Unleash the power of agentic AI in post-merger integration

Leading deal professionals are increasingly using next-gen technologies, including generative AI, in the various stages of their mergers and acquisitions (M&A) deal cycle.

In a recent Accenture survey, 82% of dealmakers say AI and advanced analytics are already accelerating their pre-deal insights. New technologies are helping deal teams identify and assess targets faster, refine bids with greater precision and craft more effective value creation plans. Where AI adoption is lagging, however, is in post-deal integration.

Dealmakers who are able to embed generative AI broadly across the deal lifecycle are 4x more likely to report that they consistently capture post-acquisition value. A leading media and telecom company for instance used generative AI-powered automation to streamline post-merger data integration, significantly boosting productivity and improving customer data accuracy.

Agentic AI is a powerful—yet still largely untapped—tool in transforming future state operating models in M&A.

Applying agentic AI to transform future state operating models

For larger mergers or acquisitions, post-deal integration can take anywhere between 9–24 months. Getting the integration done often means companies take their eyes off the roadmap of where they want to be—and halt innovation as a result.

What many executives don’t realize is that M&A integration presents an opportunity to drive innovation. The engine cover has already been lifted, as you will, so it’s the perfect time to immediately pursue changes that will drive greater efficiency and boost performance.

Agentic AI can make driving such improvements more straightforward by introducing autonomous “AI agents” that take on multi-step workflows—serving like tireless analysts. As Justin Boitano, VP of enterprise software at NVIDIA, states: “Agentic AI is enabling enterprises to enhance productivity with intelligent agents capable of handling complex, multi-step challenges." By leveraging agentic AI, companies can avoid merely patching up existing processes in their post-deal integration. Instead, they’re able to build a forward-looking operational environment that is fit for purpose and drives real value.

Our research shows that the number of companies with fully modernized, AI-led processes has nearly doubled from 9% in 2023 to 16% in 2024. Agentic AI can optimize both end-to-end processes, such as procure-to-pay, leading to better economics and faster integration, as well as enhance go-to-market capabilities like pricing, leading to increased revenue.

The key benefits of applying agentic AI in M&A integration are threefold: faster integration of systems, greater cost and revenue synergies and enabling leapfrog innovation. Compared to peers, we see organizations that have developed intelligent operations achieve 2.5x higher revenue growth, 2.4x greater productivity and 3.3x greater success at scaling generative AI use cases.

End-to-end processes example: Procure to pay

During the planning process, agentic AI can analyze the procurement and payment processes of both companies to identify redundancies, inefficiencies and opportunities for optimization. This analysis helps in designing a new, streamlined procure-to-pay (P2P) process that integrates the best practices from both organizations.

Post-merger, agentic AI can be applied to automate the P2P workflow—from purchase order creation to payment processing—by intelligently routing tasks, flagging anomalies and predicting potential issues. It can also enhance supplier management by continuously monitoring supplier performance and suggesting improvements. This approach not only accelerates the integration but also drives better economics and operational benefits.

Common challenges

Dealmakers face several common challenges when integrating agentic AI in M&A. These include large volumes of scattered and/or incomplete data, the selection of the right technology and partners, as well as differences in technological maturity levels and culture between the two companies. Dealmakers often wrestle with the fundamental question of whether to build, buy or partner with technology solutions.

To address these challenges, leading dealmakers are taking a structured approach. They ensure their teams have the necessary training and hands-on experience to effectively use agentic AI tools. They also embed agentic AI into core deal activities, such as screening, diligence and value creation planning, and standardize and centralize deal data to unlock smarter insights. Furthermore, they pilot new technologies in live deal processes to test and refine their impact before full deployment. By creating a culture of innovation and continuous learning, they can better navigate the complexities to drive greater value from their deals. Agentic AI is relatively cheap and fast to design, build and deploy—and you can begin reaping benefits if you get started early in the planning process.

Linking M&A to transformative future state operating models

During M&A, the effort of integration is already significant, so leveraging agentic AI to drive material change can pay off by reducing costs and improving the overall economics of the deal. It can not only streamline processes but also create a more integrated and forward-looking operational environment that strengthens resilience sets the new company up for long-term success.

We recommend leveraging agentic AI to optimize operating model innovation in the integration phase of your next merger or acquisition—and unlock alpha along the way.

Written in collaboration with Austin Corbett and J. Neely.