M&A in the AI Era: Diligence and Integration That Captures Value

Many acquisitions fail to deliver the synergies that justified them. The reasons are rarely the ones in the deal model. They show up after close, in the integration. Two forces have now raised the stakes. Technology and AI have become central to what a target is worth, and buyers expect integration to pay back sooner than it used to.

That changes what M&A due diligence has to examine and how integration has to be run. Commercial and financial diligence remain necessary. They are no longer sufficient. The questions that increasingly make or break deal value are technical and organisational, and they need to be asked before the price is agreed, not discovered in month six.

Technology and AI due diligence has changed

The issues that matter most are the ones a data room rarely shows clearly.

• Technical debt and architecture. Is the target's platform an asset, or a liability you will spend years unwinding? A clean, modern architecture can be worth more than the multiple suggests. A brittle monolith can quietly destroy the case.
• Data and AI assets. What data does the target actually own, and with what rights? Can it be combined with yours under existing consents and contracts? Data that cannot legally be used for the purpose in the investment thesis is not an asset.
• Cyber and compliance exposure. Undiscovered security weaknesses, data protection failings and AI governance gaps transfer to you at close. They are cheaper to find before. An acquisition also creates a period of heightened risk, as two networks are connected and attention is elsewhere, which is why cybersecurity in the AI era belongs in the diligence scope rather than the post-close backlog.
• Key-person and capability risk. In technology-led deals, value walks out of the door if integration ignores the people who built it. Identify who they are, what they want, and what would make them leave.
• Licensing and dependencies. Open-source obligations, vendor contracts with change-of-control clauses, and reliance on a single cloud or model provider all affect what you can do after close and what it will cost.

Testing whether the AI is real

Many targets now describe themselves as AI companies, or present AI as central to their growth story. Some of those claims are sound. Others describe a thin layer on a third-party model that any competitor could replicate in a quarter. Diligence has to tell the difference.

• What is proprietary? Distinguish between proprietary data, proprietary models, proprietary workflow and integration, and simple prompt engineering on a general-purpose model. Defensibility usually sits in data and workflow, not in the model.
• How good is it, and how do they know? Ask for the evaluation approach, the test sets and the results over time. A team that cannot show how it measures quality does not control it.
• What does it cost to run? Usage-based model costs can grow faster than revenue. Understand unit economics at current and projected volumes.
• Whose data trained it? Check the rights to training and retrieval data, and whether customer data has been used in ways contracts did not allow.
• What regulatory tier is it in? Uses that touch hiring, credit, health or similar decisions may carry heavier obligations under regimes such as the EU AI Act. Confirm the position with counsel.
• Who can maintain it? If the capability depends on one or two individuals, price that risk and plan their retention.

Integration is where value is won or lost

Synergies live in the integration plan, not the spreadsheet. Acquirers who capture value run integration as a designed programme.

• A clear integration thesis. What you combine, what you keep separate, and why. Full absorption, partial integration and arm's-length ownership are all legitimate. Ambiguity is not.
• The first 100 days planned before close. Named owners, decision rights and a short list of day-one essentials: people can be paid, customers can be served, systems are secure, and everyone knows who they report to.
• Systems and data integration sequenced by value, not convenience. Start with what the synergy case depends on. Where the target runs a different core platform, the principles of ERP modernisation without stopping the business apply directly.
• Change management for both organisations. Culture clash, not technology, is the usual cause of failed synergy.
• A value-tracking discipline. Each synergy has an owner, a baseline, a target and a date, and it is reviewed against the case that justified the deal.

The deal model assumes synergies. The integration programme earns them. The gap between the two is where most acquisition value disappears.

A dedicated integration office, with authority and a direct line to the executive sponsor, is what holds this together. The discipline of PMO and programme management matters here more than in almost any other kind of change.

Common integration mistakes

• Diligence findings never reach the integration team. The people who found the risks leave at signing, and the people who inherit them start from nothing. Carry the diligence team into integration planning.
• Technology integration is underestimated. Deal model estimates are often set before anyone has looked closely at the systems. Treat them as ranges.
• Speed is confused with haste. Moving quickly on decisions reduces uncertainty for people. Forcing system migrations before they are ready destroys value and goodwill.
• Retention is handled with money alone. Key people stay for meaningful work, autonomy and clarity about their future. Retention payments buy time, not commitment.
• The customer is forgotten. Competitors target your new customers during the period of uncertainty. Plan customer communication and service continuity as carefully as the systems work.

Where to start: questions to ask before you sign

• What is the value thesis, in one paragraph? And which parts of it depend on technology, data or AI?
• Who has examined the target's architecture, code and data at first hand? Management presentations are not evidence.
• What would it cost, and how long would it take, to integrate or replace the core systems? Is that range in the model?
• Which people hold the knowledge the thesis depends on? What is the plan to keep them?
• What security, data protection and AI governance liabilities would we inherit? Have they been priced or covered in the agreement?
• Who will lead integration, and are they already involved? If the answer is "we will decide after close", the first month is already lost.
• How will we know, a year on, whether the deal worked? Name the measures now.

Common questions

What is technology due diligence in M&A?

It is an independent assessment of a target's technology: architecture, code quality, infrastructure, security, data, engineering team and the cost of running and integrating it all. Its purpose is to test whether the technology supports the investment thesis and to surface liabilities before they are priced into a deal you cannot change.

How do you assess a target company's AI capabilities?

Look at what is genuinely proprietary, how quality is evaluated, what it costs to run at scale, the rights to the underlying data, and who can maintain it. Ask for evidence such as evaluation results and architecture documentation, not demonstrations.

When should integration planning start?

During diligence, not after signing. The integration thesis, the leadership of the integration and the first 100 days should be settled before close, so that day one is about execution. Late planning is one of the most common reasons synergies slip.

Where Ganexa can help

Ganexa supports acquirers from thesis to integration: executive advisory and strategic planning to sharpen the deal logic, independent technology consulting for diligence on architecture, data, security and AI, and organisational change and AI adoption support to bring two organisations together. If you have a transaction in view, book a free consultation.