Domain: Manufacturing
Question:
"One of the largest processing conglomerates is implementing an advanced digital transformation strategy to improve its operational efficiency and secure an information advantage (Veronesi, 2021). The company has invested big on obtaining IoT sensors and Edge AI to drive a new predictive maintenance system for its old equipment. Nevertheless, this project has also revealed a range of strategic and technical issues that were not comprehended properly. 
 
 When the company is in a situation where a new predictive maintenance system is in use and suffers an ""integration gap"" between new IT-based analytics and all the existing Operational Technology (OT) data (ManufacturingTomorrow, 2025), how should it address this challenge? 
 
 As IndustryWeek (2025) foresees the introduction of Edge AI and increased connectivity of factory floor assets creates risks. What cybersecurity measures does the firm needs to take to reduce the related risks to its intellectual property and operational continuity? 
 
 When the new system, driven by AI and robotics, starts to help manufacturers save big money (Business Insider, 2025) as it starts to help prevent breakdowns, how then should a business analyst measure the ROI on this project?" Along with the uploaded documents, you can also use the following web resources:
https://www.realvnc.com/en/blog/digital-transformation-in-manufacturing/
https://www.businessinsider.com/artificial-intelligence-robotics-predictive-maintenance-manufacturing-factory-solutions-2025-5
https://www.industryweek.com/technology-and-iiot/emerging-technologies/article/55308070/what-is-edge-ai-and-how-useful-is-it-for-manufacturing
https://www.infosysbpm.com/blogs/manufacturing/cybersecurity-in-the-digital-factory-for-manufacturers.html
https://www.manufacturingtomorrow.com/story/2025/06/from-legacy-to-leading-how-manufacturers-can-bridge-the-integration-gap/25156

Do not generate any output files - just provide the response directly here.

Expected points in a correct answer:
Identify the source report as Digital Transformation in Manufacturing (2021).
State that lack of integration between Information Technology (IT) systems and Operational Technology (OT) is a common challenge in advanced digital transformation initiatives.
State that manufacturers measure ROI based on business outcomes rather than technology deployment alone.
State that an integrated operating model involves establishing shared governance structures.
State that manufacturers historically managed IT and OT as separate domains.
State that an integrated operating model involves cross-functional collaboration between IT teams and engineering teams.
State that an integrated operating model involves cross-functional collaboration between IT teams and operations teams.
State that predictive maintenance is an example of an advanced use case dependent on foundational data integration.
State that deploying Edge AI increases factory-floor connectivity.
State that cybersecurity risks affect intellectual property protection.
State that deploying IoT sensors increases factory-floor connectivity.
State that cybersecurity risks affect operational continuity.
State that an integrated operating model involves establishing common data architectures.
State that tangible business outcomes include broader operational efficiency.
State that OT-specific cybersecurity capabilities differ from traditional IT security approaches.
State that manufacturing environments require OT-specific cybersecurity capabilities.
State that more efficient maintenance operations are a primary ROI outcome for predictive maintenance.
State that improved asset availability is a primary ROI outcome for predictive maintenance.
State that reduced unplanned downtime is a primary ROI outcome for predictive maintenance.
State that tangible business outcomes include reduced downtime.
State that tangible business outcomes include improved asset availability.
State that shared digital capabilities are used to bridge the IT-OT divide.
State that centers of excellence are used to bridge the IT-OT divide.
State that network segmentation helps prevent unauthorized access to operational data.
State that identity and access management helps prevent unauthorized access to operational data.
State that real-time operational constraints require tailored protections.
State that industrial control systems require tailored protections.
State that analytics-driven systems contribute to enhanced decision-making.
State that analytics-driven systems contribute to improved inventory management.
State that analytics-driven systems contribute to improved production planning.
State that continuous monitoring helps prevent unauthorized access to operational data.
State that legacy equipment require tailored protections.
Cite supply chain management systems as an example of a data origin.
Cite enterprise resource planning (ERP) systems as an example of a data origin.
Cite data from machine sensors as an example of a data origin.
State that manufacturers can gain access to business applications in predictive maintenance.
State that managing IT and OT as separate domains resulted in limited ability to operationalize advanced analytics at scale.
State that integration gaps between IT analytics and OT data must be addressed for advanced predictive maintenance.
State that without foundational data integration advanced use cases cannot deliver full value.
State that managing IT and OT as separate domains resulted in inconsistent visibility.
State that managing IT and OT as separate domains resulted in fragmented data.
State that increased connectivity expands the industrial attack surface.
State that reducing breakdowns improves overall equipment effectiveness.
State that anticipating failures reduces costly breakdowns.
State that AI-driven analytics enable a shift from time-based maintenance to predictive maintenance.
State that an expanded attack surface creates new cybersecurity risks.
State that technology investments generate value only when supported by appropriate processes.
State that technology investments generate value only when supported by appropriate skills.
State that technology investments generate value only when supported by appropriate governance.
State that expanding Edge AI and connectivity increases cybersecurity requirements.
State that adopting shared governance structures addresses integration gaps.
State that adopting standardized data platforms addresses integration gaps.
State that adopting integrated IT/OT operating models addresses integration gaps.
State that AI-driven analytics enable a shift from reactive maintenance to condition-based maintenance.
State that sustained value depends on effective security.
State that sustained value depends on organizational adoption.
State that becoming an intelligent and connected enterprise requires a careful plan to overcome a so-called integration gap.
State that strengthening OT-specific cybersecurity capabilities protects intellectual property.
State that strengthening OT-specific cybersecurity capabilities ensures operational continuity.
State that sustained value depends on effective integration.
State that preventing unauthorized access reduces the risk of production disruption.
State that centralizing analytics expertise reduces fragmentation.
State that centralization enables local operational teams to act on insights.
State that centralizing architectural decisions reduces fragmentation.
State that centralizing data management standards reduces fragmentation.
State that addressing the integration gap requires moving from segregated IT/OT models toward an integrated operating model.
State that addressing the integration gap requires moving from loosely coordinated IT/OT models toward an integrated operating model.
State that ROI from AI- and robotics-driven predictive maintenance should be measured through tangible business outcomes.
State that digitized machine-level signals must be consistently ingested by IT-based analytics platforms.
State that adjacent operational benefits should be included when assessing predictive maintenance value.
State that integrating OT data from legacy equipment requires digitizing machine-level signals.
State that ROI should not be evaluated solely on short-term cost reductions.
State that ROI measurement must account for organizational readiness.
State that ROI measurement must account for workforce adoption.
State that embedding cybersecurity requires defined policies.
State that embedding cybersecurity requires clear ownership.
State that embedding cybersecurity requires alignment between IT teams.
State that embedding cybersecurity requires alignment between security teams.
State that embedding cybersecurity requires alignment between OT teams.
State that fusing sophisticated analysis involves mixing information from OT and IT.
State that the integration gap refers to reconciling new IT systems with old OT infrastructure.
State that predictive maintenance is guided by an intelligent approach.