WSP is expanding Microsoft 365 Copilot across its global workforce as part of a **seven-year, $1 billion strategic partnership with Microsoft**. The goal is to give tens of thousands of engineers and scientists more time to focus on innovation and client impact. In practice, WSP teams are using Copilot to:

• Automate repetitive tasks that previously took up a lot of time.
• Improve grammar and clarity in emails and documents.
• Find answers and information quickly across their content and tools.
• Support multi-language communication and translation for global projects.
• Assist with coding, including PowerShell, T-SQL, Kusto, and Excel formulas.

According to WSP’s internal surveys, **84% of Copilot users report saving time every day**. That time is being reinvested into:

• Deeper collaboration with clients.
• Training and upskilling engineers and scientists.
• Higher-value work such as complex problem-solving and design.

For WSP, Copilot is helping to **reshape productivity norms** in an industry where overall productivity has been largely flat for more than 50 years, while keeping quality and accountability non-negotiable.

WSP is already seeing AI move from concept to measurable impact, particularly in large infrastructure and mining projects. Transport validation in South America
For metro, subway, high-speed rail, and urban transit projects, WSP must run rigorous compliance checks against hundreds or thousands of variables before opening systems to the public. These final validation phases can take weeks, months, or even quarters. In a recent South American transportation project, WSP piloted an AI-enabled approach that showed teams could complete the final validation phase in just **10–15% of the usual cycle time**—while keeping quality management non-negotiable. This kind of acceleration means:

• Infrastructure can open sooner.
• Communities gain faster access to safer, more efficient transport.
• Project teams can redirect effort to higher-value engineering and design work.

Mining and tailings management
In the mining sector, WSP is working with Microsoft to **reimagine how tailings are managed** over the 50–100 year life of a mine. By using AI to analyze thousands of data points—inspections, assessments, and regulatory requirements—WSP aims to:

• Identify patterns that support more informed decisions.
• Contribute to reduced climate risk and improved financial resilience.
• Enhance environmental safeguards around tailings facilities.

These examples illustrate how WSP is using AI not just to streamline internal work, but to deliver faster, more informed outcomes for clients and communities.

WSP treats AI with the same sense of accountability that engineers apply to physical infrastructure. The company has built an AI framework that aims to align with the **EU AI Act** and is anchored on four non-negotiable principles:

• Innovation-friendly – Teams are encouraged to move quickly, experiment, and learn from real-world application, without losing sight of risk.
• Proportional – Risk management scales with the potential impact of each project, so higher-stakes work gets more rigorous oversight.
• Transparent – WSP maintains openness with clients and communities about how AI is used and where it adds value.
• Human-centric – Health, safety, and human rights remain at the center of every AI-related decision.

WSP describes its approach as keeping “tech in the middle”:

• Engineers and scientists stay fully accountable for what goes into AI models and what comes out.
• AI supports, but does not replace, professional judgment.

The company is also piloting ways to **capture and amplify institutional knowledge**. Around **300 engineers and scientists** are testing AI tools that learn from senior specialists, so that:

• Colleagues can get instant, always-on answers to technical questions.
• Younger professionals can access expert insight more easily.
• Clients benefit from faster, more consistent expertise across disciplines.

By combining this governance model with a focus on measurable value, WSP aims to build an AI culture that is both agile and accountable, supporting long-term innovation in infrastructure, energy transition, and climate resilience.