AI Implementation Roadmap¶
You've identified good first use cases. You understand the importance of change management. You've evaluated vendors carefully.
Now you need a practical roadmap for actually implementing AI: running pilots that generate real learning, making sound scale decisions, and building assurance practices that become business-as-usual.
This page provides that roadmap with specific guidance on sizing, timeframes, success criteria, and what "done" looks like at each stage.
Who this page is for¶
This page is for:
- Project managers running AI pilots
- Transformation teams planning AI rollouts
- Business owners moving from pilot to production
- Anyone asking "what do we actually do, and in what order?"
If you're ready to move from planning to doing, this is for you.
The four-stage roadmap¶
This roadmap is intentionally lightweight. The goal is to move forward deliberately, not to stall waiting for perfection.
Each stage has clear objectives, typical timeframes, and "what done looks like" criteria.
Overview¶
- Explore and pilot (2-4 months) – Learn what works in your environment
- Standardise and embed guardrails (3-6 months) – Make safety and governance routine
- Scale what works (6-12 months) – Expand successful patterns
- Continuously assure and improve (ongoing) – Refine based on experience
Stage 1: Explore and pilot (2-4 months)¶
Objective: Run small, focused pilots that generate real learning about what AI can and can't do in your specific environment.
What to do¶
Identify 2-3 low-risk, high-learning use cases: - Review Safe AI Adoption - Getting Started for good first uses - Choose cases that are reversible and low-stakes - Pick at least one that builds resilience, not just cuts cost
Set up your AI System Register: - Use the AI System Register Template to track pilots - Even a simple spreadsheet is fine at this stage - Key fields: use case, owner, risk rating, status, decisions made
Run small pilots with clear parameters:
Pilot group size: 5-15 people - Why this size: Large enough to generate meaningful data, small enough to support intensively - Too small (1-3 people): Doesn't reveal process or workflow issues - Too large (30+ people): Support burden becomes overwhelming, problems scale quickly
Pilot duration: 4-8 weeks for initial learning - Week 1-2: Setup, training, initial use with high support - Week 3-5: Regular use, feedback collection, issue resolution - Week 6-8: Evaluation, decision preparation - Longer pilots (8-12 weeks): Appropriate for defensive/security use cases or complex workflows
Success criteria examples: - "Reduces document drafting time by 20% while maintaining quality" - "Maintains 90% accuracy with human review required for all outputs" - "Staff report tool is helpful, not frustrating, in week 4 survey" - "Zero security incidents during pilot period" - "80% of pilot participants want to continue using the tool"
Exit conditions (when to stop): - "If error rate exceeds 15% after 4 weeks of tuning" - "If staff satisfaction score below 3/5 after training period" - "If integration issues remain unresolved after 6 weeks" - "If cost per task exceeds current manual process cost"
Complete AI Readiness Checklists: - Use the AI Readiness Checklist for each pilot - Document risk assessment, privacy considerations, approval - Lightweight but systematic
Capture lessons learned: - Weekly pilot review meetings (30 minutes) - End-of-pilot retrospective (document what worked, what didn't, what surprised you) - Feed insights back into system register and planning
What "done" looks like¶
At the end of Stage 1, you should have:
✓ Data on whether pilots met success criteria (yes/no with supporting metrics)
✓ Documented lessons learned (what worked, what didn't, what you'd do differently)
✓ Clear recommendation for each pilot (scale / adjust and retry / stop)
✓ Updated system register with pilot outcomes and decisions
✓ Staff feedback on actual experience vs expectations
✓ Initial view of cost vs benefit for each use case
Common reasons to stop a pilot: - Tool doesn't fit actual workflow (looked good in demo, awkward in practice) - Error rate remains too high despite tuning - Staff find it more frustrating than helpful - Integration problems too complex to resolve quickly - Cost exceeds benefit even at best-case adoption
Common reasons to scale: - Meets or exceeds success criteria - Staff want to keep using it - Clear path to broader adoption - Cost-benefit case holds at larger scale - Manageable support and governance requirements
Common reasons to adjust and retry: - Core value is there but implementation needs refinement - Different use case or workflow would work better - Tool configuration needs adjustment - Training approach needs improvement
Stage 2: Standardise and embed guardrails (3-6 months)¶
Objective: Make AI governance and safety part of business-as-usual, not special projects.
What to do¶
Introduce approval and risk checks for new AI projects: - Expand the AI Readiness Checklist into standard project approval process - New AI initiatives can't start without basic documentation and risk assessment - Keep it lightweight – appropriate to risk level, not bureaucratic
Make the system register part of business-as-usual: - Assign an owner for the register (someone senior enough to have authority) - Quarterly review of all active AI systems (more frequently for high-risk ones) - Clear process for adding new systems and updating status
Establish incident reporting: - Deploy AI Incident Report Form - Make it clear this is learning-focused, not blame-focused - Review incidents quarterly to identify patterns and improvement opportunities - Feed lessons back into training, documentation, and tool configuration
Publish AI use guidelines for staff: - Dos and don'ts for common use cases - When to use AI vs when not to - How to review AI outputs (what to look for) - Where to report problems or concerns - Keep it to 1-2 pages, practical not theoretical
Example guidelines snippet: - ✓ DO use AI for first drafts of routine documents - ✓ DO review all AI outputs before using them - ✓ DO report errors or concerning outputs - ✗ DON'T use AI for decisions about people without human review - ✗ DON'T copy-paste AI outputs without checking for accuracy - ✗ DON'T put confidential or sensitive data into unapproved AI tools
Set up review cadences: - Monthly: Review active pilot metrics and issues - Quarterly: Review system register, incident reports, update risk assessments - Annually: Review AI use guidelines, governance framework, vendor relationships
What "done" looks like¶
At the end of Stage 2, you should have:
✓ Clear approval process – new AI initiatives require documented risk assessment
✓ Maintained system register – single source of truth for all AI use
✓ Working incident reporting – staff know how to report issues, reports are actually reviewed
✓ Published guidelines – staff know what's expected when using AI tools
✓ Regular review cadence – governance isn't "set and forget"
✓ Systematic risk management – you can answer "where do we use AI and what are the risks?"
You should be able to answer: - What AI systems are we using and who owns each one? - What risks have been identified and how are we managing them? - How many AI incidents have we had and what did we learn? - What are staff allowed and not allowed to do with AI tools? - When was each system last reviewed?
Stage 3: Scale what works (6-12 months)¶
Objective: Expand successful patterns while maintaining safety and learning from experience.
What to do¶
Expand successful pilots to broader teams: - Don't scale everything – focus on what clearly worked - Plan for 2-3x more training and support time than pilots required - Identify team-level champions (not just organisation-level) - Expect slower adoption in scaling phase than pilot phase
Invest in training and enablement: - Develop self-service resources (written guides, video tutorials, FAQs) - Regular training sessions for new users (monthly or as new teams onboard) - "Office hours" or drop-in support sessions - Update training based on common issues and questions
Prioritise defensive and resilience use cases: - Now that you've learned from productivity use cases, consider defensive ones - Security operations support, fraud detection, risk and compliance assistance - See Safe AI Adoption - Getting Started for resilience-focused uses
Track organisation-level metrics:
Monitor across four dimensions: - Adoption: How many staff are actively using tools? What percentage of eligible workflows? - Value: Time saved, quality improvements, cost savings - Risk: Incident rates and severity, staff confidence in outputs - Support: Request volumes, common issues, user satisfaction
Track these monthly and look for trends rather than snapshots.
What "done" looks like¶
At the end of Stage 3, you should have:
✓ Multiple teams using AI tools confidently – not just pilot volunteers
✓ Clear metrics on value delivered – can articulate ROI and benefits
✓ Risk managed systematically – incidents tracked and learned from
✓ Training embedded – part of onboarding and team practices
✓ Balanced portfolio – productivity AND resilience use cases
✓ Shared knowledge – lessons learned documented and accessible
Warning signs to watch for: - High support burden not decreasing over time (indicates training or tool issues) - Low adoption rates despite availability (change management problems) - Increasing incident rates (inadequate controls or risky use cases) - Staff finding workarounds (tool doesn't fit real workflow)
Stage 4: Continuously assure and improve (ongoing)¶
Objective: Maintain safety and effectiveness as AI use matures and evolves.
What to do¶
Schedule periodic assurance activities:
Quarterly reviews for high-risk systems: - Review incident reports and near misses - Check compliance with risk controls - Verify system performance vs expectations - Update risk assessment if context changes
Annual reviews for low-risk systems: - Confirm system still appropriate for use case - Review vendor relationship and costs - Check for regulatory or technology changes - Update documentation
Ad-hoc reviews when triggered: - Significant incidents or near misses - Major system updates from vendor - Regulatory changes - Workflow or organisational changes
Refine guardrails based on experience: - What controls are working well? What's pure overhead? - Where are gaps emerging? - What have incidents taught you? - Update guidelines, checklists, and processes accordingly
Adjust roadmap as context evolves: - New regulations (track Australian AI legislation development) - New technology capabilities (AI is evolving rapidly) - Changed organisational priorities or risk appetite - Lessons from your experience and others'
Share lessons learned: - Internally: cross-team learning from experiences - Externally: consider contributing to industry knowledge - Help others avoid your mistakes, learn from your successes
What "ongoing success" looks like¶
In the continuous improvement stage, you should have:
✓ Mature governance – safety and assurance are routine, not special effort
✓ Learning culture – incidents lead to improvement, not blame
✓ Adaptive approach – adjusting to new regulations, technology, experience
✓ Balanced adoption – moving forward deliberately, not recklessly or too cautiously
✓ Clear accountability – everyone knows their role in safe AI use
✓ Documented track record – can demonstrate responsible AI adoption
Your next steps based on where you are¶
If you haven't started pilots yet:¶
- Review Safe AI Adoption - Getting Started and complete AI Readiness Checklist
- Set up your AI System Register
- Evaluate 2-3 vendors using AI Vendor Selection Guide
- Design pilot: 5-15 people, 4-8 weeks, clear success criteria and exit conditions
If you're running pilots now:¶
- Confirm you have clear success criteria, exit conditions, and a decision timeline
- Collect feedback weekly and document lessons as you go
- Review change management against AI Change Management
- Update your system register with pilot status and findings
If you're ready to scale:¶
- Confirm pilot met success criteria with evidence (don't scale based on enthusiasm alone)
- Review guardrails from Safe AI Adoption - Getting Started
- Develop training materials and identify team-level champions
- Set up incident reporting and plan for 2-3x more support
Key takeaways¶
The roadmap: - Four stages: Explore → Standardise → Scale → Assure - Each stage has clear objectives and "done" criteria - Don't skip stages to move faster – each builds on the previous
Pilot design: - 5-15 people, 4-8 weeks - Clear success criteria and exit conditions - Weekly feedback, documented lessons - Be prepared to stop pilots that aren't working
Scaling decisions: - Scale based on evidence, not enthusiasm - Plan for more support than pilots required - Expand gradually, don't try to scale everything at once
Ongoing assurance: - Regular reviews (quarterly for high-risk, annual for low-risk) - Learn from incidents, refine controls - Adjust to regulatory and technology changes
Remember:
Perfect is the enemy of good. The goal is deliberate progress with appropriate safeguards, not flawless execution. Start small, learn fast, scale what works, and build assurance into your routine.