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Rethinking synergies for the agentic deal
AI is changing the economics of work, technology and operations. What does that mean for how we identify and quantify M&A synergies?
5-MINUTE READ
October 19, 2026
In brief
- AI is changing the assumptions underpinning traditional synergy estimates, from workforce and technology to procurement and operations.
- Dealmakers need to distinguish the AI value each business could achieve independently from the additional value created by combining them.
- The biggest opportunities may come where combined scale, data and the buyer's capabilities create value neither business could achieve alone.
Beyond traditional synergies: the AI opportunity
Synergy assessments traditionally focus on overlapping functions, consolidating technology and suppliers, purchasing scale and revenue opportunities.
These remain important sources of value. But AI is challenging the assumptions behind conventional synergy estimates.
In The dawn of the agentic deal, we explored how agentic AI could take M&A beyond process efficiency, creating opportunities to rethink operating models and identify sources of value that traditional approaches might overlook. Our research found that leading acquirers are 2.7 times more likely to use agentic AI as a catalyst for integration value.
How should we assess combination value when AI is changing what each business could achieve independently?
Start with the right baseline
The distinction between standalone value creation and deal synergy has always mattered in M&A. AI makes it more challenging.
Both buyer and target may have significant opportunities to improve their businesses independently through AI. As we discussed in Beyond the hype: Unlocking mid-market value through AI, these can range from improving core processes to developing new products and services.
But an opportunity either company could pursue independently should not count as deal synergy.
AI is changing what each business could achieve independently by altering the cost and capacity required to run its business processes.
A more useful comparison is therefore between what each business could achieve through AI on its own and what becomes possible when they come together. The difference is where genuine AI-enabled synergies may lie.
Look beyond the existing organizations
Consider combining two finance functions. Rather than simply identifying overlapping roles and applications, AI allows us to redesign workflows around people, agents and technology before determining the combined organization.
Accenture's When everyone has AI, your operating model will set you apart explores how AI is changing the relationship between headcount and productive capacity. That changes how dealmakers estimate workforce savings based on overlapping roles.
Similar questions arise elsewhere. In technology, the choice may no longer be simply which company's applications to retain. In procurement, there may be opportunities to eliminate demand for certain services rather than negotiate better rates. And in operations, combining two businesses creates an opportunity to redesign processes rather than merely standardize them.
Our earlier work on agentic AI in post-merger integration discusses this in the context of procure-to-pay.
The implication is that AI changes how we assess existing synergy levers, rather than necessarily creating an entirely new category.
Where the combination changes the economics
Combining businesses can improve AI economics. Greater transaction volumes help spread investment in data, integration, orchestration and governance, particularly when capabilities can be reused across workflows or acquisitions.
This is especially relevant for serial acquirers. As highlighted in The dawn of the agentic deal, leading organizations increasingly treat their digital core and reusable AI capabilities as assets that can support successive transactions.
The potential will also vary between buyers. An acquirer with established AI capabilities, common platforms or complementary proprietary data may be able to realize greater value from the same target, potentially faster and at lower cost.
Combining data can create opportunities too. Bringing together complementary customer, transaction and operational information may improve AI-enabled decisions, pricing or customer propositions. The question is whether that additional context produces a measurable improvement, not simply whether more data becomes available.
Focus on the opportunities that matter
Deal teams should focus on the largest cost and revenue opportunities, particularly high-volume, repeatable workflows where combining the businesses creates additional value.
These opportunities also need to be quantified carefully. Estimating savings by applying an automation percentage to current labor costs is rarely sufficient. The business case needs to reflect the full cost of the future workflow, including AI consumption, infrastructure, human oversight and the investment required to implement it.
Importantly, AI benefits should be captured within the relevant cost or revenue synergy levers, rather than added as a separate category and potentially counted twice.
In Unlocking alpha in deals, we found that dealmakers embedding GenAI broadly across the transaction lifecycle were four times more likely to report consistently capturing post-acquisition value.
That reinforces the case for assessing AI-enabled synergies during diligence and underwriting, rather than leaving them until integration planning.
Traditional synergy analysis asks where two businesses overlap. AI gives us a reason to ask something further: what can these businesses achieve together that neither could achieve as effectively independently?
The answer may change not only the size of the synergy opportunity, but also where dealmakers choose to look for it.