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Starting a career in accounting is no easy feat. Whether you’re a recent graduate, a career changer, or someone who’s still figuring out their path, the journey to landing that first role can feel like navigating a maze. Accounting is a dynamic field with countless opportunities, but the sheer number of options—paired with the demand for experience, certifications, and networking—often leaves aspiring professionals wondering, Where do I even begin? I’ve seen this challenge firsthand. A student recently asked me, “How can I land my first accounting-related position when I don’t have relevant experience?” Their uncertainty is common among newcomers to the profession, regardless of their background. Even those with traditional academic credentials can feel unprepared when faced with the demands of job applications, networking, and the competitive market. The good news? Breaking into accounting is possible for anyone willing to define their goals, build a strong foundation, and take intentional steps toward success. Whether you’re starting from scratch or pivoting from another career, the path to your first accounting role starts here.

Should Accountants be Teaching Their Clients? We can use teaching as we way to align everyone to the same business goals. If we're teaching our team and at the same time teaching clients those same concepts, they're now speaking a common language. They're now working off the same metrics and the same KPIs and the same business goals. The goals could be to increase cash flow, increase profitability, pay off debt, make a strategic acquisition or make a sale of the business. Those are just really powerful messages to align the whole team to the same goals.

A dive into the evolving regulatory landscape of artificial intelligence, exploring key laws and regulations shaping AI governance today. Learn about groundbreaking legislation like the EU AI Act, which became effective in August 2024, and its risk-based framework for AI applications, including unacceptable and high-risk use cases. We also discusses how existing laws like GDPR impact AI and what’s on the horizon in the U.S., such as California's draft regulations on Automated Decision-Making Technology and Colorado's pioneering AI Act. Discover how these regulations address transparency, bias, and accountability in AI systems and why businesses must prepare for a patchwork of compliance challenges.

We look at some real-world examples where AI governance fell short, highlighting why robust governance frameworks are essential. We discuss three cases that showcase the consequences of poor accountability, transparency, and oversight in AI systems: 1. Air Canada Chatbot Case (2024): A man relied on misinformation from an AI chatbot about bereavement discounts, leading to a lawsuit that underscored the importance of accountability in AI-driven customer interactions. 2. Apple Card Bias Controversy (2019): Allegations of gender bias in credit limit decisions revealed challenges in explainability and transparency, despite no evidence of intentional discrimination. 3. COMPAS Algorithm in Criminal Justice: A ProPublica investigation into the proprietary COMPAS algorithm raised concerns about bias in predicting recidivism rates, highlighting the critical need for transparency in high-stakes AI applications. We connect these cases to governance principles such as accountability, transparency, and fairness, emphasizing their role in maintaining trust, balancing value and risk, and upholding societal ethics.

A brief intro to the concept of governance and frameworks for AI governance.   We explore the critical topic of AI governance—what it is, why it matters. AI governance refers to the frameworks, principles, and practices that guide the ethical and responsible development, deployment, and use of artificial intelligence. AI systems have the potential to drive innovation, efficiency, and significant business outcomes, but they also pose risks—ranging from biases in decision-making to legal and reputational liabilities. Without proper governance, these risks can outweigh the benefits. This discussion sets the stage for deeper conversations on how organizations can align their AI practices with global frameworks and navigate the challenges of responsible AI deployment.

As artificial intelligence continues to revolutionize industries, organizations must navigate the complexities of AI governance to ensure ethical, transparent, and responsible use. This presentation will explore the key components of AI governance, including risk management, regulatory compliance, data privacy, and accountability frameworks. Includes best practices for implementing governance structures that balance innovation with ethical considerations, enabling organizations to harness the power of AI while managing potential risks. The session will also address the role of continuous monitoring, stakeholder engagement, and strategic alignment in maintaining effective AI governance. Learning Objectives: • Understand the fundamentals of AI governance • Identify key risk factors and regulatory considerations • Explore best practices for implementing AI governance frameworks • Examine the role of continuous monitoring and compliance • Engage stakeholders in AI governance initiatives