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We assess workflows, data, systems, governance, risk and team readiness, then prioritise the AI opportunities worth piloting, preparing, buying, building or avoiding.
If several of these sound familiar, the constraint is not AI capability. It is AI clarity.
Select the symptoms you recognise to see where an audit would start.
The AI Readiness Audit is a fixed-scope, fixed-price diagnostic for businesses that want a commercially grounded starting point for AI. We review your workflows, tools, systems, data, current AI usage, team capability, risks and commercial priorities, then identify which AI opportunities are valuable, feasible and safe enough to pursue, and which should be avoided, deferred or prepared for later. The audit exists to reduce the pressure to “do AI” indiscriminately: it protects against disconnected pilots, inappropriate AI use, weak data foundations, use cases with no owner, unmeasured value and insufficient human oversight.
Every candidate use case starts with the commercial question: what would this improve (capacity, cost, service or decision quality) and by how much.
Not every use case should proceed. Each entry in the opportunity register lands on one of these recommendations, including the last one.
The value, data and ownership are ready: pilot with acceptance criteria agreed before anything is built.
The opportunity is real, but the data or workflow needs preparation before it can support AI reliably.
An existing product already solves it: configure that rather than building something custom.
The use case is valuable, specific to how you operate, and justified as a custom system.
Staff are already using AI here: the priority is structure, guidance and oversight, not new tooling.
Worth revisiting when the data, workflow or team readiness changes, with the triggers named.
A simpler fix (rule-based automation, process change or better search) solves the problem with less cost and risk.
A prioritised, costed roadmap you own, build with us or without us.
The audit is diagnostic before it is prescriptive. The sequence reflects that.
We start with the commercial problem AI is supposed to improve (capacity, cost, service or decision quality) and the constraints leadership already knows about.
We map the workflows in scope as they actually run day to day, including the informal AI experiments already underway across your team.
We review the systems, tools and data behind those workflows: what is accessible, what is structured, and what would need preparation.
Candidate use cases go into an opportunity register, each scored on value, feasibility, risk, data readiness, adoption effort and time to evidence.
We assess accuracy, privacy and oversight requirements per use case, so the roadmap reflects what is safe to pursue, not just what is technically possible.
Each use case lands on one of seven conclusions: pilot, prepare, buy, build, govern, defer or do not use AI. Most ideas do not make the first 90 days; that is the point of the exercise.
We work the findings through with leadership, so the roadmap is understood, challenged and owned before any budget moves.
The AI Readiness Audit produces a prioritised adoption roadmap. Where it leads depends on what it concludes. Select a pathway to see how it connects.
Owns: commercial value · workflow fit · data readiness · governance · adoption · measurement.
The roadmap is approved and you're ready to build.
Assistants, automations, document systems, retrieval and integrations.
The audit decides what to build; AI Engineering builds it.
Payment Processing Cost Reduction. An ecommerce retailer was losing a significant percentage of revenue to payment processing and invoice platform fees. Web Lifter redesigned the entire sales and payment workflow, replacing Stripe and Paycove with a direct Westpac PayWay integration and a custom-built invoicing platform. The new architecture reduced transaction costs, streamlined operations, and delivered immediate profit improvements without requiring any increase in sales volume.
Read the case“We can't recommend Web Lifter highly enough … a digital partner who could understand our operations, connect the dots between marketing and backend systems, and deliver real results.”
The audit is the fixed-scope, fixed-price diagnostic: a point-in-time assessment that produces a prioritised AI roadmap, delivered and done. Technology Strategy & Roadmapping is the deeper consulting engagement that often follows. It sequences AI adoption alongside your wider systems, integration and platform decisions, with governance and prioritisation as pilots run and priorities shift. If you are unsure where to start, start here: it is the lowest-risk entry point.
The audit suits established businesses with real operational complexity: typically $2M+ in revenue, multiple systems and workflows, and a leadership team that needs a defensible plan. If your operation is simpler than that, a fixed-scope diagnostic is probably more than you need, and we will say so on the scoping call.
No. Implementation, prototypes, integrations, data preparation, training and governance documents sit outside the audit unless explicitly scoped. The audit exists to decide what should be piloted, built, bought, governed, trained or avoided. Building the approved solution is AI Engineering's job, and keeping that separation is what keeps the recommendations honest.
A chatbot request is usually a symptom of a use case, not a definition of one. The audit asks what it would need to know, who would own it, how its answers would be checked and whether a simpler fix serves better, then scores it against the alternatives. If it earns its place, it proceeds with acceptance criteria; if not, you have avoided an expensive lesson.
Then that is what the roadmap says. Part of the deliverable is showing where the business is not ready (usually data quality, workflow structure or governance) and what preparation would change that. A deferred recommendation with a preparation path is a far better outcome than a failed implementation.
It is common, and it is one of the strongest reasons to run the audit. Informal use shows where staff feel the friction: useful signal. But ungoverned use also carries accuracy, privacy and data-handling risk. The audit reviews current usage and recommends governance direction, so experimentation can become structured rather than shut down.
No. Assessing your data is part of the audit. The data readiness assessment shows whether your data can support the use cases worth pursuing, and what preparation would be required where it cannot. If foundational data work is needed, that is scoped separately as Data Engineering.
The audit is fixed-scope and fixed-price, so you know the full cost before committing. The exact figure depends on the size and complexity of your operation (the number of workflows, systems and teams in scope) and is confirmed on a short scoping call.
Access and context: time with the people who run the workflows in scope, honest process detail, and visibility of the systems and data involved. No confidential data is requested through the website. Scope and data sensitivity are agreed before anything is shared. The quality of the roadmap depends on it.