AI in M&A

As pressure for transformational M&A grows from investors¹, embedding AI throughout the M&A process is viewed as a way to unlock greater value across the deal lifecycle. Our recent survey and interviews of Swiss M&A leaders show a clear pattern: firms are adopting AI in the M&A workflows, but mostly where it is easiest to deploy, rather than where it can have a transformational impact. For M&A leaders the question is no longer whether AI has a role in the deal lifecycle or which use case to prioritise. The challenge is turning isolated applications into capabilities that support value creation across the entire deal lifecycle. Achieving this demands more than deploying new technologies; it requires an operating model that redefines the systems, processes and capabilities through which deals are executed with AI.

Survey insights

Firms are adopting AI where implementation is easiest, not necessarily where value creation is greatest

Our survey shows that 78% of Swiss M&A practitioners are already using AI in the pre-deal phase. This suggests that M&A AI is already delivering practical benefits. However, our interviews indicate that these benefits are concentrated in specific standardised pre-deal activities, such as due diligence, target screening and document review, where deployment is easier: teams are smaller and fully dedicated, so the work lends itself more naturally to efficiency gains than in the post-deal phase. This finding is particularly striking when compared with other geographies. In post-close integration, 52% of respondents in our US study² report using GenAI, compared with just 8% of Swiss organisations in our survey. In practice, many Swiss M&A teams are realising early gains through productivity improvements rather than through capturing additional value across the deal lifecycle. The strategic question is whether firms can move beyond incremental efficiencies and apply AI where its transformational impact forces change in the M&A operating model itself.

The challenge is not only adoption. It is the ability to tailor and build AI for real deal settings

In our survey 38% of respondents cite data security as the main barrier to adoption which resonates with our experience delivering complex cross-border M&A programmes: in M&A the challenge is not just whether AI in M&A workflows can be adopted at all, but how it can be tailored, developed, and used in the context of confidential deal work. Our interviews demonstrate that the barriers are people and processes, rather than the AI model itself. Key barriers include uncertainty around using large language models (LLMs) for sensitive information, limited capacity to develop new use cases while meeting day-to-day delivery demands, and gaps in AI capabilities. These constraints not only affect rollout but also limit the ability to create higher-value use cases and deliver better deal economics, faster.

AI will not transform deal value creation without a change in the M&A operating model

Looking forward, our survey shows that Swiss M&A leaders intend to leverage AI more in due diligence workflows (48%), while the ambition to use AI in post-merger integration (PMI) and value realisation is significantly lower (7%). This discrepancy is noteworthy because both due diligence and PMI value realisation are highly document and communication intensive activities, making them well suited to the capabilities of today’s LLMs. The lower ambition for post-deal AI usage therefore reflects organisational constraints rather than limitations of the underlying technology. It is evident from due diligence use cases that firms are deploying AI in complex, time-bound, tightly scoped and repeatable workflows. The challenge is to replicate that success in the less structured post-deal environment, where value realisation typically occurs. Here, fragmented data, diffuse ownership, and evolving decision rights between deal teams and integration teams make AI significantly harder to embed into everyday ways of working.

Conclusion: Towards a new AI operating model

Firms that continue to treat post-deal AI as a collection of siloed use cases will miss out on AI’s transformational potential. The greater prize will go to those organisations that consciously invest in embedding AI into their M&A operating model, thereby opening new opportunities to connect capability verticals to value delivery.

To capture post-deal value, Swiss M&A leaders need to evolve from using AI for productivity to leveraging AI as a strategic value creation capability, transforming around a new, fit-for-purpose M&A operating model:

• Systems: build a secure M&A data layer that connects diligence outputs, integration plans and live functional data, allowing AI to track risks, synergies, and value creation beyond signing
• Processes: redesign M&A workflows so that AI converts diligence findings into structured post-deal actions and value levers, reducing the hand-off risk between the deal team and the business owners
• People: dedicate capacity to cross-functional M&A AI use case development and workflow redesign, combining deal, integration, functional, data, risk and technology expertise
• Governance: establish clear rules for data access, confidentiality, model use, human review, ROI tracking and decision rights under the new M&A operating model
• Scope: broaden the scope of the M&A function through new AI-enabled activities, such as AI upside assessments of the target operating model

AI needs to be not just a toolkit but an enterprise’s way of working.

How we can help

We help clients to translate their M&A AI ambition into a value-led transformation agenda through a vision-to-value approach. This starts with identifying where AI can move beyond efficiency gains and create transformational value across the deal lifecycle, and which conditions need to be in place to capture it at scale. Deloitte’s experience drawn from the most significant and complex cross-border M&A deals combined with our AI expertise delivers integrated value to our clients.

Methodology

This report is based on the findings of a survey of 66 Swiss M&A leaders conducted between February and April 2026. Quantitative results were complemented by two in-depth interviews and a broader set of client conversations, which refined and validated the findings. The sample spans organisations in Industrials, Information Services, and Software Development.