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AI Education Sessions

The top three education topics selected by the
Working Group to be delivered in Phase 1

1

AI that Actually Works for Your Brokerage

Equip brokers with hands-on knowledge to leverage off-the-shelf AI tools in day-to-day tasks. Cover how to write effective prompts, apply practical prompt hygiene, and provide guidance for general use.

Delivery Date: October 1

Format: 40min live + 20min Q&A Format

Facilitator: Jerome Hector

2

Managing AI Risks to Safeguard Your Brokerage

Understand the strategic, operational, and regulatory risks associated with AI adoption, explore approaches to assess and mitigate those risks effectively.

Delivery Date: October 15

Format: 40min live + 20min Q&A Format

Facilitator: Kareem Sadek & Kartik Gupta

3

Leading Your Team through the AI Shift

Explore strategies for upskilling workforce, building AI literacy, and managing change to support successful integration of AI across the workforce. Consider how to understand and response to workforce sentiment along the way

Delivery Date: October 29

Format: 40min live + 20min Q&A Format

Facilitator: Natalie Witiuk

All session recordings are available through your provincial broker association.

Al sessions presented by subject
matter experts

Jerome Hector

Jerome is a Senior Manager in KPMG’s AI Advisory practice, specializing in AI, digital transformation, and automation. He serves as Product Lead for Kleo, KPMG Canada’s GenAI Platform, and has extensive experience guiding organizations across financial services, retail, and telecommunications through AI adoption. Jerome focuses on equipping the workforce with practical AI skills for impactful use of AI tools in day-to-day work — from writing effective prompts to applying prompt hygiene and ensuring responsible AI practices.

Sadek Kareem

Kareem is a Partner in KPMG Canada’s Advisory practice, now serving as the National Risk Services AI and Innovation Leader and the National Lead for the Trusted AI practice across Canada. With over 22 years of professional services experience in Emerging Technology Risk, Crypto & Blockchain, and Trusted AI, he leverages a wealth of expertise to help organizations adopt innovative and secure practices in the digital transformation landscape.

Kartik Gupta

Kartik is a Senior Manager in KPMG’s IT Risk Consulting practice, with over 12 years of experience in AI governance, IT business transformation, and risk management. His expertise lies in developing tailored Responsible AI frameworks that address regulatory, operational, and ethical challenges. Kartik focuses on aligning AI practices with both technical and ethical principles, ensuring that organizations are prepared to responsibly navigate the complexities of emerging technologies.

Natalie Witiuk

Natalie is a Director in KPMG’s People & Change Advisory Services, specializing in organizational transformation, workforce enablement, and change management. She has led large-scale initiatives across financial services, utilities, and the public sector, with expertise in HR transformation, leadership development, and technology adoption. Natalie focuses on equipping employees with AI literacy, new skills, and the confidence to embrace change, ensuring the successful integration of technology with workforce engagement.

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Aligning with Regulatory Frameworks and Standards

Principles will align with international AI guidelines (e.g., ISO 42001, OECD frameworks) and industry-specific regulations (e.g., RIBO, OSFI, AMF) upheld by stakeholders. Adherence to legal standards will help organizations navigate jurisdictional requirements, promote sustainable practices, and prevent misuse of AI data

Encouraging Responsible Al Innovation

Principles will encourage the broker community to innovate responsibly by developing AI systems that prioritize consumer well-being, inclusivity, and fairness, while also assessing the societal, environmental, and economic impacts of their AI solutions

Promoting Accountability in AI Oversight

AI principles will reinforce accountability across all levels of organizations and third-party collaborators. By defining roles, ensure human oversight in AI processes, this enhances traceability, enables informed decision-making, and embeds mechanisms for ethical redress when errors or adverse outcomes occur

Ensuring Consumer Trust and Fairness

AI governance principles support commitment to transparency, fairness, and accountability. By requiring explainable outcomes and proactive consumer communication, it fosters trust among the broader broker community, their clients, and external stakeholders

Supporting Ethical Standards and Stakeholder Collaboration

By incorporating AI governance principles, broker members can align with its mission of fostering an ethical culture among its stakeholders Address biases, safeguard consumer protection, and promote inclusivity, which will reinforce commitment to ethical AI practices in collaboration with industry stakeholders, regulators, and third-party solution providers

PoC Use Case Overview: Al-Assisted Coverage Discovery & Gap Analysis

Technical Requirements

AI models with context on industry benchmarks and policy structures to interpret existing policy terms, endorsements, and clauses.

Integration with BMS to retrieve client profiles, exposure information, and historical policy data.

Data ingestion and continuous updates to ensure alignment with typical coverage patterns and industry guidance.

Data security and compliance features to protect client information.

Functional Scope

Analyze client-submitted data, including exposures, business context, and other relevant information.

Extract and interpret existing policy terms (e.g., endorsements, exclusions, clauses).

Benchmark against typical coverage patterns and industry guidance to identify coverage gaps.

Prioritize identified coverage gaps and provide rationale based on considerations such as industry standards and risk.

Recommend relevant products and coverage options tailored to the client profile, and generate summaries for client discussions.

AI-Powered Client Onboarding & Data Intake – Overview:
Proof of Concept (PoC) Use Case

Technical Requirements

BMS Integration: Integration with BMS for secure data capture and storage.

Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.

Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.

Extensibility Framework: Modular architecture to support future enhancements and additional automation.

Functional Scope

AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.

Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.

Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.

Quotes Generation: Generate quotes based on collected information.