AI Development in Sydney: Enterprise Solutions Guide
Sydney is Australia's enterprise capital. The big four banks, major insurers, Telstra, Qantas—they're all headquartered here. And they're all trying to figure out AI.
The challenge? Enterprise AI development is different from startup AI development. The stakes are higher, the systems are more complex, and "move fast and break things" isn't an option when you're handling millions of customer records.
After working with Sydney enterprises on AI development projects, here's what we've learned about what works.
The Enterprise AI Reality
Let's be clear about what enterprise AI development actually involves:
It's not just building models: Maybe 20% of the work is the actual AI. The other 80% is integration, security, compliance, change management, and operations.
It's not greenfield: You're not building from scratch. You're integrating with decades-old core systems, legacy databases, and existing processes.
It's not one team's problem: AI projects touch data teams, IT, business units, compliance, legal, and often the board. Alignment is half the battle.
It's not done when it launches: Enterprise AI needs ongoing care—model monitoring, retraining, drift detection, incident response.
If someone tells you enterprise AI is easy, they haven't done it.
What Sydney Enterprises Are Actually Building
Based on what we're seeing in the market:
Document Intelligence: Extracting data from contracts, invoices, claims, applications. High volume, clear ROI, relatively contained risk. This is where many enterprises start.
Customer Service Augmentation: Not replacing call centres, but making them more efficient. AI that surfaces relevant information, drafts responses, handles simple queries. Our customer service AI work fits here.
Process Automation: Taking existing manual processes and adding AI decision-making. Loan approvals, insurance underwriting, compliance checks.
Predictive Analytics: Forecasting demand, churn prediction, risk scoring. Often enhancing existing analytics rather than replacing them.
Knowledge Management: Making institutional knowledge accessible. Finding answers in policy documents, precedents, past decisions.
What we're not seeing much of (yet): fully autonomous systems making high-stakes decisions without human oversight. Most enterprises are rightly cautious about this.
Choosing an AI Development Partner
For Sydney enterprises, the choice usually comes down to:
Big consulting firms (Accenture, Deloitte, etc.): Deep enterprise relationships, broad capabilities, eye-watering rates. Good for strategy and program management. Often subcontract the actual development.
Global tech vendors (Microsoft, Google, AWS): Platform-specific solutions. Great if you're already deep in their ecosystem. Risk of lock-in.
Specialist AI firms: Focused expertise, often better price-performance. May lack enterprise experience or struggle with integration complexity.
Software development companies with AI capability: (This is us.) Build custom solutions, understand integration, pragmatic approach. May not have cutting-edge research capabilities.
The right choice depends on your situation. For most enterprises, a combination works: strategy from a big firm, platform from a vendor, custom development from specialists.
Questions to Ask Any Partner
"Show me an enterprise deployment that's been running for 12+ months." Demos are easy. Production is hard. Ask about what happened after launch.
"How do you handle our existing systems?" If they want to rip and replace everything, run. If they can't articulate an integration approach, worry.
"What's your approach to compliance and security?" In Sydney, this means APRA, ASIC, Privacy Act, and often industry-specific regulations. Generic answers are red flags.
"Who actually does the work?" Meet the team. Not the partner, not the sales lead—the people writing code.
"What happens when something goes wrong?" Because it will. What's the support model? The escalation path? The SLA?
The Build Process
Enterprise AI development should follow a phased approach:
Phase 1: Discovery (4-8 weeks)
- Define the specific problem and success metrics
- Audit available data and systems
- Assess technical feasibility
- Identify integration points
- Map stakeholders and governance
Deliverable: Business case, technical design, project plan
This phase is critical. Skipping it leads to expensive surprises later.
Phase 2: Proof of Concept (6-12 weeks)
- Build working AI on representative data
- Validate accuracy and performance
- Test integration approach
- Refine requirements based on learning
Deliverable: Working prototype, validated metrics, refined plan
Go/no-go decision point. Some projects should stop here.
Phase 3: Production Build (12-20 weeks)
- Full development with enterprise standards
- Security hardening
- Integration with production systems
- Testing (unit, integration, UAT, performance)
- Documentation and training
Deliverable: Production-ready system
Phase 4: Deployment & Stabilisation (4-8 weeks)
- Staged rollout
- Monitoring and alerting setup
- Performance tuning
- Incident response procedures
- Knowledge transfer
Deliverable: Operational system, trained team
Ongoing: Operations
- Model monitoring
- Periodic retraining
- Continuous improvement
- Incident management
This isn't a phase—it's permanent.
Sydney-Specific Considerations
Regulatory environment: APRA-regulated entities (banks, insurers, super funds) have specific requirements around model risk management. If this applies to you, build compliance in from the start.
Data sovereignty: Some enterprises require data to stay in Australian regions. This affects model selection (some cloud AI services route data overseas) and infrastructure choices.
Talent market: Sydney has strong AI talent but intense competition. Your development partner's ability to attract and retain good people matters.
Executive expectations: Sydney boards have seen the AI hype. They expect results but may have unrealistic timelines. Managing expectations is part of the job.
Cost Expectations
For Sydney enterprise AI development:
Discovery phase: $50,000-$150,000 Proof of concept: $100,000-$300,000 Production build: $300,000-$1,500,000 (highly variable based on scope) Annual operations: $100,000-$500,000
These are real numbers from real projects. Your mileage will vary based on complexity, compliance requirements, and integration challenges.
If a quote seems dramatically lower, ask what's not included. If dramatically higher, ask whether you're paying enterprise tax for a mid-market problem.
Common Mistakes
Starting with technology: "We want to use GPT-4" isn't a project brief. Start with the problem.
Underestimating integration: The AI is the easy part. Connecting it to your core banking system is the hard part.
Over-scoping phase one: The first project should be small, contained, and successful. Save the transformation for later.
Ignoring change management: The best AI system fails if nobody uses it. Plan for adoption.
No ongoing investment: AI isn't a capital project you finish. Budget for operations.
Getting Started
If you're a Sydney enterprise exploring AI development:
- Identify a specific problem with clear business value and measurable success
- Audit your data and systems—know what you're working with
- Talk to multiple partners—at least 3, different types
- Start with discovery—invest in getting the plan right
- Plan for the long term—not just the build
We work with Sydney enterprises on AI development projects from discovery through deployment and beyond. Happy to discuss your specific situation.