OpenAI vs Copilot: Lessons for AI in Finance Teams

La Trobe University is planning to roll out 40,000 ChatGPT Edu licences by the end of FY27, with 5,000 targeted by the end of this financial year. That’s not a pilot. That’s an institutional bet.

What caught my attention isn’t just that ChatGPT beat Microsoft Copilot on campus after Copilot had a head start. It’s what that decision pattern tells us about how large organisations actually choose generative AI tools when they’re serious about adoption.

If you work in banking, lending, payments, wealth, regtech, or fintech operations, this is a useful mirror. Universities and financial institutions are different beasts, but they share three traits: complex governance, high data sensitivity, and a constant fight for user adoption. La Trobe’s move offers a clean case study in AI tool selection, rollout strategy, and the reality of “AI-first” programs.

Why La Trobe’s decision matters to financial services

A university choosing ChatGPT Edu over Microsoft Copilot matters because it highlights the real battleground: distribution at scale and daily usage, not vendor slide decks.

Financial services leaders often frame the choice as “Microsoft ecosystem vs standalone AI.” The reality is closer to: Which tool gets used by the most people, for the most valuable work, with the least friction—without blowing up risk and compliance?

Here are the parallels that make this relevant to AI in finance and fintech:

• Identity and access patterns are similar: large workforces, role-based controls, mixed device environments.
• Data classification matters: PII, commercially sensitive data, research/strategy docs, customer communications.
• Outcomes are measurable: productivity, cycle time, error rates, customer satisfaction.
• User behaviour decides success: staff will adopt what feels helpful today, not what’s strategically “neat.”

La Trobe is also deploying Copilot to staff while pushing ChatGPT Edu at scale across students and staff. That “two tools, different jobs” approach is one I’ve seen work in finance—when it’s done deliberately.

What “AI-first” really means (and what most organisations get wrong)

Most companies get “AI-first” wrong because they treat it like a platform purchase.

An AI-first strategy is closer to an operating model change: you’re redesigning workflows so AI becomes a default step for drafting, analysis, triage, summarisation, and decision support—while keeping humans accountable for outputs.

AI-first in finance: the 4 layers you can’t skip

If you want generative AI adoption that survives audits, incidents, and executive turnover, build it in layers:

1. Use-case design (value first)
   • Examples: complaint handling summaries, credit memo drafting, policy Q&A, fraud case narratives, call centre agent assist.
2. Controls (risk always)
   • Logging, retention rules, prompt/data policies, red-teaming, model output constraints.
3. Enablement (people always)
   • Training that is role-specific: fraud analysts don’t need the same playbook as relationship managers.
4. Measurement (proof always)
   • Time saved per process, reduction in rework, quality scores, resolution times.

La Trobe’s scale goal (40,000 licences) implies they believe enablement and everyday utility will beat “small excellence centres” that never spread.

OpenAI vs Microsoft Copilot: the practical difference finance teams feel

The practical difference isn’t branding. It’s where AI shows up in a user’s day.

Copilot is strongest when your work is already trapped inside the Microsoft stack—Outlook, Word, Excel, Teams, SharePoint—and you want AI to act directly in those surfaces.

ChatGPT (and ChatGPT Edu/Enterprise-style offerings) tends to win when people want:

• A fast, general-purpose reasoning assistant
• Better long-form drafting and iteration
• More flexible “workbench” behaviour (structured prompts, reusable templates, multi-step analysis)

The finance angle: Excel-native AI vs “thinking partner” AI

For finance teams, the difference often lands like this:

• Copilot: great for “turn this email thread into actions,” “summarise this Teams meeting,” “draft a Word memo,” “help me with an Excel formula.”
• ChatGPT: great for “analyse this policy, propose exceptions, draft options, write customer-friendly explanations, generate test cases, review controls language.”

Neither is universally better. But adoption at scale tends to follow the tool that feels more helpful across more workflows, especially when users are still learning what generative AI is good for.

That’s why La Trobe’s decision is interesting: it suggests ChatGPT’s value proposition is easier to understand broadly, even when Copilot is already available.

A rollout pattern finance leaders should copy: dual tooling with clear lanes

Running both Copilot and ChatGPT sounds messy. It can be. But it can also be the most realistic approach—especially in regulated environments.

The key is setting clear lanes for each tool.

A simple “lane” model (works well in banks and fintechs)

• Copilot lane (Microsoft-native work):

  • Meeting notes and action capture
  • Email drafting and summarisation
  • Word/PowerPoint first drafts
  • Excel productivity support

• ChatGPT lane (analysis + drafting workbench):

  • Credit narrative drafting and scenario explanations
  • Fraud investigation narratives and case summaries
  • Policy interpretation Q&A with citations to internal docs (via approved retrieval)
  • Customer communication variations and tone rewrites

• No-AI lane (restricted data):

  • Highly sensitive customer data not yet covered by controls
  • Secrets, credentials, unreleased market-sensitive material
  • Any dataset without an approved governance path

This reduces the “shadow AI” problem because people understand what’s allowed and where.

Governance isn’t a blocker—bad governance is

Financial services sometimes treat governance like a reason to wait. That’s backwards. The best time to implement governance is while usage is still forming habits.

Here’s the governance checklist I’ve found most useful for generative AI in banking and fintech deployments:

Minimum viable governance (MVG) for generative AI

• Data handling rules: what’s permitted, prohibited, and conditionally allowed
• Audit logging: prompts, outputs, user IDs, timestamps, retention policies
• Model risk management: evaluation, drift monitoring, known limitations
• Human accountability: who signs off outputs in regulated processes
• Incident playbook: what happens if sensitive data leaks or outputs mislead

If you’re a lender, add credit-specific safeguards:

• Document the boundary between decision support vs automated decisioning
• Ensure adverse action reasons and explanations remain compliant
• Maintain an evidence trail for how narratives were produced

Universities worry about academic integrity and research IP. Finance worries about privacy, market conduct, and consumer harm. Different risks, same discipline.

Where education adoption predicts finance adoption (and where it doesn’t)

Education is a good leading indicator because it’s a high-usage environment: thousands of people testing limits every day. That pressure exposes what’s usable and what’s brittle.

What carries over cleanly

• Adoption follows convenience. If AI requires too many steps, people revert.
• Training must be contextual. Generic “AI awareness” doesn’t change behaviour.
• Tool choice becomes cultural. Once a workforce standardises on a prompt style and workflow, switching costs rise.

What doesn’t carry over

• Finance has sharper consequences for mistakes. A hallucinated citation in an essay is bad; a hallucinated policy interpretation in customer remediation is worse.
• Finance also has heavier third-party and regulatory scrutiny. You need stronger evidence trails.

That said, the directional signal is clear: large institutions are no longer asking “should we use generative AI?” They’re asking which tool becomes the default.

“People also ask” (the questions finance teams raise first)

Can we use ChatGPT in a bank without leaking customer data?

Yes—if you use an enterprise-grade setup with approved data controls, retention, and access governance, and you implement strict rules on what data goes in. Most failures come from policy vagueness and weak enforcement, not from the model itself.

Should we standardise on one vendor for generative AI?

Only if it genuinely covers your core workflows. Many finance organisations end up with a “suite + specialist” model: one default tool for productivity, another for deeper analysis or development, both governed centrally.

What’s a sensible first use case for generative AI in fintech ops?

Pick something frequent, text-heavy, and measurable: complaint triage summaries, KYC document explanation drafts, fraud case narratives, or customer email response drafting with human approval.

What to do next if you’re evaluating AI tools for finance

If La Trobe’s story pushes one idea to the top, it’s this: adoption is the product. Fancy capabilities don’t matter if people don’t use them weekly.

Here’s a practical evaluation sequence you can run in a bank or fintech within a quarter:

1. Choose 3 workflows (not 30) and define success metrics (time, quality, rework).
2. Run a side-by-side bake-off: Copilot vs ChatGPT-style tool, same tasks, same user groups.
3. Instrument everything: usage, output quality scoring, escalation rates, policy breaches.
4. Decide lanes: which tool owns which workflows, and where AI is banned.
5. Scale training: role-based prompt patterns, approved templates, review checklists.

The goal isn’t to pick a winner in the OpenAI vs Microsoft Copilot debate. The goal is to build a governed system where generative AI improves customer outcomes and reduces operational drag.

If you’re planning your 2026 roadmap right now, ask your team one forward-looking question: Which AI tool will employees reach for first when the clock is ticking—and have you made that choice safe?