Building a Cross-Functional AI Working Group

Learning Objectives

By the end of this lesson, learners will understand the steps involved in establishing a cross-functional AI working group, be able to outline a suitable structure and assign appropriate roles, and appreciate the value of multi-departmental representation for ethical, effective, and sustainable AI adoption within their organisation.

  • Identify AI opportunities and challenges: Gather input from across the organisation to understand where AI can add value, as well as potential risks and hurdles.
  • Secure executive sponsorship: Gain support from senior leadership to give the group authority and access to resources.
  • Select representatives: Invite key staff from IT, legal, compliance, HR, operations, and frontline teams. Ensure diversity of perspectives and skills.
  • Define clear roles and responsibilities: Assign roles such as project lead, data steward, legal adviser, ethics champion, and business liaison to clarify accountability and avoid silos.
  • Establish governance framework: Set out rules and processes for decision-making, risk assessment, and reporting, tailored to your organisation’s needs.
  • Facilitate regular collaboration: Schedule recurring meetings, use collaborative tools, and maintain open channels for feedback and discussion.
  • Monitor, learn, and adapt: Track progress, document lessons learned, and adjust group structure or processes as the organisation’s AI journey evolves.

Building a Cross-Functional AI Working Group Overview

As organisations increasingly seek to leverage artificial intelligence for competitive advantage and operational excellence, it has become critical to ensure these initiatives are responsibly, efficiently, and collaboratively managed. Rather than being isolated within the IT department, AI projects require input and direction from a range of business units to align with organisational goals, comply with regulations, and support employees at all levels.

By forming a cross-functional AI working group, organisations can bring together expertise from departments such as IT, legal, compliance, HR, operations, and those working directly with customers or products. This collective approach not only drives successful AI implementation but also builds trust, advocates best practice, and empowers staff through inclusive decision-making.

Commonly Used Terms

Let’s clarify some key terms used when discussing a cross-functional AI working group:

  • Cross-Functional: Involving members from different departments or areas of expertise within an organisation.
  • Governance: The system of policies and processes that ensures AI activities align with legal, ethical, and operational standards.
  • Stakeholder: Anyone with an interest in or affected by the outcomes of the AI project, such as employees, customers, or regulators.
  • Data Steward: A person responsible for overseeing the organisation’s data, ensuring it is managed and used appropriately and securely for AI projects.
  • Ethics Champion: A team member who advocates for ethical considerations and ensures responsible AI use.
  • Representation: Ensuring all relevant viewpoints are included, so decisions reflect the organisation as a whole.

Q&A

Why can’t our IT or data science team handle all AI initiatives alone?

While technical teams play a vital role, AI projects impact a range of business areas—legal, compliance, HR, and frontline operations all bring unique perspectives. By involving these areas, organisations can spot risks early, ensure legal and regulatory compliance, anticipate workforce changes, and develop solutions that better meet user needs. A cross-functional team makes AI initiatives more successful and widely supported.

How do we decide who should be in the AI working group?

Begin by mapping which departments are most affected by or can contribute to AI projects. Prioritise including IT, legal, compliance, HR, operations, and at least one representative from teams interacting with customers or core processes. Choose members who are open to collaboration, can communicate well, and are empowered to represent their area’s interests.

What if some departments don’t see the value in joining the group?

It’s not uncommon for some areas to feel AI is outside their remit. Address concerns by explaining how AI projects can affect everyone—through changes in workflows, compliance requirements, or customer experience. Emphasise that sharing ideas early leads to smoother implementation and better, more inclusive results for the whole organisation.

Case Study Example

Example: A UK Financial Services Firm Builds an AI Working Group

Facing pressure to deploy AI for customer insight and operational efficiency, a leading UK financial services firm recognised the risks surrounding data privacy, regulatory compliance, and employee impact. Executive leadership commissioned a cross-functional AI working group to guide responsible adoption. Members were drawn from IT, compliance, legal, HR, operations, and customer service frontline staff, ensuring a broad spectrum of expertise and on-the-ground perspectives.

The group began by mapping all anticipated AI use cases across departments. The compliance officer ensured adherence to FCA and GDPR regulations, while HR represented workforce concerns around skill needs and change management. Legal advised on third-party contracts and IP risks, IT members provided technical feasibility insights, and frontline staff flagged practical considerations. Through regular workshops, the group drafted policies, designed pilot projects, and established a review process for future AI initiatives. The result was more robust, compliant, and widely accepted AI solutions that carried the confidence of the entire organisation.

This approach has since become a key best practice for the firm. The working group remains active, revisiting policies as technologies and rules evolve, and serves as a knowledge hub for other innovation projects, ensuring long-term AI capability and readiness.

Key Takeaways

  • AI initiatives benefit from input across multiple departments, reducing risks and improving outcomes.
  • A successful AI working group needs executive backing, clear roles, and structured collaboration.
  • Involvement from IT, legal, compliance, HR, operations, and frontline staff delivers a balance of expertise, legal rigour, and practical insight.
  • Governance frameworks and ongoing communication are critical for sustainable AI progress.
  • Cross-functional groups help nurture a culture of trust, transparency, and continuous learning regarding AI within the organisation.

Reflection Question

How might involving diverse departments and roles in your AI initiatives change the outcome and acceptance of these projects within your organisation?

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