In every merger and acquisition transaction, data management is a critical step. It remains fundamental, as it enables both merging entities to operate using a common language.
The use of artificial intelligence is set to significantly transform this process and accelerate transactions—particularly for SMEs and mid-sized companies.
Insights and analysis by the experts at AURIS Finance, a leading M&A advisory firm.
In M&A transactions, value creation largely depends on the ability to rapidly integrate information systems and databases. However, this phase remains one of the most complex and time-consuming. In many cases, integration is only partially achieved and may take months—or even years—after the transaction is completed.
CIOs: a highly strategic function
During a merger, the multiplicity of tools, the heterogeneity of formats and inconsistent data quality significantly complicate the work of IT teams. Aligning information systems is nonetheless a fundamental prerequisite for successful integration. Without this alignment, the two entities cannot operate effectively together, delaying the realisation of synergies.
According to a study by McKinsey & Company, organisations that are most advanced in the use of generative AI in M&A are already experiencing substantial productivity gains. The use of AI can reduce transaction costs by an average of 20% and accelerate deal cycles by up to 50%.
Cost reduction and faster execution
In practical terms, AI enables companies to:
- Rapidly analyse large volumes of heterogeneous data
- Identify inconsistencies or duplicates
- Generate mappings between systems
- Automate certain migration processes
According to Brett Wilson, two main approaches are emerging in the use of AI in M&A.
The first involves bypassing full system integration by using AI to fill gaps and provide rapid access to key information, without the need for heavy consolidation projects. The second approach focuses on accelerating full integration by automating tasks such as data mapping, interface creation and testing.
In both cases, AI enables time savings, cost reduction and faster post-acquisition value creation.
Accelerating value creation
By automating data analysis, structuring and migration, generative AI significantly reduces integration timelines. Teams can more quickly access a unified view of customer, financial and operational data, while limiting the risk of errors or information loss.
For SMEs and mid-sized companies, which are often less technologically equipped than large corporations, this contribution is particularly decisive.
According to the McKinsey study, within the next two to three years, AI agents capable of automating more than 50% of integration tasks are expected to emerge—covering screening, due diligence and post-closing phases. However, full automation will not be achievable without human involvement. Structuring incoming data, particularly in a context of accelerated consolidation, remains a key success factor.
Our experts by your side
Generative AI is gradually becoming a key lever for accelerating data integration and securing value creation in M&A transactions. While productivity gains are already tangible, achieving them requires rigorous data structuring and a balanced combination of technology and human expertise.
In a market where speed of execution is increasingly critical, companies capable of mastering these new tools will gain a clear competitive advantage.
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