Choosing GenAI Platforms: Lessons for Australian Finance

40,000 licences is a strategy, not an experiment.

That’s what jumped out at me in La Trobe University’s plan to roll out ChatGPT Edu to 40,000 staff and students by the end of FY27, with 5,000 targeted by the end of this financial year. The interesting part isn’t that a university is adopting generative AI at scale. It’s that OpenAI’s ChatGPT has become the dominant tool there despite Microsoft Copilot having the head start.

If you work in banking, fintech, or financial services, this is familiar terrain. You’re not choosing “an AI.” You’re choosing a platform that will shape productivity, risk controls, talent development, vendor dependence, and how fast teams can move. The La Trobe decision is a clean case study in how institutions are making those calls—often in ways that have nothing to do with brand loyalty and everything to do with adoption mechanics.

La Trobe’s move signals what buyers actually optimise for

Answer first: Large organisations pick the generative AI platform that spreads fastest with the least friction—and then they backfill governance.

La Trobe’s reported approach is telling: Copilot remains for staff, while ChatGPT Edu is deployed “at scale” to students and staff (including researchers). That’s not a “winner takes all” decision; it’s a portfolio architecture. One tool sits inside the Microsoft productivity stack. The other becomes the broad, daily “thinking and writing” assistant across a diverse population.

Finance leaders should read that as a pattern:

• One vendor for embedded workflow assistance (email, documents, meetings)
• Another for flexible, cross-domain reasoning and experimentation (research, analysis, customer language, policy drafts)

The reality? Many banks already run like this—best-of-suite for office productivity, best-of-breed for specialist risk tooling. Generative AI is going the same way.

What “dominant tool” really means

Dominance usually isn’t about model benchmarks. It’s about where users go first when they want help.

In financial services, the “dominant” genAI tool becomes the default for:

• summarising customer interactions
• drafting internal memos and credit submissions
• generating first-pass incident reports
• synthesising policy changes into operational checklists
• turning product requirements into user stories

Once that habit forms, switching costs appear fast—training materials, prompt libraries, reusable templates, and internal champions all become tied to that platform.

Why education adoption maps cleanly to finance adoption

Answer first: Universities and banks share the same adoption constraint: you’re rolling out to thousands of people with wildly different roles, and you can’t afford a governance failure.

Education looks “open” compared to banking. But in practice, a large university has plenty of sensitive data (student records, research IP, health-related information) and a complex governance environment. That’s closer to finance than many people assume.

Here’s the parallel I see:

1) Students are like retail staff and customers—volume drives everything

La Trobe’s big bet is about scale of hands-on use. In banks, the equivalent is enabling large cohorts:

• branch and contact centre staff
• operations teams handling exceptions and disputes
• relationship managers preparing advice and proposals
• analysts writing insights for business stakeholders

If your platform can’t be rolled out with a simple user experience, clear guardrails, and straightforward licensing, it won’t become habitual. And if it doesn’t become habitual, it won’t generate ROI.

2) Researchers are like fraud teams—specialised work needs flexibility

La Trobe explicitly includes researchers. In finance, substitute:

• fraud detection and AML analysts
• risk and compliance teams
• quant and treasury functions
• cyber, insider threat, and investigations

These teams don’t just want “help writing emails.” They need deeper capability: structured analysis, document synthesis, controlled experimentation, and the ability to work with internal knowledge safely.

3) Curriculum integration is like operating model integration

La Trobe plans to incorporate ChatGPT and other OpenAI tools into curriculum, including an AI-focused MBA program.

Banks doing this well treat genAI as more than a tool rollout. They bake it into:

• onboarding and role-based training
• standard operating procedures
• model risk management and third-party risk processes
• secure-by-default data handling

That’s how you avoid the worst outcome: one pilot succeeds, the organisation celebrates, and then adoption stalls because nobody changed how work actually flows.

The real platform decision: control points, not features

Answer first: In finance, the best generative AI platform is the one that gives you enforceable control points—identity, data boundaries, auditability, and model governance.

Most platform evaluations still start with “Which model writes best?” That’s fine for a demo. It’s the wrong centre of gravity for a bank.

When I see institutions choose one tool over another, the deciding factors are typically:

Identity and access management

• Can you enforce SSO, MFA, and conditional access?
• Can you segment by role (e.g., retail vs. risk vs. finance)?
• Can you restrict high-risk capabilities (file upload, connectors, browsing)?

Data boundaries and retention

• What happens to prompts and outputs?
• Can you enforce retention limits and legal hold policies?
• Can you ring-fence regulated data (PII, card data, account identifiers)?

Auditability and monitoring

• Can you log prompts and outputs appropriately?
• Can you detect policy violations or sensitive data exposure?
• Can you trace which version of the model was used for a decision-support output?

Integration strategy (where value compounds)

• Can it connect to internal knowledge bases safely?
• Can it sit inside case management, CRM, or fraud operations?
• Can you deploy it into secure environments for sensitive workflows?

A lot of buyers learn this late: genAI value compounds where it’s integrated into “systems of record,” not where it’s used as a clever chat window.

A practical scorecard for banks choosing OpenAI vs Copilot (or both)

Answer first: Treat the choice as a segmentation problem—map platforms to user groups and use cases, then enforce consistent governance.

La Trobe’s “both, but different scope” approach is a useful template. Here’s a scorecard you can adapt for AI in finance and fintech evaluations.

Step 1: Classify use cases by risk and by repeatability

Use a simple matrix:

• Low risk / high repeatability: policy summaries, meeting notes, internal comms
• Low risk / low repeatability: brainstorming, research drafts
• High risk / high repeatability: complaint handling responses, collections scripts, credit memo drafting
• High risk / low repeatability: investigations, large exposure reviews, suspicious matter narratives

The trick: don’t ban high-risk use cases. Design the workflow so the model supports humans without pretending it’s the final authority.

Step 2: Assign platforms to segments

A common pattern looks like:

• Copilot for deeply embedded productivity tasks where the Microsoft ecosystem is already dominant
• ChatGPT-style environments for analysis, drafting, synthesis, and cross-domain work—especially where teams need reusable prompt playbooks and flexible interaction

This is not about “which is better.” It’s about where each tool fits the operating model.

Step 3: Standardise guardrails across tools

If you run multiple genAI platforms, inconsistency becomes your biggest risk. Standardise:

• a single data classification policy for prompts
• mandatory user training and attestations
• approved use-case catalogues (what’s allowed, what’s restricted)
• escalation paths for “AI output caused an issue”

A bank’s genAI program fails when governance is a PDF and usage is a habit.

Step 4: Measure adoption like a product team

If your goal is outcomes, track:

• weekly active users by role
• task completion time reductions (e.g., claims summaries)
• rework rates (how often outputs are discarded)
• risk events (sensitive data incidents, policy breaches)
• customer impact metrics (complaint resolution time, NPS lift, fewer callbacks)

If you can’t measure it, you can’t defend it to the board—or the regulator.

“People also ask” (and what I tell finance teams)

Is generative AI safe enough for banking?

Yes, if you design it as decision support, restrict data exposure, and log usage. The unsafe version is uncontrolled, shadow usage with no monitoring.

Should banks standardise on one AI vendor?

Not automatically. Standardise on governance and control points first. Multi-vendor is fine when it’s intentional and managed.

What’s the fastest way to get ROI from genAI in financial services?

Start where work is text-heavy and repetitive: operations, contact centres, dispute handling, KYC narratives, policy interpretation, and internal knowledge retrieval. Then integrate into workflows.

What can fintechs learn from universities rolling out genAI?

Fintechs can move faster, but they often underinvest in governance. Universities show that scale adoption requires training, clear policy, and a platform strategy—even when users are enthusiastic.

What La Trobe’s choice implies for finance leaders in 2026 planning

Answer first: Platform decisions are becoming talent decisions—and talent decisions become competitive advantage.

La Trobe is preparing graduates for an AI-heavy workplace. That means banks and fintechs hiring in 2027 will increasingly recruit people who already have strong defaults: they know how to prompt, how to validate outputs, how to work with AI without trusting it blindly.

Financial institutions that lag on tooling and training will feel it in two places:

• productivity: teams take longer to produce the same outputs
• retention: top performers won’t stay where their workflow feels outdated

If you’re mapping your 2026 roadmap now, treat genAI platform selection as part of workforce strategy, not just IT procurement.

A better way to approach your genAI platform decision

Pick the platform strategy that matches how work actually gets done, then enforce governance so people can use it daily without creating risk.

La Trobe’s rollout is a reminder that adoption isn’t driven by internal strategy documents. It’s driven by whichever tool makes sense to the user at the moment they need help—and is available at scale.

If you’re leading AI in finance and fintech initiatives, the question to ask your team this quarter is simple: Which platform will become the default for our highest-volume workflows, and what controls will prove it’s safe?

If you want help pressure-testing your platform scorecard—use cases, risk tiers, governance controls, and a rollout plan that actually gets adoption—build that as a two-week sprint, not a six-month committee. The organisations moving fastest in 2026 will be the ones that keep decisions practical and measurable.