Healthcare has been quick to understand and apply artificial intelligence (AI) in many of its operational functions. It helps detect diseases earlier, improve customer decision-making via digital platforms, and automate tasks in organizational functions, such as HR and other administrative areas. However, AI’s application in financial strategy and asset valuation, particularly in the domain of financial due diligence, remains an untapped opportunity, even as the adoption of AI in the sector tends to lag other industries, according to the World Economic Forum.^[1] 

This is also where AI can provide a distinct competitive advantage.

AI is creating new financial levers and asset classes within the life sciences and healthcare sectors that can have a profound impact on how assets are valued and can create new financial insights to enhance growth. In other words, AI is not merely a tool for operational improvement but a fundamental catalyst for advanced financial decision-making. It provides a road map for leaders to build a defensible, differentiated strategy that leverages AI to create, value, and monetize assets in the new healthcare economy. Above and beyond this, the technology gives investors and consultants a tool to quantify the financial impact of AI in the financial due diligence process with a direct and positive effect on EBITDA.

Redefining Financial Due Diligence in Healthcare

In any deal, conducting traditional due diligence on a company’s financial, operational, legal, and intellectual property is limited by time and resources. Risks and red flags are often missed; even if identified, they may not be fully understood or robust enough to improve decision-making.

AI allows investors to conduct more sophisticated financial due diligence by analyzing complex data, uncovering risks, and identifying opportunities to improve value that might be missed through traditional merger and acquisition (M&A) analyses. But more importantly, it’s a performance improvement tool that can be wielded in due diligence and also realized post-transaction. It gives investors and consultants a mechanism to make better decisions, significantly enhance risk detection, and can improve EBITDA substantially.

 The benefits include:

Improving decision-making through analyses: Large language model technology and similar AI platforms can enhance efficiencies to uncover insights that might be missed through resource constraints and time. These tools can analyze proprietary data, summarize documents, and flag inconsistencies or abnormal transactions in a company’s record database. AI can read more data to uncover hidden risks compared with sample data models, helping transaction teams work smarter as they are able to more quickly highlight opportunities for incremental findings that enhance decision-making for leaders and create value.

Creating comprehensive, sector-specific checklists: By uploading data and information based on the evaluator’s expertise in the industry, AI can generate comprehensive checklists of financial areas of note, including those most applicable to the company’s sector. This expertise and sector experience leads to better prompts to get the answers needed to improve efficiency in financial due diligence. The checklist, however, is not the goal. It’s simply a way to ensure that anomalies aren’t missed, which can be done because the right data and questions have been plugged in at the start of the process.

Enhancing regulatory, industry-specific, and reimbursement insights: AI allows leaders to optimize their intelligence surrounding upcoming healthcare regulation changes, reimbursement shifts, and industry trends that may affect the value of the asset or its inherent risks. It can also spotlight hidden inefficiencies and lead to solid value creation. AI provides a way to get smarter faster in this area of healthcare due diligence.

AI offers the ability to analyze historical transactions to identify risks, patterns, and benchmarks against peers in the sector. Moreover, it gives better visibility into risk factors, such as inefficient collections and coding misalignment. However, the value of the insights during due diligence is dependent on the quality of the data. Healthcare leaders must also consider the challenges of privacy regulations when conducting financial due diligence.

Quantifying Financial Impact—the ROI of AI

What are the measurable impacts of AI on M&A outcomes, asset valuation accuracy, and strategic decision quality? They include revenue, margin, and enterprise value. While it’s easy to identify potential productivity gains due to technology adoption, quantifying the return on investment (ROI) is far more challenging. The real transformation lies not in automation itself, but in how AI fundamentally improves the quality of financial decisions, risk identification, and value creation strategies.

The question has never been whether AI creates value; it's about quantifying how that value manifests in healthcare M&A. The ROI emerges through three strategic dimensions:

1. Enhanced decision precision
2. Superior risk detection
3. Optimized allocation of expert judgment

AI plays a critical role across key dimensions of healthcare financial due diligence, revenue integrity, EBITDA quality, working capital, operations synergies, and regulatory risk:

Revenue integrity: Organizations typically write off 3% to 5% of patient charges as bad debt. When AI is applied during financial due diligence, AI can identify systematic inefficiencies like under-collection against contracted rates, pinpoint claims denial patterns, and detect coding inefficiencies. These insights can recover 20% to 30% of the write-offs or 0.5% of revenue leakage. This translates to significant EBITDA improvement.

Risk identification and due diligence quality: By analyzing historical transactions, AI can now deliver 85% or higher risk identification accuracy. AI identifies patterns that enable healthcare investors and consultants to ask fundamentally better questions during due diligence to detect subtle margin compression trends, shifts in payer mix, or degradation in revenue cycle metrics that traditional analysis overlooks. These insights transform subjective assessments into objective, defensible positions during valuation discussions and negotiations.

EBITDA quality improvement: An average of 8% to 12% of total claims are denied across hospitals and physician groups. Traditional reviews rely on small samples; however, AI can analyze most of the claim and denial data, including 837 and 835 digital data transmission files as well as clearinghouse logs. Predictive analysis can reduce denial rate by at least 1%, improving EBITDA by 0.3% to 0.4%.

The measurable ROI of AI in healthcare M&A is realized through:

• Better decisions: More precise, data-driven insights
• More accurate valuations: Reduction of subjectivity in financial forecasting
• Operational intelligence: Turning hidden inefficiencies into value-creation opportunities

Moving AI From Operations to Advance Financial Strategies

Deployed as a tool during financial due diligence, AI can be a fundamental catalyst for significantly improving decision-making for healthcare acquisitions. It mitigates the constraints of time and resources during the process and provides a comprehensive way to identify problem areas and revenue-enhancing opportunities.

AI streamlines M&A by saving time and resources while uncovering challenges and opportunities to boost revenue, before and after the deal.

How Can A&M Help?

A&M’s Healthcare and Life Sciences transactional team creates solutions for clients by providing sophisticated diligence, that delivers richer insights and better decision-making. The combined expertise and specialized experience of our professionals in growth expansion, process improvement, strategic advisory, operational intelligence, M&A, and private equity are focused on successful transactions and value creation after the deal.

[1] https://www.weforum.org/stories/2025/08/ai-transforming-global-health/