The Future of Construction Productivity with AI: Hype, Reality, and the Way Ahead

Submit your manuscript here from 12 March 2026!

Introduction

Construction productivity has stagnated for decades, even as AI promises transformative gains. Yet the evidence base remains thin: fewer than 15% of AI-construction studies provide empirical productivity measurements, leaving practitioners unable to distinguish genuine advances from aspirational claims. 

This special issue directly addresses that gap by demanding rigorous, evidencebased contributions that centre measured productivity outcomes as the non-negotiable standard. Research aims include establishing an empirical evidence base through case studies with measured outcomes, identifying methodological advances for productivity assessment, and documenting implementation barriers and success factors across different organisational contexts. 

Practice aims focus on providing realistic guidance for practitioners on when, where, and how AI investments yield measurable productivity returns, documenting implementation pathways from pilot to scaled deployment, and offering economic models grounded in actual project data. Teaching aims seek to equip educators with evidence-based content that moves beyond technology hype and fosters critical thinking about technology adoption. 

The special issue makes a distinctive contribution by explicitly centring productivity as the core outcome measure. Unlike technology-centric collections, it examines implementation pathways, business cases, organisational enablers, and scaling challenges that determine whether AI pilots translate into sustained productivity gains. By actively soliciting critical perspectives and comparative studies, it moves beyond promotional discourse to establish an evidence-based understanding of AI’s actual—not promised—contributions to construction productivity. 

List of Topic Areas

1. Measuring and Evidencing AI's Productivity Impacts: Metrics, methodologies, and measurement challenges for assessing AI-enabled productivity gains across project phases and organisational levels. 
2. Implementation Pathways – From Pilots to Scaled Deployment: Organisational factors, change management strategies, and conditions enabling successful transition from experimental AI applications to scaled productivity improvements. 
3. Case Studies of AI Deployment with Measured Outcomes: Documented implementations specifying AI applications, productivity metrics, measurement approaches, and contextual factors influencing outcomes. 
4. Economic Models and Business Cases for AI Adoption: Costbenefit analyses, ROI frameworks, and financial modelling for AI investments grounded in actual project economics rather than aspirational projections. 
5. Critical Perspectives – Interrogating Hype and Reality: Systematic examination of gaps between AI promises and delivered productivity, including failed implementations, unexpected challenges, and limitations of current approaches.
6. Strategic Roadmaps and Conditions for Productivity Gains: Industry-level strategies, policy interventions, and enabling conditions required for AI to deliver sustained productivity improvements at scale.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/ecaam
Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/ecam
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Opening date for manuscripts submissions: 12/03/2026
Closing date for manuscripts submission: 12/06/2026