
arXiv:2605.20198v1 Announce Type: cross Abstract: Augmented analytics has transformed how business intelligence (BI) systems support managerial decision-making. This is especially true for users without technical backgrounds, who increasingly rely on automated insights rather than manual analysis. BI research has previously concentrated on system adoption and user intention, with very little research examining the impact of AI-enabled analytics on decision quality and the cognitive mechanisms in between. Using the theory of cognitive delegation, this paper investigates the role of trust in aug
The proliferation of AI-enabled analytics in business intelligence is reaching a point where understanding user trust, especially among non-technical users, is critical for effective decision-making and adoption.
This research highlights the evolving human-AI interaction in critical business processes, suggesting that trust in AI systems directly influences decision quality and organizational efficiency.
The focus for AI adoption shifts beyond mere technical capability to include psychological factors like trust, which become central to realizing the full potential of augmented analytics.
- · AI/BI platform providers who prioritize explainability and trust-building featur
- · Organisations with strong AI literacy and change management programs
- · Consultants specializing in AI adoption and human-AI interaction
- · AI/BI systems lacking transparent decision-making processes
- · Organisations pushing AI without considering user trust and cognitive mechanisms
- · Traditional BI tools requiring deep technical expertise
Increased demand for explainable AI (XAI) features in business intelligence tools to foster user trust.
New standards and best practices emerging for Human-AI teaming in decision-making contexts within enterprise settings.
A potential widening of the gap between companies that effectively integrate trusted AI and those that struggle with user adoption and decision quality.
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