To build an AI strategy, work through eight steps in order: assess your processes, data, systems and skills; define measurable business objectives; map processes to what AI can do; prioritise by impact and feasibility; build the business case; design the architecture; set governance; and plan a phased roadmap. Skipping from assessment straight to technology is the most common failure.
McKinsey's State of AI 2025 found 88 per cent of organisations use AI in at least one business function, yet only around 6 per cent attribute more than 5 per cent of earnings to it. Gartner predicted in 2024 that 30 per cent of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data, rising costs and unclear value. RAND (2024) noted estimates that more than 80 per cent of AI projects fail, about twice the rate of other IT projects.
The pattern is familiar. Someone builds a prototype, leadership gets excited, budget is allocated, and six months later it stalls because nobody worked through data access, governance, integration or the link to business objectives. This guide is for COOs, CFOs, CIOs and MDs at mid-sized and enterprise Australian organisations.
Key takeaways
- Start with business outcomes tied to a measured baseline, never with a tool.
- Score opportunities on impact and feasibility; the first project should show value in 8 to 12 weeks.
- Model benefits conservatively and include downside scenarios and a 90-day decision point.
- Decide data location, integration approach and monitoring before building.
- Run one project at a time with one named owner.
What are the eight steps to build an AI strategy?
- Step 1: AssessCurrent state audit
- Step 2: DefineBusiness objectives
- Step 3: MapProcesses to AI
- Step 4: PrioritiseImpact vs feasibility
- Step 5: BuildBusiness case
- Step 6: DesignTechnical architecture
- Step 7: GovernGovernance framework
- Step 8: RoadmapImplementation plan
Each step builds on the one before. Jumping from Step 1 to Step 6 is how projects derail.
Step 1: How do you assess your current state?
Cover four areas.
- Processes. Document manual, repetitive or error-prone workflows that consume staff time out of proportion to their value: invoice processing, timesheet reconciliation, compliance reporting, customer onboarding, quoting. Look for the step where "a person retypes information between systems".
- Data. Where does it live, how clean is it, and can you reach it through APIs or only spreadsheets? Gartner (February 2025) found 63 per cent of organisations lacked, or were unsure they had, AI-ready data management practices.
- Systems. Map the stack: Xero, MYOB, SAP, Business Central, a CRM, practice or field service software. Do systems talk to each other, or do staff copy data between them?
- Skills. You do not need data scientists on staff, but you need people who understand what AI can and cannot do and can manage vendors.
Our AI readiness checklist and AI readiness assessment guide give a scored way to run this.
Step 2: How do you define AI business objectives?
Most organisations start with technology ("we should use AI") instead of an outcome ("cut invoice handling time and errors"). Useful objectives fall into four groups:
- Revenue: faster quoting, better lead qualification.
- Operational efficiency: less rekeying, faster approvals, fewer errors.
- Customer experience: faster, more consistent responses.
- Compliance and risk: automated reporting, audit trails, fewer errors.
Make each specific and measurable. "Improve efficiency" is not a strategy. "Cut accounts payable handling from a measured 12 minutes per invoice to under 3, across 4,000 invoices a month, with a person approving each" is.
Compliance carries extra weight in Australia: Privacy Act reforms, Fair Work record obligations and ATO data matching all create work AI can reduce, if a person stays accountable.
Step 3: Which processes suit AI?
Match documented processes against what AI can do today.
AI does well at:
- extracting structured data from documents (invoices, contracts, receipts)
- pattern recognition across large datasets (anomalies, demand forecasts)
- language tasks (summarising, classifying, translating, drafting)
- repetitive decisions with clear rules (routing, categorising) under human review
- triaging email and enquiries for a person to action.
AI struggles with:
- novel situations needing judgement
- tasks without enough history
- decisions the law requires a person to make or explain
- processes that change constantly
- strategic or genuinely creative work.
We will not build AI that makes a decision the law says a person must make.
Step 4: How do you prioritise AI opportunities?
Score each opportunity 1 to 10 on two axes.
Business impact: savings or revenue, staff affected, customer experience, compliance risk reduced.
Feasibility: data quality and access, integration complexity, change effort, tool maturity.
How does the opportunity score?
- High impact + High feasibilityStart here: first project with clear return
- High impact + Low feasibilityStrategic project: plan for a 6-12 month horizon
- Low impact + High feasibilityNice-to-have: only if capacity allows
- Low impact + Low feasibilityAvoid
The first project should be high impact and high feasibility, showing value within 8 to 12 weeks. See AI quick wins for mid-market organisations.
Step 5: How do you build the AI business case?
Speak the CFO's language: measured numbers, realistic timelines, honest risk.
- Baseline: what the process costs today, at loaded staff cost, plus error correction, delays and compliance exposure.
- Benefits: be conservative. Model well below vendor-claimed gains, and validate against a pilot.
- Costs: licences, integration, data preparation, training, ongoing operation, and change management.
- Risk: what if it takes 50 per cent longer, or adoption is lower than expected?
- Measured baseline for the chosen processTime, errors, cost
- Conservative benefit modelValidated in a pilot
- Full cost including integration and changeNot just licences
- Downside scenariosSlower, lower adoption
- Decision point at day 90Continue, change or stop
Our AI business case template has a board-ready structure, and the ROI calculator helps model the baseline.
Step 6: How do you design the technical architecture?
This is where build versus buy matters.
What is your situation?
- Standard process + mature SaaS tools existBuy: proven platforms or add-ons
- Standard process + no good tool existsBuild light: workflow automation (Power Automate, Make, n8n)
- Unique process + competitive advantageBuild custom: purpose-built AI using your data
- Complex integration + legacy systemsHybrid: buy the core, build the connections
For the first two or three projects, "buy" or "build light" is usually right. Custom AI makes sense when the process is unique and a source of advantage. See our build vs buy TCO guide.
Document four decisions:
- Data location: where personal information is processed, in line with the Privacy Act 1988. Many organisations keep AI on their own infrastructure in Australia (their cloud or their own servers). See our data sovereignty guide.
- Integration: API-first where possible; budget for connections where systems lack APIs.
- Scale: design for growth in volume.
- Monitoring: how accuracy, performance and drift will be measured from day one.
Step 7: What should AI governance cover?
Governance keeps AI legal, ethical and aimed at business objectives. Australia relies on existing law plus the National AI Centre's Guidance for AI Adoption (October 2025).
- Data governance: ownership, access, classification, retention.
- Output governance: how outputs are validated, who reviews, when a person overrides, escalation paths.
- Risk management: what happens if the model is wrong, and the reputational and compliance exposure.
- Bias testing: especially for HR and customer-facing uses.
- Ongoing review: quarterly checks against baseline, because performance drifts.
Start with a one-page AI policy covering acceptable use, data handling and human oversight. See our AI governance framework.
Step 8: What does an AI implementation roadmap look like?
- Weeks 1-4FoundationAssessment, objectives, first project selected, governance basics
- Weeks 5-8First Project PilotBuild and run alongside the team, measure against baseline
- Weeks 9-12Validate and RefineReview results against the business case, refine, document lessons
- Months 4-6Scale First WinRoll out to the full team or more sites, begin second project
- Months 7-12Expand PortfolioAdd projects from the priority list, build internal capability
- Start with one project. One success teaches more than five half-finished ones.
- Name a single owner, not a committee.
- Set 90-day checkpoints and be willing to stop.
- Budget for change. Adoption is where most projects stall.
- Document lessons so the next project goes faster.
What difference does an AI strategy make?
| Without Strategy | With Strategy | Impact | |
|---|---|---|---|
| Starting point | A tool someone liked | A measured business problem | Clear purpose |
| Data | Assumed ready | Checked before building | Fewer stalls |
| Project choice | Loudest idea wins | Scored on impact and feasibility | Better odds |
| Governance | Added after an incident | Designed in | Lower risk |
| Results | Unclear | Measured against baseline at day 90 | Provable |
McKinsey's high performers share clear strategy, prioritised use cases, strong data foundations and executive commitment, arrived at deliberately.
When should you bring in outside help for AI strategy?
Building strategy internally works when you have experienced technology leadership, time to commit and a team that has delivered change before. Consider outside help when:
- you lack a technology leader with AI experience
- previous technology projects stalled or overran
- you need an objective view of readiness
- your sector has specific compliance demands (financial services, healthcare, government)
- the board wants an AI plan and nobody owns it.
Across our consultants' careers, including enterprise data and reporting programs at BHP and Rio Tinto, the pattern is consistent regardless of size: strategy comes before technology. Our AI strategy service follows this framework, usually compressing steps 1 to 4 into a short fixed-fee phase.
What can you do this week?
- List your ten most manual, repetitive processes and estimate staff time on each.
- Check whether core data is reachable through APIs or trapped in spreadsheets.
- Define three measurable outcomes AI could deliver.
- Score them on impact and feasibility.
- Decide whether to build the strategy internally or with support.
Pair this with why AI projects fail.
How Solve8 can help
Our consultants work through these eight steps with your operations, finance and IT leads, and our engineers then build the first project on your infrastructure in Australia, with a person approving outputs until it proves accurate. See our AI strategy service, or contact us to scope the first phase.
