Automation and artificial intelligence (AI) are reshaping industries across Canada and beyond. These technologies offer significant enhancements in both efficiency and quality of work. Automation streamlines repetitive, rule-based tasks—freeing up time and reducing errors. AI, on the other hand, augments human capability by learning from data, interpreting complex patterns, and supporting decision-making. Together, automation and AI are catalysts for workplace efficiency and innovation.
The question isn’t whether these technologies will change the workforce, but how we’ll prepare people to lead in a rapidly changing environment. As organizations respond to these changes, the integration of automation and AI is becoming essential for continued growth and progress.
This transformation is not without its challenges. Organizations today face two converging trends: an experienced generation nearing retirement, and a surge of digital tools redefining work. While this is undeniably a disruption, it also presents a powerful opportunity to build continuity and resilience. By capturing the knowledge of senior staff through digital systems and investing in early-career pathways, we ensure expertise is preserved and new talent is empowered. For instance, by working together, junior staff with digital skills can help transform the experience and expertise of senior colleagues into documented processes, digital tools, and knowledge platforms. This not only preserves critical know-how, but also allows senior staff to focus more on client needs and complex problem-solving. In turn, early-career professionals gain a deeper understanding of best practices by codifying and building on the work of their mentors—often developing new solutions that previous generations didn’t have the time or tools to create.
As the lead of WSP Canada’s Future Ready® program, I witness these transformative forces every day. Our teams thrive by blending innovative thinking and technical expertise from new hires with the mentorship and strategic guidance of experienced professionals. This balance—between leveraging automation and drawing on human creativity and judgment—lays the foundation for a truly resilient workforce, well-equipped to deliver lasting value for clients and shape the future of work.
The evolving early career experience
Entry-level roles are changing. Tasks that once served as proving grounds—like data entry, drafting reports, or routine coding—are increasingly automated by tools like ChatGPT and GitHub Copilot. While this boosts efficiency, it also means young professionals must develop new skills to thrive. Employers are shifting from traditional credentials to a skills-first approach, valuing digital literacy, data analysis, creative problem-solving, communication, and adaptability. In parallel, entirely new roles are emerging—such as prompt engineer, AI ethics officer, and sustainable AI analyst. At WSP, early-career professionals aren’t just learning how to use AI and automation—they’re helping shape how we apply these tools, ensuring responsible integration and knowledge capture for future teams.
During this transition, mentorship is evolving from a one-way transfer of knowledge to a dynamic, two-way exchange. Early-career professionals help experienced colleagues explore new tools, while mentors provide context and strategic guidance. Structured mentoring and knowledge-sharing platforms sustain culture, improve engagement, and retain top talent. This partnership accelerates learning and ensures that both automation and AI are used to their fullest potential. At the same time, leveraging technology for impact means empowering young professionals to drive innovation while ensuring knowledge is shared across generations.
Our teams are leveraging automation to streamline data capture and routine processes, while AI is being used to interpret complex information. For example, young professionals are applying automation, computer vision, and machine learning to simplify key technical tasks, such as automating field data collection and interpretation or developing platforms that improve consistency and efficiency. By utilizing AI-driven tools for image and sound analysis, teams are minimizing repetitive manual work, enhancing data quality, and speeding up project delivery—strengthening WSP’s reputation for innovation and operational excellence.
Preparing for the future
As we look ahead, it’s clear that building a resilient workforce requires more than just adapting to new tools; it demands a commitment to responsible innovation, continuous learning, and a focus on delivering value to clients. AI is not automation, it’s augmentation. Responsible adoptions require clear governance, ethical guidelines, and continuous upskilling. Expert evaluation remains essential to ensure quality and context, especially as AI systems become more integrated into our workflows. By piloting AI initiatives thoughtfully, we build trust, equity and accountability, and position ourselves as strategic advisors to our clients.
Clients expect cost efficiency and transparency, especially when AI is involved. By automating routine tasks, we can deliver projects more efficiently. By rather than simply passing along cost savings, we reinvest these efficiencies into higher-value outcomes—such as enhancing project resiliency, accelerating delivery, or introducing innovative solutions that support our clients’ long-term goals. This approach ensures our clients receive greater value and see us as trusted partners in a rapidly evolving landscape.
The future of work is not about choosing between AI and opportunity, it’s about integrating both to build a resilient, adaptable workforce. By investing in young professionals, prioritizing mentorship, and embracing ethical AI, organizations can ensure that the next generation is prepared not just to survive, but to thrive in the age of AI.
Acknowledgements
This article draws on interviews with WSP experts in innovation, digital tools, and mentoring young professionals across multiple sectors. Thank you to the following colleagues who contributed insights to this article: Abdé Essaidi, Andrea Dorton, Anna Robak, Anthony Man, Bryan Waller, Carla Evans, Cheryl Trent, David Carnovale, Herman Won, Iveta Demirova, Jean-Frederick Duquette, Jean-Luc Daviau, Joel Inkster, Mahaut Ricciardi-Rigault, Maya Watson, Melissa Gatineau, Ross Taylor, Sawan Dhaliwal, and Shawn Chow.
