Technology can play a critical role in mitigating risks, relaying signals across the project lifecycle much like a digital nervous system, asserts Mahim Chaturvedi.
Capital projects are inherently complex, involving numerous stakeholders, dependencies, uncertainties, and varied data sources. Increasing resource shortages, external challenges, and interface issues add to this complexity. Traditional governance relies on lagging indicators, such as progress reports, variance analysis, and milestone delays, to assess performance. The primary challenge with such mechanisms is that risks are identified either when they have materialised or too late in the project cycle, resulting in inevitable delays and cost overruns. Project governance must, therefore, evolve from reactive oversight to predictive, risk‑anticipating decision‑making.
Currently, organisations use technology in projects primarily as point solutions or, at most, to drive efficiencies, such as automating reports or digitising documents. Further, while organisations have built interactive dashboards to monitor projects, much of the interpretation, analysis, and outcome prediction is still left to users. These tools, while useful, seldom enable proactive identification of challenges and risks.
Digital Nervous System
In capital programmes, risk builds gradually through all its interrelated workstreams. Examples include design changes in engineering, procurement bottlenecks, diminishing schedule buffers across project activities, and resource changes in construction. Traditional risk registers often miss this accumulation, relying primarily on periodic updates and reports.
Technology can play an important role in this respect by relaying dynamic risk signals throughout the project lifecycle, much like a digital nervous system. Building such a digital nervous system requires defining leading indicators and key performance indicators (KPIs) and embedding risk management across all workstreams in the project lifecycle, making each project activity a source of risk signal.
Project Planning
From a governance perspective, planning forms the foundation of the digital nervous system, where baseline benchmarks and underlying assumptions are subjected to stress tests. AI‑driven scenario modelling and schedule stimulations reveal risk and critical path weaknesses. Rather than assuming a fixed timeline, probabilistic stimulations model uncertainties like procurement delivery, monsoons, and labour productivity to quantify project completion risks.
Engineering & Design
In addition to the traditional engineering KPIs that measure planned vs. completed drawings or weighted average engineering s‑curves, organisations should also monitor forward‑looking risk indicators such as design change trends in interface areas or specific packages. These analytics highlight how these changes may impact commissioning or rework risks in the future.
Integrated data platforms with 5D building information modelling (BIM) act as early warning sensors, helping avoid costly clashes and reworks at a later stage through digital stimulations related to the constructability of the project.
Procurement & Contracts
In addition to the lagging indicators, such as percentage of purchase orders (PO) planned vs. actual, organisations must evaluate leading indicators as well, such as long‑lead exposure, post‑order risk, and buffer erosion risk, on account of incremental delays in interrelated activities in the procurement process.
AI‑powered digital tools are central to such procurement governance. These tools evaluate procurement turnaround time (TAT), vendor progress data, vendor engineering approval timelines, and manufacturing inspection changes to predict delivery and installation risks.
Construction & Execution
Execution is when problems usually become obvious, but with AI‑powered digital tools, these issues are spotted early. These tools capture progress, analyse productivity drifts, and monitor how workers and equipment are used, sending out quick risk alerts to avoid delays. AI looks at the information to spot drops in productivity, clusters of rework, and unstable resources at critical job sites before these issues cause missed milestones or project setbacks.
Hence, in addition to traditional KPIs of percentage progress completion, milestone achievement, and non‑conformance reports, organisations must evaluate leading indicators, such as productivity change, workfront readiness of critical zones, resource utilisation, and rework trends.
Regulatory & Stakeholder Interfaces
Regulatory approvals and stakeholder interfaces often introduce unpredictable shocks. Digitally‑enabled mechanisms can map dependencies between statutory approvals and engineering, procurement, and construction activities, proactively flagging risks and converting them into a forward‑looking process.
Transitioning to Digital Governance
Many capital programmes today are labelled ‘digital’ because they deploy tools for digital reporting. Yet digitisation alone does not transform governance. The real transformation lies in building an AI‑driven digital nervous system. This integrated architecture continuously senses engineering instability, procurement fragility, construction productivity drift, rework concentration, and shrinking schedule buffers. When leading indicators are embedded across the lifecycle, risk is detected while still emerging rather than after milestones are missed.
Ultimately, the goal is not just to execute digital projects, but to institutionalise digitally governed programmes where transparency, accountability, and predictability are embedded into the fabric of delivery.
ABOUT THE AUTHOR:
Mahim Chaturvedi, Partner, Consulting, EY India, has led major expansion programmes across metals, chemicals, discrete manufacturing, and infrastructure over a career spanning nearly 25 years.