AI Project Phases - From Discovery to Production

Every AI project, whether it is a document extraction system or a multi-agent workflow, follows the same fundamental phases. The details vary. The structure does not.

After delivering dozens of AI projects for Australian businesses, I have learned that the companies who understand these phases upfront make better decisions, set better expectations, and get to production faster. Here is the breakdown, phase by phase, with honest timelines and the lessons we have learned along the way.

Phase 1 - Discovery

Duration: 1-3 weeks

Discovery is about answering three questions: What problem are we solving? Is AI the right tool? Do we have what we need to build it?

What Happens in Discovery

Problem definition. This sounds simple but consistently takes longer than people expect. The business says "we want to automate our invoice processing." But which invoices? From which suppliers? In which formats? With what validation rules? What happens to exceptions? The detailed problem definition is where you find out whether this is a 4-week project or a 4-month one.

Process analysis. We observe and document the current process - not the process as it is documented in the manual, but the process as people actually perform it. In every single project, we find steps, workarounds, and decision logic that are not written down anywhere. An operations manager at a financial services firm once told us their process had 12 steps. After shadowing the team for two days, we documented 23.

Data assessment. What data does the AI system need? Where does it live? What format is it in? How much of it exists? Is it accessible? Is it compliant? Data readiness is the strongest predictor of project timeline. Clean, accessible data means fast delivery. Fragmented, unstructured data means weeks of preparation work before you can write a line of AI code.

Technical landscape review. What systems does the AI need to integrate with? What cloud infrastructure is available? What security and compliance requirements apply? Are there existing APIs or do we need to build connections from scratch?

Feasibility assessment. Based on all of the above, is this project feasible? Is AI the right approach, or would a simpler solution work? What are the risks? What is the estimated timeline and investment?

Discovery Deliverables

• Problem statement with measurable success criteria
• Current-state process documentation with baseline metrics
• Data readiness assessment
• Technical architecture recommendation
• Feasibility report with go/no-go recommendation
• Project plan with timeline and resource estimates

Common Discovery Mistakes

Skipping it. Some companies want to jump straight to building. In our experience, every week invested in discovery saves 2-3 weeks during development. The projects that skip discovery are the ones that end up rebuilding things mid-stream.

Treating it as a formality. Discovery is not about filling in templates. It is about genuinely understanding the problem. If your discovery phase produces a 50-page document that nobody reads, it has failed regardless of how thorough it looks.

Not involving the right people. Discovery needs input from three groups: the business stakeholders who own the problem, the operational staff who do the work today, and the technical team who will support the system. Missing any of these perspectives creates gaps that surface later.

Phase 2 - Proof of Concept

Duration: 2-4 weeks

The proof of concept phase answers one question: does the AI approach work with this data and this problem?

What Happens in the PoC

Model selection and configuration. Based on the discovery findings, we select the right AI approach. This might be a foundation model like GPT-4o or Claude with carefully engineered prompts, a fine-tuned model trained on domain-specific data, or an agentic architecture that coordinates multiple AI components. At Team 400, we often work with Azure AI services for enterprise clients, but we select the technology that fits the problem, not the other way around.

Data pipeline construction. Build the pipeline that gets data from its source into the AI system in the right format. This often involves extraction, transformation, cleaning, and validation steps. For document processing projects, this is where we handle the conversion of PDFs, images, and scanned documents into machine-readable text.

Core capability development. Build the AI function itself - the extraction, classification, generation, or agentic workflow that does the actual work. This is typically the most visible part of the PoC but often not the most time-consuming.

Testing against real data. Run the system against actual production data (or a representative sample) and measure performance. Compare accuracy, speed, and error rates against the baseline established in discovery.

PoC Deliverables

• Working prototype running against real data
• Performance metrics (accuracy, speed, error rates)
• Comparison to baseline metrics
• List of edge cases and limitations
• Go/no-go recommendation with rationale
• Estimated timeline and cost for production development

What a Good PoC Looks Like

A good PoC is not impressive - it is informative. It tells you:

• Whether the core AI capability works (not perfectly, but well enough)
• What the accuracy ceiling is likely to be with further refinement
• What the major challenges are for production development
• Whether the business case holds up based on actual (not theoretical) performance

We have delivered PoCs that achieved 70% accuracy on the first pass and still recommended proceeding - because the data showed a clear path to 95%+ with refinement. We have also delivered PoCs that achieved 85% accuracy and recommended not proceeding - because the remaining 15% represented high-stakes cases that could not tolerate errors.

The numbers alone do not tell the story. The analysis of what the numbers mean for your specific business context is what matters.

Phase 3 - Development and Iteration

Duration: 4-10 weeks

This is the longest phase and the one where the real engineering happens. You take the PoC and turn it into something production-worthy.

What Happens in Development

Accuracy improvement. Refine the AI system to handle the full range of inputs, including edge cases. This is iterative work - test, analyse failures, adjust, re-test. Expect 4-8 iteration cycles. Each cycle improves accuracy, but the rate of improvement slows over time. Going from 80% to 90% might take one iteration. Going from 95% to 98% might take four.

Error handling and escalation. Build the logic for what happens when the AI is uncertain or wrong. In production systems, graceful failure is as important as correct operation. Define confidence thresholds, escalation paths, and fallback behaviours.

Integration engineering. Connect the AI system to the upstream systems that provide data and the downstream systems that receive outputs. This includes APIs, databases, message queues, file systems, and whatever else the architecture requires. Integration work typically consumes 30-40% of the development phase.

User interface development. If the system has a user-facing component, build it. This might be a web application, a dashboard, a chat interface, or an integration into an existing tool. The interface needs to be functional, intuitive, and designed for the actual workflow - not a generic template.

Security and compliance. Implement access controls, data encryption, audit logging, and whatever other security measures your organisation requires. In regulated industries, this can include specific AI governance requirements around transparency, explainability, and bias testing.

Performance optimisation. Make the system fast enough and cost-effective enough for production use. This involves optimising token usage, implementing caching strategies, managing concurrent requests, and tuning infrastructure for the expected load.

Development Deliverables

• Production-ready AI system
• Integration with upstream and downstream systems
• User interface (if applicable)
• Security review and compliance documentation
• Performance benchmarks
• Deployment documentation
• Test suite and quality assurance results

The Iteration Cycle

Development is not a straight line from start to finish. It follows an iterative cycle:

1. Test - Run the system against a representative dataset
2. Analyse - Identify failure modes, accuracy gaps, and performance issues
3. Prioritise - Decide which issues to fix first based on impact and effort
4. Fix - Make the changes
5. Re-test - Verify the fix works and has not broken anything else
6. Repeat

We typically run this cycle weekly. Each cycle produces measurable improvement and a clearer picture of remaining work. This structure gives stakeholders visibility into progress and confidence that the project is on track.

Phase 4 - Pilot

Duration: 4-8 weeks

The pilot takes the production-ready system and tests it with real users in real conditions before committing to a full rollout.

What Happens in the Pilot

User selection and training. Identify the pilot group (typically 5-20 users), train them on the system, and set up support channels. Choose users who are representative of the broader user base - not just the most tech-savvy or the most enthusiastic.

Staged deployment. Start with human review of every AI output, then gradually increase autonomy over the pilot period. This builds trust and catches issues before they affect business operations.

Continuous monitoring. Track all defined success metrics in real time. Performance dashboards, error logs, user feedback - everything visible and reviewed daily in the first two weeks, then weekly.

Feedback loops. Collect structured feedback from pilot users throughout the pilot. What is working? What is frustrating? What edge cases are they hitting? Feed this back into the development process for real-time improvements.

Parallel operation. Run the AI system alongside the existing process so you can compare outputs and measure improvement directly. This adds workload for the pilot users, so keep the pilot duration reasonable.

Pilot Deliverables

• Performance data from real-world operation
• User satisfaction metrics
• Comparison to baseline (before-and-after)
• List of issues identified and resolved
• List of remaining issues for post-pilot resolution
• Go/no-go recommendation for full deployment
• Full rollout plan

Pilot Success Criteria

Define these before the pilot starts:

• Must-have: Accuracy meets threshold, processing time meets threshold, no data privacy incidents
• Should-have: User satisfaction above target, cost per transaction below target
• Nice-to-have: Edge case handling at target level, full autonomy achieved

If the must-have criteria are met, you proceed. If they are not, you either fix the issues and extend the pilot or stop and reassess.

Phase 5 - Production Deployment

Duration: 2-6 weeks

Full production deployment is an engineering exercise. The AI works - now you need to make it work at scale, reliably, and securely.

What Happens in Production Deployment

Infrastructure scaling. Size the infrastructure for production load. This means handling peak volumes, not just average volumes. A system that processes 100 documents per day in the pilot needs to handle 5,000 per day at full scale without degradation.

Rollout execution. Deploy to the full user base, typically in stages. Start with the departments or teams closest to the pilot group and expand outward. Each stage gets abbreviated training and support.

Monitoring and alerting. Set up production monitoring with automated alerts for accuracy drops, latency spikes, error rate increases, and cost anomalies. The monitoring you had during the pilot is a starting point, but production monitoring needs to be more automated and less dependent on manual review.

Documentation and handover. System documentation, runbooks, troubleshooting guides, user manuals, and training materials. If the system will be maintained by an internal team, knowledge transfer sessions are part of this phase.

Change management. Communicate the rollout plan across the organisation. Address concerns. Celebrate early wins. Make it easy for people to get help when they need it.

Production Deliverables

• Fully deployed AI system at scale
• Monitoring and alerting infrastructure
• Documentation suite
• Training materials and user support resources
• Post-deployment review plan

Phase 6 - Ongoing Operations

Duration: Ongoing

AI systems require ongoing attention. They are not fire-and-forget software.

What Ongoing Operations Involves

Performance monitoring. Regular reviews of accuracy, speed, cost, and user satisfaction. We recommend weekly reviews for the first month post-deployment, then monthly.

Model maintenance. Foundation models are updated by their providers. Fine-tuned models may need retraining as data patterns change. Prompts may need adjustment as business processes evolve. Budget for ongoing model work.

Continuous improvement. Analyse failure modes, collect user feedback, and make incremental improvements. The best AI systems get better over time because they benefit from real-world data and user corrections.

Cost management. Monitor and optimise AI service costs. Token usage, API calls, infrastructure - all of these have cost implications at scale. Small optimisations can produce significant savings when multiplied across thousands of daily operations.

Expansion planning. Once one AI system is running successfully, the conversation naturally turns to "what else can we do?" Use the learnings from the first project to identify and prioritise the next opportunity.

Budgeting for Ongoing Operations

Plan for 15-25% of the initial project cost per year for ongoing operations. This covers:

• Monitoring and incident response
• Model updates and prompt refinement
• Infrastructure maintenance
• Minor feature additions and improvements
• Regular performance reviews

This percentage decreases over time as the system stabilises, but the first year requires the most attention.

End-to-End Timeline Summary

Phase	Duration	Key Output
Discovery	1-3 weeks	Feasibility report and project plan
Proof of Concept	2-4 weeks	Working prototype with metrics
Development	4-10 weeks	Production-ready system
Pilot	4-8 weeks	Real-world validation data
Production Deployment	2-6 weeks	Fully deployed system
Ongoing Operations	Continuous	Maintained and improving system
Total to production	13-31 weeks

Simple projects (single-purpose extraction or classification) land at the lower end. Complex projects (multi-agent systems with extensive integrations) land at the upper end or beyond.

How Team 400 Manages These Phases

At Team 400, we have delivered projects across all of these phases for Australian businesses in financial services, resources, manufacturing, and professional services.

What makes our approach different from the large consulting firms:

We build, not just advise. Our team includes the engineers who write the code, not just the consultants who write the recommendations. The same people who run discovery build the production system.

We move fast. A typical AI development engagement with Team 400 reaches production pilot in 8-12 weeks. We have done it in as few as 6 weeks for well-scoped projects with clean data.

We are honest about feasibility. If the data is not ready, or AI is not the right approach, or the timeline is unrealistic, we will tell you in discovery rather than letting you find out in month 4.

We stay involved. We do not hand over a system and disappear. Our ongoing support ensures the system continues to perform and improve over time.

If you are planning an AI project and want to understand what each phase will look like for your specific use case, get in touch. We will give you a realistic roadmap before you commit to anything.