ECIS 2026 (2026) AI Processed
HOW GENERATIVE AI DRIVES FIELD-LEVEL CHANGE IN MANAGEMENT CONSULTING
This qualitative multiple-case study examines how the integration of generative AI into daily practices reconfigures routines in professional service firms. Analyzing 31 semi-structured interviews across two European management consultancies, the study develops a multi-level framework linking micro-level routine changes to broader industry-level shifts.
Problem
While generative AI is rapidly transforming knowledge-intensive work, current research remains fragmented across different levels of analysis, leaving its broader impacts poorly understood. Specifically, there is a lack of explanation regarding how local, day-to-day adaptations of routines aggregate to reshape industry-wide practices and value creation logics.
Outcome
- The study identified four core mechanisms of change—negotiated legitimacy, provisionality of reliance, skill polarity, and contestation of value—which were qualitatively supported by empirical interview data.
- Under the 'provisionality of reliance' mechanism, temporal pressures cause consultants to bypass validation routines, creating fragile outputs that risk client trust (supported qualitatively).
- Under the 'skill polarity' mechanism, generative AI simultaneously expands short-term capabilities and erodes long-term analytical thinking and mentoring routines (supported qualitatively).
- Under the 'contestation of value' mechanism, the commoditization of standardized tasks shifts the focus of consulting value toward human-centered contextual judgment and experience (supported qualitatively).
- Quantitative hypothesis testing was not performed due to the study's qualitative, inductive methodology, which limits statistical generalizability to other contexts or regions.
- Under the 'provisionality of reliance' mechanism, temporal pressures cause consultants to bypass validation routines, creating fragile outputs that risk client trust (supported qualitatively).
- Under the 'skill polarity' mechanism, generative AI simultaneously expands short-term capabilities and erodes long-term analytical thinking and mentoring routines (supported qualitatively).
- Under the 'contestation of value' mechanism, the commoditization of standardized tasks shifts the focus of consulting value toward human-centered contextual judgment and experience (supported qualitatively).
- Quantitative hypothesis testing was not performed due to the study's qualitative, inductive methodology, which limits statistical generalizability to other contexts or regions.
What it means for you
- CIO / IT Executive: Integrate an automated 'Validation Gateway' into the firm’s proprietary GenAI workspace that blocks users from exporting drafts into client-ready formats (like PPTX or PDF) until they complete a digital checklist verifying the accuracy and sources of the underlying data.
- IT Manager: Set up a weekly automated audit report in your GenAI usage analytics platform to identify teams with high-volume AI prompt generations but low average document-editing times, and flag these teams for manual quality-assurance reviews.
- Business Strategist: Redesign the firm's standard client engagement proposal template to phase out hourly billing for commoditized deliverables (like market research summaries) and replace it with value-based pricing focused on human-led strategic judgment workshops.
- Researcher: Draft a quantitative survey instrument targeting 500 management consultants across North America and Asia to test if the qualitative mechanisms of 'skill polarity' and 'provisionality of reliance' negatively correlate with long-term client retention metrics.
- Policymaker: Introduce an industry-standard certification framework for professional service firms that mandates documented 'human-in-the-loop' validation protocols and minimum structured mentoring hours to protect junior workforce development against generative AI skill erosion.
Transcript
Host: Welcome to A.I.S. Insights - Turning IS Research into Business Action. I'm Anna Ivy Summers. Today, we are discussing a study presented at ECIS 2026, the European Conference on Information Systems. The study is titled "HOW GENERATIVE AI DRIVES FIELD-LEVEL CHANGE IN MANAGEMENT CONSULTING." It examines how integrating generative AI into daily routines reconfigures professional service firms, linking micro-level routine changes to broader industry-wide shifts. Joining me to discuss this is our resident expert, Alex Ian Sutherland. Welcome, Alex.
Expert: Thank you, Anna. It is great to be here. This study provides a useful look at how daily adaptations of generative AI are beginning to reshape the management consulting industry.
Host: Let's start with the real-world problem. What specific challenge does this study address for business professionals?
Expert: While many of us see how generative AI affects individual tasks, like drafting emails or creating slide outlines more quickly, existing research has been quite fragmented. We have lacked a clear explanation of how these day-to-day, local adaptations of work routines aggregate over time to reshape industry-wide practices and value creation. The study addresses this gap by exploring how individual, routine-level changes build up to transform the broader consulting field.
Host: To help us understand the context of these findings, can you explain the study's approach and methodology?
Expert: Yes. The researchers conducted a qualitative, inductive multiple-case study of two European management consultancies. They collected data through 31 semi-structured interviews across various hierarchical levels, from interns to partners, and analyzed internal company documents. Because this was a qualitative, inductive study, no quantitative hypothesis testing was performed. This means the findings cannot be statistically generalized to other regions or industries, but they offer rich, contextual insights into the mechanics of organizational change.
Host: That is helpful context. Now, what did the study actually find? What are the key outcomes?
Expert: The study identified four core mechanisms of change, which were qualitatively supported by the interview data. The first is negotiated legitimacy, which describes how consultants navigate conflicting expectations from clients and professional standards regarding the use of AI. The second is the provisionality of reliance. Under this mechanism, temporal pressures often cause consultants to rely spontaneously on generative AI to meet short-term deadlines. As a result, standard review and validation routines are shortened or bypassed, which can produce fragile outputs that risk client trust if errors are not caught.
Host: Bypassing quality checks due to time pressure is a very practical risk. What are the other two mechanisms?
Expert: The third is skill polarity. The qualitative data showed that generative AI simultaneously expands short-term capabilities, allowing junior employees to tackle complex tasks earlier, but also erodes long-term analytical thinking and traditional mentoring routines. When junior consultants use AI shortcuts, they may skip the critical thinking practice loops necessary for their professional growth. The fourth mechanism is the contestation of value. As standardized tasks become commoditized through AI, the focus of consulting value shifts away from basic analysis and toward human-centered contextual judgment and experience.
Host: This brings us to the practical implications. What can business leaders and practitioners actually do with this knowledge?
Expert: The study suggests actions at three levels: operational, tactical, and strategic. At the operational level, consultancies need to explicitly structure how AI is integrated into daily work. Rather than just providing access to tools, managers should adapt training programs to focus on critical evaluation, validation, and sustained ownership so that short-term efficiency does not lead to skill erosion.
Host: And what about the tactical and strategic levels?
Expert: Tactically, firms must explicitly communicate AI use in proposals and project agreements to clarify responsibilities and data handling, aligning client expectations early on. Strategically, leaders must redefine how they generate value. Because standardized offerings are becoming commoditized, consultancies need to focus on human-centered activities, such as tailoring solutions to a client's unique organizational context, as their primary basis for differentiation.
Host: So, to summarize, managing generative AI effectively requires balancing immediate speed gains against long-term reliability, protecting junior skill development, and shifting the business focus to contextual judgment. Alex, thank you for sharing these insights with us today.
Expert: Thank you, Anna.
Host: And thank you to our listeners. Join us next time on A.I.S. Insights, where we bring you the latest information systems research to help drive your business action. See you next time!
Expert: Thank you, Anna. It is great to be here. This study provides a useful look at how daily adaptations of generative AI are beginning to reshape the management consulting industry.
Host: Let's start with the real-world problem. What specific challenge does this study address for business professionals?
Expert: While many of us see how generative AI affects individual tasks, like drafting emails or creating slide outlines more quickly, existing research has been quite fragmented. We have lacked a clear explanation of how these day-to-day, local adaptations of work routines aggregate over time to reshape industry-wide practices and value creation. The study addresses this gap by exploring how individual, routine-level changes build up to transform the broader consulting field.
Host: To help us understand the context of these findings, can you explain the study's approach and methodology?
Expert: Yes. The researchers conducted a qualitative, inductive multiple-case study of two European management consultancies. They collected data through 31 semi-structured interviews across various hierarchical levels, from interns to partners, and analyzed internal company documents. Because this was a qualitative, inductive study, no quantitative hypothesis testing was performed. This means the findings cannot be statistically generalized to other regions or industries, but they offer rich, contextual insights into the mechanics of organizational change.
Host: That is helpful context. Now, what did the study actually find? What are the key outcomes?
Expert: The study identified four core mechanisms of change, which were qualitatively supported by the interview data. The first is negotiated legitimacy, which describes how consultants navigate conflicting expectations from clients and professional standards regarding the use of AI. The second is the provisionality of reliance. Under this mechanism, temporal pressures often cause consultants to rely spontaneously on generative AI to meet short-term deadlines. As a result, standard review and validation routines are shortened or bypassed, which can produce fragile outputs that risk client trust if errors are not caught.
Host: Bypassing quality checks due to time pressure is a very practical risk. What are the other two mechanisms?
Expert: The third is skill polarity. The qualitative data showed that generative AI simultaneously expands short-term capabilities, allowing junior employees to tackle complex tasks earlier, but also erodes long-term analytical thinking and traditional mentoring routines. When junior consultants use AI shortcuts, they may skip the critical thinking practice loops necessary for their professional growth. The fourth mechanism is the contestation of value. As standardized tasks become commoditized through AI, the focus of consulting value shifts away from basic analysis and toward human-centered contextual judgment and experience.
Host: This brings us to the practical implications. What can business leaders and practitioners actually do with this knowledge?
Expert: The study suggests actions at three levels: operational, tactical, and strategic. At the operational level, consultancies need to explicitly structure how AI is integrated into daily work. Rather than just providing access to tools, managers should adapt training programs to focus on critical evaluation, validation, and sustained ownership so that short-term efficiency does not lead to skill erosion.
Host: And what about the tactical and strategic levels?
Expert: Tactically, firms must explicitly communicate AI use in proposals and project agreements to clarify responsibilities and data handling, aligning client expectations early on. Strategically, leaders must redefine how they generate value. Because standardized offerings are becoming commoditized, consultancies need to focus on human-centered activities, such as tailoring solutions to a client's unique organizational context, as their primary basis for differentiation.
Host: So, to summarize, managing generative AI effectively requires balancing immediate speed gains against long-term reliability, protecting junior skill development, and shifting the business focus to contextual judgment. Alex, thank you for sharing these insights with us today.
Expert: Thank you, Anna.
Host: And thank you to our listeners. Join us next time on A.I.S. Insights, where we bring you the latest information systems research to help drive your business action. See you next time!