AI Readiness Assessment

AI adoption is a programme governance challenge, not a technology challenge. The same disciplines that determine whether an ERP or HRIS implementation succeeds apply here: data governance, change readiness, strategic alignment, and accountable oversight. This assessment evaluates whether those foundations are in place. For the seven dimensions in full, and four questions that test readiness against evidence rather than intent, read how to assess AI readiness below.

21 questions 4 minutes Instant results
0 of 21 answered
1

Data Readiness

Does your organisation have the data governance, quality, and integration capability to support AI workloads?
DR-01
Your organisation has a clear data governance framework that defines ownership, stewardship, and quality standards across all business units.
DR-02
Your organisation systematically captures, stores, and maintains high-quality data across operational systems.
DR-03
Your organisation has the technical infrastructure to securely integrate and analyse data from multiple sources.
2

AI Governance Framework

Are the policies, decision structures, and accountability mechanisms in place to govern AI adoption responsibly?
AG-01
Your organisation has defined policies and decision-making structures for AI adoption, including approval workflows and risk escalation paths.
AG-02
Your organisation has appointed an AI governance lead or committee with clear accountability and authority to oversee AI initiatives.
AG-03
Your organisation monitors AI-related risks and maintains audit trails for AI systems and decisions made by AI tools.
3

Talent and Skills

Does your organisation have the people and capability to guide, build, and sustain AI initiatives?
TS-01
Your organisation has people with expertise in AI, data science, machine learning, or LLM deployment to guide AI adoption.
TS-02
Your organisation has a plan to upskill existing staff in AI capabilities and change readiness across different seniority levels.
TS-03
Your organisation has the capacity to attract or retain AI and data talent in a competitive market.
4

Technology Infrastructure

Does your organisation have the compute, integration, and security foundations to run AI workloads safely?
TI-01
Your organisation has cloud or on-premises computing infrastructure capable of supporting AI workloads and real-time analytics.
TI-02
Your organisation has APIs, data pipelines, and integration frameworks to connect AI systems with existing enterprise applications.
TI-03
Your organisation has cybersecurity and data protection measures in place to support AI use cases and regulatory compliance.
5

Strategic Alignment

Is AI adoption aligned with business objectives, and does the leadership team share a common understanding of what AI will deliver?
SA-01
Your organisation has a documented AI strategy that aligns with business objectives and defines target use cases and success metrics.
SA-02
Your executive leadership team is aligned on the business case for AI investment, including budget allocation and risk tolerance.
SA-03
Your organisation has identified quick wins and early pilot projects to demonstrate AI value and build stakeholder confidence.
6

Ethics and Risk Management

Has your organisation considered the ethical implications of AI and put safeguards in place for bias, compliance, and transparency?
ER-01
Your organisation understands and has processes to mitigate risks such as AI bias, model drift, and unintended consequences in decision-making.
ER-02
Your organisation complies with relevant regulations such as privacy laws, anti-discrimination requirements, and industry-specific AI standards.
ER-03
Your organisation has governance around transparency and explainability of AI systems, especially for high-stakes decisions.
7

Change Readiness

Is your organisation prepared for the cultural and operational change that AI adoption demands?
CR-01
Your organisation has a track record of successfully managing large-scale technology and process changes.
CR-02
Your workforce is open to innovation and has demonstrated willingness to adopt new ways of working.
CR-03
Your organisation has communication and engagement plans to address workforce concerns and ensure adoption of AI-enabled processes.
All 21 questions must be completed.
The Argument

Why AI Readiness Is a Governance Question

Three minutes on what the Auditor-General counted at the Australian Taxation Office, the distinction between a model being sound and an organisation being able to show it, and the seven dimensions this assessment measures.

The Governance Arrives Last: What AI Readiness Measures

Transcript

The Governance Arrives Last 10 chapters

Forty-three models in production 0:00

43 artificial intelligence models were running in production. 32 of them had no completed data ethics assessment. The framework existed. It had not been pointed at them. That is the Auditor-General, counting the Australian Taxation Office's models in 2024.

What a data ethics assessment is 0:22

A data ethics assessment is how an organisation shows a model is fair, explainable and contestable. The audit was not about whether those models were unfair. It was about whether the Tax Office could show they were not.

No defined approval arrangements 0:36

The Australian National Audit Office found no clearly defined arrangements for testing, review and approval. And no evidence of regular monitoring once a model was in production. Capability was never the problem. The Tax Office had built 43 of them. Deployment had run ahead of the governance.

What the audit also said 0:56

The Tax Office was already building the monitoring the audit found missing. It agreed to all 7 recommendations. And the rules for government AI were still being written while the audit ran.

Assigning accountability for AI 1:08

Since 2024, Commonwealth agencies have had to assign accountability for AI. That is a governance instrument, not a technical one. A model in production has already changed a default. And a changed default needs what every programme needs.

What a decision needs 1:26

Someone accountable, and a record of what was approved. A review cadence, and a way to contest the result. This is the same discipline an ERP or HRIS programme needs. The platform is different. The gap is not.

Seven dimensions of AI readiness 1:43

7 dimensions decide whether an adoption holds. Data readiness. Whether what sits underneath is governed, not just available. AI governance framework. Whether anyone holds clear accountability for AI decisions. Talent and skills. Whether you have the people to guide this, and can keep them.

Technology infrastructure 2:04

Technology infrastructure. Whether it can be integrated and secured. Strategic alignment. Whether the executive agrees what AI is meant to deliver. Ethics and risk management. Whether bias and drift are somebody's job. Change readiness. Whether the people working alongside it were prepared.

Symptoms on your own programme 2:25

A tool was approved for a pilot and it is now across the business. Your vendor has added AI to a module you already license, and nobody signed anything. Your privacy team is still forming a view. Your delivery team has booked the go-live.

Twenty-one questions, four minutes 2:41

21 questions. 7 dimensions. 4 minutes. Only two of them are technical. That distribution is the finding. The technology arrives on its own. The framework has to be pointed at it.

How to assess AI readiness

An AI readiness assessment is only useful if it tests behaviour rather than intent. Four questions do most of the work, and none of them requires a specialist to answer.

Where is AI already active in the platforms you run? Most organisations cannot produce the list. Features have been enabled by default, switched on during configuration, or delivered in a release note nobody read. An inventory of where the technology is already making or shaping decisions is the first honest measurement of readiness, and it is usually the most uncomfortable.

For each of those, who approved the delegation? Not who configured it. Who decided that this class of decision should move. If the answer is the implementation partner or the release cycle, the delegation happened without a decision.

What is the standard the output is checked against? A recommendation is only as good as the reference point used to challenge it. Organisations that cannot name the standard are not reviewing outputs. They are receiving them.

What happens when it is wrong? Follow one hypothetical error end to end. Who notices, how quickly, what gets rolled back, who is accountable to the customer or the employee affected. If the path breaks at any point, that break is the readiness gap and no amount of infrastructure closes it.

Four questions, all answerable from evidence that already exists. An assessment that scores intention will return a comfortable result. An assessment that scores these will return a useful one.