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Where AI can help during an M&A integration
Post-merger technology work often means reconciling systems, records, processes, and rules that were designed separately. AI can reduce manual effort in particular tasks, especially where teams must find patterns or synthesize large amounts of information. It should support accountable integration teams, not replace their decisions.
Map data between systems
AI-enabled data tools can propose how fields and records in one system correspond to those in another. This can help when finance or customer relationship management (CRM) platforms use different structures or taxonomies. People who understand the business still need to check proposed matches, investigate anomalies such as duplicate client records, and approve the resulting definitions and mappings.
Nash Squared CIO Ankur Anand told CIO in its March 18, 2026 feature that his company used Nextgenlytics’ AI-enabled BlueGecko platform after acquisitions brought together different finance and CRM systems, operating models, taxonomies, and security policies. Anand reported that the tool completed about 80% of the mapping for that use case, with his team reviewing the work, and reduced traditional data-mapping effort by about 30%. Those are company-reported results for Nash Squared’s process, not a general benchmark for M&A integrations.
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Review documents and synthesize processes
AI can help teams search and summarize diligence materials, operating-model documents, and process documentation. That may make it easier to spot questions for specialists or build an initial view of how work is done across the two businesses. Outputs should be treated as leads for investigation: an automated summary can miss exceptions, interpret terms incorrectly, or omit context that matters to a decision.
In the same CIO feature, Mark Davis of Egremont Group described AI use in synthesizing fragmented operating-model and process information into performance data. Thomson Reuters CTO Joel Hron said the company’s corporate development team was developing an AI system intended to support due diligence and make deal evaluation, risk discovery, and mitigation more consistent. As reported in March 2026, that system was in development; it should not be treated as a generally available product or a proven outcome.
Assist with interfaces, tests, and planning
For a full integration, AI may help teams map data, draft interface work, generate candidate system tests, and assemble an initial roadmap. These are accelerators for technical teams, not substitutes for architecture decisions, security review, testing, or sign-off. Teams should validate generated work against the target design and the consequences of failure.
Make policies easier to understand
Nash Squared also uses Microsoft Copilot to summarize internal rules and regulations for employees. A plain-language summary can support onboarding and help people navigate unfamiliar policies, but it should point back to the authoritative policy and make clear when an employee needs advice from a responsible team.
Choose the integration path before choosing the AI task
The right use of AI depends on what the deal is meant to achieve. McKinsey partner Brett Wilson described two broad paths in CIO’s March 2026 feature: bridge systems so teams can answer business questions without moving everything onto one platform, or pursue fuller integration. Neither is the right answer for every transaction. EY’s M&A technology integration guidance similarly recommends aligning technology decisions and governance with the business integration goals and deal strategy.
| Decision factor | Bridge systems first | Full integration |
|---|---|---|
| Near-term objective | Surface useful information across businesses without first consolidating every platform. | Move toward the selected future-state systems and operating model. |
| Potential AI contribution | Help connect or interpret information across systems while teams retain separate platforms. | Assist with data mapping, interface work, candidate tests, and an initial integration plan. |
| Key trade-off to assess | Ongoing dependence on legacy systems and interfaces, plus the governance and access complexity of connecting them. | Upfront effort and migration risk, including the complexity of aligning data, systems, and users. |
| Questions for the deal team | How soon is useful business insight needed? How long can the organization operate the bridges safely? | Does the target architecture fit the deal’s intended operating model? Can migration be sequenced around complexity, security, and affected people? |
These are planning considerations, not a formula for selecting an approach. Compare expected time to useful insight, upfront and ongoing effort, security and access controls, legacy-system dependence, fit with the intended architecture, resilience, and adoption. An adaptable architecture can also matter to organizations that acquire businesses repeatedly.
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Put data, governance, and human review around the tools
AI cannot settle disagreements about who owns a dataset, what a field means, or which team is allowed to use it. Before scaling a use case, establish how its output will be governed and acted on.
- Define ownership and the operating model. Name the business and technical owners who resolve conflicting definitions, approve mappings, and maintain the resulting data.
- Align standards and measures. Agree on relevant governance rules, security policies, data definitions, taxonomies, and KPI definitions across the integration workstreams.
- Clean and validate with experts. Standardize and harmonize data where needed; have cross-business specialists review anomalies and consequential AI-generated matches or summaries.
- Set access and review rules. Decide which information a tool may process, who can see its outputs, where those outputs may be used, and who must approve a result before it changes a system or business decision.
- Sequence migration rather than forcing a “Big Bang.” Anand’s advice in CIO’s March 2026 feature was to avoid a Big Bang integration. Prioritize work using agreed standards, complexity, security, and the people affected.
Involve cybersecurity from diligence through migration
M&A can bring sensitive personal information, trade secrets, and operationally important systems into new hands or new connections. EY’s technology integration guidance calls for cybersecurity involvement from diligence through planning, migration, and integration; it also flags risks such as ransomware disruption and threats to operational continuity.
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AI may assist with document analysis during diligence, or be used to explore attack scenarios and analyze software vulnerabilities. EY describes these as potential applications, not guarantees that automated analysis will find every weakness. Security specialists must validate findings, set access controls, and decide how identified risks affect the integration plan.
The 2024 EY CIO Sentiment Survey figures reported in that guidance provide context for the role: 96% of surveyed CIOs said they were involved or would be involved in a corporate transaction, and 64% said they had been involved with six or more. Yet 32% said they had significantly met deal objectives such as technology synergies and closing on time in past transactions, while 37% said they were engaged in the post-close phase. More than 53% saw cybersecurity as a top challenge in the M&A lifecycle. These are survey responses reported by EY, not proof that a particular AI tool changes deal outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure the task, not just the deployment
A tool being installed—or employees saying they use it—does not show that integration improved. Define a baseline for the workflow before introducing AI, then compare like with like and retain human review for errors that matter.
- For data mapping: track time spent per mapping, the share of proposed matches accepted after review, the types and consequences of corrections, and unresolved records.
- For document review or summaries: track review time, material issues found in subsequent expert review, and whether the output helped the responsible team make a decision.
- For generated tests or interface work: track review and rework, test coverage against agreed requirements, and defects found before and after release.
- For employee policy support: track whether employees can find the authoritative answer and whether questions are resolved or correctly escalated—not merely how many summaries the tool produces.
Also observe adoption and confidence: successful use may require changing workflows, aligning teams, and helping employees understand when to rely on an AI-assisted result. CIO’s March 2026 feature cautions that many organizations are seeing incremental efficiency improvements rather than clear headline outcomes such as faster deal closure or day-one readiness. The evidence described there does not establish that AI broadly shortens integration time, reduces total deal cost, or increases realized synergies across completed transactions.
A practical sequence for CIOs
- Translate the deal thesis into a technology outcome. Identify what must be connected, retained, or consolidated and when the business needs the result.
- Select the integration path. Decide whether bridging or fuller integration better serves the future state, then identify the work that path actually requires.
- Choose a bounded workflow. Start with a defined, reviewable task—such as proposing data matches or summarizing a policy—rather than asking AI to own an entire workstream.
- Set owners and controls. Agree on data definitions, permitted access, security review, human sign-off, and how to handle uncertain or incorrect outputs.
- Run a measured, phased implementation. Establish a baseline, test with representative data and users, document corrections, and expand only when quality and controls are acceptable.
- Reassess the value and risk. Compare the observed workflow results with the baseline and consider ongoing integration burden, resilience, and adoption before making the next migration decision.
The practical opportunity is to remove avoidable manual work from integration tasks while keeping strategy, risk acceptance, and business decisions with accountable people. CIOs should treat each AI use as a workflow to govern and measure, not as evidence by itself that a merger will be easier or more valuable.
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