Deloitte’s 2025 Survey of GenAI in M&A found that 86% of organisations used generative AI in their daily workflows. In the UK, a 2026 study showed that almost a quarter of retail investors were using AI to support their investment decisions. And although the contexts are slightly distinct, the broader development is clear: AI is becoming increasingly influential in financial analysis.
AI is moving beyond experimentation and becoming an increasingly important part of the due-diligence toolkit, although its relevance and application will vary by transaction. So what can AI do, and where can it add the most value?
Faster review
Every merger or acquisition can involve thousands of files. It’s no surprise that these contracts, financial documents, company policies and operational reports all require rigorous oversight. And as we highlighted in our dry powder blog, this already slow process has been made slower by persistent exit bottlenecks.
AI can help teams sift through these materials and locate relevant information more efficiently; information such as:
- Change-of-control or termination clauses
- Required or missing documentation
- Inconsistencies across reports
AI can also summarise large volumes of information quickly. This doesn’t mean financial, legal and operational checks are completely outsourced to a model. Instead, AI can provide a more efficient starting point, quickly finding and highlighting potential problems.
Supporting commercial assessment
Not only can AI review disparate data, it can draw connections. By reviewing market research, competitor analysis and internal sales data, a well-honed model can help deal teams examine:
- Whether growth is sustainable
- Which customer or market segments are outperforming
- How the target compares with competitors
These questions can help deal teams test management assumptions, identify gaps in the investment case, and determine where further analysis is required.
Businesses that use AI effectively may gain an advantage through faster and more extensive analysis. However, adopting AI does not create an advantage on its own. Its value depends on the quality of the data, the controls surrounding its use and the expertise of the people interpreting its findings.
And individuals who have proven their ability to originate and execute deals based on that data will be in demand.
READ MORE: What could a career in transaction advisory services look like?
Pattern analysis
AI can identify patterns across quantities of data that would be difficult for any one individual to review manually.
In M&A, pattern analysis can reveal valuable information about a prospective asset. Is employee turnover unusually high? Are there gaps in intellectual-property ownership or licensing?
This does not outsource critical thinking. It gives decision-makers more evidence to draw from. By outlining risks and providing comparison points, deal teams have more context to help determine which areas require prioritisation.
Cultural and talent assessment
Financial performance and technological capability are only part of an acquisition. Buyers also know that the target has the leadership, workforce and culture required to deliver the investment plan. Although these factors can be difficult to quantify, AI can analyse specific metrics across workforce data and extrapolate from there.
Employee surveys, interview notes, public reviews – all of these can be analysed and assessed for patterns, helping to identify recurring themes across the workforce. This can reveal retention risks, leadership gaps or potential integration challenges.
READ MORE: Explore the wider M&A Consulting Trends shaping the dealmaking landscape in 2026.
Managing the risks
AI must be treated as a tool rather than a single solution. It cannot be a substitute for professional judgement, and it cannot take accountability for an investment decision.
Generative AI can produce inaccurate information, invent sources, summarise incorrectly, or present inaccurate answers with absolute confidence. There is also the inherent confidentiality risk in uploading sensitive documents to public tools; it would be counterproductive for the use of AI during due diligence to undermine the governance and information-security standards the process is intended to uphold.
AI due diligence in M&A
AI may accelerate the diligence process, but the quality of an M&A decision still depends on experienced professionals who can challenge assumptions, interpret risk and lead the business through change.
Whether you are strengthening a corporate finance team, preparing for integration or seeking your next role in M&A, Gambit Search connects specialist finance professionals with businesses at critical stages of growth and transformation. Upload your CV, or browse our selection of corporate finance opportunities.
Looking to build a career in M&A? Our guide to the key roles in mergers & acquisitions is the perfect place to begin.
