Bringing People Along: Human-Centred Leadership in the Age of AI
Future of Work
In this article, Urmilla Mahabirsingh reflects on the responsibility of HR leaders to guide ethical decision-making in an era where AI is advancing faster than regulation or public understanding. She explores the risks of using AI without proper oversight, the need to actively prevent bias, and the emotional toll of accelerated digital workplaces. She highlights the importance of designing inclusive systems that bring everyone along. The article calls on HR professionals to protect trust, fairness, and human dignity as technology reshapes the future of work.
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We’re living in a time where technology is advancing faster than most of us can keep up. It’s moving ahead of regulation, ahead of policy, and sometimes ahead of our ability to fully understand what it means. Artificial Intelligence is no longer something on the horizon; it’s already here, woven into the tools we use, the choices we make, and the way our workplaces operate.
As someone working in HR and leadership in North America, I find myself asking what this means for the future of work and, more importantly, what it asks of us right now. HR has always been about people, culture, and ethics, but when machines start to make decisions that used to rely on empathy and human judgment, we have to think carefully about our role.
1 Clarity Lags Behind Innovation
Across every industry, AI is developing faster than our ability to regulate it. In Canada, there’s no single law that clearly explains how artificial intelligence should be used. The federal government is trying to create a framework through the proposed Artificial Intelligence and Data Act, which is part of Bill C-27. This draft legislation is focused on high-impact AI tools and aims to promote transparency, reduce bias, and maintain human oversight ("The Artificial Intelligence and Data Act (AIDA) – Companion document," 2023).
But while this work is in progress, different sectors are figuring things out on their own. In healthcare, agencies and provinces are exploring how AI can help with diagnostics and patient care, but their approaches differ. In education, school boards are testing AI for personalized learning, though many are still unsure how to manage privacy and fairness. In finance, national regulators have issued broad advice, but the details are still vague and open to interpretation ("Artificial Intelligence in Finance requires specific safeguards: OSFI and GRI report - Explainability among key principles for gaining confidence in AI," 2023).
For those of us in HR, this leaves a gap. Many companies are introducing tools like AI resume screening, automated training platforms, or systems that analyze employee sentiment. But there’s often little clarity around accountability or risk. When AI is involved in hiring, promotions, or performance reviews, the stakes are high. We can’t afford to get it wrong.
This isn’t a call to reject progress; it’s a reminder that we need to pause and ask important questions. What happens when we move forward without proper guardrails? And are we ready, as HR professionals, to bridge the gap between what technology can do and what our values demand?
2 Innovation Without Infrastructure
One mistake I see over and over is when companies roll out new AI tools without thinking about the people and processes that need to support them. A recruitment system might claim to reduce bias by hiding names or other personal details. That sounds promising. But who’s testing the algorithm? Who’s monitoring the outcomes? And how do we know that the system is actually working the way it says it is?
The truth is, even the most advanced AI tools are not neutral. They’re shaped by the data we feed them and by the assumptions built into their design. If that data reflects past discrimination, even subtly, then the system can end up repeating or reinforcing those patterns, just with more speed and less visibility.
Guaranteeing that an AI tool is free from bias isn’t a one-time task. It’s a continuous process that requires regular audits, transparent reporting, and clear accountability. It means involving people with diverse backgrounds and perspectives in the design and testing of the tool. It means comparing outcomes across different groups, looking for disparities, and being willing to act when something does not look right.
We also need to remember that bias is not always about what's included. Sometimes it's about what's missing. If a system hasn’t been trained on data that reflects certain communities, abilities, or experiences, it may fail to understand or respond to them accurately. Inclusive design is so important, not just in theory, but in practice.
In performance management, for example, an AI-generated dashboard might point to trends in productivity. But who decides what counts as ‘productive’? And who translates those data points into decisions about promotions, feedback, or support? Without human judgment and a careful eye on context, we risk turning complex people into one-dimensional scores.
Having spent years helping organizations grow strong, inclusive leadership, I believe that innovation on its own will never be enough. It needs structure and intention. That means setting clear policies, offering ongoing training, building strong oversight, and being ready to make adjustments when things go wrong. Otherwise, we risk treating technology like a shortcut when what we really need is deeper reflection and care.
3 The Hidden Cost of Speed
AI and other digital tools often promise to make work more efficient. But what they sometimes deliver instead is speed without support. In HR, we’re seeing more burnout, more stress, and more employees who feel disconnected or overwhelmed. Some feel like they are being watched more than they are being helped. We often talk about employee engagement as a number. But behind that data is a deeper story. People want to feel seen. They want to feel like their work matters to someone, not just to a program tracking output. They want to be trusted and to have some control over their own work experience.
If we’re going to invest in AI, we also need to invest in well-being. That includes mental health programs, safe and open workplaces, and leadership that understands how to balance technology with empathy.
4 Bringing People Along
Technology holds great promise, but inclusion must be intentional. In Trinidad, the current government has taken meaningful steps to bridge the digital divide. By distributing personal laptops to students across various stages of primary and secondary education, they are expanding access to digital learning and promoting greater equity in education. At the same time, public services are being modified with care, not by shifting everything online, but by thoughtfully considering what works best for all citizens. While digital systems can enhance efficiency, not everyone finds them easy to navigate. Older adults, in particular, may face difficulties with online forms and platforms. That’s why a measured, responsive approach is so important, ensuring that seniors and others are not left behind as the country moves forward with digital progress.
We’re seeing similar patterns elsewhere, including in Canada. Digital inclusion goes beyond devices; it’s about providing the right supports: multilingual resources, in-person help when needed, and flexible systems that adapt to different needs. It also means listening to community feedback and making changes when something isn’t working.
True innovation isn’t about moving fast, it’s about moving forward together. When we design with empathy and inclusion, we build systems that empower everyone.
Final Thoughts
In this moment, leadership isn’t just about embracing the latest technology. It’s about deciding what to protect. Values like trust, accountability, and dignity aren’t just nice ideas; they’re the foundation of healthy workplaces. As HR professionals, we’re in a unique position. We stand where technology, regulation, and human needs all meet. Our role isn’t just to help people keep up with change. It’s to shape change in ways that reflect care and fairness. People don’t resist change just because it’s unfamiliar; they resist it when it causes harm or leaves them behind. So, the real question is, what are we protecting, and why?
The future will ask us how we handled this moment. Let’s be ready with an answer we can be proud of.
Bibliography
The Artificial Intelligence and Data Act (AIDA) – Companion document. (2023). Retrieved from https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document
Artificial Intelligence in Finance requires specific safeguards: OSFI and GRI report - Explainability among key principles for gaining confidence in AI. (2023). Retrieved from https://www.osfi-bsif.gc.ca/en/news/artificial-intelligence-finance-requires-specific-safeguards-osfi-gri-report-explainability-among
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