AI evidence extraction

Consulting in the Age of AI: A Qualitative Study on the Impact of Generative AI on Management Consultancy Services

Frédéric Tronnier, Sascha Löbner, Kai Rannenberg · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

3.75

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.24251/hicss.2025.017

Methodology & findings

Study design

Qualitative content analysis based on 15 semi-structured expert interviews with German-speaking management consultants, conducted online via Microsoft Teams from February 5, 2024, to August 21, 2024.

Sample

N = 15, 1 group

Primary method

Qualitative content analysis

Main result

The study found that "GenAI impacts MCs in all three areas of value creation, value proposition, and value capturing and will likely continue to do so, with technological improvements and MCs searching for new use cases." The results indicate that "GenAI are currently primarily used for two tasks in the value chain of MCs: to serve as an assistant by taking over routine tasks, such as creating templates, performing analysis and administrative work, and to significantly improve the knowledge management in MCs." Additionally, "all experts argued that GenAI cannot entirely replace consultants, as they remain the main source of value for their customers."

Reports effect sizes.

Research paradigm

Interpretivist/Qualitative

Author conclusions

"Fifteen expert interviews were conducted and analyzed using qualitative content analysis, following Mayring (2014). The interviews demonstrate that GenAI impacts MCs in all three areas of value creation, value proposition, and value capturing and will likely continue to do so, with technological improvements and MCs searching for new use cases." The authors conclude that "While the most important resource in MCs, the consultant, will remain indispensable, their work will shift towards more value-creating and creative tasks" and that "Industry-specific knowledge and project experience could be sold as a software product, creating new business processes and revenue streams."

Risk of bias

Selection bias: Sample of 15 consultants may not be representative of all management consultancies; Potential response bias from consultants discussing emerging AI technology in their field; Selection bias: Experts contacted via email, personal networks, or LinkedIn may not represent broader consulting industry perspectives; Gender bias: Only two female experts participated despite aiming for equal representation; Geographic bias: Limited to German-speaking consultants; Informant bias: Acknowledged and addressed through purposive sampling from diverse MCs with varying seniority; Interviewer bias: Potential influence from researcher during semi-structured interviews; Selection bias: Purposive sampling of experts may not be representative of all management consultants; Gender bias: Only 2 out of 15 interviewees were female despite aiming for equal representation; Geographic bias: Limited to German-speaking consultants only; Respondent bias: Self-selection of consultants willing to participate (not compensated); Informant bias: Although efforts made to reduce through sampling diverse firms and seniority levels; Translation bias: Interviews conducted in German then translated to English using DeepL, potential meaning distortion; One-sided perspective: Data collected only from consultant viewpoint, not client perspective; Selection bias: Purposive sampling of experts may not represent all consultant perspectives; Gender bias: Only 2 female experts among 15 participants; Informant bias: Experts self-selected to participate, potentially skewing toward those with stronger GenAI interest or adoption; Single-sided perspective: Data collected only from consultant side, not from client perspective; Selection bias: Non-random sampling via purposive sampling from personal networks and LinkedIn; limited to German-speaking consultants only; Informant bias: Only 2 female experts out of 15 despite stated objective to obtain equal gender representation; Geographical bias: Sample limited to German-speaking consultants; Respondent bias: Interviewees self-selected (volunteered); not compensated, potentially attracting those with stronger interest in AI; Researcher bias: Only consultant perspective captured; no client perspective included; Small sample size (n=15) limits generalizability across consulting fields and countries; Translation bias: Original German interviews translated to English using DeepL for analysis; Interview timing bias: Interviews conducted February-August 2024, during rapid AI development, making time-sensitive statements potentially outdated; Selection bias: Purposive sampling of consultants who self-selected to participate; only 15 German-speaking experts; Gender bias: Only 2 female experts among 15 participants despite efforts to recruit equally; Informant bias: Geographic and language limitations (German-speaking only); Potential geographic bias: Appears focused on German-speaking regions without explicit geographic distribution details; No client perspective: Study gathered data from consultant side only, missing client viewpoint

Limitations

"This qualitative study focuses on MC and has gathered data from the consultant side only, thus no insights and reports from the client side are considered
Interviews were limited to 15 German-speaking expert, a majority of which have been male." The authors note that "while the qualitative approach chosen naturally does not aim to formulate statistically representative statements, we aimed to anonymously sample respondents from different consultancies, of different size and with differing seniority." Additionally, "Significant discrepancies in the degree of expected productivity gains were noted by experts, with only limited research attempting to quantify them."

Open questions raised

Authors identify need for quantitative and mixed-methods approaches to investigate the topic in greater detail and to quantify the impact GenAI might have on MCs. Future research should observe differences between specific fields of consulting, such as finance or IT-security. From a theoretical perspective, the socio-technical system representing the interplay of consultants, clients, and GenAI applications is identified as a worthwhile avenue for future research.
Limited existing research on digital transformation impact on management consulting business models
Need for quantitative and mixed-methods approaches to investigate the topic in greater detail
Quantification of GenAI impact on management consultancies
Observation of differences between specific fields of consulting (finance, IT-security)
Socio-technical system analysis: interplay of consultants, clients and GenAI applications

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