You know your organisation needs to start leveraging AI. The potential is clear: automating repetitive work, unlocking insights from your data, and improving how your teams access information. But with so many options available, figuring out where to start can feel overwhelming.
Should you roll out Microsoft Copilot since you’re already using Office 365? Set up ChatGPT Enterprise for your teams? Or invest in building something custom?
Each approach has genuine strengths, and the right choice depends entirely on what you’re trying to achieve.
This guide breaks down the three main paths to enterprise AI platforms, helping you understand what each offers, what it costs, and most importantly, which one fits your needs.
Understanding your three options
Before diving into technical comparisons, let’s establish what each option actually is:
Option 1: Microsoft Copilot
AI embedded directly into the Microsoft 365 tools you already use: Word, Excel, Outlook, Teams, and PowerPoint. It’s designed to make your existing workflows faster and smarter without requiring you to learn new software.
Option 2: ChatGPT Enterprise
A powerful general-purpose AI assistant from OpenAI. Think of it as having an incredibly knowledgeable colleague available around the clock for research, writing, analysis, brainstorming, and problem-solving. You can also create Custom GPTs tailored to specific tasks.
Option 3: Custom AI solutions (using AWS Bedrock)
Purpose-built AI applications designed specifically for your organisation’s internal needs. Several platforms can deliver custom solutions, including Azure OpenAI and Google Vertex AI, but for this comparison, we’ll use AWS Bedrock as our reference. Bedrock provides access to multiple AI models from providers like Anthropic (Claude), Meta (Llama), and Mistral, along with the infrastructure to build production-grade applications. This is the path when you need AI that serves your entire workforce, including staff without M365 licences, or requires deep integration with your existing systems.
A quick decision framework
Each platform has a sweet spot, and understanding the trade-offs upfront can save significant time and money. Here’s an assessment of where each genuinely excels, and where it falls short:
Microsoft Copilot is likely your best fit if:
- You’re already invested in Microsoft 365 and want AI embedded in familiar tools
- All users who need AI access already have Microsoft 365 licences
- Your use cases centre on document summarisation, email drafting, meeting notes, and Teams collaboration
- You want to pilot AI quickly to validate whether it can add value to specific workflows
- You have Australian government or IRAP PROTECTED, compliance requirements
Copilot may not be the right fit if:
- You have a large workforce without M365 licences (site-based staff, contractors, frontline workers)
- You need AI to integrate with non-Microsoft systems
- You need deeply customised responses based on your specific documentation
- Your pilot results have been underwhelming due to generic responses.
ChatGPT Enterprise is likely your best fit if:
- Your primary need is general knowledge work: research, writing, brainstorming, analysis
- Your team wants an intuitive assistant they can start using immediately
- You want to quickly test AI concepts before committing to a larger implementation
- Creating Custom GPTs without coding provides enough customisation for your needs
ChatGPT Enterprise may not be the right fit if:
- You have Australian government compliance requirements (no IRAP certification)
- You need the AI to integrate deeply with internal systems
- Per-user licensing becomes prohibitive at your scale
- You need responses that are specific to your organisation rather than general knowledge.
A custom AI Bedrock solution is likely your best fit if:
- You’ve validated that AI can help, and now need a production solution that delivers consistent, reliable results
- You have staff who need AI access to internal documentation but don’t have M365 licences, such as site-based workers, contractors, or frontline teams
- You need AI to integrate with existing systems like ERP, asset management, safety systems, or proprietary platforms
- Generic responses aren’t good enough: you need AI trained on your specific procedures, policies, and documentation
- Consumption-based pricing makes more sense than per-seat licensing for your user base
Bedrock may not be the right fit if:
- You’re still in the early validation stage and want to test concepts quickly
- Your needs are fully met by Microsoft 365 productivity tools
- You don’t have internal development capability or budget for a technology partner.
Security and compliance considerations
This is often the first question from IT and security teams, and rightly so. The good news: all three platforms have committed that Enterprise customer data is not used for model training. However, there are important differences in compliance certifications and data residency.
When it comes to Australian Government Agencies, Defence Industry Partners, and APRA-regulated Financial Institutions handling PROTECTED-level data, compliance clarity is everything.
Microsoft Azure currently holds the strongest and most mature position for regulated AI workloads.
In February 2024, Microsoft became the first hyperscaler to complete an IRAP assessment at the PROTECTED level that explicitly includes both Azure OpenAI Service and Copilot for Microsoft 365. This milestone gives Australian public sector and critical infrastructure organisations a clear, well-documented compliance pathway for deploying enterprise-grade generative AI today.
AWS Bedrock is a very close second and rapidly catching up.
Amazon Bedrock is available in the Sydney region and supports leading models, including Anthropic Claude, Meta Llama, and Amazon Titan. Bedrock was formally added to AWS’s IRAP PROTECTED scope in the August 2024 assessment and remains covered in all subsequent reports (now 168+ AWS services assessed at PROTECTED). As with all cloud services, agencies should download the latest IRAP package from AWS Artefact and confirm model-specific controls align with their risk posture.
Direct OpenAI services (ChatGPT Enterprise or OpenAI API) are not currently IRAP-assessed.
While OpenAI introduced Australian data residency for at-rest storage in 2024, there is still no IRAP assessment or certification for the OpenAI platform itself. For workloads subject to the Australian Government Information Security Manual (ISM), PSPF, or APRA CPS 234, organisations typically need to use Azure OpenAI Service (which benefits from Microsoft’s IRAP coverage) rather than connecting directly to api.openai.com.
Bottom line for Australian regulated entities in late 2025:
Using direct OpenAI APIs → suitable for commercial or low-sensitivity use cases, but not yet for official or PROTECTED government workloads.
Need guaranteed IRAP PROTECTED coverage today with the broadest service inclusion → choose Microsoft Azure OpenAI and Copilot for Microsoft 365.
Happy to validate the latest AWS IRAP package and comfortable with Bedrock’s control set → AWS is now a very strong and compliant alternative.
Understanding the true costs
The pricing models are fundamentally different, which has significant implications as you scale.
Microsoft Copilot charges approximately $45 AUD per user per month. This is straightforward to budget, but adds up quickly. For 1,000 users, you’re looking at over $500,000 AUD annually in licensing, before training, change management, and data preparation costs.
ChatGPT Enterprise uses custom pricing, reportedly starting around $90 AUD per user. There’s also a lower commitment option: ChatGPT Team at approximately $38 AUD per user per month for smaller teams.
AWS Bedrock uses consumption-based pricing, meaning you pay for what you use rather than per seat. A medium volume knowledge base or chatbot handling 100,000 queries monthly might cost $250-500 AUD per month in AI processing. This model tends to become more economical at scale, though it requires upfront development investment.
Key insight: Per-user licensing is easier to budget but can become expensive at scale. Consumption-based pricing requires more monitoring but offers significant optimisation opportunities. Bedrock features like prompt caching and intelligent model routing have helped some organisations achieve 80% cost reductions.
The real difference: Why most AI pilots never reach production
Here’s a sobering statistic: according to recent research from S&P Global and MIT, between 46% and 95% of enterprise AI pilots never make it to production.
The failure rate for AI projects is more than double that of traditional IT projects. Why? In most cases, it’s not that the technology doesn’t work. It’s that generic AI tools deliver generic responses that don’t solve real business problems.
This is the heart of the matter. Copilot and ChatGPT are genuinely impressive tools. They can summarise documents, draft emails, and answer general questions remarkably well. But ask them something specific to your business, and you’ll often get a response that’s technically correct but not quite right.
What “customisation” actually means for your business
When we talk about AI customisation, we’re not talking about technical model parameters. We’re talking about whether the AI actually understands your business. Consider these scenarios:
A site worker asks: “What’s the isolation procedure for the SAG mill?” A generic AI might explain what a SAG mill is and describe general isolation principles. A customised AI trained on your procedures will return your specific isolation steps, reference the correct permit requirements, and link to the relevant safety documentation.
An employee asks: “What’s our policy on contractor access to restricted areas?” A generic AI will give you a general answer about contractor management. A customised AI will tell you your company’s specific policy, who approves exceptions, and what forms need to be completed.
The difference between these responses is the difference between AI that impresses in a demo and AI that actually gets used in production.
Why off-the-shelf tools often underwhelm
Research from NTT Data found that off-the-shelf AI programs tend to have lower adoption rates and efficiency gains than custom-built enterprise tools. MIT’s research identified that generic tools excel for individuals but stall in enterprise use because they don’t learn from or adapt to specific workflows.
This is why so many organisations run an AI pilot, get underwhelming results, and conclude that “AI isn’t ready yet” or “it doesn’t work for our business.” The issue isn’t AI itself. It’s that generic tools were asked to solve specific problems they weren’t designed for.
The right tool for each stage
Copilot and ChatGPT are excellent for validation. They’re fast to deploy, require minimal setup, and let you quickly test whether AI can add value to a particular workflow. They’re ideal for answering the question: “Could AI help here?”
Custom Bedrock solutions are built for production. When you need AI that understands your specific documentation, integrates with your systems, and delivers responses your teams will actually trust and use, you need a solution designed around your data and workflows. This is where you answer the question: “How do we make AI work at scale?”
The pattern we see repeatedly: organisations pilot with Copilot or ChatGPT, validate that AI can add value, then discover that moving to production requires deeper integration with their data and systems than off-the-shelf tools can provide.
Five questions to guide your decision
Before committing to any platform, work through these questions:
- Are you validating or deploying? If you’re still exploring whether AI can help with a particular workflow, start with Copilot or ChatGPT. They’re fast and low risk. If you’ve validated the concept and need a production solution, that’s when custom development makes sense.
- Who needs access? If it’s only office-based staff who already have M365 licences, Copilot is a natural fit. If you need to include site workers, contractors, or other staff without M365 licences, a custom solution avoids the cost of licensing everyone and can be designed for their specific needs.
- How specific do the answers need to be? General productivity assistance works well with off-the-shelf tools. If users need precise answers about your specific procedures, equipment, policies, or systems, you’ll need AI that’s been trained on your documentation.
- What systems does the AI need to connect to? If the answer is just Microsoft 365, Copilot makes strong sense. If it’s your ERP, asset management system, safety database, or legacy systems, you’re looking at custom integration work.
- What does success look like at scale? A successful pilot with 50 users is very different from a production rollout to 5,000. Consider the licensing costs, the depth of customisation needed, and whether the solution will still deliver value when it’s handling real workloads across your entire workforce.
Comparison at a glance
Getting started
There’s no universally “best” platform, only the right choice for your specific stage and needs. Many organisations find success with a phased approach: use Copilot or ChatGPT to quickly validate whether AI can help with a particular workflow, then invest in custom Bedrock solutions when you’re ready to deploy at scale with the integration and customisation that production demands.
The most important insight from the research is this: AI pilots fail not because the technology doesn’t work, but because generic tools are asked to solve specific problems. If your pilot delivered underwhelming results, the answer might not be “AI isn’t ready.” It might be that you need a solution designed around your data and workflows.
Need help moving from pilot to production?
Horizon Digital helps organisations turn AI from potential into performance. We bridge the gap between ambition and execution, combining technical depth with commercial understanding to deliver solutions that create real, measurable value. If you’re ready to move from exploration to outcomes, we’d welcome the conversation.
Let’s talk → https://horizondigital.au/get-in-touch/
