Private equity is entering a structural shift. As highlighted in our recent piece “Reinventing Operational Due Diligence with AI”, rising interest rates, longer hold periods, and compressed value-creation margins mean traditional deal playbooks, reliant on financial engineering or macro tailwinds are increasingly inadequate.
In 2025 and beyond, the edge will come from operational insight, execution discipline and most of all, data-driven intelligence. That is why AI is no longer just a back-office efficiency tool. AI is becoming a full partner at the deal table. Not to replace human judgement but to amplify it, enabling deeper understanding of value, risk, and growth, especially in asset-heavy and industrial sectors where we operate.
From Static Snapshot to Strategic Partner
In our previous article we explained how AI transforms operational due diligence (ODD). Traditional ODD — a static, time-bound review — is giving way to continuous operational visibility: AI-enabled ingestion of ERP data, IoT sensor logs, maintenance records, supply-chain and quality metrics allowing investors to detect inefficiencies or risks before they show up on financial statements.
This shift created the groundwork for a new era when you can monitor operations in (near) real time instead of relying solely on historical data, you gain the ability to take action rather than simply watch.
The natural progression now is to embed that operational intelligence throughout the entire deal cycle, from sourcing to governance, and to treat AI not as a supplementary tool but as a true co-pilot, co-investor, and “silent partner” in both decision-making and post-deal value creation.
AI as a Sourcing and Screening Partner: Spotting Opportunities Humans Miss
Traditional deal sourcing largely depends on relationships, networks, and manual screening, slow, resource-intensive, and often biased toward known circles. AI changes that dynamic drastically.
AI-powered deal-origination and screening platforms, scan large and varied data sources such as public filings, media reports, supply-chain data, web traffic, hiring patterns, patent or IP activity, satellite or geospatial data, and more. For example, AI can:
- Identify a mid-market manufacturer showing early signs of demand surge or improved production efficiency.
- Detect an infrastructure company under-investing in maintenance or quietly accumulating supplier concentration risk.
- Spot a renewable energy project with fresh interconnection rights or favorable regulatory shifts.
These early signals allow PE firms to approach targets earlier, often before they enter traditional auction pipelines, offering first-mover advantage and better pricing discipline.
In effect, AI becomes a sourcing partner, expanding the funnel and improving the quality of leads. This aligns directly with the competitive pressure described in our prior article: in today’s market where value-creation margin is tighter, sourcing advantage can decide which deals deliver real returns.
From Document-Heavy “Checklists” to Predictive, Data-Rich Insight
Once a target is identified, the traditional due diligence process often involves weeks of manual document review, financial statements, contracts, supplier agreements, compliance filings, followed by spreadsheets and human judgement.
But AI changes the game. As shown by multiple recent studies and industry reports:
- AI-driven due diligence can reduce time in manual processes, by automating contract review, financial cross-checks, supplier and vendor analysis becoming document streamliner for business according to KPMG’s report “The Impact of AI on the Deals Market”.
- AI also supports deeper, multi-dimensional risk assessment such as supply-chain vulnerabilities, ESG exposures, maintenance backlogs, hidden liabilities which is often invisible in traditional diligence.
Thus, AI becomes a diligence partner to delivering speed, breadth, and depth far beyond what a team of human analysts can realistically achieve within typical deal-process timelines.
Pre-Closing Value-Creation Planning
Our earlier article described how AI enables creation of “operational twins”, pre-close digital models of the target company that simulate operations, supply-chain flows, maintenance, output, and stress-test value levers.
This capability unlocks a critical advance: by the time the deal closes, the PE firm is not scrambling to define post-close priorities. The value creation plan is already mapped out from the first day.
With AI as a partner, the deal team can simulate various scenarios:
- What happens if production is optimized via better maintenance scheduling?
- What if inventory and working capital are optimized via supply-chain improvements?
- What if ESG compliance upgrades reduce regulatory risk and increase valuation?
This transforms VCP planning from hypothesis-based assumptions into quantified, data-backed roadmaps, giving sponsors and limited partners greater conviction in the “why” and “how” of value creation even before capital is committed.
In compressed markets, like those we operate in Asia, where macro tailwinds are uncertain, and hold periods longer, this early VCP clarity is not just a nice-to-have, it is a competitive differentiator.
Reducing Overpayment Risk
In current market conditions, paying too much for a target, especially one with operational or structural risk that can destroy returns over a typical private equity holding period. That risk is higher today than in past cycles.
With AI enabling detailed diligence, predictive modeling, and scenario stress-testing, investors can apply more conservative, risk-adjusted valuations. They can stress-test downside scenarios, adjust for operational volatility, and factor in hidden liabilities or capex needs.
AI becomes more than an analyst, it becomes a discipline-enforcing partner at the table, helping counteract optimism bias, and improving valuation rigor.
AI Is Not a Silver Bullet — The Hybrid Model Prevails
It is crucial to emphasize what we said before that AI is not a substitute for domain expertise, deep operational know-how, or human judgement.
Data quality, system integration, governance frameworks, and organizational readiness remain major constraints. Especially in emerging markets or industrial sectors where digitization is patchy, data fragmentation or poor record-keeping can limit what AI can achieve.
What makes the difference between success and failure is the hybrid model. AI handles high-volume data ingestion, anomaly detection, predictive modeling meanwhile humans with domain experience will interpret results, provide context, make judgment calls, and execute strategic decisions.
For firms like Gunung Capital with deep industrial know-how, regional presence, and a sustainability- and operations-driven mandate, this hybrid approach offers a real competitive advantage.
What the Deal Table of 2026 Looks Like
The next generation of deal-making won’t just gather underwriters, bankers, and lawyers. It will gather:
- Operators and industrial experts — those who know assets, plant operations, capex cycles, ESG realities
- Deal teams and financial professionals — structuring capital, taxation, exit strategy, returns
- Data scientists, AI engineers, and operating-systems architects — building the data pipelines, models, and dashboards that power predictive operations
AI will no longer be a side-project or efficiency lever. It will be a full, strategic partner involved in sourcing, diligence, valuation, value creation, and governance.
For firms ready to embrace it, especially in asset-heavy, industrial, or sustainability-driven sectors, the payoff can be significant such as faster deal cycles, smarter underwriting, cleaner operations, better value creation, and ultimately a stronger, more resilient returns.
At Gunung Capital, we believe this hybrid “human + AI” model is happening. It is fundamental to delivering value, managing risk, and driving the green, sustainable transition that defines our institutional mandate.
