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KPMG’s approach

to enabling AI adoption

KPMG’s approach to enabling AI adoption

To adopt Gen AI effectively, a human-centric strategy must be tailored to your workforce’s specific needs. Our five-phase ‘make it’ change methodology provides a comprehensive framework for adoption and change management success.

Key Activities

Make it clear

Align leaders around the strategic aims, ambition and scale of change.

  • Leadership Strategic Alignment
  • Strategic Narrative Development
  • Stakeholder Analysis
  • Change Metrics & Definitions
Make it known
Communicate the change vision and case for change and create ownership.
  • Change Impact Analysis
  • Identify and build personas
  • Communication and engagement strategy and plan
  • Communications deployment
Make it real
Translate the change vision into reality for people in the organization.
  • Training strategy & plan targeted by persona
  • Ongoing communications and engagement
  • AI Champion activation
Make it happen
Work towards the end state and equip people to work in new ways.
  • Training launch
  • Pulse survey dev & deployment
  • Recognition program development
Make it stick
Ensure there is capability in the organization to sustain the change.
  • Program metric dashboard and adjustments based on results
  • Continuous pulse surveys
  • Recognition programs execution
  • AI metrics and goals integrated into performance management

Change Principles

Equip leaders to lead
Ensure leadership across all levels is equipped to lead and visibly supports the AI transformation by advocating for the change and role modeling new ways of working
Engage early and often
Prepare people by driving awareness, understanding and commitment through multi-channel, cross-cultural communications.
Enhance Change capability
Leverage our AI transformation to strengthen your organization’s change management capabilities across the entire enterprise.
Reward and Recognition
To maintain momentum, shine a light on success and celebrate milestones.

KPMG's approach

KPMG’s approach to delivering change management is based on a behavioural change management methodology, that we refer to as  the “make its”.

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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.