Case Study: Predicting Project Margin Erosion Before It Happens
For engineering and project-driven enterprises, protecting margins is one of the most pressing commercial challenges. Despite strong governance frameworks, unexpected write-offs and sudden drops in project profitability remain common, often surfacing too late for meaningful intervention. With portfolios spanning multiple geographies and delivery models, the complexity of managing risk across projects only intensifies.
insightfactory.ai has now worked with multiple engineering, infrastructure services companies to change the equation. By embedding predictive intelligence into its project oversight process, the organisation gained earlier visibility of commercial risk, enabling proactive interventions, stronger governance, and greater confidence in its ability to protect margins at scale.
insightfactory.ai has repeatedly partnered with heavy asset industries, including engineering, infrastructure, and related sectors, to help these organisations better understand performance risk across large portfolios of in-flight projects. The foundation of these solutions is the integration of multiple critical data sources into a single, accessible platform. This often includes project valuation, risk, planning, scheduling, and data quality, all brought together into one consolidated access point that simplifies oversight and decision-making.
Building on this foundation, predictive models are developed to assess both the likelihood and potential magnitude of a project write-off or margin drop before it occurs. These insights provide executives and project leaders with foresight and confidence to act proactively, protecting margins and improving portfolio outcomes. This case study reflects one such engagement representative of the work insightfactory.ai has delivered for multiple clients facing similar challenges across complex project environments.
Peter Inge - CEO (insightfactory.ai)
The Problem
A leading engineering services company (like many) was managing a complex portfolio of concurrent projects, each carrying its own risks, resource demands, and financial pressures. Despite having robust controls in place, the organisation was increasingly challenged by unanticipated project write-offs and margin erosion, undermining profitability across engagements.
Commercial risk management remained largely reactive, with limited foresight into emerging issues. Disparate data sources made it difficult to correlate planning, performance, and delivery risk in a meaningful way, restricting the ability of leaders to make proactive decisions. The business urgently needed earlier visibility of potential risks, supported by predictive insights, to prevent margin decline and guide intervention strategies with greater confidence
The Solution
insightfactory.ai designed and deployed a machine learning–driven solution capable of predicting which projects were most likely to experience a margin drop within the next 90 days. Delivered on the Insight Factory platform, the model integrated historical and in-flight project data spanning financials, planning schedules, resource utilisation, and risk indicators to build a comprehensive view of project health.
Using advanced predictive modelling techniques, the system identified patterns linked to margin erosion and flagged at-risk projects well in advance, giving leaders the ability to act proactively rather than reactively. The solution was deployed into full production, continuously retraining on new data to refine its accuracy and maintain relevance over time.
Commercial and project leaders were equipped with intuitive dashboards and automated alerting mechanisms, embedding the tool directly into the governance process. Today, it operates as a core part of project oversight, enabling data-informed decisions and more confident intervention strategies across the company’s portfolio.
The Value Delivered
The production-grade solution is already delivering benefits across the organisation. By surfacing earlier visibility of commercial risk, leaders now have the time and insight needed to intervene before margins are eroded whilst having a centralised view of financials, risk, planning and data quality in a single location. The platform has also fostered greater alignment between delivery, commercial, and executive teams, ensuring that risks and interventions are addressed consistently across the portfolio.
Equally important, the solution provides confidence in data-driven decision-making, with explainable model outputs giving stakeholders transparency into the factors driving predictions. Beyond immediate impact, the approach has established a scalable foundation for expanding predictive risk models into other dimensions of project health, such as schedule risk and resourcing gaps.
This engagement demonstrates how insightfactory.ai helps engineering-led enterprises apply AI to their most critical challenges, delivering foresight, control, and measurable commercial value.
