Kailash Sadangi on Aligning Financial Modelling with Delivery Strategy across Megaprojects and Capital Projects

Executive Abstract

Many capital projects succeed on spreadsheets but fail in execution. This article explores why static financial models often underestimate delivery complexity, cost escalation, scheduling risk, and governance failures—and what investors must do differently.

The stakes are substantial: research shows that 70% of major infrastructure projects experience cost overruns, average delays exceed 20 months, and the global construction sector loses more than $1.6 trillion annually to productivity gaps. (Source)

Bridging this disconnect demands more than sharper forecasts—it requires reference-class benchmarking, dynamic models that adapt as conditions evolve, disciplined front-end loading, and execution governance that ties financial assumptions directly to delivery accountability. For CFOs and investors, the question is no longer whether to close the gap, but how quickly they can build the discipline to do so.

Keywords

Capital projects, financial modelling, megaprojects, infrastructure risk, delivery strategy, forecasting

Pull Quote

“The best financial model in the world has limited value if project reality is ignored.”

The Scale of the Problem

Infrastructure and capital projects are increasingly shaped by sophisticated financial models. Yet despite advances in modelling capability, many projects continue to suffer cost overruns, delays, disputes, and underperformance. According to Kailash Sadangi, the growing disconnect between financial models and project execution realities is one of the most significant risks facing investors, sponsors, and policymakers.

McKinsey research shows that large capital projects typically take 20% longer to finish than scheduled and can run up to 80% over budget. Source

This underperformance is not incidental. Research by Bent Flyvbjerg demonstrates that cost overruns and delays are systematic across megaprojects, driven by optimism bias and flawed forecasting. Source

Models vs Reality

At the heart of the issue is a structural disconnect: financial models are theoretical, while project delivery operates in uncertain, dynamic environments.

Early-stage models often assume stable conditions. In practice, projects face volatility in procurement, labour, approvals, and stakeholder dynamics.

As per a report from McKinsey, poor early-stage planning and inaccurate assumptions are major drivers of cost overruns and delivery failure. Source

Why Financial Models Fail

1. Cost Assumptions Become Outdated Construction markets are highly volatile. KPMG’s Global Construction Survey identifies cost inflation, supply chain disruption, and labour shortages as leading risks impacting project delivery globally.⁴
2. Schedule Assumptions Are Unrealistic Financial models often assume linear execution timelines. However, McKinsey analysis shows that complexity and coordination challenges routinely lead to significant schedule delays in large projects.
3. Risk Registers Are Incomplete Risk modelling frequently fails to capture real-world uncertainties. PwC notes that many organisations lack mature risk management practices, leading to reactive decision-making during project delivery.
4. Revenue Forecasts Are Overly Optimistic Demand forecasting is often biased. Flyvbjerg’s research shows that optimism bias leads to systematically inflated demand and revenue projections in infrastructure projects. Source
5. Governance Is Undervalued Execution failure is often a governance issue rather than a financial one. The Project Management Institute (PMI) reports that poor governance and lack of alignment are key drivers of project underperformance. Source
6. Digital & Data Gaps Limit Visibility Modern project environments require real-time insight. McKinsey highlights that limited use of digital tools and data transparency reduces productivity and increases execution risk in construction projects.

The Consequences

When financial models fail to reflect execution realities, projects experience delays, cost overruns, and reduced returns. Kailash Sadangi notes that when assumptions diverge from delivery conditions, investors face lower returns, lenders reassess risk, and management teams are forced to make reactive decisions.

McKinsey estimates that the majority of large projects fail to meet cost and schedule targets, reinforcing the systemic nature of the issue.

What Needs to Change

1. Reference-Class Forecasting
   Using comparable project data improves accuracy. Flyvbjerg emphasises that reference-class forecasting reduces bias and improves reliability in project estimates. Source
2. Dynamic Financial Modelling
   Static models are insufficient in volatile environments. PwC highlights the importance of continuous monitoring and updating of project assumptions throughout the lifecycle. Source
3. Front-End Loading
   Early-stage planning is critical. McKinsey finds that projects with strong upfront planning perform significantly better in cost and schedule outcomes. Source
4. Integration Between Finance and Delivery
   Misalignment between teams creates unrealistic expectations. KPMG identifies lack of integration across project functions as a key barrier to successful delivery. Source

5. Stronger governance improves outcomes.

PMI shows that organisations with robust governance frameworks achieve higher project success rates. Source

Conclusion

The evidence is consistent across global research:

• Cost overruns are common
• Schedule delays are systemic
• Forecasting bias is persistent

Financial models remain essential, but only when grounded in operational reality.

Bridging the gap between models and execution is now one of the defining challenges in modern infrastructure investment.