“We Still Use Spreadsheets.” Understanding Business Decision-Makers’ Perceptions and Barriers to AI Analytics

Key findings from this study

This research indicates that:

• Business decision-makers regularly use data analysis for decision-making but predominantly rely on traditional spreadsheet tools rather than advanced analytics or AI systems.
• Adoption barriers include skill gaps in statistical methods, difficulty comprehending complex analytical models, and insufficient familiarity with sophisticated analytics platforms.
• Many decision-makers either delegate analytical work to specialists or perform basic analyses themselves using legacy tools, creating dependence on manual processes.

Overview

This empirical study examined perceptions and adoption barriers regarding analytics and AI systems among business decision-makers in non-technical roles. The research surveyed 65 participants from business domains and conducted in-depth interviews with 18 participants to understand their current practices, attitudes toward data-driven decision support, and obstacles to adopting advanced analytics tools.

Methods and approach

The study employed mixed methods combining survey data from 65 business professionals with qualitative interviews from 18 participants. Participants represented various business domains where decision-making typically occurs. The research evaluated their current analytics usage patterns, perceptions of AI and data-driven approaches, and identified specific factors inhibiting adoption of advanced systems.

Results

Business decision-makers, including sales professionals, actively incorporate data analysis into their decision processes. However, most rely on traditional tools such as spreadsheets rather than data-driven or AI-based analytics platforms. The low adoption of advanced analytics stems from multiple barriers: insufficient technical skills, difficulty understanding complex statistical models, and unfamiliarity with sophisticated tools. Many decision-makers either delegate analytical tasks to dedicated analysts or perform basic analyses themselves using familiar, legacy software.

The findings indicate a substantial gap between available AI analytics capabilities and the actual tools deployed in business decision-making contexts. Despite organizational investment in advanced systems, frontline decision-makers continue using spreadsheets and manual processes. This persistence reflects not merely preference for simplicity but genuine obstacles related to skill requirements, cognitive demands of understanding complex methodologies, and lack of integration between AI systems and existing workflows.

Implications

The research suggests that successful AI analytics adoption requires human-centered system design addressing decision-makers' actual capabilities and constraints. Current AI tools prioritize technical sophistication over accessibility for non-expert users, creating a mismatch between system capabilities and user needs. Future development should emphasize reducing cognitive load, lowering skill requirements for effective use, and designing interfaces that integrate naturally with existing business processes.

Organizations seeking to increase analytics adoption should reassess assumptions about user technical proficiency and willingness to learn complex tools. Training and support alone may prove insufficient without simultaneous redesign of analytics systems to accommodate users' existing mental models and workflows. The persistence of spreadsheet usage signals that decision-makers require systems matching their operational context and skill levels, not merely more powerful alternatives to current tools.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.