FAQs
RICS AI Compliance
The RICS AI Professional Standard sets out the principles and expectations for the responsible use of artificial intelligence within regulated surveying practice. It provides guidance on governance, transparency, accountability, risk management, and professional oversight when using AI tools.
The standard came into force in March 2026 and applies to firms and professionals operating within RICS regulated practice.
Yes. The principles apply proportionately across firms of all sizes. Even smaller practices are expected to understand and manage AI-related risks appropriately.
The standard applies broadly to AI systems used within surveying practice, including generative AI tools, automated reporting systems, valuation support tools, data analysis platforms, and workflow automation technologies.
No. The focus is on responsible professional practice rather than technical development. Surveyors are expected to understand the risks, limitations, and governance implications of the AI tools they use.
AI compliance helps firms protect professional standards, maintain client trust, reduce operational risk, and demonstrate responsible decision-making in an evolving regulatory environment.
No. Professional judgement and human oversight remain essential. AI should support professional decision-making, not replace it.
Potential risks include inaccurate outputs, bias, data security concerns, lack of transparency, regulatory breaches, and reputational damage.
Firms should maintain clear governance policies, risk registers, due diligence procedures, staff training records, and documented oversight processes.
Yes. OXC.AI offers additional consultancy and compliance support for firms requiring assistance with implementation, documentation, and governance development.
Courses & Training
OXC.AI provides professional AI training courses, including the RICS AI Compliance Course, introductory webinars, and specialist programmes focused on AI governance, sustainability, and responsible adoption.
The courses are designed for professionals, business leaders, surveyors, compliance teams, and organisations seeking practical understanding of AI in professional environments.
Yes. No prior coding or technical background is required for most introductory and compliance-focused courses.
OXC.AI offers both online live delivery and selected classroom-based training formats, depending on the programme.
Yes. Many courses include recognised Continuing Professional Development (CPD) learning hours.
Training is delivered by multidisciplinary experts, including Chartered Surveyors, AI specialists, academics, and governance professionals.
The training combines practical professional application with academic insight, regulatory understanding, and real-world governance experience.
Yes. Group and organisational bookings are available, including discounted rates for larger teams.
Yes. Courses include practical resources, guidance materials, templates, and implementation tools where relevant.
Yes. Bespoke training can be developed around specific organisational requirements, sectors, and risk profiles.
AI Governance & Assurance
AI governance refers to the structures, policies, controls, and oversight processes used to manage AI responsibly within an organisation.
Good governance helps organisations manage risk, maintain transparency, support compliance, and ensure AI is used ethically and professionally.
AI assurance involves evaluating and validating AI systems to ensure they operate reliably, safely, transparently, and in line with organisational requirements.
A robust framework typically includes policies, accountability structures, risk assessments, staff training, documentation processes, and review procedures.
Policies should be reviewed regularly to reflect changes in technology, regulation, organisational practice, and emerging risks.
An AI risk register is a documented record of identified AI-related risks, their impact, likelihood, mitigation measures, and oversight responsibilities.
Firms should conduct due diligence covering data handling, security, transparency, supplier accountability, and operational risks.
No. Governance is relevant to organisations of all sizes using AI tools within professional workflows.
Yes. Clear governance demonstrates professionalism, accountability, and responsible use of emerging technologies.
Yes. OXC.AI provides consultancy and practical support to help organisations establish proportionate governance and assurance processes.
Consultancy Services
OXC.AI provides consultancy across AI strategy, governance, compliance, responsible adoption, assurance, training, and organisational transformation.
Consultancy services are suitable for businesses, regulated firms, public sector organisations, and professional practices exploring or implementing AI.
Yes. Consultancy support includes strategic planning for AI adoption aligned with organisational objectives and operational realities.
Yes. OXC.AI supports organisations in understanding and implementing AI compliance requirements, particularly within regulated sectors.
Yes. Support may include governance frameworks, internal policies, risk registers, and operational guidance.
Yes. Consultancy engagements often involve directors, compliance teams, operational leaders, and decision-makers.
Yes. Recommendations and frameworks are developed proportionately around each organisation’s needs, risk profile, and sector.
Yes. Existing workflows, governance arrangements, and AI usage can be reviewed to identify strengths, gaps, and improvement opportunities.
Yes. OXC.AI can provide ongoing support during implementation and organisational adoption phases.
You can contact the OXC.AI team directly through the website contact form, email, or by arranging a call.
Booking, Fees & Certification
The current fee for the professional compliance course is £350 plus VAT.
No. VAT is applied separately at the standard UK rate.
Yes. Group bookings may qualify for discounted pricing.
Yes. The RICS AI Compliance Webinar is offered free of charge.
Yes. Participants may request a certificate of completion for CPD and professional development purposes.
The courses are structured to support Continuing Professional Development requirements.
Yes. Private and bespoke sessions can be arranged for teams and organisations.
Courses can be booked directly through the OXC.AI website course pages.
Participants receive confirmation details, joining instructions, and access information prior to the session.
Yes. OXC.AI encourages organisations and individuals to contact the team to discuss suitable options before booking.
Responsible Use of AI
Responsible AI refers to the ethical, transparent, accountable, and proportionate use of artificial intelligence technologies.
Responsible use helps protect individuals, organisations, clients, and wider society from unnecessary risk and unintended harm.
Yes. Responsible AI includes consideration of data privacy, confidentiality, and secure information handling.
Yes. AI systems can reflect or amplify bias present in training data or operational processes.
Transparency supports trust, accountability, and informed professional decision-making.
Human oversight ensures professional judgement, ethical consideration, and accountability remain central to decision-making.
Yes. Clear policies help establish consistency, accountability, and governance across the organisation.
No. Responsible AI also involves leadership, culture, ethics, governance, and professional conduct.
Yes. Responsible practices can strengthen confidence among clients, stakeholders, regulators, and staff.
Organisations should begin with education, governance, risk assessment, and clear operational frameworks.
Technical & Professional Requirements
No. The courses are designed for professionals rather than software developers or data scientists.
Participants typically require a stable internet connection, a computer or laptop, and access to video conferencing software.
Yes. The content is designed to be accessible and professionally relevant without requiring technical expertise.
Yes. Technical concepts are explained in practical, understandable language suitable for professional audiences.
Most courses do not require formal technical qualifications or prior AI experience.
The courses are suitable for surveyors, consultants, managers, compliance professionals, directors, and wider professional services teams.
No. The courses are valuable both for organisations already using AI and those exploring adoption.
No. Foundational concepts are introduced as part of the learning process.
Yes. Sessions are designed to encourage discussion, questions, and practical engagement.
Yes. Guidance and support are available to help participants access and engage with the training effectively.
Glossary
- A
- Agentic AI
AI systems capable of acting with a level of autonomy, carrying out tasks, making decisions, and responding dynamically to changing conditions.
- AI Adoption
The process of introducing and integrating AI technologies into day-to-day business operations and workflows.
- AI Assurance
Independent evaluation and validation processes that help organisations trust the safety, reliability, and performance of AI systems.
- AI Compliance
The process of ensuring AI systems, workflows, and organisational practices meet legal, regulatory, and professional standards.
- AI Ethics
The study and application of moral principles relating to the design, deployment, and impact of artificial intelligence.
- AI Governance
The frameworks, policies, oversight, and accountability measures used to ensure AI is deployed responsibly, ethically, and safely.
- AI Literacy
A practical understanding of AI concepts, opportunities, risks, and limitations, enabling individuals and organisations to engage confidently with AI technologies.
- AI Strategy
A structured organisational approach for adopting, integrating, and scaling AI technologies to achieve measurable business value.
- AI Transparency
Ensuring organisations clearly communicate how AI systems operate, use data, and influence decisions.
- Artificial Intelligence (AI)
Technology designed to perform tasks that would normally require human intelligence, such as recognising patterns, understanding language, making decisions, and learning from experience.
- Automation
The use of technology to complete repetitive or structured tasks with minimal manual intervention.
- D
- Data-Driven Insights
Findings and recommendations generated through the analysis of large volumes of structured or unstructured data.
- Decision Support Systems
AI-powered tools designed to assist people in making faster, more informed, and evidence-based decisions.
- Digital Transformation
The integration of digital technologies, including AI, into business operations, workflows, and decision-making processes.
- E
- ESG Reporting
Environmental, Social and Governance reporting used by organisations to measure and communicate sustainability performance.
- Explainability
The process of making AI outcomes understandable and transparent for users, organisations, and regulators.
- H
- Human-Centred AI
AI designed around human needs, behaviour, trust, and usability, ensuring technology enhances rather than replaces human judgement.
- I
- Interpretability
The ability to understand and explain how an AI system reaches its conclusions or decisions.
- L
- Large Language Models (LLMs)
Advanced AI models trained on vast amounts of text data, enabling systems to understand, generate, summarise, and respond to human language.
- M
- Machine Learning (ML)
A branch of AI where systems learn from data and improve performance over time without being explicitly programmed for every task.
- Meta-Learning
Often described as “learning to learn”, this area of AI focuses on systems that improve how quickly they adapt to new tasks or environments.
- P
- Predictive Analytics
Using AI and data analysis to identify patterns, forecast trends, and support more informed decision-making.
- Prompt Engineering
The process of designing clear, structured instructions that help AI systems produce more accurate, useful, and reliable outputs.
- R
- Reinforcement Learning
A machine learning method where AI systems learn through trial and error by receiving rewards or penalties based on outcomes.
- Responsible AI
An approach to AI development and deployment that prioritises ethics, transparency, fairness, accountability, and societal impact.
- S
- Sustainability in AI
The consideration of environmental, social, and economic impacts associated with AI technologies and infrastructure.