve customer relationship management through integrated records, automated communication and analysis of customer behaviour. In operations, sensors and analytics can support predictive maintenance by identifying signs of equipment failure before a breakdown. In human-resource management, digital learning systems can recommend training, while workforce analytics can identify staffing patterns. In finance, automated reconciliation and anomaly detection can reduce errors and fraud.

Remote and hybrid work require managers to redesign coordination rather than simply transfer office routines online. Clear objectives, appropriate communication channels, agreed availability, documented decisions and outcome-based performance measures are important. Excessive digital monitoring may undermine trust and encourage unhealthy work intensity. Managers should distinguish legitimate performance information from intrusive surveillance.

Digital platforms can expand market access and reduce transaction costs. A small producer may sell through an online marketplace without building its own global distribution network. However, platform dependence can expose the producer to changing fees, ranking algorithms and contractual conditions.

9. Examples

A retail chain may use point-of-sale data to forecast demand. If forecasts are accurate, stores can reduce stockouts and waste. However, managers must account for unusual events, promotions and data limitations rather than treating the forecast as certain.

A logistics company may use route-optimisation software. The system can reduce fuel use, but drivers may know about road closures, safety risks or customer circumstances that are not included in the data. A responsible system therefore combines algorithmic recommendations with human discretion.

A college may introduce an AI tutor to provide immediate feedback. The tutor can support learning outside classroom hours, but it should not replace academic judgement. Students need to know when content is AI-generated, how their data is used and how to obtain human assistance.

10. Technology and Digital Applications

Relevant technologies include cloud computing, mobile applications, collaboration platforms, enterprise resource planning systems, customer relationship management systems, robotic process automation, machine learning, generative AI, Internet of Things sensors, dashboards, data warehouses and cybersecurity tools.

Business intelligence dashboards convert data into visual reports and indicators. They are useful when indicators are clearly defined and linked to decisions. A dashboard can also mislead when managers focus on easily measured outputs while ignoring service quality, employee wellbeing or long-term capability.

Generative AI can draft documents, summarise information, support coding and provide ideas. It may produce inaccurate, biased or fabricated content, so outputs require human verification. Confidential information should not be entered into an AI service unless the organisation has approved appropriate protections.

11. Strategic / Managerial Importance

Digital capability can create competitive advantage through lower costs, superior customer experience, faster innovation, better decisions and greater organisational agility. It can also improve strategic visibility by allowing managers to analyse operations in near real time.

Digital management affects every managerial function. Planning uses forecasts and scenarios. Organising requires new roles, workflows and decision rights. Leading involves building trust, communicating change and supporting learning. Controlling uses data, performance indicators and risk monitoring. Managers must therefore become translators between business needs, technical specialists and affected employees.

Strategic value does not arise automatically from owning advanced technology. It depends on complementary resources: capable people, reliable processes, useful data, leadership commitment and a culture that supports responsible experimentation.

12. Detailed Case Study

The following case is fictional and illustrative.

Background

BrightHome Supplies is a medium-sized distributor of household products with 180 employees. It receives orders through telephone, email and a basic website. Staff manually re-enter order information into a warehouse system. Senior managers believe that an integrated digital platform could improve customer service and reduce costs.

Problem

The company experiences frequent order-entry errors, delayed responses and excess inventory. The proposed solution is an AI-enabled platform that predicts demand, recommends stock levels and automatically classifies customer enquiries. Some employees fear job losses, while the sales director wants implementation within three months.

Analysis

An initial process review finds that many errors originate in inconsistent product codes and incomplete customer information, not in the absence of AI. The data is fragmented and contains duplicates. The organisation also lacks a formal privacy policy and has weak access controls. Warehouse employees possess valuable knowledge about seasonal demand, but they were not consulted during initial planning.

A technology–organisation–environment analysis shows potential competitive benefit, but organisational readiness is limited. The socio-technical analysis indicates that the platform should support employees rather than replace their judgement. A risk assessment identifies privacy, cybersecurity, inaccurate forecasts, supplier dependence and employee resistance.

Strategy / Solution

Management changes the programme from a rapid technology purchase to a staged transformation. First, it standardises product codes, cleans customer records and defines data ownership. Second, it introduces role-based access and multi-factor authentication. Third, employees from sales, warehousing, customer service and finance participate in designing workflows.

The demand-forecasting tool is piloted for two product categories. Its recommendations are reviewed by inventory specialists, and decisions are recorded so that forecast errors can be analysed. The enquiry-classification tool suggests categories but does not send automatic responses for complaints or vulnerable customers. Employees receive training in data literacy, system use and escalation procedures. New performance measures include order accuracy, response time, stock availability, customer satisfaction and employee workload.

Outcome

After six months, order-entry errors fall, stock availability improves and customer-response time decreases. The company does not eliminate customer-service positions. Instead, routine classification is automated and employees spend more time resolving complex cases and maintaining customer relationships. Forecasts are useful in stable periods but less reliable during unusual promotions, so managerial review remains mandatory.

Learning Points

The case demonstrates that digital transformation begins with process and data quality. It also shows that implementation speed must be balanced against readiness, security and human consequences. AI should be treated as a decision-support capability rather than an unquestionable authority. Employee participation, staged pilots and measurable governance controls improve both adoption and results.

13. Advantages / Benefits

Digital management can produce the following benefits:

• faster access to accurate operational information;
• reduced administrative effort and transaction costs;
• improved forecasting, coordination and resource allocation;
• more personalised customer service;
• flexible remote and hybrid working arrangements;
• improved quality through automated checks;
• new products, services and business models;
• wider market access through digital platforms;
• improved safety through remote monitoring and predictive maintenance; and
• opportunities for employees to perform more creative and analytical work.

These benefits depend on appropriate design and implementation. Technology may increase workload or create new risks if systems are poorly integrated or if performance measures encourage undesirable behaviour.

14. Limitations / Challenges

Digital transformation can require substantial investment, specialised skills and continuing maintenance. Legacy systems may be difficult to integrate. Employees may resist change because of fear, low confidence or previous negative experiences. Digital systems can fail because of poor data, unreliable connectivity or unrealistic expectations.

Automation may displace particular tasks and create transitional unemployment. New roles may require skills that existing employees have not had the opportunity to develop. Remote work can create isolation, communication problems, unequal visibility and blurred boundaries between work and personal life.

AI systems may generate inaccurate outputs, reproduce historical discrimination or operate in ways users cannot easily explain. Cyberattacks, ransomware, data breaches and supplier outages can interrupt operations. Platform workers may experience insecure income and limited employment protection. Managers must therefore evaluate social and organisational costs, not only financial returns.

15. Ethical and Governance Considerations

Privacy requires organisations to collect and use personal data for legitimate, clearly communicated purposes. Data minimisation means collecting only what is reasonably necessary. Access should be limited according to role, and information should not be retained indefinitely without justification.

Algorithmic bias can arise from unrepresentative data, biased historical decisions, inappropriate variables or unequal error rates. For example, a recruitment model trained on past hiring decisions may reproduce a preference for candidates from groups historically favoured by the organisation. Fairness testing should be performed before and after deployment, and affected people should have a route to appeal.

Transparency does not always require publishing technical source code, but it does require meaningful information about the system’s purpose, data use, major factors and limitations. High-impact decisions should include human oversight and documented accountability.

Cybersecurity governance includes risk assessment, secure design, access control, encryption, staff awareness, incident response and recovery testing. Employees should be trained to recognise phishing and social engineering. Senior managers remain responsible for ensuring that cybersecurity is treated as a business risk rather than only a technical concern.

Responsible AI governance should address validity, reliability, safety, fairness, privacy, explainability, accountability and human control. Organisations should maintain an inventory of AI systems, classify systems according to risk, approve acceptable uses and review performance throughout the system life cycle.

16. Current and Emerging Trends

Hybrid work is becoming a long-term organisational design issue rather than a temporary arrangement. Managers are experimenting with flexible schedules, digital workspaces and outcome-based performance management.

Generative AI is expanding rapidly across administration, marketing, education, software development and customer service. The main managerial challenge is to identify appropriate human–AI division of labour while protecting confidential information and verifying outputs.

Low-code and no-code tools allow non-specialists to create applications and automate workflows. They can increase innovation but may produce uncontrolled systems, duplicated data and security weaknesses if governance is absent.

The Internet of Things, edge computing and digital twins are increasing the ability to monitor physical assets and simulate operations. Sustainable digital management is also receiving attention because data centres, devices and electronic waste have environmental costs.

Workforce analytics, digital credentials, immersive collaboration and autonomous systems may further change recruitment, training and operations. Regulation concerning AI, privacy, platform work and cybersecurity is also becoming more significant.

17. Best Practices

Managers should:

1. Begin with a clearly defined organisational problem and desired outcome.

2. Link every major technology investment to strategy and measurable value.

3. Involve users and affected stakeholders in design and testing.

4. Improve data quality before applying advanced analytics or AI.

5. Use pilots, independent evaluation and gradual scaling.

6. Establish clear ownership, access controls and audit trails.

7. Keep humans responsible for high-impact decisions.

8. Test systems for bias, security, reliability and accessibility.

9. Provide training, reskilling and realistic implementation support.

10. Measure employee wellbeing and customer outcomes as well as productivity.

11. Maintain contingency plans for outages, cyberattacks and supplier failure.

12. Review digital systems regularly as technology, risks and regulations change.

18. Future Scope

Future managers will increasingly work in environments where humans and intelligent systems collaborate. Routine information processing is likely to become more automated, while demand grows for judgement, creativity, relationship management, ethical reasoning and complex problem-solving. Managers will need data literacy without assuming that all important knowledge can be quantified.

Organisations may become more modular, with work distributed across employees, contractors, platforms and AI systems. This may create flexibility but will raise questions about accountability, worker protection and organisational identity. Digital inclusion will remain important because unequal access to devices, connectivity and skills can widen economic and social inequality.

The most valuable future capability may be responsible adaptability: the capacity to experiment with new technology while protecting people, maintaining trust and learning from evidence.

19. Chapter Summary

Digital management integrates technology, data, people, processes and strategy. Digital transformation is broader than digitising records because it changes how organisations operate and create value. Management information systems support operations and decisions, while analytics and AI identify patterns, make predictions and automate selected tasks. Digital platforms and remote work offer flexibility and market opportunities but require careful coordination and governance.

Successful transformation depends on strategic alignment, data quality, cybersecurity, user involvement, organisational readiness and continuous learning. Automation should be analysed at the task level and accompanied by job redesign and reskilling. Responsible management requires privacy protection, fairness testing, transparency, human oversight and clear accountability. Technology creates significant benefits, but managerial judgement determines whether those benefits are realised safely and sustainably.

20. Key Terms

1. Artificial intelligence (AI): Computer capability to perform tasks associated with human intelligence.

2. Automation: Use of technology to perform tasks with limited human intervention.

3. Algorithmic bias: Systematic unfairness produced or reinforced by an algorithm or its data.

4. Business analytics: Use of data, statistical methods and models to support decisions.

5. Cloud computing: On-demand access to computing resources through a network, usually the internet.

6. Cybersecurity: Protection of digital systems, networks and data from threats.

7. Data governance: Policies and responsibilities concerning data quality, access, use, security and retention.

8. Digital platform: A technology-enabled intermediary that connects two or more user groups.

9. Digital transformation: Organisational change created by integrated use of digital technologies.

10. Generative AI: AI that creates new content such as text, images, audio or code.

11. Internet of Things: Network of connected physical objects that collect and exchange data.

12. Machine learning: AI approach in which systems learn patterns from data.

13. Management information system: System that collects, processes and distributes information for organisational management.

14. Network effect: Increase in a platform’s value as more users participate.

15. Predictive analytics: Analysis used to estimate likely future events or outcomes.

16. Remote work: Work performed away from the organisation’s conventional workplace using digital connectivity.

17. Robotic process automation: Software automation of repetitive, rule-based digital tasks.

18. Socio-technical system: A system in which social and technical elements are designed together.

21. Review Questions

Short Answer Questions

1. Define digital transformation and distinguish it from digitisation.

2. What is the purpose of a management information system?

3. Explain two principles of responsible AI.

4. What is meant by the task-based view of automation?

5. Identify two management challenges associated with hybrid work.

Long Answer Questions

1. Explain the role of data quality, governance and cybersecurity in digital management.

2. Discuss how digital platforms create value and identify their principal risks.

3. Evaluate the benefits and limitations of AI-supported decision-making.

4. Describe the stages of a responsible digital-transformation process.

5. Explain how managers can redesign jobs and develop employee capabilities in response to automation.

Analytical / Application Questions

1. A bank plans to use AI to screen loan applications. Analyse the potential benefits, risks, fairness issues and governance controls that should be considered before implementation.

2. A manufacturing business wants to introduce sensors and predictive-maintenance software, but employees distrust management. Recommend an implementation approach using socio-technical principles.

3. An online retailer has rising sales but depends heavily on a dominant digital marketplace. Analyse the strategic advantages and platform-dependence risks, and propose actions for management.

22. Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes digital transformation from digitisation?

A. Digital transformation only involves purchasing newer computers.

B. Digital transformation changes organisational processes, capabilities or business models through integrated digital use.

C. Digital transformation means converting paper records into electronic files.

D. Digital transformation is limited to social-media marketing.

Correct Answer: B. Digital transformation changes organisational processes, capabilities or business models through integrated digital use.

Question 2

A manager wants to introduce an AI forecasting system, but the organisation’s sales data contains many duplicate and incomplete records. What should be done first?

A. Deploy the system immediately to obtain results quickly.

B. Replace all sales employees with data scientists.

C. Improve data quality and establish data governance before relying on forecasts.

D. Ignore the problem because machine learning automatically corrects all data errors.

Correct Answer: C. Improve data quality and establish data governance before relying on forecasts.

Question 3

Which practice best demonstrates responsible use of an AI system in a high-impact decision?

A. Allowing the system to make unreviewable decisions automatically.

B. Keeping the system’s purpose secret from affected individuals.

C. Providing human oversight, documenting accountability and allowing appeals.

D. Using historical decisions without testing them for bias.

Correct Answer: C. Providing human oversight, documenting accountability and allowing appeals.

Question 4

According to the task-based view of automation, automation most commonly does what?

A. Eliminates every task in an occupation at the same time.

B. Changes the mix of tasks by substituting for some and complementing others.

C. Makes managerial judgement unnecessary in all situations.

D. Affects only manufacturing jobs.

Correct Answer: B. Changes the mix of tasks by substituting for some and complementing others.

Question 5

Which measure is most appropriate for managing a hybrid team?

A. Evaluating employees solely by the number of hours they appear online.

B. Banning all remote communication.

C. Using clear objectives, agreed communication practices and outcome-based measures.

D. Requiring employees to respond instantly at all times.

Correct Answer: C. Using clear objectives, agreed communication practices and outcome-based measures.

Question 6

What is a major risk of relying on a digital platform for access to customers?

A. The organisation becomes completely independent of the platform owner.

B. The organisation may face changing fees, ranking rules or contractual conditions.

C. The platform eliminates the need for customer trust.

D. Network effects always reduce the platform’s value.

Correct Answer: B. The organisation may face changing fees, ranking rules or contractual conditions.

Question 7

Which example best illustrates algorithmic bias?

A. A dashboard displays monthly sales totals.

B. A recruitment model reproduces historical preferences for a particular demographic group.

C. A company encrypts employee records.

D. A system automatically backs up data.

Correct Answer: B. A recruitment model reproduces historical preferences for a particular demographic group.

Question 8

What is the primary purpose of a pilot in digital transformation?

A. To avoid consulting users until after full implementation.

B. To test technical, organisational and risk assumptions on a limited scale.

C. To guarantee that the final system will have no defects.

D. To replace the need for performance measures.

Correct Answer: B. To test technical, organisational and risk assumptions on a limited scale.

Question 9

Which principle is most closely associated with socio-technical systems theory?

A. Technology should be designed independently of employees and work practices.

B. Social and technical elements should be designed together.

C. Only financial outcomes matter in system design.

D. Employees should never participate in implementation decisions.

Correct Answer: B. Social and technical elements should be designed together.

Question 10

Why is human verification important when using generative AI?

A. Generative AI always refuses to produce content.

B. Generative AI can produce inaccurate, biased or fabricated outputs.

C. Human verification prevents any need for cybersecurity.

D. Generative AI can only process numerical data.

Correct Answer: B. Generative AI can produce inaccurate, biased or fabricated outputs.