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5 Ways Leaders Can Support Adoptive Parents

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It's Time to Rethink Job Descriptions for the Digital Era

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Why Executive Pay Is Failing

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Greener B-Schools, Greener Employees

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NFL Owners Can’t Manage Their Players Without Better Rules

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  • November 22, 2021

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For Professionals Returning to Work, There’s Power in the Cohort

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  • March 30, 2015

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Why – and How – to Hire Young People Without Diplomas

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  • April 28, 2015

After Your "Daddy Days" End

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  • January 20, 2014

Competing for Talent in Every Geography

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  • November 23, 2016

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Want Fewer Employees to Quit? Listen to Them

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  • April 22, 2019

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Bring in Outside Experts to Mentor Your Team

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  • January 11, 2017

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Technology Can Save Onboarding from Itself

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  • March 25, 2015

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Excelco Equipment: Attracting Young Talent in Singapore

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  • April 15, 2020

Shanghai Diligence Law Firm (B)

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  • Catherine Zhang
  • July 11, 2012

Entrepreneurial Sales and Marketing Vignettes

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Bias Interrupted: Creating Inclusion for Real and for Good

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Positivity and Growth (HBR Emotional Intelligence Series)

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Home » Blog » Dissertation » Topics » Management » Talent Management » 99 Talent Management Dissertation Topics | Research Ideas

research topics on talent management

99 Talent Management Dissertation Topics | Research Ideas

By Liam Oct 14, 2023 in Management , Talent Management | No Comments

If you are a student embarking on your journey towards completing a dissertation in talent management and looking for research topics, you have arrived at the perfect resource. Whether you are pursuing an undergraduate, master’s, or doctoral degree, selecting an engaging and relevant topic is crucial to the success of your research. Talent management is […]

talent management dissertation topics

If you are a student embarking on your journey towards completing a dissertation in talent management and looking for research topics, you have arrived at the perfect resource. Whether you are pursuing an undergraduate, master’s, or doctoral degree, selecting an engaging and relevant topic is crucial to the success of your research.

Talent management is a rapidly evolving field that encompasses various aspects of attracting, developing, and retaining talented individuals within organizations. It offers a multitude of opportunities to explore the strategies, practices, and challenges associated with managing and leveraging human capital effectively. Current talent management dissertation topics focus on organizational futuristic anticipations of human resources based on organizational growth. Project topics on talent management relate to processes and methodologies as much as procedural evaluations to reinvent organizational capabilities.

Our comprehensive list of topics is designed to inspire and guide you in selecting a dissertation topic that aligns with your interests, expertise, and academic level. Each topic presents a unique avenue for exploration, allowing you to make valuable contributions to the field of talent management. The following is a compilation of some of the most current research topics on talent management to enable you to gain perspective and choose one that matches your area of interest.

Download Talent Management Dissertation Sample

List of talent management dissertation topics:

The influence of talent management on organizational agility: A quantitative examination.

Reviewing the impact of talent management on leadership development and succession planning.

Analyzing the effectiveness of talent management in global organizations.

The impact of remote talent acquisition and virtual recruitment processes on talent management outcomes.

Exploring the impact of talent management on organizational performance: A quantitative analysis.

Investigating the role of talent management in driving innovation and organizational agility.

Talent management and HRM – two sides of the same coin? A discussion.

Examining the impact of talent management on organizational resilience and change management.

How do employees view talent management? A primary investigation in the UK service sector.

Examining talent management strategies in the public and private sectors: A comparative study.

Exploring the role of talent management in addressing skill gaps and upskilling challenges post-COVID.

Exploring talent management practices in a multinational technology company: A case study analysis.

An essay on the historical context of talent management and future implications.

The relationship between talent management practices and employee performance: An empirical analysis.

A systematic review of talent management practices in the healthcare industry: Lessons learned and best practices.

Examining the impact of talent management on employee satisfaction and organizational commitment.

Employer branding- scope of understanding developed through talent management literature.

A comparative analysis of talent management practices in multinational corporations: Case studies of different industries.

A review of academic literature on talent management in the past decade.

How does expertise in HR practices empower organizations in talent management? A primary study from the UK.

Developing a theoretical framework for talent management in the era of artificial intelligence and automation.

A comparative review of scope and diversity in talent management between developed and emerging economies.

Analyzing the effectiveness of talent management in building a culture of innovation and creativity.

Developing a theoretical framework for talent management in the healthcare sector.

Examining talent management practices in a nonprofit organization: A case study of a charitable foundation.

Talent management in economically challenged situations- review of the literature.

A comprehensive review of talent management strategies for knowledge transfer and organizational learning.

A theoretical framework for talent management in the gig economy: Challenges and opportunities.

A systematic review of talent management practices in the digital age: Trends and future directions.

Developing a theoretical framework for talent management in multinational corporations.

Analyzing the effectiveness of talent management in addressing skills gaps and future workforce needs.

The influence of talent management practices on organizational culture: An empirical examination.

Reviewing the effectiveness of talent management in addressing skill gaps and future workforce needs.

Investigating the role of talent development programs in fostering employee career growth and advancement.

A critical discussion of talent management frameworks.

Exploring the role of virtual onboarding in talent management: Best practices and challenges.

A comparative analysis of talent management approaches in developed and emerging economies.

Analyzing the effectiveness of talent retention strategies in reducing turnover and increasing employee loyalty.

Exploring the role of talent management in managing a multigenerational workforce.

Examining the role of talent management in building a culture of innovation and adaptability post-COVID.

Examining the role of a psychological contract in talent management: A theoretical framework.

The impact of talent management on succession planning and leadership development.

Analyzing talent management strategies in a public sector organization: A case study of a government department.

Organizational strategy driven talent management- how leadership informs organizational capabilities in the UK.

The role of talent management in promoting diversity and inclusion: A case study of a multinational corporation.

A comparative analysis of talent management practices in the banking and manufacturing industries.

Talent management New term, old principle? A discussion.

The role of talent management in enhancing employee engagement and retention.

Investigating the relationship between talent management and employee well-being: An empirical study.

Analyzing the effectiveness of talent development programs on employee engagement: An empirical analysis.

Investigating the similarities and differences in talent management strategies across different cultural contexts.

Investigating talent management in the creative industries: A case study of a design agency.

Analyzing the effectiveness of talent management in promoting diversity and inclusion in the workplace.

The impact of remote work on talent management strategies: Lessons learned from the COVID-19 pandemic.

Exploring the influence of talent management on employee well-being and work-life balance.

Examining the effectiveness of talent management in nonprofit organizations and for-profit companies: A comparative analysis.

Best practices in global talent management of Multinationals- a systematic review.

Talent management through role specific contextual characteristics- what are organizations looking for?

Investigating the impact of talent management on employee retention: A quantitative study.

Emotional and professional attitude of expatriates and implications for talent management- a global perspective.

Examining the impact of talent management on succession planning: A quantitative analysis.

Investigating the role of talent management in building a culture of continuous learning and development.

Investigating talent management in a global consulting firm: A case study of a leading management consultancy.

Analyzing the role of talent management in promoting employee well-being and work-life balance: A literature review.

Investigating the role of talent management in promoting employee engagement in remote and virtual work environments.

Risk factors in talent management- an investigative analysis.

Exploring the role of talent analytics in identifying and managing high-potential employees.

The role of talent management in fostering knowledge transfer and organizational learning.

Examining the influence of talent management on employer branding and reputation.

A comparative analysis of talent management practices in the public and private education sectors.

A comparative study of talent management practices in the healthcare and technology sectors.

Technological support in talent management- a primary study from the pharmaceutical sector in the UK.

Examining the influence of the gig economy on talent management strategies in the aftermath of COVID-19.

A systematic focus on the current trends and challenges in talent management across developed countries.

Talent management in SMEs- primary research in the UK.

Talent management in the UK hospitality sector- a primary review.

Analyzing the impact of organizational restructuring on talent management practices in a post-pandemic world.

Analyzing the impact of digital transformation on talent management practices in the post-COVID era.

The impact of talent management on employee performance and productivity.

Examining the relationship between talent acquisition strategies and organizational performance: An empirical analysis.

Investigating the differences in talent management practices between small and large organizations: A comparative study.

Family owned businesses’ and attitudes towards talent management- perspectives from the UK.

Talent management consultation companies- a discussion on roles, responsibilities and ethics within the UK context.

Investigating the influence of crisis management and business continuity on talent management strategies.

Investigating talent acquisition and retention strategies in a fast-growing startup: A case study.

Exploring the role of employer branding in talent management: A theoretical framework.

Investigating the influence of talent management on employee motivation and engagement.

A comprehensive review of talent management frameworks and models: Advancements and practical implications.

Reviewing the role of talent management in fostering employee engagement and motivation.

Investigating the impact of talent management on organizational innovation: A quantitative study.

Analyzing the effectiveness of talent acquisition strategies in attracting and selecting top talent.

Analyzing the impact of digital transformation on talent management practices: A review of the literature.

Analyzing talent development programs in the healthcare sector: A case study of a leading hospital.

A theoretical framework for talent management in the context of sustainable development.

The impact of talent management on employee engagement in a retail company: A case study analysis.

Exploring the role of social capital in talent management: A theoretical framework.

Analyzing the effectiveness of talent analytics in predicting employee potential and performance.

Examining the impact of industry-specific factors on talent management practices: A comparative study.

Examining talent management practices in the financial services industry: A case study of a prominent bank.

There you go. Use the list for dissertation topics in talent management well and let us know if you have any comments or suggestions for our topics-related blog posts for the future or looking to get help with dissertation writing , send us an email at [email protected] .

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Talent management in marketing: A framework and agenda for future research

  • Theory/Conceptual
  • Published: 06 November 2023
  • Volume 13 , pages 327–349, ( 2023 )

Cite this article

  • Stacey L. Malek   ORCID: orcid.org/0000-0002-8680-0951 1 ,
  • Georgia Kirilova 2 &
  • Shikhar Sarin 3  

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Marketing managers and academics are recognizing a widening gap between contemporary demands of markets and the capabilities of the marketing function, calling for greater attention to identifying, developing, and retaining ‘talent’ in the marketing organization. However, the marketing literature provides limited and fragmented guidance on what talent management in marketing entails, how to cultivate it, and how to leverage it to positively impact marketers and key marketing outcomes. Drawing on a review of 71 articles published in top marketing and human resource management journals since 2000, we introduce the concept of Talent Management in Marketing Systems (TALMARKS). We identify six core elements of TALMARKS: attracting, recruiting and selecting, training and developing, evaluating and compensating, recognizing and rewarding, succession planning and promoting, and measuring and reporting. We further develop a comprehensive conceptual framework of TALMARKS, outlining its key antecedents, outcomes and boundary conditions. Finally, we propose a future research agenda of avenues that might provide guidance on expanding this fundamental and underdeveloped topic.

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research topics on talent management

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Malek, S.L., Kirilova, G. & Sarin, S. Talent management in marketing: A framework and agenda for future research. AMS Rev 13 , 327–349 (2023). https://doi.org/10.1007/s13162-023-00264-4

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Published by Grace Graffin at January 6th, 2023 , Revised On August 18, 2023

Introduction

The subject of management involves an in-depth understanding of the various aspects of business management, such as employee management, risk management, organisational behaviour, and many more.

When choosing a topic for your management dissertation, make sure to consider diverse topics that explore both the theoretical and practical aspects of management.

We understand that getting a dissertation topic approved can be extremely challenging as academic supervisors require students to research a unique case.

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Review the full list of  dissertation topics for 2022 here.

How to Select the Best Management Dissertation Topic?

A dissertation topic must be selected based on research interests, availability of data, time limitations, and the research’s scope and significance. The following management dissertation topics are carefully shortlisted while considering all these parameters. Please review these topics and let us know if you have any queries.

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2022 Management Research Topics

Topic 1: an evaluation of organizational change management- why do people tend to oppose change.

Research Aim: The research will aim to assess the structure of organizational change management and to find the reasons why people resist or oppose the changes in an organization. There are many reasons through which change in organization’s management becomes important but some employees’ does not accept that changes. There are many reasons why people resist changes on organization. In certain circumstances, resistance to change might be beneficial. Resistance to change is, in fact, a crucial feedback mechanism that must not be neglected.

Topic 2: Investigating the effectiveness of customer relationship management in airlines

Research Aim: The research will aim to study the efficiency of CRM in airlines. Customer relationship management has evolved into a critical technique used by every corporation to better its operations and obtain a competitive advantage over competitors. Customer relationship management has evolved into a key priority for airline firms and an integral part of airline businesses’ corporate strategy to distinguish themselves from rivals in the eyes of the consumer. The goal of facility organisations, such as airlines, is to provide services that attract and maintain satisfied, loyal customers who promote the airline.

Topic 3: How does leadership affect employees’ productivity? A case of IT firms

Research Aim: This research will focus on leadership positions in IT organisations with the goal of increasing staff productivity and performance. Leadership is essential for increasing employee retention, career drive, and efficiency. Most companies’ progress is accelerated by effective leadership. As a result, it is critical to organisational success. Employee performance, on the other hand, is a critical pillar of every firm, and companies must examine the variables that contribute to great performance. Leadership is based on confidence, which is based on skill, sincerity, ethics, transparency, reactivity, empathy, and kindness.

Topic 4: The effect of organisation advancement tools on business performance

Research Aim: The research will aim to find the effect of organization advancement on business performance. Organizational tools are objects that assist you in organising your workspace, resources, and tasks in order to make your workday more effective. Physical instruments, planners, and software platforms are examples of what they can be. Organization advancement tools are a great source to improve your business performance as they help you in managing your daily tasks and workforce.

Topic 5: The importance of leadership and social skills in new entrepreneurs: An investigative study

Research Aim: The research will aim to investigate the importance of leadership and social skills in new entrepreneurs. Developing talent, introducing innovative goods and services, delivering efficiency, and gaining market share all benefit from improved leadership qualities. If you wish to stay small, you might be able to get away with not growing your leaders. Otherwise, it will restrict your progress. Social skills enable entrepreneurs to interact with customers more effectively, resulting in more agreements and more profitability.

Covid-19 Management Research Topics

Crisis management during covd-19.

Research Aim: This study will identify crisis management aspects during COVD-19, including its challenges and solutions.

Business management during COVID-19

Research Aim: This study will review business executives’ challenges in various scale industries and how they are recovering from the loss. How far did they succeed?

Hospital and medicine management during COVID-19

Research Aim: This study will highlight the role of hospital management during COVID-19, the challenges they came across, and the ways to overcome those challenges.

Educational management during COVID-19

Research Aim: This study will address the issues faced by students and educational institutes. How are they trying to overcome the challenges of imparting education during the coronavirus pandemics?

Maternal health care management during COVID-19

Research Aim: The lockdown situation has been an issue of concern for the patients, including pregnant women. This study will address the role of Maternal health care management during COVID-19.

Management Dissertation Topics for 2021

Topic 1: analyzing the traditions and trends in public administration and management in post-wwii europe.

Research Aim: The purpose of the research will be to analyze the characteristics of cultural and national communities that have influenced public administration and management in the 1970s and 1980s in Europe. The study will be carried out using a systematic literature review.

Topic 2: The Impact of Gender-inclusive Gatekeeping and Predecessors Influence on the Success of Female CEOs

Research Aim: The purpose of the research will explore how local organisational agents and contexts can help women leaders overcome barriers and achieve success at higher levels in corporate firms. The study will focus on CEO succession events and predecessor CEOS factors and their influence on women post-succession. The research design will be developed qualitatively.

Topic 3: Analysing the Impact of Daily Psychological Power on Organisational Leaders

Research Aim: The research will use quantitative techniques to analyze power-holders relational and interdependent work contexts. The study will examine the effect of daily psychological power using the factors of abusive behaviour and perceived incivility.

Topic 4: Examining the Impact of Cultural Diversity on Interaction Process and Performance

Research Aim: Using quantitative techniques, the research will analyse the interaction process and performance factors in two groups of employees in the services industry – homogenous and culturally diverse. The effectiveness in operation and arrangements will be examined.

Topic 5: Analyzing the Impact of ‘Voice’ and ‘Silence’ on Destructive Leadership

Research Aim: The research will examine the limited and biased view of silence in management literature. The study will also analyse the impact of silence in an organisation in terms of a functional value using quantitative research techniques. Furthermore, how silence in organisations can be used as a strategic response will be discussed.

Topic 6: Examining the Relationship between Productivity, Management Practices, and Employee Ability in the UK Setting

Research Aim: Using quantitative techniques, the study will analyse a relationship between productivity, management practices, and employee ability using data from management practices surveys and employees’ longitudinal earnings records.

Topic 7: Analysing the Factors that Impact International Differences in Gender Pay Gap

Research Aim: The research will use quantitative techniques to analyse microdata from various countries between 1980 and 2010. The study will use the factors of wage structures, net supply, wage compression, collective bargaining coverage, and unionised wage setting to identify the lower gender pay gap internationally.

Topic 8: The Impact of Psychosocial Hazards on Workplace Risk Management

Research Aim: The study will investigate workplace risk management practices in industry sectors with a high risk of musculoskeletal disorders (MSDs) and mental health disorders (MHDs) and the extent to which they may rise from psychosocial hazards. The research will be conducted using qualitative research techniques.

Strategic Management and Organisational Behavior Dissertation Topics

Strategic management and organisational behaviour can be described as the actions a firm takes to achieve its business objectives primarily derived from competitive markets’ dynamic behaviour. Following are some interesting dissertation topics under this field of study;

Topic 1: The Impact of Organisational Goals on Organisation Behavior

Research Aim: The primary focus of this research will be to combine factors from the theory of action, phases and self-determination theory to develop a motivational model that will explain the relationship between organisational goals setting process that lead to organisational behaviour. The research will be conducted using mixed methods of research techniques.

Topic 2: Integrating the Principles of Strategic Human Capital and Strategic Human Resource Management to Improve Organisational Performance

Topic 3: comparing the impact of family and non-family firm goals on strategy, family and organisational behavior.

Research Aim: This research will analyse the differences between family and non-family business goals and their impact on how businesses develop strategies. Also, the research will assess how these developed strategies would affect family and organisational behaviour. This research will use quantitative research techniques.

Topic 4: Analyzing the Effect of Strategy, Innovation, Networks and Complexity on Organisational Adaptability – The Mediating Effect of Leadership

Research Aim: The current study will use empirical analysis to examine the effects of strategy, innovation, networks, and complexity of organisational adaptability using leadership as a mediation factor.

Topic 5: Examining the Effect of Appointment of a Racial Minority Female CEO on White Male Top Manager Intrapsychic and Behavioral Responses

Research Aim: This research will examine white male managers’ behavioural responses to a female racial minority CEO’s appointment. The behaviour that the research will analyse is the amount of help that the white male top manager provides to their fellow executives. The research will be conducted using quantitative techniques.

Topic 6: Analysis of the Effectiveness of an Affect-Based Model to Portray Recipients Responses to Organisational Change Events

Research Aim: The study will use the Affect-Based Model developed by Oreg et al. (2016) to analyse if it is useful in documenting and portraying the recipient responses to organisational change events. The research will use factors of valence and activation to assess the effectiveness of the model. The study will be conducted using quantitative techniques.

Topic 7: Evaluating the Relationship between the personality of a CEO and Employee Motivation

Research Aim: This research will investigate the relationship between a CEO’s personality and employee motivation. The core of this study will be to assess whether a CEO’s character possesses the power to influence employee motivation or not. Case studies from various companies will be used in this study.

Topic 8: Assessing the Role of Managers in Bringing and Implementing Technological Change in an Organisation

Research Aim: This research will focus on how managers implement technological change in their organisations. Change management is challenging as not all employees are open to accepting change. This research will focus on various ways through which managers successfully implement technological change in their companies.

Topic 9: An Analysis of Organisational Change Management: Why Employees Resist Change?

Research Aim: This research will focus on why employees resist change in organisations, i.e., why employees dislike change. Different causes and factors will be discussed in this study, and the research will conclude why employees do not wholeheartedly accept the change.

Knowledge Management Dissertation Topics

The importance of knowledge management for organisations can’t be understated because this aspect of management enhances the workforce’s capabilities and overall productivity. It leads to a competitive advantage and provides the basis for differentiating an organisation from its competitors. Some interesting dissertation topics under this field are;

Topic 1: Examining the Impact of Enterprise Social Networking Systems (ESNS) on Knowledge Management and Organisational Learning

Research Aim: The research will investigate the effect of ESNS on knowledge management processes and organisational learning. The research will use knowledge creation and sharing to play the mediating role in analysing the proposed relationship. The proposed study will use empirical research methods.

Topic 2: A Review of Knowledge Management Research

Research Aim: The research paper will use a systematic literature review technique for the proposed study. The research will review the last twenty years of knowledge management literature to assess the presence of bias in explaining knowledge integration over research by exploring knowledge differentiation processes.

Topic 3: The Impact of the Internet of Things (IoT) on Innovation and Knowledge Management Capacity

Research Aim: The purpose of this research will be to investigate the plausible relationship between knowledge management systems, open innovation, knowledge management capacity, and innovation capacity in firms. The research will be conducted using empirical techniques to draw reliable conclusions.

Topic 4: The Impact of Strategic Knowledge Management on MNC and their Subsidiaries Performance

Research Aim: The research will develop a model to test the possibility of a relationship between strategic knowledge management (SKM) processes and organisation performance compared between multinational companies and their subsidiaries. The research will also analyse the impact of relational context on knowledge creation and transfer.

Topic 5: Analyzing the Relationship between Knowledge Management Practices and Knowledge Worker Performance - The Mediating Role of Organisational Commitment

Research Aim: The study will analyse the role of knowledge management practices to address the issues of insufficient organisational commitment and knowledge workers’ performance in the UK’s public sectors. The proposed study will use quantitative research techniques to fulfil its aim and objectives.

Topic 6: The Relationship between Knowledge Management Processes and Sustainable Competitive Advantage in Private Business Schools in the UK

Research Aim: The proposed research will explore the impact of knowledge management processes on sustainable completive advantages by using knowledge-based view (KBV) and resource-based view (RBV) as mediators in the relationship. The research will be conducted using quantitative techniques of data collection (i.e. questionnaire) and analysis (i.e. structural equation modelling).

Topic 7: The Impact of Strategic Knowledge Management on Manufacturing Firm’s Performance

Research Aim: The purpose of the study will be to empirically investigate the relationship between the availability and use of IT solutions for strategic knowledge management and a manufacturing firm’s performance, which will be measured in unit production. The research will use the resource-based view and the knowledge-based theory to develop a conceptual framework to analyze this relationship’s effect.

Topic 8: Evaluating how Knowledge Management Impacts Company Performance: A Case Study of Sainsbury

Research Aim: This research will discuss the basic concepts of knowledge management. The study will also discuss the impact knowledge management has on a company’s performance, i.e. how it helps companies achieve their goals. The main focus of this research work will be on Sainsbury’s knowledge management framework.

Topic 9: Knowledge Management as a Core Competency? Evaluating the Application and Benefits of Knowledge Management

Research Aim: This research will uncover how companies utilise knowledge management as their core competency and how it benefits their business operations. This study’s main focus will be on applying the various concepts of knowledge management and their implication for businesses.

Topic 10: Exploring the Managerial Concerns and Issues in Knowledge Management and Their Impact on Organisations

Research Aim: This research will explore the managerial concerns and issues related to knowledge management. The study will also focus on assessing the impact of these issues on businesses and how they can influence day-to-day operations. This will be an evidence-based study where evidence from different companies and various situations will be evaluated.

Leadership and Management Information System Dissertation Topics

Leadership drives the organisational agenda and is regarded as one of the most influential factors in streamlining organisations’ processes. Good leadership results in better performance of any organisation because it gives direction to the business activities under the market conditions and requirements.

Similarly, management information systems are pivotal to any organisation’s success and successfully implementing them can benefit the organisation in many ways. Following are some dissertation topics under the subject of leadership and management information systems;

Topic 1: The Role of Information Systems (IS) in Enterprise Architecture and its Impact on Business Performance

Research Aim: This study will examine the relationship between IS Enterprise Architecture and business performance using technical alignment and IS capabilities mediators. This research will be conducted using quantitative techniques to fulfil its aim.

Topic 2: Exploring The Relationship between Ethical Leadership and Employee Knowledge Sharing

Research Aim: This research will use social learning theories and self-determination to investigate the relationship between ethical learning and employee knowledge sharing. The study will be conducted using empirical research techniques.

Topic 3: Analysing the Impact of Relationship Leadership and Social Alignment on Information Security Systems Effectiveness in Private Organisations

Research Aim: This research will use social capital theory as its theoretical foundation to explore the impact of relational leadership on social alignment between business and IT executives. The relational model will study the factors of integrated knowledge, information security system effectiveness, and organisational performance. This research will use empirical techniques.

Topic 4: Examining the Relationship between Operating Room (OR) Leadership and Operating Staff Performance

Research Aim: This research will analyse the relationship between Operating Room leadership and operating staff performance. This will be done using emotional intelligence and collaboration variables to assess staff performance, using recovery numbers. The relationship will also be examined through the mediating role of leadership principles. The data will be collected and assessed using quantitative research techniques.

Topic 5: The Role of Transformational Leadership as a Mediating Variable in the DeLone and McLean Information Success Model.

Research Aim: The research will use the DeLone and McLean Information Success Model to analyse if productivity software implemented in an organisation can improve its performance. However, the research will also evaluate the model and propose modifications to include transformational leadership as a mediating factor in the information success model. The research will be quantitative in nature.

Topic 6: Assessing the Role of Leadership in an Organisation to Help Adopt Advanced Technological Systems

Research Aim: This research will assess the role of leadership in an organisation to help companies realise the importance of innovative, technologically advanced systems. Many companies today are still naive to the ever more important role of technology. Thus this research will aim to help companies adopt innovative technological systems through leadership. The research will be evidence-based in nature.

Topic 7: Evaluating How Changing Business Leadership Impacts Technological Organisational Performance

Research Aim: Changing leadership in organisations can prove a disaster if not handled properly. The transition process is extremely challenging, and companies should have the capability to handle this phase. This research will explore how their decision to change leadership impacts technological and organisational performance and how to optimise the process. This research will be quantitative in nature.

Topic 8: Can Information Systems in Organisations Be Considered a Competitive Advantage?

Research Aim: Information systems, if implemented successfully, benefit organisations immensely. The impact that an information system has and its results help companies stay ahead of their competitors. This research will assess how companies can turn their information systems into a competitive advantage, and most importantly, whether they or not information systems should be considered a competitive advantage.

Topic 9: Understanding the Leadership Challenges of Implementing and Managing an Advanced Information System in an Organisation

Research Aim: This research will help explain the challenges that managers and the entire leadership of an organisation face when implementing an advanced information system. Bringing a change in a company is challenging, and throw in a technology to implement, the process becomes even more challenging. This study will explore in detail all related challenges through quantitative research.

Topic 10: Do all Business Processes in an Organisation need Information System Management?

Research Aim: It is often argued that not all business processes require information systems. However, when talking about today’s world and the technological advancements taking place, it is recommended that business processes in organisations adopt the technology. This research will be a comparative analysis of whether companies are successful and profitable with information systems or without them.

Also Read: Business Dissertation Topics

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Organisational Culture and International Business Dissertation Topics

Organisational culture shapes the work ethics and helps in defining the professional image of organisations. Organisational culture plays a huge role in international business.

Organisations that adopt the country’s culture they are operating in are known to run their operations more successfully. The following topics are related to organisational culture and international business and help students choose an appropriate topic according to their interests.

Topic 1: The Impact of Organisational Culture of Collaborative Networks Influence on IT Governance Performance in Large Enterprises

Research Aim: This research will explore the influence of collaborative networks’ organisational culture on IT governance performance. The study will use a case study to analyse multinationals as they have a wide working network. The purpose of the research will be to determine whether or not organisational culture helps businesses effectively use IT in business operations. The research will be conducted using mixed methods research.

Topic 2: Analysing the Relationship between Supervisor’s Job insecurity and Subordinates’ Work Engagement

Research Aim: The purpose of this research is two-fold. The research will analyse the relationship between the supervisor’s job insecurity and subordinates’ work engagement using a mediator and a moderator. The research will first examine the mediating role of subordinate’s pro-social voice between supervisor job insecurity and subordinates’ work engagement. Next, the research will examine the moderating role of organisational culture between the supervisor’s job insecurity and sub-ordinates pro-social voice. The research will be conducted through quantitative techniques.

Topic 3: Analysing the Impact of Individual Perception of Organisational Culture on the Learning Transfer Environment

Research Aim: The research will be conducted empirically to assess the relationship between culture (as perceived by employees) and the work environment based learning factors (i.e. learning transfer environment [LTE]) in the organisation). LTE is measured using feedback and coaching factors that received resistance or openness to chance, personal outcomes, and supervisor and peer support.

Topic 4: The Role of Organisational Culture on the Development of Psychological Distress in the Workplace

Research Aim: The purpose of the study will be to analyse how organisational culture may cause the symptoms of psychological distress in the workforce. The study will use corporate culture and work organisation conditions as base factors to relate them to employees’ psychological distress. The research will be conducted using quantitative research techniques.

Topic 5: Analysing the Role of Leadership and Organisational Culture

Research Aim: The research will examine the relationship between organisational culture, leadership and employee outcomes. The paper will focus on the mediator of leadership processes and their impact on the relationship between culture and employee outcomes. The study will be conducted using quantitative research techniques.

Topic 6: The Role and Relationships among Strategic Orientations, Cultural Intelligence, International Diversification and Performance of Organisations

Research Aim: The research will aim to understand the drivers of the international expansion of globalised firms. The research will explore the relationship between strategic orientations and cultural intelligence as drivers and international diversification and firm performance. Strategic orientations used in the study include international market orientation (IMO) and entrepreneurial orientation (IEO). The study will be conducted using quantitative research techniques.

Topic 7: Dynamics of Corruption Culture Distance to Core Values

Research Aim: The research will examine how corporate bribery is impacted by cultural distance between multinational enterprises (MNEs) in their home and host countries. The research will also analyse the organisational distance to core value between MNE’s entry into the host country and its headquarters. The research will use empirical data collection and analysis techniques.

Topic 8: Examining Organisational Export Performance by International Business Competencies

Research Aim: The study aims to explore the relationship between international business competencies and export performance. The research will also analyse export performance by singular analysis or combined analysis of the competencies. The research will be conducted using empirical data.

Topic 9: Does Organisational Culture Influence the Leadership Type that a Company Should Adopt?

Research Aim: This research will argue whether companies should hire leaders concerning their culture or not. Organisational culture and leadership are interconnected. Thus companies that do not operate according to their culture struggle to grow exponentially. This research will aim to focus on the possible relationship between leadership and organisational culture. The research will be evidence-based.

Topic 10: Organisational Culture and International Business Competition: Are they Interrelated?

Research Aim: Organisational culture plays a huge role in making a company competitive internationally. When a business’s culture is motivating to all employees and identifies the right culture for its employees, there is every likelihood of rapid growth for both the company and the employees. The research will explore how the two concepts are interrelated.

Important Notes:

As a management student looking to get good grades, it is essential to develop new ideas and experiment with existing management theories – i.e., to add value and interest to your research topic.

The management field is vast and interrelated to many other academic disciplines like operations management , business , business administration , MBA , human resource management and more. That is why creating a management dissertation topic that is particular, sound, and actually solves a practical problem that may be rampant in the field is imperative.

We can’t stress how important it is to develop a logical research topic based on your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, there is a possibility that the study is not viable.

This impacts your time and efforts in writing your dissertation , as you may end up in the cycle of rejection at the initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

Keeping our advice in mind while developing a research topic will allow you to pick one of the best management dissertation topics that fulfil your requirement of writing a research paper and adds to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and be practically implemented. Please look at some of our sample management dissertation topics to get an idea for your own dissertation.

How to Structure your Management Dissertation

A well-structured dissertation can help students to achieve a high overall academic grade.

  • A Title Page
  • Acknowledgements
  • Declaration
  • Abstract: A summary of the research completed
  • Table of Contents
  • Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems. An outline of the structure of a dissertation can also be added to this chapter.
  • Literature Review : This chapter presents relevant theories and frameworks by analysing published and unpublished literature on the chosen research topic to address research questions . The purpose is to highlight and discuss the selected research area’s relative weaknesses and strengths while identifying research gaps. Break down the topic and key terms that can positively impact your dissertation and your tutor.
  • Methodology : The data collection and analysis methods and techniques employed by the researcher are presented in the Methodology chapter, which usually includes research design , research philosophy, research limitations, code of conduct, ethical consideration, data collection methods, and data analysis strategy .
  • Findings and Analysis : Findings of the research are analysed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include graphs, charts, and tables in this chapter to identify meaningful trends and relationships.
  • Discussion and Conclusion : The researcher presents his interpretation of results in this chapter and states whether the research hypothesis has been verified or not. An essential aspect of this section is establishing the link between the results and evidence from the literature. Recommendations with regards to implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
  • References : Make sure to complete this by your University’s requirements
  • Bibliography
  • Appendices : Any additional information, diagrams, and graphs used to complete the dissertation but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

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Frequently Asked Questions

How to find dissertation topics about management.

To find management dissertation topics:

  • Research recent management challenges.
  • Explore industry trends and innovations.
  • Analyze organizational behavior or strategies.
  • Examine cross-cultural management issues.
  • Investigate sustainability and ethics.
  • Consult academic journals and experts.

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The talent management field is advancing rapidly when it becomes easier to find talent around the world. The management of talent is a subcategory of human resources that is not specific to any particular industry.

There is a rite of passage in selecting a dissertation topic in the academic world. A well-chosen subject may also lead to research questions that will keep one’s intellectual curiosity alive for years to come.

Employment law governs the relationship between employers and employees largely. A contract outlines what employers expect from their employees, what they may ask them to accomplish.

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100+ Management Topics For Your Research Papers

management topics

As a student, you are probably very interested to learn about more ways to improve your grades. Management students, like all other students, should focus on learning. It is the best way to improve your grades, after all. However, there is another simple way you can improve the grades you get on your academic papers: finding exceptional management topics. Let’s talk about topics and show you why they are so important for students of all ages.

Great Management Topics = Top Grades

Finding great project management research topics usually means getting a top grade on the essay. Did you notice that some of your classmates are getting high grades without even trying too much? How do they do it? The simple answer is that they manage to impress the professor. It’s not easy, but it can be done. And you can do it too. For example, if you manage to find some business management research topics that nobody in your class thought about, you are almost guaranteed a top grade.

Your topic will pique the interest of your professor and will keep him or her reading. This means you will instantly get bonus points. It also shows to your professor that you’ve really strived to come up with an interesting topic. Of course, a unique topic also means you’ll submit an entirely original essay.

Where Can You Find the Best Management Topics?

Whether we’re talking about research topics in healthcare management or other management topics, you need to know where to find the best topics. Yes, you could try the Internet. However, keep in mind that most of your peers are using the same source to get topics. A good approach would be to ask industry experts for some interesting business management research paper topics. For example, you can ask around in forums and on blogs. Also, we advise you to check our great list of business topics .

Another great way to get some ideas is to read as many articles on management topics as possible. You’ll find plenty of examples and you can tweak them as much as you like. You’ll probably end up with a long list of unique topics.

However, the best and easiest way to get the best topics is to get in touch with an expert academic writer. A professional writer can come up with a list of research topics in management for you in no time. After all, this is what these people do. They have extensive experience writing academic papers for their clients. This being said, all you have to do is get in touch with an academic writing company and ask them for a list of management topics for a research paper.

Management Training Topics

Looking for amazing management training topics? Why don’t you pick one of the following examples:

  • The best employee management tactics every company should employ in 2022.
  • The various leadership styles around the world.
  • How does a company train its HR department adequately?
  • Is employee training necessary for coronavirus eradication?
  • How do employees adapt to change without proper training?
  • Does training influence employees’ loyalty?
  • The importance of evaluating the performance of a team.
  • Evolving technologies require thorough training programs.

Project Management Topics

Finding great project management topics can be a daunting task. To help you out, our expert writers have put together this list:

  • Effective ways to manage international teams of remote workers.
  • Effective project management methods that will work best in 2022.
  • Define the Distance Education Management System.
  • The Starbucks project management team: a case study.
  • Global projects are the new form of organization.
  • Outsourcing management problems in 2022.
  • The negative side of outsourcing to India.
  • The negative effects of stress and excessive work in the modern world.

Healthcare Management Research Paper Topics

Are you in need of excellent healthcare management research paper topics? Here are some of the best examples we can think of:

  • Top methods to increase the effects of preventative care.
  • Complying with the new privacy laws as a public hospital.
  • How do hospitals acquire talent?
  • Best way to retain talent in the healthcare industry.
  • What are hospital ratings and how do their effect these institutions?
  • Patient safety should be a manager’s primary concern.
  • Can manpower be used to gauge a hospital’s performance?
  • Case Study: The supply chain of drugs and medicine in a large hospital.

Risk Management Topics

When it comes to risk management topics, we doubt you will find topics more interesting than the following:

  • Methods of accounting in case of natural disasters.
  • How to protect assets as part of an integrated financial risk management strategy.
  • Grouping clients into risk pools: best practices.
  • What are the factors that influence risk management in the healthcare industry?
  • Big data and its effects on risk management in the UK.
  • The effects of advancing technology on risk management.
  • 3 ways to tackle the supply chain risk in 2022.

Management Meetings Topics

What do you talk about during a management meeting? Let’s find out! Here are some of the most interesting management meetings topics:

  • What do managers like to discuss during meetings?
  • Supply chain and operations management can be integrated.
  • Methods to discuss a change of pace in the company.
  • Analyzing the marketing activities and their impact.
  • The main steps of product development.

Sport Management Research Topics

Our sport management research topics are unique and original. You can use any of the following topics for free right now:

  • The influence of music during training.
  • Managing water intake during weightlifting exercises.
  • Does mathematic coaching help with sports performance?
  • Risk management in a sports organization.
  • The legal risks sports organizations face in the UK.
  • Discussing safety issues in football.
  • The different leadership methods in sports management.

Operations Management Topics

It can be very difficult to find original operations management topics, we know. This is why we are here to help. Take a look at these new topics:

  • Should you manufacture in-house or outsource?
  • The difference between the main operations management strategies.
  • What is the scope of operations management in the financial sector in the United States?
  • The best research methods for operations management.
  • Methods for bottleneck elimination.
  • What is Lean manufacturing and what does it imply?
  • The impact of the IT industry on operations management.

Management Topics for Presentation

Are you looking for management topics for the presentation? Although not the easiest thing to do, our expert managed to create some unique topics for you:

  • The recruitment process in the Al Etihad company.
  • The unique problems of organizing a sports organization.
  • How do hospitals plan for biological attacks?
  • Are business ethics the same for employees and managers?
  • Managing lawsuits as a pharmaceutical company.
  • Managing waste disposal as a fertilizer company in the UK.
  • How does the COVID-19 virus affect the management of organizations?
  • Using social media for company management purposes.

Research Topics in Human Resource Management

When you need to research topics in human resource management, you need our list. Here are some topics that we think you will find very interesting:

  • Discussing equal employment opportunities in the UK.
  • What is strategic HR planning?
  • How is talent aligned to business?
  • What are soft skills and hard skills?
  • The many benefits of salary bonuses.
  • Eliminating sexual harassment at the workplace.
  • Eliminating drug use at the workplace.

Stress Management Topics

Do you need to write an amazing essay about stress management? In this case, you need to write on one of our stress management topics:

  • Progressive muscle relaxation therapy.
  • The best breathing exercises for stress.
  • Keep a positive attitude to reduce stress.
  • The effects of physical exercise on stress levels.
  • Does discrimination cause stress?
  • The link between stress and depression.
  • Top 4 methods to manage stress effectively.

Controversial Topics in Business Management

Writing about controversial topics in business management will surely catch the eye of your professor. Here are some examples:

  • What is sexual harassment at work?
  • Let’s discuss the paid parental leave policy.
  • The importance of health insurance coverage in the US.
  • The worst cases of public relations blunders in the UK.
  • Is workplace violence real?
  • What causes drug use at the workplace in the US?
  • Is bureaucracy useful in 2022?

Anger Management Group Topics

Don’t waste your time looking for anger management group topics and just write a paper on one of the original topics:

  • Here is why you should think before you speak.
  • Does exercise relieve anger?
  • The best strategies for anger management in 2022.
  • Playing video games can help you manage your anger.
  • Is anger your friend?
  • The best relaxing exercises for anger management.
  • Are our anger management groups working?

Financial Management Topics

It’s difficult to find original financial management topics, but we managed to put together some unique topics for you:

  • The difficulties of financial management in emerging countries.
  • Work from home and the financial aspect of teleworking.
  • How does a remote financial department work?
  • Managing the finances of an SME during its expansion phase.
  • Taking loans or self-funding your operations?
  • A report on the financial workings of Netflix.
  • How does Tesla get money without making a profit?
  • Ford finances in Europe: a case study.

Easy Management Topics

Don’t want to spend too much time on the essay? Here are some easy management topics for you:

  • Using computers in the learning process is not as easy as you think.
  • Do you study abroad just to have an excuse to travel more?
  • What are the various drinking styles when discussing college students?
  • The sustainable organizations of the twenty-first century.
  • Analyzing supply chain management from an environmental perspective.
  • The main factors that affect the success of women managers.
  • Methods of strategic planning in new SMEs.

Hot Topics in Pain Management

Here are the hot topics in pain management that you should be writing an essay on right now:

  • The role of nurses in pain management in the United States.
  • Defining modern pain science.
  • Low back pain is more complex than you think.
  • How do pain management drugs work?
  • Should painkillers be prescribed to children?
  • How addictive are painkillers?

It doesn’t matter that you need human resource management topics or management meeting topics. What matters the most is to learn how to pick the right topic. You can read dozens of articles on how to pick the right topic and waste hours learning the basics. Or you can just pick one of the topics our thesis writers thought of. It’s 100% free!

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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