AI readiness is not a single infrastructure purchase or a decision to adopt a particular model. It is the organisation’s ability to select a useful problem, provide appropriate data and access, assess risks, operate the system and review its results. A readiness review should consider technology alongside governance, people, suppliers and business ownership before a pilot becomes part of a real workflow.
Start with the use case
Describe the work the AI system is intended to support, who will use it and what decision or task it will influence. Identify the expected benefit, the people affected, the information involved and the consequences if the output is wrong, delayed or unavailable.
Decide how success will be evaluated. A technical demonstration may show that a model can produce an answer, but it does not establish that the answer is reliable enough for a business process. Define who will review outputs, when a human must intervene and how users can report errors.
Understand the data and information flows
Map the sources of data, who owns them, how they are accessed and whether they are suitable for the proposed use. Check quality, freshness, permissions, retention and any contractual, privacy or regulatory constraints that apply to the information. Confirm where prompts, files, outputs and logs are stored and which service providers can process them.
Do not assume that data already held by the organisation can be used for any AI purpose. The organisation should confirm the lawful and authorised use of information, and establish how sensitive data is handled. Review the provider’s terms and configuration for the specific service before entering confidential or personal data.
Plan identity, access and security
AI systems may connect to cloud services, collaboration platforms, business applications, data stores and external APIs. Apply access controls to users and service identities, and limit permissions to the data and actions required by the use case. Review how credentials are stored, how access is revoked and how activity is logged.
Consider risks such as unintended disclosure, excessive permissions, unsafe output handling, prompt injection and misuse of connected tools. The controls depend on the system design. A standalone assistant, a retrieval system connected to internal documents and an agent that can take actions create different access and operational questions.
Check the technology foundation
Review whether the cloud environment, network paths, identity systems, devices, data services and monitoring can support the intended workload. Estimate demand, latency, storage, integration and operating costs. Establish how the service behaves during an outage and what users should do if it is unavailable.
Not every AI project requires new infrastructure. Some services are delivered through existing cloud or software platforms, while other workloads may require additional compute, data pipelines or specialised environments. Match the architecture to the use case and verify the provider’s requirements rather than assuming all AI workloads need the same capacity.
Establish governance and responsibility
Assign an accountable business owner, a technical owner and a risk or compliance reviewer appropriate to the use. Document who approves a new use case, what types of information may be used, what outputs require review and how issues are reported. Include suppliers and third-party services in the assessment.
NIST’s AI Risk Management Framework is voluntary guidance intended to help organisations manage AI risks and consider trustworthiness in the design, development, use and evaluation of AI systems. It organises risk work around Govern, Map, Measure and Manage.^1 It is not a certification or a checklist that guarantees a system is safe. Organisations should adapt its outcomes to the use case and their own risk process.
Prepare people and operations
Identify the skills needed to use, support and evaluate the system. Users may need guidance on appropriate inputs, output limitations, verification and escalation. Support teams need to understand how to diagnose access or service issues and who owns the model, integration and underlying data.
Business Chamber Queensland’s public summary of its 2025 Digital Future of Work Report says seven in ten Queensland businesses have a basic or below understanding of AI and 3.3% consider themselves experts. The same public summary reports a strong or critical need for new skills and retraining among more than half of businesses.^2 Those figures describe the Chamber’s survey summary, not a readiness score for an individual organisation.
Pilot, monitor and decide whether to scale
Start with a bounded pilot, test representative tasks and document failures as well as successful outputs. Review accuracy, user impact, data handling, access, cost, support effort and performance against the criteria set at the beginning. Decide whether to improve, expand, pause or stop the use case.
If the pilot scales, keep monitoring it. Models, data, providers, costs and user behaviour can change. Review roles, access, incident handling and output quality as the system becomes part of day-to-day work.
Build readiness around the work
A useful readiness review connects the business goal to data, identity, infrastructure, risk, skills and ownership. It avoids buying technology before understanding what it should do and how the organisation will operate it.
Read what cloud providers do not cover and how identity controls access. For AltTab’s current cloud, Microsoft, identity and workplace planning scope, see Cloud and Microsoft services.