AI doesn’t make integration intelligent by design. It just makes the gaps harder to ignore.

In mergers and acquisitions, technology doesn’t rescue a poorly prepared integration, it exposes whether two companies were ever ready to operate as one.

Fragmented systems, inconsistent data, weak governance and misaligned access controls: none of that disappears after the deal closes. It sits there, undermining value.

McKinsey’s 2025 State of AI survey found that nearly nine in ten companies now use AI in at least one business function.

Separately, Bain’s 2026 M&A report found that AI adoption in M&A more than doubled last year, with one in three dealmakers now systematically deploying it inside the deal process and across the post-deal operating model.

That acceleration is significant, because many companies are deploying AI before they have resolved whether their data, permissions and governance can support it. In integration work, this becomes visible very quickly.

AI turns M&A fragmentation into business risk

Every acquisition entails some operational overlap that is hard to avoid. The problem is that unmanaged AI use can turn overlapping into an operational contradiction.

Diverse data definitions across the now-integrated businesses can produce inconsistent outputs; different access controls create permission risk; and conflicting governance models leave accountability unclear.

Accounting for duplicate systems that create cost and process drag, AI accelerates these problems rather than resolves them.

When AI draws on inconsistent data across a combined business, its outputs are not obviously unreliable. They look authoritative but misinform decision-making before anyone identifies the contradiction underneath.

Boston Consulting Group analysis found that six in ten companies have yet to show measurable results from AI investments, with poor data quality, inadequate architecture and fragmented governance among the most cited barriers. In M&A, those weaknesses are not inherited once they are inherited twice. Each company brings its own version of the problem, and the merged organisation multiplies every gap.

The risk is not that AI fails outright - it is that AI scales operational fragmentation faster than the business can control it.

Consider two companies that are individually well governed: their permission structures still conflict when merged, data definitions diverge and ownership blurs. Every AI workload layered onto the combined organisation deepens the friction.

This is not a problem of poor management on either side it is structural, and it surfaces the moment companies attempt to operate as one.

After a deal closes, the pressure is immediate. Leadership teams want to combine workforces, standardize systems and start using AI across the new business. Speed is paramount. But AI introduces questions that cannot be deferred.

Who can access which data? Which data is AI allowed to use? Who owns AI outputs? Who audits the decisions AI informs? Which policies govern the new operating environment and who intervenes when outputs are wrong?

These are not questions that resolve themselves over time. Left unanswered, they become embedded in how the combined business operates.

Why this becomes a deal-value problem

This is where deal theory starts to weaken. Synergies depend on shared processes, data and operating discipline. If AI is asked to operate across fragmented foundations, costs become less predictable, integration timelines stretch and time-to-market slows. Security exposure widens as uncontrolled data flows multiply across two estates.

We see this most clearly when companies try to scale AI across a combined business before agreeing on the basic operating rules beneath it. A deal can look attractive on paper, but if the merged organisation cannot produce reliable data flows, consistent governance and stable access controls, AI initiatives will struggle to deliver the value the deal was built on. The gap between what leadership expects and what operations can deliver grows each quarter.

For buy-and-build strategies, the risk compounds with every acquisition. If each new business brings its own systems, data rules and access logic, AI becomes harder to govern with every deal. Without a disciplined approach to operational readiness, the cost of integration escalates faster than the value it was supposed to generate.

What operational maturity looks like in AI-led M&A

The task is not to slow AI adoption. It is to decide what must be standardized before AI is scaled.

For leadership teams, these questions matter most:

1. Can we trust the data? Have the systems and data estates across both companies been fully mapped before any AI workload touches the combined environment?

Without this, AI draws on sources that may conflict, producing outputs that appear reliable but are built on inconsistencies that cannot be traced or corrected.

2. Is ownership clear? Who governs AI outputs, who audits decisions and who is accountable when something goes wrong?

In the absence of defined ownership, errors compound silently and post-incident remediation becomes exponentially more costly than prevention.

3. Is access controlled? Are permissions standardized so that AI draws only on data it is authorized to use, across an environment where the rules are consistent?

Inconsistent access controls are not just a governance risk they create direct security exposure as AI workloads traverse data boundaries that were never designed to be shared.

When these three questions are resolved, cost becomes predictable and the business can scale with confidence. When they are not, every new AI initiative adds risk. The companies that create value fastest from M&A will not be those that apply AI most aggressively.

That means mapping before scaling, standardizing before deploying and resolving ownership before delegating decisions to automated systems. Deal value depends not only on what a business acquires, but on how quickly the combined company can operate intelligently.

AI will not hide operational fragmentation it will put a spotlight on it.

We review and rank the best cloud storage software.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Field CTO & Global Practice Head for Intelligent Systems and Operations at Altimetrik.