This article was originally published on CIO.com and includes insights from Ankur Anand, Chief Information Officer at Nash Squared.
Many digital leaders believe gen AI can transform M&A processes. Some pioneers already use emerging technology to integrate systems and data, but wider adoption means overcoming significant obstacles.
One of the hardest parts of M&A activity is integrating systems and data, and inheriting a company’s IT architecture means absorbing a complex array of platforms and processes. However, research suggests growth-focused companies can use targeted AI to overcome M&A challenges.
McKinsey reported last year that 42% of business leaders believe gen AI has the potential to transform the deal-making process. Survey respondents using gen AI in their M&A activities reported average cost reductions of roughly 20%, and 40% said gen AI enabled up to 50% faster deal cycles.
McKinsey further suggests the next era of M&A will be defined by teams that ride gen AI rather than wait on the sidelines. Brett Wilson, partner at the firm, says two paths are emerging. First, some firms use AI as an alternative to traditional system integration.
“They bridge the gaps so they can answer key business questions without forcing everything onto a single platform,” he says. “This approach takes the place of expensive multi-year system consolidation programs, and brings value-driving insights to the forefront much earlier at typically a much lower cost.”
The second path, where full-scale integration is the goal, uses AI to accelerate work by mapping data across systems, building interfaces, generating system tests, and reducing manual effort. AI is also used to create an initial integration plan or project roadmap much faster than teams could do previously on their own.
“When paired together, these incremental improvements are starting to add up to real-time savings in the process,” he says. “In practice, the potential impact across both paths isn’t just defining the integrated systems landscape, but also a faster path to value.”
CIOs are also considering how AI can help overcome M&A challenges, with pioneering digital leaders using emerging technology to integrate systems and data. However, there are obstacles along the way, and CIOs should take heed of crucial best-practice lessons.
Finding a better way to integrate data
Mark Davis, VP at business transformation consultancy Egremont Group, recognises the scale of the M&A situation. Post-acquisition, CIOs are expected to integrate systems and data at pace, often while the organisation forms a view on strategy, governance, and operations. AI can add value by supporting decision-making during integration.
“Rather than simply mapping systems, organisations are using AI to synthesise large volumes of fragmented information from operating models, and process documentation into performance data,” he says. “This approach is helping leadership teams, including CIOs, build clearer pictures of how work gets done across both organisations, and where the real points of friction and dependency sit.”
Richard Corbridge, CIO at property specialist Segro, recognises the potential for AI to assist with the M&A process. The direction of travel for transformation at his firm is threefold: joining up point solutions, making the most of enterprise data assets, and implementing new technology alongside trusted partners. While Corbridge hasn’t explored AI for managing M&A activities, he can see the potential.
“I think this idea that you have to go away and fix all your data before you can plug AI in isn’t true,” he says. “I think you can work out which bits you need. AI is a good tool for bringing organisations together, and we look at how we can use it to bring our disparate geographical data sets together, so it feels logical that the same could be said for M&A.”
Nick Pearson, CIO at Ricoh Europe, has gained M&A experience in his current role and earlier positions at major blue-chip companies such as PepsiCo and Vodafone. He says a lift-and-shift approach is the traditional model for post-merger system integration. Now, instead of standardising ERP data across a six-month schedule, companies have an alternative.
“AI is the magic dust on top,” he says. “I think we’re starting to see a change, and it’s not an IT change but a change in the mindset. Instead of waiting six or 12 months, people are recognising they can potentially use AI to access data more quickly. So it’s a shift in the integration team logic as well as an AI shift.”
Barry Panayi, group chief data officer at insurance firm Howden, is another digital leader who believes AI can be a crucial ally in the M&A process. The firm employs about 23,000 people, up from about 10,000 five years ago, and data and technology sit at the heart of the company’s growth agenda, including integrating systems with emerging technology.
“Buying companies and growing should be a competitive advantage, not a liability, because we’ve now got all this data to ingest, and it’s all very hard,” he says. “AI should be seen as a brilliant opportunity, because every new bit of data we get can be triangulated, and that means using knowledge graphs and thinking about generating insight, as opposed to how to smash data together in a data warehouse.”
Using AI in the M&A process
Ankur Anand, CIO at technology and talent solutions provider Nash Squared, has helped his company integrate data and systems post-integration. In 2022, the company acquired Het Flexhuis, a managed service provider of talent and recruitment services. A year later, Nash Squared purchased cloud and data solutions provider Knoldus.
One of the major issues was bringing together disparate finance and CRM systems. Each firm had its own operating model, with associated platforms, taxonomies, and security policies. These models fostered an internal culture that wasn’t necessarily commensurate with the host business. Anand had to integrate systems and processes to benefit Nash Squared.
Using BlueGecko, an AI-enabled data management platform from technology specialist Nextgenlytics, Anand’s team automated time-consuming data-mapping processes. The system helped reduce the effort and created more accurate results.
“The technology understands the systems with some AI inputs,” he says. “And through that approach, BlueGecko completes about 80% of our data mapping. My team then reviews the output, agrees with it, and that process reduces the traditional effort on the data-mapping side by about 30%.”
Joel Hron, CTO at business information services specialist Thomson Reuters, is another digital leader who helps his growth-focused firm integrate systems and data. He says it’s important to be aggressive about security and compliance gaps, and addressing those concerns in the early days of the acquisition is crucial because it’s harder to tackle gaps later, especially when joined-up firms start to roll out new capabilities.
To help smooth post-merger processes, Hron says Thomson Reuters’ corporate development teams are developing an AI system to assist with due diligence. The tool, which will work with the firm’s HighQ productivity platform, will drive consistency in how employees evaluate deals, uncover risks, and mitigate concerns.
“As you might expect, in various parts of the business, there can be different ways of evaluating M&A across the teams managing the process,” he says. “If you can drive more consistency and better assessment and mitigation of risk, you do better deals, so we’re excited about what they’re doing.”
Best-practice lessons for applying AI
As McKinsey’s Wilson suggests, there isn’t yet a single AI-enabled solution for end-to-end integration. Instead, companies report gradual improvements using existing tools, often in specific parts of the process. While the improvements are real, they don’t translate into clear headline outcomes, such as closing a deal faster or being ready to operate from day one.
“As a result, many organisations aren’t yet making wholesale improvements to how they integrate, or how work gets done,” he says. “Instead, they’re capturing incremental efficiency gains within existing processes, rather than redesigning those processes around AI’s full potential. This limits impact to small gains from existing technology, rather than enabling a true step change in integration planning performance.”
McKinsey’s research suggests just 30% of respondents engage with gen AI at moderate to high levels. Wilson says successful adoption of AI for M&A activities requires pausing to rethink workflows, align teams on new ways of working, and build confidence before running at full speed.
As an executive who’s delivered results in these high-pressure situations, Nash Squared’s Anand offers crucial advice to other digital leaders aiming to apply AI to M&A processes. First, define the operating model. Technology consolidation will only succeed when governance, security policies, and KPI definitions are fully aligned. Second, invest in data standardisation and harmonisation. Taxonomies must be clear and consistent across organisations. Third, focus on data cleansing. Important stumbling blocks, such as duplicate client details, can be missed if you don’t use a team of cross-business experts to ensure AI tools remove anomalies.
He also says employees of an acquired company can struggle to understand weighty documentation detailing internal policies and processes, but AI can be an assistant here. Nash Squared uses Microsoft Copilot to summarise rules and regulations. In short, suggests Anand, inserting AI into an M&A requires a focus on progression, processes, and people.
“Try to avoid a Big Bang integration,” he says. “Define a sequence based on standards, where you appreciate the complexity and the security, and through that, you migrate the technology. That approach is important because you need to carry the people along with you, not just integrate the business.”
