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AI monetisation is becoming measurable, Australian banks, businesses and data centre investment accelerate, supporting productivity gains while reshaping the economy and labour market.
- Corporate AI benefits become measurable
- Are Australian Banks positioned for agentic AI benefits?
- Data centre boom adds to inflation and RBA pressure
By Danielle Ecuyer
The many faces of AI monetisation
As the US reporting season begins, investors are increasingly asking for a “show me the money” moment as evidence of AI monetisation emerges, particularly for Big Tech.
Monetisation of AI is multifaceted, ranging from big tech companies investing billions to remain at the forefront of change, or risk becoming technology dinosaurs, through to the application of AI in businesses as a productivity tool.
Morgan Stanley recently analysed 17,000 earnings calls and conference transcripts using a large language model to assess the progress on AI adoption, the measurable benefits being achieved and AI’s impact on labour.
There is a certain irony in that Morgan Stanley used AI to analyse AI adoption, perhaps providing one of the clearest examples yet of the technology already delivering productivity gains.
One recent concern has been the cost associated with developing and implementing this new technology, with notable examples of Meta and Amazon curbing employee AI token usage due to budget overruns.
Nevertheless, Morgan Stanley’s findings show companies are reporting increasingly measurable returns from AI adoption irrespective of more disciplined AI spending.
Among the companies identified as AI adopters, 40% reported at least one quantifiable benefit in 2Q2026, up from 21% a year earlier and 37% in 1Q2026.
Across the S&P500, the percentage of companies reporting measurable AI benefits rose to 25% from 14% a year earlier.
On a sector basis, Technology remains the largest beneficiary, with 51% indicating measurable AI gains, followed by Communication Services at 44% and Financials at 37%.
The analysis highlights Financials recorded one of the strongest year-on-year increases in AI benefit disclosures as AI adoption accelerated.
AI applications are becoming more deeply embedded across fraud detection, compliance, underwriting, customer service, software development, document processing, coding assistance and personalised financial advice.
Financials are also becoming one of the clearest examples of AI improving employee productivity rather than replacing workers, translating AI investment into measurable business benefits.
Australian banks & agentic AI
Citi observed that offshore banks, which have been quicker to adopt AI, have generated an estimated 3%-5% uplift in profit before tax over a one-to-three-year period across various use cases spanning revenue, costs, fraud and even “enterprise value”.
In Australia, Citi sees the rising application of agentic AI benefiting the major banks.
Due to limited disclosure, it is difficult to establish the relative winners at this stage, but Commonwealth Bank ((CBA)) and National Australia Bank ((NAB)) appear further ahead in terms of AI infrastructure and data than ANZ Bank ((ANZ)) and Westpac ((WBC)).
Offshore banks including JPMorgan Chase, Goldman Sachs and other large financial institutions have reported using generative AI to improve software engineering, coding productivity, research and internal knowledge management.
Other examples include Equifax, which has announced expected AI productivity benefits of US$75m, while American Express and Mastercard are using AI across fraud detection, payment intelligence, cybersecurity and customer offerings.
Citi points out global banks are adopting AI at different speeds, both in terms of embedding applications and explaining and quantifying the benefits to investors.
The broker highlights the Evident Insights AI Index as the closest barometer to a global AI leaderboard, ranking banks across talent (45% weighting), innovation (30%), leadership (15%) and transparency (10%).
The rising implementation of agentic AI is shifting banks away from enterprise-wide productivity tools, such as Microsoft Copilot, towards more function-specific, workflow-embedded AI.
Evident Insights estimates around one in three incremental banking AI use cases now involve agentic AI.
Meanwhile, a Cambridge Centre for Alternative Finance survey describes agentic AI as the “clearest technological frontier in the survey data”, with around nine in ten respondents expecting meaningful deployment by 2030.
That survey covered around 630 financial institutions and found highly specialised agentic applications were producing greater productivity gains than more generic automation.
Following discussions with Australian banks, Citi expects higher adoption of agentic AI over the next year, accompanied by more quantifiable performance targets.
The broker believes investors should assess Australian banks’ AI preparedness based on infrastructure, data quality and their readiness to adopt and manage change.
Equally, Citi believes Australia is moving towards a stricter AI governance framework.
Both APRA and ASIC have warned governance is failing to keep pace with AI deployment and boards should not become complacent regarding cybersecurity, resilience, accountability and AI governance.
Given Australian banks possess large proprietary data sets, an oligopolistic market structure and relatively simple product offerings compared with global peers, Citi analysts believe they are structurally well positioned to benefit from AI.
Commonwealth Bank is viewed as the most advanced across cloud infrastructure, data quality and AI talent, and is ranked fourth globally by Evident Insights for AI capability, well ahead of its domestic peers and ahead of Morgan Stanley, Citibank, UBS and Goldman Sachs, in order of rankings.
NAB is ranked in thirty-fourth position, ANZ in thirty-seventh and Westpac, forty-fifth. JP Morgan has the top rating.
National Australia Bank has been building stronger AI foundations through what Citi describes as a modular technology strategy. Around 90% of applications have migrated to a multi-cloud environment, while its Databricks-based Ada platform is improving data management.
NAB is considered the second-most advanced Australian bank and is expected to quantify AI benefits over the next year.
ANZ Bank faces greater challenges from maintaining parallel legacy and new banking systems, which could constrain AI deployment. Westpac has improved its cloud infrastructure, but Project Unite remains a bottleneck to AI deployment at this stage.
Drawing from offshore experience, Citi estimates AI could deliver a 3%-5% uplift in profit before tax across the Australian major banks over a one-to-three-year period as agentic AI matures.
Commonwealth Bank is viewed as the clear leader.
The strongest AI adopters are highlighted as gaining momentum ahead of peers, boosted by investment in both talent and innovation.
Broader impacts on businesses and employment
Turning back to Morgan Stanley’s analysis, the most commonly cited AI benefits were improved financial outcomes, including revenue growth, cost savings and capital efficiency, followed by productivity gains.
As AI models improve and deployment broadens, the broker expects more measurable AI benefits to emerge.
Regarding the scope for job losses, Morgan Stanley’s findings suggest narratives around widespread employment impacts are overstated.
Its analysis found only around 10% of S&P500 companies discussed AI’s labour implications during 2Q26, although this was up from around 6% a year earlier.
Companies identified as AI adopters recorded a higher figure of 18%, implying labour impacts may become more evident as AI implementation matures.
Companies also discussed slower hiring ahead of redundancies, revenue growing faster than headcount, redeploying staff into higher-value activities, higher revenue per employee and greater automation of repetitive workflows.
The National Bureau of Economic Research published a working paper in March 2026 examining AI adoption across 6,000 senior business executives in the US, UK, Germany and Australia to develop new evidence on AI’s impact on jobs, productivity and output.
The study found 69% of firms were using AI, with adoption highest among younger and more productive businesses.
More than two-thirds of executives regularly use AI, although average usage remains only around 1.5 hours per week.
Nine in ten firms reported no measurable impact on employment or productivity to date, but executives expect sizeable impacts over the next three years. On average, they expect AI to lift productivity by 1.4%, increase output by 0.8% and reduce employment by -0.7%.
Employees, by contrast, expect AI will increase employment by 0.5% over the next three years, highlighting differing expectations between employers and employees.
The working paper incorporated results from the Business Outlook Scenarios Survey (BOSS) in Australia, with Australian findings generally more optimistic, particularly regarding employment, than those in other countries.
Around 59% of Australian businesses are using at least one AI technology, below the US at 78%, the UK at 71% and Germany at 65%, although adoption continues to increase.
Large language models are the most common AI application at 30%, followed by machine learning for data processing at 22% and visual content creation at 20%.
Interestingly, Australia recorded the largest realised productivity gain from AI at around 0.49% over the past three years, compared with 0.245%-0.29% in the US, UK and Germany.
The survey found Australian executives expect AI to generate higher productivity without significant job losses, making Australia the most optimistic of the four countries surveyed on the outlook for the labour market.
Adding fuel to inflationary pressures
AI implementation, however, is only part of Australia’s story. UBS uses AI-related investment in the Information, Media & Telecommunications (IMT) sector to gauge the scale of AI capex.
In 1Q2026, IMT capex tripled to $8.7bn, a rise of more than $5bn y/y, with UBS estimating most of the increase was AI-related.
The IMT sector has risen to around 1.25% of quarterly nominal GDP, an increase of approximately 0.75 percentage points over the past year.
The net impact on GDP is expected to be more moderate due to the high import intensity. UBS estimates AI-related imports, using ADP equipment as a proxy, rose to around $8.2bn in 1Q2026, double the year-earlier trend of around $4bn.
Equipment investment is estimated at around $6bn, or more than 0.8% of quarterly GDP, while buildings and structures have increased to almost $3bn, or around 0.4% of quarterly GDP.
Work under construction in other commercial buildings, now largely data centre-related, rose to $21.6bn in 1Q2026, up $15bn y/y. Building approvals suggest construction could double again over the next one to two years to above 1.5% of GDP.
Comparing the AI boom with the mining boom, UBS believes today’s boom represents Australia’s most significant investment cycle since that period, although the broader economic spillovers are likely to be less pronounced.
AI build-outs are less labour-intensive, while completed data centres require relatively little labour.
On the labour market, Australian government research has found no broad disruption from AI to date. Only 4% of the domestic workforce is considered highly exposed to AI automation, while around 21% has medium-to-high exposure.
Around 90% of jobs are expected to experience AI augmentation rather than outright replacement.
UBS, by contrast, estimates AI could replace around -10%-15% of the workforce at the companies covered by its analysts over the next three to five years under a moderate adoption scenario.
As AI investment continues to ramp up and potentially doubles over the next couple of years, the near-term effects are viewed as more inflationary, adding to the case for another RBA rate hike.
UBS continues to forecast a further 25 basis point increase at the August meeting.
Longer term, the impact on productivity and the labour market remains uncertain, but the findings from Morgan Stanley, Citi and UBS suggest AI is already moving beyond experimentation to deliver measurable benefits across corporate earnings, investment and the broader economy.
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