AI brings clarity to asset management M&A

"AI lets us process that volume while freeing deal teams to focus on what actually drives value in asset management deals: advisor performance and retention dynamics, organic versus inorganic growth, technology integration complexity, customer preferences and portability risk. It’s not replacing existing diligence — it’s enhancing it.”

Ronan Curran 

Principal, Transaction Advisory Services
Grant Thornton Advisors LLC

When deal competition is fierce, teams may rely on surface-level assessments like satisfied client references, positive advisor feedback and strong recent performance.

“Buyers may rationalize it as, ‘We already have a platform in this market. Given the level of competition for this asset, let’s keep things moving and just make a few reference calls,’” Bansal said.

But the factors that truly determine asset quality often hide in management dashboards and internal systems that outside-in diligence doesn't reach. By the time gaps in talent retention, technology architecture, data integrity or operational processes come to light, integration plans are underway and valuation assumptions are at risk.

"Outside-in due diligence often fails to answer some critical questions, such as, ‘What's the real retention rate among top-producing advisors? What keeps them at the firm versus vulnerable to poaching? How much growth is organic client acquisition versus market appreciation? How concentrated is revenue among the top 20% of advisors or client relationships?’" Bansal said.

Bansal and his team work to answer those questions early in the deal lifecycle to bring greater clarity to buyers. But to uncover that level of detail, due diligence requires:

1. Access to detailed and high-quality data. Under exclusivity agreements, buyers can request raw data in whatever format exists, whether it’s CRM exports, compensation spreadsheets or client account databases. Large language models can normalize and process unstructured and structured data. “If a target says that data is scattered across systems, ask for it anyway because AI can churn through it,” Bansal said.
2. Benchmarking against deal trends. Firms that have access to proprietary data on transaction outcomes can benchmark targets against results from other deals. "AI and target data are table stakes now," Curran said. "Winning firms are benchmarking the data they review against actual outcomes from comparable deals."
3. The ability to turn data into insights. Access to data is only as valuable as a team’s ability to process it into meaningful takeaways. “Effective diligence now goes beyond processing data. It means starting with hypotheses about what could make or break a deal — client stickiness, advisor retention, organic growth rates — then using AI to test those hypotheses against actual performance data,” Bansal said.

"Ten years ago, time limited how deep we could go. … Now, data analytics lets us consume general ledger detail and transaction-level data within the same tight LOI exclusivity window."

John Cristiano 

Partner, Transaction Advisory Services
Grant Thornton Advisors LLC

“Ten years ago, time limited how deep we could go," Cristiano said. "We’d review trial balance summaries and sample transactions. Now, data analytics lets us consume general ledger detail and transaction-level data within the same tight LOI exclusivity window. ”

That depth reveals what summary financials hide:

• AI can analyze every client account to measure true retention and identify at-risk relationships, rather than relying on management's reported aggregate retention rate.
• Transaction-level expense analysis reveals cost structure inefficiencies or one-time items buried in operating expenses
• Pattern recognition flags anomalies like revenue timing inconsistencies, unusual fee adjustments and expense reclassifications that warrant investigation

"Buyers used to depend on whatever analysis the target provided," Cristiano said. "Now they can request raw data and develop independent insights. Information that used to surface months post-close now informs valuation and deal structure before signing."

“Understanding the journey an entity needs to go through earlier drives significantly more value once the acquisition takes effect.”

Ross Sheridan 

Partner, Advisory
Grant Thornton Ireland

“Understanding the journey an entity needs to go through earlier drives significantly more value once the acquisition takes effect," said Ross Sheridan, Grant Thornton Ireland Advisory Partner.

Additionally, integrating data, systems and processes post-close can take time, and as time goes on, deal value can decline. AI and analytics help integration teams move faster by unifying data environments, revealing process misalignments early and giving leadership clear visibility into how the combined business is actually performing.

"We're looking for synergies and misalignments across processes, data and technology," Sheridan said. "Even when both firms use the same ERP or portfolio management system, there can be drastic differences in how those systems are configured and used."

Sheridan’s team uses AI throughout the integration process, from assessing support functions to building integration roadmaps. They employ a rapid assessment framework combining traditional assessment tools with AI to evaluate support functions end-to-end across finance, HR, IT and compliance, which generates substantial data but often uncovers the context that isn’t documented. 

"We conduct extensive conversations to capture that context, then use AI to transcribe, analyze and synthesize that information to profile processes and structure process documentation," Sheridan said. "AI helps us feed insights into our assessment templates and quickly map out support function structures, identify gaps and spot process variations that could cause friction during integration. It also allows us to link that contextual information with quantitative data and KPIs, helping organizations better interpret, control and improve performance"

For asset managers, weak process documentation can be common, even at large firms. "AI expedites process documentation and design work that used to take weeks," Sheridan said. "We can complete assessments, develop benchmarks and create integration roadmaps much faster, which means operational teams can start executing sooner."