Matt Sharp

‘STAIRWAY has transformed RLB's AI maturity, delivering efficiency gains, upskilling our workforce, and positioning us as a leader in ethical AI innovation’

Part of the AI Leadership Index – a curated list of the most inspirational figures shaping the AI revolution.

Matt Sharp is Chief Digital Officer (CDO) of RLB UK. He is strategic advisor to the RLB UK Board on matters regarding Digital, Data, Technology, IT and AI.

Matt chairs the RLB Global Digital Transformation Committee, Global AI Community, Global Data Programme and Global Cyber Security Community advising the Global Board of RLB, and is also the founder of RLB Digital - RLB's dedicated Digital advisory practice to the Construction and Built Environment Industry.

Matt is passionate about ensuring the Construction industry can go on the digital transformation journey including AI and has shared the RLB AI story at Digital Construction Week as well as briefing professional bodies like the RICS and APM.

Please share a case study of how you have you innovated with AI over the last year?

Over the past 12 months, RLB has implemented STAIRWAY, our Strategic AI Research programme, designed to embed artificial intelligence across the organisation in a structured, ethical, and impactful way.

• Step 1: AI Enablement We laid the foundations by creating an AI Strategy, Policy, and Principles, ensuring responsible and transparent adoption. We launched company-wide AI awareness briefings and established a multi-disciplinary AI Council to govern all AI activities and safeguard client interests. Our data architecture was strengthened through Microsoft Fabric, enabling secure and scalable AI integration.

• Step 2: AI Capability To build expertise, we recruited dedicated AI and Data specialists and introduced AI learning tracks via our RLB Academy in partnership with Multiverse. Department-specific training and interactive "promptathons" accelerated practical skills in tools like Microsoft 365 Copilot.

• Step 3: Pilot Deployment We piloted M365 Copilot across business functions, identifying high-value use cases and building the business case for enterprise-wide adoption.

• Step 4: Off-the-Shelf Solutions We evaluated and implemented approved AI-enabled solutions for tasks such as bid automation and image-based inspections, delivering immediate project benefits.

• Step 5: AI R&D Our dedicated AI team advanced into agentic development, grounding, and fine-tuning LLMs, tackling targeted use cases that solve client challenges and prove our AI capability.

STAIRWAY has transformed RLB's AI maturity, delivering efficiency gains, upskilling our workforce, and positioning us as a leader in ethical AI innovation.

What is the biggest challenge you have faced with AI innovation and how you have overcome it?

The most significant challenge was low organisational AI maturity and inconsistent understanding across our business. AI was often perceived as a purely technical initiative rather than a strategic enabler, creating resistance to adoption and uncertainty about its value. This was compounded by risks around data integrity, confidentiality, and ethical use, which raised concerns among colleagues and clients.

We tackled this challenge through the structured, people-first STAIRWAY programme. This is a stepped approach to increase AI awareness and capability, implement an AI ecosystem, activate AI quick wins, evaluate OTS AI solutions and drive our own AI R&D.

• AI Governance Framework: We established robust guardrails and principles to ensure AI usage aligned with compliance, ethics, and business objectives. This framework provided clarity and mitigated risks.
• AI Council & Policy: A cross-business AI Council was formed to own governance, manage escalations, and embed accountability. We published an AI Policy and integrated it into existing processes to reinforce trust and transparency.
• Change Management: We designed a people-first change plan to build awareness, desire, and capability across teams. This included workshops, training, and clear communication of benefits.
• Practical Enablement: We prioritised "turn on AI" solutions in existing Microsoft tools (Teams transcription, MS365 Copilot) to deliver immediate productivity gains and demonstrate tangible value. This built confidence and paved the way for specialist AI solutions for high-impact use cases.

By combining governance, cultural change, and quick wins, RLB transformed AI from a perceived risk into a trusted innovation driver, unlocking efficiency, reducing manual effort, and positioning the business for long-term success.

How do you balance innovation with ethical safeguards?

At RLB, innovation and ethics are inseparable. We recognise that AI can unlock significant efficiencies and new capabilities, but without robust safeguards, it poses risks to data privacy, fairness, and trust.

To balance these priorities, we implemented a Responsible AI Framework built on five principles: Responsible, Transparent, Ethical, Safe, and Accountable. These principles guide every AI initiative, from ideation to deployment.

We established an AI Council to oversee governance and ensure compliance with legal and ethical standards. This includes rigorous Data Protection Impact Assessments (DPIAs) for projects involving personal data, supported by clear policies aligned with GDPR and UK Data Protection Act requirements.

• Practical measures include guardrails and policies: Defined acceptable use, bias mitigation, and IP protection.

• Human Oversight: AI outputs are reviewed by subject matter experts to prevent errors or unethical outcomes.

• Transparency: Clear communication to clients and teams about how AI is used and its limitations.

By embedding ethics into our operating model and combining governance with education, we enable innovation that is safe, compliant, and trusted ensuring AI delivers value without compromising integrity.

How do you believe the artificial intelligence space will change over the next 12-24 months?

The AI landscape will evolve rapidly, driven by three key shifts: Generative AI at RLB will move beyond experimentation into core business workflows. We already have pilot groups exploring M365 Copilot and the use of agents. Use cases are becoming apparent that drive personal productivity and process efficiency. Specialist AI applications will gain traction in construction and consultancy.

Our own AI R&D will bear fruit and demonstrate art of the possible for how AI can be used. We will differentiate RLB AI solutions from generically available AI ensuring RLB quality and value.

With increasing adoption, we will see ongoing evolution of governance, regulation and insurance positioning around the use of AI. Facing significant initial uncertainty, protective guards have been set high.

As the use of AI technology becomes more common place and use cases are tested and proved we will see an industry acclimatisation to the use of the technology and adaptation of our professional guidelines. Human-AI Collaboration will define success. Rather than replacing roles, AI will augment expertise, enabling our professionals to focus on client relationship and strategic value. We will deliver further significant awareness raising and upskilling of all our colleagues in the next couple of years.