Dr Sidney Shapiro
Most organisations have already run AI pilots. The harder question is what happens after the proof of concept ends. Procurement standards stay unclear, accountability for AI-assisted decisions is unassigned, and the governance frameworks people quote in slides do not survive contact with real workflows. Leadership teams cannot say with confidence which decisions AI should be trusted with and which it should not.
Sidney Shapiro helps leadership teams move past AI hype into governed, responsible deployment, drawing on a decade in data science and his current research at the University of Lethbridge.
Full Profile
Why organisations work with Sidney Shapiro
- Argues openly against AI hype. His featured keynote “Stop Believing the AI Hype” treats vendor claims as a leadership evaluation problem, which is the conversation regulated buyers and procurement teams actually need to have.
- Has published governance work in peer-reviewed venues. A Routledge book chapter on AI governance for higher education institutions, and a forthcoming IGI Global chapter on the governance of generative AI, give him a documented position on what responsible deployment looks like in practice.
- Brings a decade of operational data science alongside the academic work. A former Data Science Manager at Sofvie building occupational health and safety software, he speaks to the integration and accountability problems that follow a proof of concept.
- Active in the working practitioner conversation. Hosts the AI in Action podcast and organises the Southern Alberta AI Conference, which together keep his perspective grounded in what organisations across healthcare, agriculture, manufacturing, and public administration are actually trying to ship.
Biography highlights
- Assistant Professor of Business Analytics, Dhillon School of Business, University of Lethbridge
- Adjunct Assistant Professor of Computer Science, University of Lethbridge
- Book chapter on AI governance frameworks for higher education published by Routledge (2025); forthcoming chapter on the governance of generative AI with IGI Global
- Host of the AI in Action podcast and organiser of the Southern Alberta AI Conference
- Former Data Science Manager at Sofvie Inc., building data systems for occupational health and safety in the mining sector
- Featured or quoted in The Globe and Mail, CBC News, Lethbridge Herald, and Canadian Family Offices Magazine
Biography
Most organisations have already run AI pilots. The harder question is what happens after the proof of concept ends. Procurement standards stay unclear, and the governance frameworks people quote in slides do not survive contact with real workflows.
This is the gap Sidney Shapiro works in. He is Assistant Professor of Business Analytics at the Dhillon School of Business at the University of Lethbridge, and Adjunct Assistant Professor of Computer Science. Published work includes a Routledge book chapter on AI governance frameworks for higher education and a forthcoming IGI Global chapter on the governance of generative AI. Both set out what responsible deployment actually requires from organisations.
His authority on the operational side comes from a decade in data-centric roles before academia. He worked as a Data Science Manager at Sofvie, an occupational health and safety software company in the mining sector, and previously coordinated the Business Analytics graduate program at Cambrian College. That background shapes how he addresses the procurement and integration problems that follow proof of concept, which is where most AI initiatives slow or stall.
Public engagement keeps the work current. Shapiro hosts the AI in Action podcast, interviewing practitioners and researchers across healthcare, education, finance, and software development on what AI deployment actually looks like in their organisations. He also organises the Southern Alberta AI Conference. Recent audiences include Indigenous Services Canada, McCain Foods, EY Calgary, CMC Alberta, and the Digital Research Alliance of Canada. These are buyers with low tolerance for vendor language and high tolerance for hard questions.
Key speaking topics
- AI governance and responsible deployment
- Generative AI in business and society
- Data privacy and trust in AI systems
- AI literacy for non-technical leaders
- Analytics leadership and digital transformation
- AI in higher education policy
Ideal for
- C-suite leadership teams and boards facing AI governance and accountability decisions
- CIOs, CTOs, and Chief Data Officers moving AI from pilot to operational deployment
- Public sector leaders, including municipal and federal government, working under privacy and equity constraints on AI adoption
- Higher education executives shaping institutional policy on generative AI in teaching and research
Audience outcomes
- A working framework for evaluating vendor AI claims against operational reality, in time to shape procurement decisions
- Sharper questions to ask of any AI initiative on data governance and where accountability sits when AI is wrong
- A grounded view of what generative AI does well today and where it still fails, with the implications for organisational adoption
- Reference points for designing operational AI governance: procurement standards, acceptable-use policies, and accountability mechanisms
Talks
A framework for separating vendor promises from operational reality, so leadership can evaluate AI initiatives on something firmer than enthusiasm.
Key takeaways:
- How to test specific vendor AI claims against what the technology actually does today
- A method for defining real success metrics for AI initiatives before procurement
- A clearer line between current AI capability and what is being oversold for tomorrow
A walk through how the governance principles in AI strategy decks become real operating decisions: oversight, acceptable-use policy, procurement, and accountability.
Key takeaways:
- How to translate high-level AI principles into specific operational decisions
- The oversight structures that actually hold AI initiatives accountable
- A working approach to AI procurement standards that risk and legal teams can stand behind
A direct account of what generative AI does well today and where it still fails, with the guardrails organisations should put in place before scaling use.
Key takeaways:
- A current picture of generative AI capabilities, with examples of strong and weak outputs
- The ethical and environmental considerations leadership teams should be tracking
- Practical guardrails for scaling generative AI use inside an organisation
A working approach to designing AI and analytics systems that earn trust from employees, customers, and regulators rather than burning it.
Key takeaways:
- Why trust failures are now a bigger barrier to AI adoption than technical limitations
- Privacy-aware design choices that protect individuals without slowing innovation
- How transparency and explainability shape actual user behaviour with AI systems