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.
21 questions4 minutesInstant 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.