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Carlos Martin Soriano

M&A Technology Integrator

Carlos Martin Soriano brings experience in M&A integration, enterprise applications and business intelligence across multiple countries. His work focuses on applying technology to complex integration challenges, with particular attention to ERP systems, data and operational efficiency.

Sharpening M&A Due Diligence

Mergers and acquisitions are traditionally viewed as financial and strategic transactions, with technology integration becoming a major focus after the deal closes. Having been involved in multiple M&A integration representing approximately $4 billion in combined transaction value, I see how quickly complexity increases as a deal moves from strategy to execution.

AI has the potential to change this model. For CIOs, it creates an opportunity to engage earlier in the M&A lifecycle while accelerating one of the most complex aspects of an acquisition: integrating a newly acquired company into the enterprise technology and ERP landscape.

Before a system gets touched, AI can strengthen the diligence process. Instead of relying solely on banker presentations and management narratives, AI tools can help model market penetration scenarios by comparing a target's customer base, territory overlap and product mix with those of the acquiring company. This can help identify where a merger expands reach and where it adds redundant capacity.

Financial due diligence can also become faster and more focused. Large language models can review accounting data, contracts and financial statements to identify inconsistencies, restated figures, unusual revenue recognition patterns and margin anomalies. This does not replace accounting or legal teams. It gives them a faster first pass so they can focus on the anomalies that require deeper review.

AI-assisted contract analysis can also help teams review large volumes of vendor contracts, licensing agreements and deal clauses under time pressure. It can surface change-ofcontrol provisions, termination triggers and non-standard terms, giving legal and IT teams an earlier view of potential renegotiation and migration requirements.

AI does not make the acquisition decision. It makes the information supporting that decision more complete and current, which can lead to a more realistic integration budget and fewer surprises later.

Accelerating the Integration

The greatest time and cost savings often emerge after the transaction closes because this is where much of the integration budget gets consumed.

Legacy ERP systems rarely document themselves well. AI models can use schemas and sample data from an acquired company's ERP to propose field-level mappings to the new system. Product catalogs can help map product hierarchies, while fuzzy matching and geolocation algorithms can help identify customer and vendor overlaps.

Once those mappings are validated, AI can generate transformation and load scripts and quickly iterate when testing reveals new edge cases in legacy data.

  AI doesn’t make the decision to acquire. It makes the information going into that decision more complete and current.  

Testing also presents an opportunity for automation. Instead of manually creating every regression test for core transactions, including order-to-cash and procure-to-pay, AI can generate and execute baseline scenarios against the new environment. This allows QA teams to focus on scenarios that are unique to the acquired business.

Change management is another area where AI can make a practical difference. Employees moving from a familiar ERP system to a new one often face a steep learning curve. An MCP server can expose the new ERP through a conversational interface, allowing employees to ask questions like how to check inventory for a specific SKU or find the status of a sales order. This can reduce the burden on trainers and help employees become productive more quickly.

The same concept can apply to organizational knowledge. An MCP server that exposes organizational structure, roles and ownership can help employees understand who is responsible for what after a merger. That can reduce one of the most common sources of post-merger friction: not knowing who owns a process or decision.

Keeping Integration Accountable

AI can also help organizations track whether the expected value of an acquisition is actually being realized. Synergies promised to the board are often tracked through spreadsheets and updated periodically. An AI-assisted dashboard can pull live information from legacy and new systems, giving CIOs and boards a more current view of whether the integration is delivering against expectations.

AI does not replace the fundamentals of M&A integration. Clear ownership, realistic timelines, executive alignment and effective change management remain essential. What AI can do is accelerate work across multiple fronts while supporting some of the more sensitive parts of integration, including the transition of employees to new systems, processes and teams.

For CIOs, that creates an opportunity to move beyond treating technology integration as a post-deal requirement. AI can make technology a more active part of the M&A process, from the earliest stages of diligence through integration and the measurement of results.