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Thanks to the recently developed large language models, or LLMs, we may be close to realizing a decades-old quest: the creation of intelligent machines. This technology, which seeks to imitate the human brain, has opened a new realm called Generative AI—software that can write coherent text and create images and computer code at a level close to that of humans.
As we ponder the vast business potential of AI, one thing is abundantly clear: AI requires sound human decision-making to curb its potential shortcomings.
So how do we harness the power of AI while managing its risks in a manner that engenders trust and not suspicion?

Regulation and transparency are likely to be part of the answer. For example, the city council of New York City recently passed a law requiring bias audits of automated employment decision tools (AEDT) used by companies for employment decisions (e.g., select job candidates from pools of applicants). Moreover, Local Law 144-21 mandates that companies publish a summary of the audit results and that they alert job applicants or employees to the use of AEDTs as well as to the “categories” and “screens” set in evaluating qualifications and background.
Federal, state, and local governments/agencies as well as international bodies are taking a closer look at how organizations use AI. For example, officials in in several states have proposed AI legislation and regulations to enhance transparency and accountability, while the National Institute of Standards and Technology has released an AI Risk Management Framework. In Europe, the Artificial Intelligence Act seeks to provide a framework to regulate AI based on the level of risk that a system might pose and to harmonize cross-border rules dealing with this technology.
This activity suggests that it’s time for companies to focus on developing or refreshing their own AI strategies as well as usage and governance models—because there’s more to concerns over AI than just bias. It’s a matter of trust—the trust between stakeholders (e.g., shareholders, employees, etc.) and companies.
AI presents new and growing challenges to data privacy, risk mitigation (including reputational risk), cybersecurity, and disruptions of operations and business models. And those may increase as the technology and use cases expand. So, what should be on a company’s AI transparency and trust agenda as it works to reimagine and manage risk mitigation, operational controls, and governance processes?
Expectations around AI continue to grow, and companies are increasingly harnessing AI and expanding its impact. Just think, AI is already capable of writing this post with some human assistance. Next time it just might. That’s a reality worth pondering.
Please read our Wall Street Journal article to learn more, and if you have any questions, please contact me.
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Ryan is an Audit & Assurance principal with more than 20 years of experience helping global institutions strengthen trust, transparency, and performance across complex analytical, financial, and artificial intelligence (AI) systems. His work brings together deep experience in risk management, governance, controls, valuation, modeling, data, automation, and emerging technology to help clients navigate transformation with confidence. Ryan serves as Deloitte’s Global AI Specialist Leader, leading a team of AI professionals who support assessments of AI systems and advise non-attest clients on large-scale AI strategy and governance transformations. His work focuses on helping organizations understand, govern, and manage the business, risk, and control implications of AI, advanced algorithms, and emerging agentic systems. Ryan also serves as Deputy Leader of Deloitte’s Valuation & Analytics practice, a global network of professionals with deep experience across traded financial instruments, data analytics, modeling, and valuation. In this role, he leads Deloitte’s Omnia DNAV AI and Derivatives technologies, which incorporate automation, machine learning, and large-scale data to enhance the delivery of valuation and analytics services. Previously, Ryan served as a leader in Deloitte’s Model Risk Management practice, where he advised financial services institutions on model development, validation, governance, technology enablement, and quantitative risk management. He has led multidisciplinary teams serving several of the top 10 US financial institutions on complex risk, control, and process transformation programs. Ryan frequently serves as a trusted advisor to CEOs, CFOs, CROs, boards, and audit committees on issues at the intersection of risk, technology, governance, financial markets, and business transformation. His experience spans AI and algorithmic risk, model risk management, financial risks and valuation. Ryan received a BA in Computer Science and a BA in Mathematics & Economics from Lafayette College. His work sits at the intersection of financial risk, valuation, modeling, data, automation, machine learning, and AI, helping clients strengthen trust, transparency, and business value across critical decision-making processes. Media highlights and perspectives Ryan has authored many thought leadership articles and blogs and his commentary has been featured in publications including Accounting Today, CFO Dive, Strategic Finance Magazine, and the Wall Street Journal. 2026 [Strategic Finance Magazine] Optimizing AI with Good Governance [Accounting Today] Internal audit’s role in guiding AI responsibly [Internal Auditor Magazine] AI Unleashed [Deloitte blog] COSO AI framework: Internal controls for generative AI 2025 [Deloitte blog] Internal Audit’s role in strengthening AI governance [Deloitte blog] The impact of AI on your audit: Supporting AI transparency and reliability in finance and accounting [Deloitte blog] Generative AI in Financial Reporting and Accounting [WSJ Risk & Compliance Journal] A Day in the Life of an Accounting Generative AI User [FEI Weekly] AI in Finance: Balancing Innovation, Accuracy, and Audit Readiness [Accounting Today] Auditors, management prepare for AI impact on financial data 2024 [Deloitte blog] Mitigating AI fraud risks [Deloitte perspectives] Underscoring the role of AI in investment management [NACD report] NACD 2024 Governance Outlook Report [FM Magazine] What CFOs Need to Know About AI Risk [Internal Auditor] The Fraudsters Have AI, Too [CFO Dive] Bolstering your cyber defenses in the age of AI 2023 [Deloitte perspectives] Perspective on New York City local law 144-21 and preparation for bias audits [Deloitte blog] Reduce AI risk and promote AI trust [Deloitte perspectives] What is an Algorithm? Let’s Demystify Algorithms and Artificial Intelligence (AI) [Pitchbook] Road to Next 2022 [Deloitte perspectives] An Auditor’s Mindset in an AI Driven World | Deloitte US [WSJ article] First Bias Audit Law Starts to Set Stage for Trustworthy AI 2021 [Deloitte perspectives] Applying COSO ERM framework principles to AI