Generative AI entered our workplaces only a few years ago, yet it has already transformed how many of us work day-to-day. The challenge is that AI is advancing faster than most organizations are built to absorb. As a result, governance, guidance, and practical guardrails have not kept pace with adoption.
Tools like Copilot appeared almost overnight within Microsoft 365 and were widely adopted with little oversight, limited training, and few clear boundaries around use. In a short period of time, we have moved from using AI for relatively simple tasks, including editing documents, drafting emails, or summarizing content, to applying it in ways that shape judgments, decisions, and outcomes.
This creates serious risks around privacy, trust, and accountability. AI systems rely on data; often large volumes of it, including sensitive information, and drawn from legacy systems where the data was not collected, structured, or governed with AI use in mind. If organizations are not clear about what data is being used (i.e., whether it is accurate, representative, and fit for purpose; whether consent is required; how outputs are monitored; and who has access), AI quickly becomes a significant risk. Those risks are compounded when employees have not been trained in data governance, privacy, and responsible AI use, and when people turn to unsanctioned tools, including personal or at-home tools, to get work done. Privacy breaches are not just compliance failures; they signal that adoption is outpacing our ethics.
We have already seen what happens when governance lags. Amazon’s resume-screening tool was trained on historical male-dominated hiring data and was found to disadvantage female candidates.[1] Facial-analysis tools used in interviews and designed to infer enthusiasm and fit have been shown to reflect racial bias and to disadvantage neurodivergent candidates.[2] Productivity-monitoring tools that track mouse movement and keyboard activity as proxies for performance have also failed to account for strategic, relational, or less computer-dependent roles.[3] These examples point to a troubling reality: when AI is not properly governed, the risk is not limited to privacy; it also becomes a reliability issue. AI does not simply reproduce flawed assumptions, but it can amplify bias, normalize distorted patterns, and scale poor decision-making. We cannot outsource human judgment to these tools without scrutiny. We need to equip people with the skills to engage with them critically and to question and validate their outputs.
This is why we need to shift our focus from AI adoption to AI governance. It is a leadership, trust, privacy, and ethics issue.
Here are five practical steps organizations can take to begin building a stronger governance foundation:
- Build a cross-functional AI ethics and governance team
AI governance should not rest solely with IT. Bring together HR, legal, privacy, data, operations, communications, and leadership so decisions reflect technical, ethical, operational, and human realities. - Inventory AI use cases and apply risk thresholds
Start by identifying where AI is already being used, then distinguish between low-risk productivity uses and higher-risk applications that influence people, rights, safety, or opportunity. A simple red-yellow-green model can help determine the level of scrutiny, approval, and human review each use case requires. - Strengthen data governance and clarify which tools can be used
AI governance starts with data governance. Organizations need to know what data is being used, where it comes from, who owns it, whether it is accurate and appropriate for AI use, and what rules apply around access, retention, consent, and sensitive information. They also need clear direction on which AI tools are permitted for work and where the use of personal or unsanctioned tools is prohibited. - Train employees and communicate the guardrails clearly
Governance only works if people understand it. Employees need practical guidance on which tools they can use, what data should never be entered, what privacy risks to watch for, how to recognize higher-risk use cases, and when to escalate concerns. Regular updates can also help by sharing progress on AI integration, reinforcing expectations, and offering practical tips for responsible use. - Create accountability, human oversight, and regular review
AI governance cannot be a one-time policy exercise. Organizations need clear ownership of AI decisions, defined escalation pathways, appropriate human oversight, procurement and vendor checks, and regular reviews on how tools are performing over time. Governance must evolve as AI use expands and risks change.
The central question is no longer whether AI belongs in our organizations. It is here to stay. The work now is to take collective action to create the structures and safeguards that allow people and technology to work together in healthy and responsible ways. The time to put meaningful guardrails in place is now, while adoption is still taking shape and before these systems become too deeply embedded in workflows, decisions, and operating models. If we want AI to create value without eroding trust, then governance, privacy, and ethics cannot trail behind innovation. They must move alongside it.
About the Author

Wylie Burke is an innovation consultant, educator, and leadership coach with over 15 years of experience in business administration, human resources, organizational design, strategic and operational planning, and leading high-performing teams. She holds an MBA from Queen’s University, an Honours Degree in Sociology from York University, and she is an Adler Trained Coach. In addition to facilitating for Queen’s University IRC, she is also a leadership coach through the University of Toronto’s Centre for Learning and Leadership. Wylie brings a unique perspective to her work, having had the pleasure of working for a diverse range of organizations, including United Way Toronto, CIBC, SickKids, WSIB, and Toronto Metropolitan University. Wylie is passionate about unleashing potential inside organizations. Her skills lie in evolving talent models, aligning strategy to organizational structure, and building outcome-focused programs and processes.
Wylie is the lead facilitator for Queen’s IRC’s AI-Powered Workforce Planning program, as well as the Talent Management and Managing Workplace Conflicts programs.
Footnotes
[1] Dastin, J. (2018b). Insight – Amazon scraps secret AI recruiting tool that showed bias against women | reuters. Reuters. Retrieved April 27, 2026 from, https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/.
[2] Engler, A. (2019, October 31). For some employment algorithms, disability discrimination by default. Brookings. Retrieved April 27, 2026 from, https://www.brookings.edu/articles/for-some-employment-algorithms-disability-discrimination-by-default/.
[3] Roh T, Esomonu C, Hendricks J, Aggarwal A, Hasan NT, Benden M (2023) Examining workweek variations in computer usage patterns: An application of ergonomic monitoring software. PLoS ONE 18(7): e0287976. Retrieved April 27, 2026 from, https://doi.org/10.1371/journal.pone.0287976.
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