Amid tariff concerns and macro volatility, a managing director at Alvarez & Marsal (A&M), Timo Schmid, said private equity in Australia is now at a “critical juncture”.
“Their [funds] ability to drive value creation and operational improvements will be key to unlocking growth and delivering transformative benefits to the broader economy,” he said.
According to the report, value creation in private equity is shifting from opportunistic gains to core drivers such as operational improvements, resilience planning and margin growth.
The report noted AI adoption, long building in the sector, has become more urgent as cost pressures rise and productivity benefits loom.
“With rising cost pressure and promising productivity gains potential, AI has moved from a talking point in investment thesis to a serious part of the value creation agenda,” it stated.
It found that half of the funds surveyed are already using AI in both pre-deal due diligence and post-deal value creation, indicating that Australian funds are just as advanced as their counterparts in more established private equity markets, such as the US and Europe.
As well as this, 37 per cent of respondents said they were exploring AI implementation even if they weren’t using it just yet, with an additional 11 per cent of funds already using AI for diligence but planning to expand to value creation.
According to the report, most AI adoption was concentrated in pre-deal work, where the technology has “solid applications” in red-flag identification, memo drafting and market scanning.
The most common AI use case, cited by 78 per cent of respondents, was in due diligence, with investment sourcing/screening and deal execution also highlighted as relevant applications.
On the other hand, in post-acquisition, the primary use of AI was in enhancing operational efficiency, with 70 per cent of respondents identifying its use in areas like automation and supply chain management.
“Many teams still struggle to identify the exact and most impactful use cases in post-deal value creation,” it stated, adding that operators are keen to incorporate AI more in post-completion asset value realisation.
Essentially, while AI has proven applications in pre-deal scenarios, its post-deal implementation has been limited, despite some usage.
A&M attributed this in part to the lack of proven and scalable AI use case models with clear return on investment, the need for substantial customisation (and often investment) to align with the specific context of each portfolio company, and a shortage of delivery talent.
These findings mirror ongoing debate about the lack of quantifiable productivity or profitability gains among companies that have invested in and integrated AI so far, despite it remaining a significant investment theme.
Additionally, the consulting firm outlined a “clear divide” in AI execution between more established or traditional businesses and digital native or carve-out assets.
While older businesses often grapple with fragmented systems and legacy data, newer and subsidiary assets offer a “blank canvas”, accelerating the deployment of AI use cases like dynamic pricing, forecasting engines or AI-driven customer segmentation. This is especially true when paired with modern cloud infrastructure and embedded digital leadership.
However, the report maintained that the deployment of AI is still possible for more established companies but requires a “patient, incremental approach” that focuses on modernising existing infrastructure.
