There's a new kid on the block, and we need to learn how to play their game.

For the past decade, digital strategy in superannuation has focused on improving the human experience. Better apps. Better retirement journeys. Better member engagement. And those things absolutely matter. But a deeper shift is emerging - one that could reshape how super funds compete. 

The decision interface between members and super funds is beginning to move from humans to algorithms. 

Increasingly, retirement decisions are being mediated by systems such as: 

• AI financial advice tools 
• robo-advisers 
• retirement modelling agents 
• adviser platform algorithms 
• portfolio optimisation engines 

In other words, the next “customer” evaluating a super fund may not be a person - It may be a machine. 

And machine customers evaluate very differently from humans. They prioritise structured data, verifiable performance, and decision efficiency. Brand narrative, sponsorships and emotional persuasion play a far smaller role. 

That shift has profound implications for the sector. 

The real platform war is machine readability 

Most funds still think the digital battleground is about frontend customer experience: apps, portals and UX improvements. And while these things are still critical to differentiate for human customers, in parralell, what we are seeing are wealth managment platforms such as Hub24 and Netwealth are growing rapidly but for different reasons, they are machine-native. 

Their platforms expose structured investment data, APIs for modelling portfolios, real-time performance feeds, and integrations with adviser ecosystems. They are designed to be queried by software as much as by people. 

Machine customers naturally favour providers where information is structured, accessible and verifiable. 

Many super funds struggle here because their infrastructure was never designed for machine consumption. Registry systems are fragmented. Investment data can be difficult to query. APIs are limited. Retirement products are often not structured in ways that algorithms can easily interpret. 

So, the real battleground is no longer: Web/App UX A vs Web/App UX B

It is rapidly becoming: Machine-readable platforms vs human-only platforms. 

Marketing is becoming algorithmic distribution 

Marketing in super has traditionally been about building trust and brand visibility through large-scale campaigns. But the decision layer is changing. In the near future, super funds will increasingly be chosen by algorithms rather than persuaded by advertising. 

We’re already seeing early signals in other territories: 

• AI retirement planners selecting drawdown strategies
• robo-advisers allocating retirement portfolios
• financial wellness tools recommending providers 
• personal finance agents comparing fees and performance 

These systems evaluate options using structured parameters: 

• fee ratios 
• performance history 
• retirement income projections 
• liquidity rules 
• risk metrics 

That shifts influence away from traditional brand marketing and towards something else entirely: data transparency, API access, and machine-verifiable performance signals. 

If an algorithm cannot access or interpret a fund’s data, that fund may never even enter the comparison set. 

Retirement is exactly the kind of decision AI will mediate 

The retirement phase is particularly suited to algorithmic decision support. Why? Because the core problems are computational. They involve probability modelling, tax optimisation, longevity forecasting, risk management and portfolio drawdown strategies. 

Imagine a near-future retirement journey: 

A retiree asks their financial AI assistant (or their financial adviser who is using an AI assistant): “What’s the safest retirement income strategy for me?” 

The system models their super balance, pension eligibility, expected returns, inflation and longevity risk. 

It then queries super providers through APIs and ranks products automatically. 

The winning provider may not be the most recognised brand. It will likely be the fund whose data is most structured, accessible and comparable. 

Regulation will accelerate this shift 

Interestingly, Australia’s regulatory environment may accelerate machine decisioning rather than slow it. 

Regulators increasingly require documented decisions, audit trails, measurable member outcomes and consistent advice frameworks. Those requirements align naturally with algorithmic systems. 

AI systems can log decision paths, validate recommendations and demonstrate consistency at scale. In fact, regulators are likley to prefer AI-assisted decision frameworks if they are transparent and explainable. 

A new competitive question for super funds 

Today, most funds ask a familiar strategic question: How do we compete for member attention? But a more important question is emerging: How do we compete for algorithmic recommendation? 

Those are very different problems. The first is marketing. The second is data architecture, trust signalling and machine-readable experience design. 

At Deepend, this is becoming an increasingly important conversation we are having with super funds. The future of digital experience isn’t just about designing interfaces for people. It’s about designing platforms that can be trusted, interpreted and recommended by machines. Because, if AI agents begin recommending retirement products on behalf of Australians, one question may determine the next decade of winners in the sector: 

Which super funds will those agents trust first - and why?