In 2025, amid higher interest rates, slower exits, and thinner value-creation margins, investors are increasingly relying on AI to strengthen operational due diligence (ODD). The result is a shift from static assessment to predictive intelligence.
From Static Snapshots to Continuous Operational Visibility
Traditional ODD relies on time-bounded reviews, but operations today are too dynamic for that. AI-enabled tools now allow investors to build near real-time operational visibility across production, maintenance, and supply-chain performance.
AI platforms can ingest ERP data, IoT sensor logs, maintenance reports, and quality metrics to detect anomalies, highlight inefficiencies, and surface risks before they show up in financials. This is especially critical in asset-heavy industries where small inefficiencies compound significantly over time.
This shift toward predictive oversight helps investors answer not just “What happened?” but “What will happen?” — strengthening risk pricing and improving deal conviction.
Shortening the Gap: Building the Value-Creation Plan (VCP) Before Closing
AI fundamentally changes the pace and depth of VCP planning. Instead of waiting until Day 1 post-close, investors can now build pre-close “operational twins” based on historical data, benchmarking, supply-chain flows, and operational metrics.
This allows deal teams to test upside scenarios, quantify operational improvements, and stress-test value levers before signing. In a world where multiple expansion is limited and hold periods are longer, this early VCP acceleration is becoming a key differentiator.
Uncovering Hidden Operational, Supply Chain & ESG Risks
AI significantly expands the scope of risk scanning in diligence. AI can review thousands of documents such as contracts, supplier agreements, maintenance logs, compliance filings are far faster than humans and with higher anomaly-detection accuracy. Industry cases show AI can replace repetitive manual review tasks and identify discrepancies faster and more reliably
Beyond structured data, AI can scan news, social sentiment, ESG disclosures, regulatory filings, and supply-chain exposures to identify reputational or compliance risks that traditional diligence often misses. For sectors like green steel, energy infrastructure, building materials, and industrial decarbonization, where regulatory, operational, carbon, and safety risks intersect, this multi-layered risk intelligence is extremely valuable.
Efficiency, Speed, and Scale: Quantifying the AI Advantage
The advantages of AI in due diligence are no longer theoretical, they are measurable. Deloitte’s latest analysis finds that 64% of private equity firms using AI have already seen improvements in the quality and efficiency of their due diligence processes, signalling a clear shift in industry norms. Accenture’s research reinforces this trend, showing that generative AI can automate up to 30% of diligence tasks and augment another 20%, particularly in areas such as document parsing, contract extraction, and scenario modelling.
These gains translate into meaningful reductions in manual workload. Automated document parsing alone can cut up to 70% of repetitive review time, allowing investment teams to redirect their focus from administrative checks to higher-value strategic judgment.
For diversified investors like Gunung Capital, operating across infrastructure, software, energy, and sustainability-oriented industries, this speed and scale isn’t just operational efficiency. It’s a competitive edge.
The Future of Operational Due Diligence
Despite its advantages, AI is not a substitute for domain expertise. Risks remain on the data quality, algorithmic bias, over-reliance on machine outputs, and regulatory issues related to privacy and AI governance, as cited from Transcript IQ. The winning model is therefore hybrid where AI handles the data-heavy analysis, while human experts apply strategic, sector-specific judgment.
This is especially crucial in industrial and infrastructure investing, where execution context, operational maturity, safety protocols, carbon impacts, and regulatory dynamics cannot be understood from data alone.
Implications for Investors
For investors across private markets, the rise of AI-enabled operational due diligence (ODD) marks a significant shift in how risk, value, and operational quality are assessed. First, AI gives investors a far deeper and more dynamic view of operational performance, particularly for assets undergoing modernization, digital transformation, or decarbonisation. Machine-driven analysis can surface hidden cost drivers, reliability issues, or process inefficiencies that traditional document-led reviews often miss.
Second, investors gain sharper visibility into maintenance, energy usage, and infrastructure-related risks. With sensors, IoT data, and AI modelling now more accessible, due diligence can evaluate real-time operational resilience rather than relying solely on management narratives.
Third, AI improves the ability to detect ESG exposures early. Whether it’s labour risks embedded in the supply chain or potential environmental non-compliance, AI tools can monitor signals at scale and flag anomalies long before they become liabilities.
Fourth, AI accelerates the development of value-creation plans (VCPs). With faster synthesis of operational baselines and predictive modelling, investors can craft more targeted, insight-driven playbooks, often within days rather than weeks. This has become particularly critical in competitive or compressed deal processes.
Finally, AI-enabled ODD strengthens downside protection. In emerging-market environments, where data quality can be inconsistent and operational surprises more common, AI reduces blind spots and improves underwriting confidence.
In short, AI transforms operational due diligence from a checklist exercise into a proactive, intelligence-driven engine for value creation and risk reduction.
