Cited by 18AI in Service InteractionsCustomer Service Quality and LoyaltyDigital Innovation in Industries

Transitioning to artificial intelligence-based key account management: A critical assessment

Open access
Prior, Daniel D., Marcos-Cuevas, Javier·2025·Industrial Marketing Management

Abstract

Research suggests that Artificial intelligence (AI) use for sales and marketing activities improves firm performance. Underpinning these AI applications are datasets that reflect large volumes of sales transactions and interactions with a broad range of customers. Conversely, key account relationships involve deep and focused engagements with a small number of strategically important customers at multiple levels, and this has important implications for AI data inputs and uses. Whether AI is appropriate or relevant to key account management (KAM) is currently unclear. In this paper, we critically evaluate AI applications for KAM. The paper highlights the amenability of a firm’s KAM capabilities to AI and evaluates the opportunities and challenges that AI-based KAM offers. The paper also outlines a set of moderating factors likely to affect the impact of AI on KAM and provides a conceptual model to better understand the potentially transformative effects of AI on KAM. The paper concludes with a set of theoretical and managerial implications of AI-based KAM and develops a comprehensive research agenda to contribute to the further exploration of AI-based KAM. • Develops an AI-based KAM capabilities framework that highlights and describes AI implications for KAM. • Describes a series of moderating factors that could impact AI-based KAM. • Outlines a series of theoretical and managerial implications for AI-based KAM. • Develops an extensive research agenda that establishes AI-based KAM as a field of research.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.indmarman.2025.02.009

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Authors
Prior, D. D., & Marcos-Cuevas, J.
Affiliations
UNSW Sydney (AU)
Corresponding author
Prior, Daniel D.
Venue
Industrial Marketing Management
Publisher
Elsevier BV
Published
2025-04
Volume
126
Pages
72-84
ISSN
0019-8501 (ISSN-L)
OpenAlex
W4407554663
Cited by
18
References
11
Citations by year
2026: 13 · 2025: 5
FWCI
7.25
Type
journal article
Language
en
Open access
Locations
  • Industrial Marketing Management (publishedVersion, cc-by)
  • UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
  • CERES (Cranfield University) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
Keywords
artificial intelligence, key account management, artificial intelligence adoption, moderators, sales and marketing, customer data, firm performance, conceptual model

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review