The Dawn of the Agentic Deal: Unlocking Value Pools Traditional M&A Cannot Reach

Ambition has outrun the data the deal is built on

Deal executives expect agentic AI maturity in the post-deal integration and value capture phase to grow by 72%. That is not a statement about pilots or proofs of concept. It is a declaration that acquirers intend to run integration with autonomous agents inside the workflow, coordinating decisions and actions rather than merely summarising documents for a human to act on.

Set against that ambition is a much quieter number, and it is the one that decides whether the ambition survives contact with a live transaction. Only around 20% of organisations use structured data effectively at the underwriting stage — the moment when value hypotheses are set and value creation plans are shaped. At the same time, two-thirds of deal executives say interoperable data systems have a strong impact on agentic AI success. The dependency is understood and unmet at once.

The mechanism matters more than the mismatch. Integration does not invent value; it executes a plan written at underwriting. Pointing agentic capability at integration while underwriting remains unstructured produces a faster delivery of a value hypothesis nobody validated. Speed applied to a wrong number is not an improvement.

One clarification before the findings. The research surveyed 650 senior dealmakers and C-suite executives from corporate development and private equity across 12 industries and 24 countries in January 2026. It is a global sample; no separate Australian sub-sample is published, and this Australia edition localises the framing rather than re-collecting the survey data. Where this page reasons about the Australian market, that reasoning is analysis, not a reported statistic.

Efficiency was the last contest, and it has already been won

Generative AI improved M&A efficiency by surfacing patterns more quickly and scaling analysis, which lifted the quality of information available for decision-making. Those gains were real, and they were concentrated where the work is structured and pre-deal: market research, diligence review and financial modelling. They are now table stakes, which means they no longer separate one bidder from another.

The adoption curve shows exactly where the effort went. The share of companies investing in generative AI for pre-deal activities rose from 31% in 2024 to 39% in 2025 and 46% in 2026. Post-deal investment moved from 18% to 21% to 27% over the same period. Both lines rise, but they do not converge, and the harder half of the deal is the half that lags.

Post-deal value realisation is a genuinely different problem, not a larger version of the same one. It requires coordinated action across systems, functions and decision rights — less structured, more operationally exposed and considerably harder to scale. That is why adoption stalls there even among organisations that have digested generative AI comfortably at the diligence stage.

The consequence shows up in the quality of what deal teams actually receive. Only 27% of deal professionals rate the quality of insights available to their leadership as strong. Factor calls the organisations behind that 27% insights-driven leaders, and the rest of this research is largely an account of what they do differently.

Treat the digital core as a deal asset, not an integration afterthought

Most acquirers still handle data and architecture as something to be sorted out after close. The research puts a figure on the cost of that habit: two-thirds of deal executives say interoperable data systems have a strong impact on agentic AI success, yet only around 20% use structured data effectively at underwriting. Diligence prices the earnings; it rarely prices the ability to act on them.

Insights-driven leaders close that gap by assessing AI readiness and data architecture alongside the financials. Governed data, interoperable platforms, modern architectures and AI-ready environments are treated as strategic enablers of deal value rather than as an IT workstream to be resourced later. The distinction is not semantic — it changes what appears in the investment committee paper.

The same discipline carries into integration design. The goal extends beyond consolidating systems to building a clean, standardised digital core that gives agentic capability something consistent to operate across. Where it makes sense, leaders combine the strongest AI capabilities from both sides into a best-of-both-plus-innovation model rather than defaulting to the acquirer's stack.

One acquirer in the research illustrates the operating tempo this implies. A US-headquartered healthcare platform pursuing an aggressive buy-and-build strategy retires legacy enterprise systems within 90 days of closing and replaces them with a standardised, AI-enabled stack, so every acquired entity runs on the same clean architecture. That is an integration cadence set by the requirements of the digital core, not by the convenience of the acquired business.

The insights-driven 27% separate on scale, not on intent

Nearly everyone is interested in agentic AI. Very few have moved past interest, and the distance between the two groups is larger than any single adoption statistic suggests. Insights-driven leaders are 4.6x more likely to have deployed and scaled agentic AI across the M&A lifecycle — not to have trialled it, but to have scaled it end to end.

The gap is visible in two specific behaviours. On tailoring generative AI to deal-specific objectives such as revenue growth or cost optimisation, 60% of insights-driven leaders do so against 25% of others, a 2.4x difference. On using agentic AI as a catalyst for integration value rather than merely as an accelerator of existing process, 49% of leaders qualify against 18% of the rest, a 2.7x difference. Both measure the same underlying choice: whether AI is aimed at the value lever or at the workflow.

That choice appears to carry into the financial statements. Organisations that scale agentic AI across integration, embedding agents into value levers rather than simply accelerating M&A processes, show 1.7x higher projected profitability margins. The word projected is load-bearing — the figure derives from Factor's analysis of S&P Capital IQ data for the public companies in the survey sample across 2024 to 2027e, using analyst consensus estimates as at January 2026. It is a forward expectation, not a realised result, and it should be read as one.

What makes the advantage durable is that it accumulates. Leaders start in targeted, lower-risk, high-impact areas to prove value, then embed those capabilities into deal theses, governance structures and value creation plans from the outset. Each transaction sharpens the agents and refines the playbook, which is why the gap is easier to open than to close.

Private equity has already turned this into a repeatable system

The clearest competitive signal in the research is not about technology at all. It is about who is moving. While many corporate deal teams remain in exploratory phases, private equity firms are 1.3x further advanced in transitioning from exploration to active piloting, because they design for value realisation from the outset and apply accumulated insight from serial acquisitions across deal types.

The structural reason is that operational value creation is already ingrained in private equity deal logic. Agentic AI is being hardwired into underwriting assumptions and post-close value delivery, with acquisitions treated as repeatable systems rather than as discrete events. A serial acquirer learns across standalone deals and buy-and-build platforms alike; a corporate acquirer doing two deals a year does not accumulate the same reps.

The edge widens through the ecosystem. Private equity firms engage strategic technology partnerships at 1.4x the rate of corporate development teams, and 75% of private equity respondents say they engage external partners to access the talent and capabilities needed to develop and deploy agentic AI during M&A and integration. Among corporate development teams, that figure drops to 52%. The scarce input here is implementation capability, and one group is systematically buying it.

For an Australian corporate acquirer, the practical reading is uncomfortable. In a contested process, the private equity bidder is more likely to have priced an agentic value lever into its model, more likely to have a partner lined up to deliver it, and 1.4x more likely to report readiness for human–agent collaboration. That is a pricing advantage before any premium is discussed.

The readiness gap runs down the org chart

Ask the C-suite whether the workforce is prepared to collaborate with AI agents and 54% say yes. Ask the people who would actually do the collaborating and only 30% agree. A perception gap of that size between the steering committee and the integration team is not a communications problem; it is a forecasting problem, because the plan is being signed off against the more optimistic of two views.

The divergence is explained by role-based accountability rather than by attitude. C-suite leaders approach AI as a strategic lever and weigh competitive positioning and long-term value, which naturally drives confidence. Deal and integration teams are accountable for timelines, compliance, system stability and execution risk, so they weigh governance gaps, model reliability, data privacy and integration disruption in daily decisions. Where executives see upside, the people on the ground see exposure.

The research also identifies what would move the ground-level view. Some 47% of deal executives say clear human-in-the-lead controls would significantly increase their organisation's willingness to adopt agentic AI in M&A. Governance is not functioning as a brake in this data — it is functioning as the precondition for scale, a pattern Factor observes across risk and control functions more broadly.

Capability is the other gate. Two-thirds of respondents — 67% — report that their teams require upskilling to collaborate effectively with agents and redesign workflows, and the honest reading of the underlying data is that most organisations are currently not prepared to work with AI agents at all. Human-in-the-lead is a real operating model with defined decision rights, not a reassuring phrase, and Factor's work on where autonomy is genuinely being delegated sets out what it demands.

The best deals leave a capability behind

Most organisations treat integration as a one-time execution exercise: a surge of activity, a set of synergy targets, then a drift back to business as usual. The enterprise absorbs the deal without changing how it works, which leaves nothing for agentic AI to attach to when the next transaction arrives. Insights-driven leaders instead build tools, workflows and governance structures that persist past close.

A global insurer in the research shows what sequencing correctly looks like. Working directly with underwriters, the team first simplified its decision standards, reducing more than 130 fragmented criteria to 70 consistent factors, then rebuilt the workflow around those core decisions — and only then introduced agents to review and structure complex broker submissions and route validated insights into underwriter decision flows with full traceability.

The outcome followed from that order of operations. Review cycles dropped from days to hours, allowing underwriters to assess every submission rather than a fraction of them. The insurer increased underwriting capacity without expanding the team and achieved more than 50% revenue growth, as underwriters shifted onto judgement, exceptions and broker relationships. Simplification preceded automation; had the agents been layered onto 130 fragmented criteria, they would have industrialised the mess.

A second case shows the compounding effect. A North American insurer used a major acquisition to accelerate its agentic roadmap, facing the transition of more than one million new policies across an 18-month renewal cycle. It deployed a third-party agentic AI tool to fast-track 70% of the translation work and expects to save approximately 50 person-years of effort — and, crucially, now holds that capability as a reusable engine for every future conversion. These cases are drawn from Factor client experience rather than from the survey.

What this means for Australian organisations

None of the figures above are Australian figures, and it would be wrong to present them as such. What the global data supplies is a set of mechanisms — the underwriting data gap, the leadership-execution perception split, the partnership differential between private equity and corporate development — and those mechanisms are structural rather than geographic. The reasonable question for an Australian board is not whether the numbers replicate here but whether the conditions that produce them are present.

Several of those conditions are arguably sharper in this market. Australian corporate development teams are typically small relative to their offshore private equity counterparts, and the local pool of people who can both structure a deal and design an agentic operating model is thin. Where the research finds 75% of private equity respondents buying that capability through partners against 52% of corporate teams, the consequence in a shallow talent market is a wider execution gap than the same figures would produce in a deeper one.

The underwriting finding also lands differently under Australian governance expectations. Boards here are accustomed to interrogating synergy assumptions in detail; they are far less practised at interrogating whether the data foundation exists to deliver a value lever that assumes agents in the workflow. If only around 20% of organisations globally use structured data effectively at underwriting, the question of what evidence sat behind the AI line in the value creation plan is one an Australian board should be asking directly.

There is a positive reading too. Because post-deal adoption sits at 27% globally, the field is not yet settled, and an Australian acquirer that fixes underwriting data and workforce readiness is not chasing a closed gap. The advantage available is the one the insights-driven leaders are taking — 4.6x more likely to have scaled agentic AI across the lifecycle — and it is available to any acquirer prepared to change what the deal is built on rather than only how it is executed. Factor examines the broader reallocation of enterprise value in The Great Value Migration.

What Australian dealmakers should do next

Move the investment upstream to underwriting before adding agents to integration. With only around 20% of organisations using structured data effectively where value hypotheses are set, agentic capability applied downstream compounds whatever was decided there. Assess AI readiness and data architecture alongside the financials, as a scored diligence workstream with its own owner.

Price the digital core into the deal model. Two-thirds of deal executives already say interoperable data systems have a strong impact on agentic AI success, so the remediation cost and the timeline to a standardised core belong in the investment case — not in a post-close IT budget discovered in month four.

Test workforce readiness below the C-suite before committing to an agent-led integration plan. The 54% executive view against the 30% view from non-C-suite deal professionals is the single most reliable early warning in this dataset, and 67% of respondents report their teams need upskilling to work with agents and redesign workflows.

Define human-in-the-lead controls at signing, not at go-live. Some 47% of deal executives name clear controls as what would significantly increase willingness to adopt, which makes governance the enabler of scale rather than the obstacle to it. Set decision rights, exception handling and accountability for agent-informed decisions before the first agent runs.

Close the ecosystem gap deliberately. Corporate development teams engage external partners at 52% against 75% for private equity, and that difference buys scarce implementation capability. Benchmark against the insights-driven 27% rather than against your own last deal — explore Factor's research programme or join a Factor corporate development or CFO event.