Implications of Artificial Intelligence for Information Systems

Boris Kantsepolsky () and Lev Topor ()
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Boris Kantsepolsky: The Academic College of Tel Aviv-Yaffo, School of Information Systems
Lev Topor: Institute for the Study of Global Antisemitism and Policy (ISGAP)

Chapter 6 in Managing Information Singularity, 2026, pp 135-186 from Springer

Abstract: Abstract This chapter argues that contemporary information systems are converging toward an information singularity, a regime in which information is predominantly generated, validated, and acted upon by machines. We distinguish classical AI agents (bounded, policy-driven executors) from agentic AI, which exhibits goal-directed autonomy by planning multi-step workflows, calling tools and APIs, using memory, and iterating on outcomes. We show how agentic architectures enhance core IS types (TPS, MIS, DSS/ESS, ERP/CRM/SCM, BI) by shifting value from isolated predictions to end-to-end operational gains: shorter cycle times, fewer escalations, and higher data quality. The same shift relocates risk from model accuracy to system-level safety, including mis-specified objectives, over-permissioned tools, prompt-/retrieval-injection, cascading failures, cross-border data issues, provenance weaknesses, and automation bias amplified by seemingly “completed” machine actions. The chapter operationalizes governance through lifecycle controls (dataset lineage, versioned pipelines, evaluation gates, least-privilege scopes, human-in-the-loop (HITL) approvals, immutable audit logs, continuous adversarial testing), linking statutory/regulatory duties (e.g., EU AI Act) with practice frameworks (e.g., NIST AI RMF). We also address debates over AI personhood and argue for human-centered accountability.

Keywords: Agentic AI; AI Agents; Automation; Bias; Provenance; Data Authenticity; Trust; Technological Over-Reliance (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-26223-3_6

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