The Impact of Intelligent Process Automation on Organizational Decision-Making and Workforce Productivity

A Conceptual Study on Automation-Enabled Decision Support and Productivity Outcomes in Modern Organizations

Nikita Katti

nikitasunilkatti@gmail.com

Department of Management Studies, Dayananda Sagar College of Engineering

Dr.Kali Charan Sabat Professor

kalicharan-mba@dayanandasagar.edu

Department of Management Studies, Dayananda Sagar College of Engineering

ABSTRACT
This paper examines the impact of intelligent process automation on organizational decision-making and workforce productivity. As organizations increasingly deploy automation technologies to manage repetitive, rule-based, and data-intensive tasks (Brynjolfsson & McAfee, 2014), attention has shifted from simple labour substitution toward a broader question: how does automation reshape the speed, quality, and locus of organizational decision-making, and what does this mean for the productivity and role composition of the human workforce (Autor, 2015)? Drawing on Bounded Rationality Theory (Simon, 1957), Socio-Technical Systems Theory (Trist & Bamforth, 1951), Diffusion of Innovation Theory (Rogers, 2003), and Dynamic Capabilities Theory (Teece, Pisano, & Shuen, 1997), this paper develops a conceptual model linking intelligent process automation to organizational decision-making speed and quality, workforce productivity, and organizational agility, moderated by change readiness and workforce skill composition.

The paper reviews the conceptual and practical literature on intelligent process automation, examines illustrative case studies from logistics, manufacturing, insurance, government, and retail settings, and develops a framework linking automation capability to organizational and workforce outcomes. Findings suggest that intelligent process automation accelerates routine decision cycles, improves consistency in operational judgement, and reallocates human effort toward higher-value cognitive and interpersonal tasks (Davenport & Kirby, 2016; Kolbjørnsrud, Amico, & Thomas, 2016), while also introducing challenges around change resistance, skill mismatches, and the risk of over-automating decisions that require contextual human judgement (Huang & Rust, 2018). Future directions, including human-in-the-loop decision automation, AI-augmented judgement systems, and organizational reskilling strategies, are discussed (Fountaine, McCarthy, & Saleh, 2019). This work contributes to the growing literature on organizational technology adoption, workforce transformation, and decision science, and holds practical implications for operations leaders, human resource strategists, and organizational researchers.

Keywords: Intelligent Process Automation, Organizational Decision-Making, Workforce Productivity, Workflow Automation, Organizational Agility, Socio-Technical Systems, Change Management