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Highlights
- The use of generative artificial intelligence (GenAI) in post-merger integration can enhance and automate tasks that were previously labour-intensive and prone to errors, potentially speeding up integration timelines and increasing the merger value.
- The volume and complexity of unstructured data such as contracts, emails, customer communication, and internal reports pose a challenge during post-merger integration. GenAI can transform the integration process with capabilities that traditional methods lack.
- Thousands of data artifacts in disparate formats inherited from an acquired entity need to be dealt with. GenAI is transforming the way this task is executed, improving the efficiency of reading the documents systemically and speeding up the process.
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Harnessing GenAI
Post-merger integration (PMI) involves the combination of operations, technology, and data of the merging organisations.
The integration of unstructured data, such as contracts, emails, customer communication, and internal reports, is expensive due to the high volume and complexity of data. The use of generative artificial intelligence (GenAI) in the PMI process can transform and automate tasks that were previously labour-intensive and prone to errors, potentially speeding up integration timelines and reducing costs of unstructured data integration by 60-70%.
The AI advantage
The use of GenAI to address unstructured data in PMI scenarios offers various advantages that transform the integration approach.
Accelerated and efficient processes
- Risk assessment: The due diligence process in PMI, involving extensive document reviews to detect risks and obligations, can be streamlined with GenAI tools. They automate contract and legal document analysis, identify key information, flag anomalies, and summarise texts, saving the review time by up to 90%. This speeds up the deal timeline and enhances risk assessment, leading to better decision-making and smoother integration.
- Data migration and cleansing: Integrating different IT systems is a common challenge in PMI, worsened by the varied formats of unstructured data. GenAI provides automated tools for data discovery, classification, and mapping. It identifies, categorises, cleanses, and standardises data before migration, ensuring higher quality and reliability. Additionally, GenAI can detect redundant, obsolete, or trivial data, helping organisations reduce storage costs and improve data governance.
- Transforming and unlocking value: GenAI can assist in realising synergies by analysing large amounts of unstructured data from the merging entities. This includes customer feedback, market analysis reports, internal communication, and operational data. By recognising patterns, sentiments, and connections within this data, GenAI can help teams identify opportunities for saving cost, increasing revenue, improving operational efficiencies, and cross-selling. This data-driven approach to synergy identification allows for more effective integration planning and a higher likelihood of achieving the strategic objectives of a merger.
Pillars of success
To fully leverage the potential of GenAI in PMI, organisations need to plan and execute carefully. Buyers should consider the following recommendations:
GenAI strategy aligned with M&A goals: Develop a strategy that defines how GenAI will support integration objectives. For example, strategic focus on data security can be addressed by identifying sensitive data in documents with AI and such documents can be secured quickly. Identify key use cases like contract analysis, communication consolidation, or knowledge base creation. Set measurable goals like document processing time, classification accuracy and create a phased plan, starting with pilot projects to show value and refine methods. This leads to purposeful and effective GenAI integration.
Prioritise data quality, governance, and security from the outset: GenAI outputs depend on input data quality. Invest in solid data governance, clear handling protocols, regulatory compliance, and strong security for sensitive information, especially when merging organisations. Transparent practices and addressing AI model biases are crucial for trust and ethical deployment.
Invest in training and personnel: Effective adoption of GenAI depends on upskilling and training teams to proficiently use AI tools and analyse their outputs. Using tools like AI opportunity radar can create measurable AI capabilities in an organisation, while standard innovation frameworks like value innovation can define high impact business use cases. Develop workflows that integrate GenAI with human expertise, enabling AI to manage repetitive tasks with humans focusing on strategic decision-making and resolving complex issues.
Generate lasting value
Integrating GenAI into post-merger activities can simplify the handling of unstructured data.
It speeds up processes, improves data quality, and provides strategic insights, enhancing the PMI efforts. A strategic vision, commitment to data excellence, strong governance, and right-skilling are essential for success of merger. Organisations adopting these principles can turn post-merger integration complexities into lasting value and competitive advantage.