This thesis seeks to explore the enhancing and inhibiting factors consultancies have to address when implementing services related to artificial intelligence.
To address the discussion of current research, whether AI is an enhancing or a disruptive innovation, the author compares the answers to the research question with existing literature on service innovation. He aims to show whether AI requires a different innovation process than other service innovations.
Firstly, the literature review addresses the theoretical background of this study by discussing literature on service innovation capabilities. Hereby, in particular, the factors that are associated to contribute as enhancing factors. Secondly, the methods section outlines the approach of the research design, data collection and data analysis. Thirdly, in the results section, the results of the study are presented, compared with the existing literature and placed in the discussion of the existing literature.
Finally, in the discussion section, the results, their implications for service innovation theory and consultancies in practise are discussed. The research concludes with its limitations and the potential areas for future research.
Table of Contents
Abstract
Introduction
Literature review
Capabilities for knowledge-based service innovations
Competence enhancing and disruptive technologies
Methods
Research design
Sampling approach
Data collection
Data analysis
Findings
Relevance perception of Artificial Intelligence
Client projects supported by Artificial Intelligence
Determinants for enhancing AI capability development
Determinants for inhibiting AI capability development
Critical remarks on internal AI capability development
Discussion
Theoretical contribution
Practical implications
Limitations and future research
Research Objectives and Core Themes
This master thesis investigates the factors that enhance or inhibit the implementation of Artificial Intelligence (AI) related services within consultancies. It aims to determine whether AI-based service innovation requires fundamentally new capabilities or if existing frameworks for service innovation are sufficient to address this technological shift.
- The impact of AI on the consulting industry and its business models.
- Identification of internal and external factors influencing AI capability development.
- Comparison between competence-enhancing and competence-disruptive technology theories.
- The role of client collaboration and data quality in successful AI service implementation.
- Practical strategies for consultancies to build and manage AI competencies.
Excerpt from the Thesis
Determinants for enhancing AI capability development
Figure 1 presents a conceptual model representing my coded categories. The model illustrates which different factors within and outside the interviewed consultancies have influenced the enhancement of Artificial Intelligence competencies. It is segmented into the aggregated dimensions 'intraorganizational', 'collaboration with clients and partners' as well as 'market related and external factors', referring to the respective origin of the enhancing or inhibiting factor.
I was able to identify a number of determinants for enhancing Artificial Intelligence competencies. Table 4 shows that the most frequently mentioned factors that are related to AI capability enhancement are ‘investing in hands-on trainings’ (13), followed by ‘applicable use-cases’ (11), ‘client- and market driven demand’ (9), the ‘recruitment of data scientists’ (8) and ‘try new things together with clients’ (7).
The factor of investment in (hands-on) trainings and leveraging existing employees in the organisation was mentioned as one of the most frequent enhancing factors. This is rather unsurprising, since these are tested means to develop new competencies or to expand existing ones. As stated by C2 it is very important “to keep learning by using online training material or platform-based training.” As it is also stated by Den Hertog et al. (2010) the capability to deliberately learn from the way service innovation is managed currently and subsequently adapt the overall service innovation process is also part of the literature.
Summary of Chapters
Abstract: Summarizes the research aim, the qualitative methodology involving 12 consultants, and the key finding that AI-related service innovation factors do not differ significantly from other service innovations, with the exception of specific co-development use-cases and data quality constraints.
Introduction: Outlines the rise of the 4th industrial revolution and the subsequent potential for AI to automate and optimize consulting services, while highlighting the scholarly debate regarding the disruptive nature of AI.
Literature review: Reviews existing theories on service innovation capabilities, knowledge-based innovation, and the specific debate surrounding whether AI is a competence-enhancing or competence-disruptive technology.
Methods: Describes the qualitative research design, the grounded theory approach used for data analysis, and the rationale behind selecting 12 expert interviewees to gain insights into AI capability building.
Findings: Presents the empirical results, categorized by relevance perception, types of AI-supported client projects, and specific factors that either enhance or inhibit the development of AI competencies.
Discussion: Synthesizes the empirical findings with current literature to argue that AI innovation does not require a radically different capability building process compared to traditional service innovations, while noting the unique criticality of data quality.
Limitations and future research: Acknowledges the constraints of the study, such as the small sample size, and suggests that future studies should utilize quantitative designs for greater generalizability.
Keywords
Artificial Intelligence, AI, Management Consulting, Service Innovation, Capability Development, Digital Transformation, Competence Enhancing, Disruptive Innovation, Grounded Theory, Data Quality, Co-development, Business Models, Machine Learning, Automation, Technological Change.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on identifying the specific factors that influence whether management consultancies succeed or struggle in implementing Artificial Intelligence related services for their clients.
What are the primary themes addressed?
The thesis explores the intersection of service innovation theory and AI implementation, specifically examining how consultancies acquire new AI capabilities and the organizational hurdles they face.
What is the main research question?
The primary research question is: "What are enhancing and inhibiting factors for consultancies to implement Artificial Intelligence related services?"
Which scientific methodology was employed?
The author utilized a qualitative research design based on grounded theory, conducting 12 semi-structured expert interviews with representatives from 10 different consultancies.
What does the main body of the work cover?
It covers a literature review on service innovation, the research design and methodology, an empirical analysis of findings including identifying specific enhancers and inhibitors, and a concluding discussion on theoretical and practical implications.
Which keywords best characterize this work?
The most important keywords include Artificial Intelligence, Management Consulting, Service Innovation, Capability Development, Digital Transformation, and Competence Enhancing Innovation.
How do 'applicable use cases' impact AI development?
The research identifies co-developing applicable use cases with clients as one of the most critical enhancing factors, as it bridges the gap between theoretical AI capabilities and practical, real-world business problems.
Why is 'data quality' considered a unique inhibitor?
Unlike other service innovations, the research identifies the lack of data quality provided by clients as a fundamental, AI-specific inhibitor, because effective AI implementation is strictly dependent on the availability of sufficient and high-quality data.
Does AI necessitate a new business model for consultancies?
While literature suggests AI could disrupt traditional hourly billing models, the study finds that internal AI usage remains limited, as many firms are focused on maintaining their existing, profitable service delivery models.
- Citation du texte
- Anonym (Auteur), 2019, Challenges of Implementing Artifical Intelligence Related Services. What Enhancing and Inhibiting Factors Need to be Addressed by Consultancies?, Munich, GRIN Verlag, https://www.grin.com/document/494307
