Abdelaal, M. (2024). AI in Manufacturing: Market Analysis & Opportunities (arXiv:2407.05426). https://arxiv.org/abs/2407.05426 
This source is relevant if you want a concise synthesis of where AI is actually being applied in factories and what’s blocking wider usage. You’ll find a literature review with market sizing, adoption patterns, and practical considerations across discrete and process industries. Two takeaways we used: adoption is uneven and concentrated in a few high-value use cases; success hinges on unifying siloed data rather than model choice alone. 

Brandom, R. (2025, February 24). AI Assistants Join the Factory Floor. WIRED. https://www.wired.com/story/ai-swaps-desk-work-for-the-factory-floor 
This article is useful for seeing real plants put conversational agents to work, not just lab demos. You’ll find reporting on Schaeffler and others using assistants to surface live manufacturing data and speed diagnoses. Two takeaways we used: shopfloor acceptance rises when answers are grounded in live systems; conversational interfaces shorten the time from question to corrective action. 

Deloitte. (2025, May 1). 2025 Smart Manufacturing and Operations Survey. https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/2025-smart-manufacturing-survey.html 
This is the benchmarking study to read if you want hard numbers on adoption and talent gaps. You’ll find executive-level stats on genAI pilots, scaling barriers, and expected ROI horizons. Two takeaways we used: only about a quarter of manufacturers have deployed genAI at scale; skill shortages remain a top obstacle to realizing value. 

Gartner. (2024, April 3). Market Guide for Conversational AI Solutions. https://www.genesys.com/resources/market-guide-for-conversational-ai-solutions 
 
This guide is relevant for understanding how analysts define the conversational AI solution landscape and what capabilities enterprises should require. You’ll find a framework that covers NLU, orchestration, connectors, guardrails, and buying considerations for enterprise deployments (registration required via an authorized distributor). Two takeaways we used: platforms must orchestrate queries across multiple systems; governance and security controls are first-class requirements in industrial settings. 

McKinsey. (2025). AI in the Workplace: 2025 Report.  
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work This report is relevant if you want to understand how AI assistants change day-to-day work and where productivity actually shows up. You’ll find a large-scale survey and case analyses covering task patterns, adoption hurdles, and the “superagency” model for empowering employees. Two takeaways we used: timetoinsight drives most of the measurable gains; assistants deliver outsized value when integrated with core enterprise systems rather than used in isolation. 

Mok, A. (2025, May 13). How AI and Robotics Prevent Breakdowns in Factories. Business Insider. https://www.businessinsider.com/artificial-intelligence-robotics-predictive-maintenance-manufacturing-factory-solutions-2025-5 This piece is helpful if you want an accessible primer on predictive maintenance and downtime prevention with AI. You’ll find case vignettes that connect sensor data, anomaly detection, and decision workflows to concrete savings. Two takeaways we used: predictive maintenance delivers quick wins when paired with operator workflows; even modest downtime reductions compound into seven-figure savings annually. 

Siemens AG. (2024). The True Cost of Downtime (White paper). https://assets.new.siemens.com/siemens/assets/api/uuid:1b43afb5-2d07-47f7-9eb7-893fe7d0bc59/TCOD-2024_original.pdf 
Read this to quantify why speed to insight matters in manufacturing. You’ll find per-hour loss figures by sector and a global estimate of annual downtime costs with methodology notes. Two takeaways we used: unplanned downtime can exceed US$2M per hour in some verticals; total annual impact exceeds a trillion dollars—justifying real-time, question-driven analytics.