PIMFA Live Online Learning: AI Leadership in Wealth Management: From Basics to Practical Implementation

This session sets the stage by demystifying AI and highlighting its transformative potential within the wealth management industry. You’ll gain a clear understanding of what AI is, explore real-world applications relevant to their field, and start to ideate opportunities for leveraging AI to enhance client services, operational efficiency, and compliance. We’ll also explore the risks and long-term consequences of failing to integrate AI into core business functions—highlighting how inaction can lead to competitive erosion, talent attrition, and diminished client trust.

Learning objectives

1. Define AI and differentiate it from related technologies.
2. Recognise key trends and applications of AI in wealth management.
3. Identify strategic benefits and potential challenges of AI adoption.
4. Understand the commercial disadvantages of delayed AI adoption

Learning outcomes
By the end of the session, you’ll be able to:
1. Articulate a clear understanding of what artificial intelligence (AI) is and distinguish it from related technologies within the context of wealth management.
2. Identify and describe key real-world AI applications that are transforming client service, operational efficiency, and regulatory compliance in the wealth management industry.
3. Evaluate the potential benefits and challenges of implementing AI solutions in a wealth management setting, considering industry-specific factors.
4. Start to generate initial ideas and strategic considerations for leveraging AI to enhance organisational performance and client outcomes.
5. Confidently take strategic ‘baby steps’ to move from passive to proactive AI exploration

AI holds great promise for wealth management – but its performance depends entirely on the quality of your data. Poor data leads to poor insights, regulatory risk, and lost trust. This session helps you understand the critical link between data management and effective AI. From governance and integrity to privacy and explainability, you’ll explore what ‘good data’ really means, why it matters to the regulator, and how to build a data culture that sets the foundation for trustworthy, impactful AI.

Learning objectives

1. Understand the connection between data quality and AI outcomes
2. Identify key risks (bias, incompleteness, inaccuracy) in wealth management datasets
3. Recognise the FCA’s expectations around data governance and accountability
4. Ask the right questions of their tech, data, and compliance teams
5. Begin to shape a “data culture” across their firm

Learning Outcomes:

1. Recognise why data quality is critical to the success (or failure) of AI tools in wealth management
2. What the FCA expect in terms of data governance, transparency, and fairness
3. How poor data can lead to biased decisions, compliance breaches, or reputational damage
4. The key attributes of high-quality, AI-ready data (accuracy, completeness, consistency, timeliness)
5. Diagnose the current state of your firm’s data readiness using a practical checklist
6. Explore how to align compliance, data, and tech teams to deliver safe and effective AI
7. Start conversations internally with data owners, compliance, and senior managers about AI readiness

Where should wealth management firms focus their efforts to get real value? This session helps you go beyond theory to understand where AI is already delivering results – and where it can be used next. Through practical use cases, peer discussions, and an “opportunity mapping” framework, you’ll will explore how to identify, prioritise, and champion AI initiatives aligned with your business needs and regulatory expectations.
Whether you’re exploring efficiency gains, enhanced client insight, or smarter compliance, this session provides a structured way to spot quick wins, avoid dead ends, and build a strategic AI roadmap grounded in your firm’s goals.

Learning objectives

1. Identify and analyse key AI use cases transforming UK wealth management.
2. Evaluate the strategic benefits and potential risks associated with implementing AI solutions.
3. Develop ideas for applying AI technologies to address specific challenges within your firm.
4. Recognise opportunities for creating a competitive edge through AI-driven innovation.

Learning outcomes

1. Identify key AI applications currently transforming wealth management, including client segmentation, portfolio optimisation, and operational automation.

2. Analyse how AI use cases can address specific business challenges and create strategic advantages within your firm.

3. Evaluate the benefits, risks, and potential impact of AI-driven solutions on client experience, operational efficiency, and regulatory compliance.

4. Generate ideas for integrating AI-driven innovations into your own firms’ strategies to enhance competitiveness and value creation.

This session guides you through the strategic process of planning and choosing the right AI tools and partners for their firm. You’ll learn how to develop clear objectives, evaluate vendors, and assess solutions against business needs, data requirements, and regulatory considerations. The focus is on creating a realistic, actionable plan that sets the foundation for successful AI implementation.

Learning objectives

1. Develop a strategic approach to planning AI initiatives aligned with your firm’s goals.
2. Identify criteria for evaluating AI solutions and vendor capabilities.
3. Understand the importance of data readiness, security, and compliance in solution selection.
4. Create an initial AI project plan with defined objectives, key performance indicators (KPIs), and implementation steps.

Learning outcomes

1. Outline a strategic approach to planning AI initiatives that align with your firm’s goals and priorities.
2. Evaluate and compare AI vendor solutions and tools based on functionality, compatibility, vendor credibility, and firm’s needs.
3. Assess data readiness, security, and compliance requirements to ensure AI solutions adhere to regulatory standards such as GDPR.
4. Develop a AI implementation plan, including clear objectives, KPIs, stakeholder responsibilities, and phased deployment steps.

When it comes to AI, talk is cheap, it’s all about turning your AI strategy into effective action. In this session, you’ll learn how to develop practical implementation plans, foster firm buy-in, and manage change effectively. Emphasising the human side of technology adoption, this session provides tools and best practices to ensure smooth integration of AI solutions into existing workflows while addressing potential resistance and stakeholder engagement.

Learning Objectives

1. Design a comprehensive implementation roadmap that guides AI deployment from pilot to scale.
2. Identify critical success factors and potential barriers in AI adoption.
3. Apply change management principles to foster stakeholder buy-in and reduce resistance.
4. Develop strategies for training, communication, and continuous improvement to sustain AI initiatives.

Learning outcomes

1. Structure a phased implementation plan with milestones, responsibilities, and resource needs.
2. Engage stakeholders across departments to build organisational support for your plan.
3. Address cultural and procedural shifts prompted by AI integration.
4. Effectively manage risks, challenges, and setbacks during deployment.
5. Establish feedback loops and continuous improvement processes.

AI is not here to replace decision-makers – it’s here to empower them. This session explores how wealth management firms can integrate AI into the decision-making process to drive better client outcomes, improve efficiency, and reduce risk. You’ll explore real examples of AI-assisted thinking, understand how to challenge algorithmic outputs responsibly, and develop the confidence to use AI as a trusted adviser—not a black box.
Through case studies and group reflection, this session helps you shift from asking, “What can AI do?” to “How should we think with it?” The result: better decisions, clearer accountability, and stronger leadership in a tech-enabled future.

Learning Objectives

1. Recognise key areas in wealth management where AI can augment decision-making.
2. Understand how to interpret and critically evaluate AI-generated insights and recommendations.
3. Identify potential biases and limitations of AI-supported decisions and strategies to mitigate them.
4. Apply principles of Responsible AI to ensure ethical and transparent decision-making processes.

Learning outcomes

1. Explain how AI can augment – not replace – human decision-making in wealth management
2. Identify areas where AI can support judgment, such as investment strategy, risk profiling, and compliance reviews
3. Evaluate the limitations and risks of over-relying on AI outputs (bias, black-box decisions, false confidence)
4. Apply principles of responsible AI use, including explainability, oversight, and accountability
5. Adopt a decision framework that blends data-driven insight with professional judgment

As AI adoption grows across financial services, the FCA’s interest in its use, governance, and risks is intensifying. This session unpacks the regulator’s evolving position on AI – from consumer protection and fairness to data integrity, explainability, and accountability. Through guidance updates, case examples, and thematic reviews, participants will gain a clear picture of what the FCA expects from firms experimenting with or deploying AI.
Whether your firm is using AI in advice, compliance, or client engagement, this session ensures you understand how to stay compliant, proactive, and prepared for scrutiny. The aim is not just to satisfy the FCA – but to use its lens to build safer, smarter AI strategies.

Leanring Objectives

1. Understand the FCA’s current position, principles, and regulatory expectations surrounding AI in financial services.
2. Explore how the FCA’s AI guidance intersects with Consumer Duty, operational resilience, and data ethics.
3. Evaluate the regulatory risks, compliance obligations, and governance frameworks associated with your firms AI deployment.
4. Identify best practices to align your AI strategy with evolving FCA expectations.

Learning outcomes

1. Articulate the FCA’s current stance on AI and its links to key regulatory themes (e.g. Consumer Duty, SMCR and operational resilience)
2. Understand key risks and concerns flagged by the FCA, including bias, lack of explainability, poor data quality, and governance gaps
3. Identify how AI interacts with existing regulatory obligations (especially around fair treatment of clients, accountability, and oversight)
4. Evaluate the implications of recent FCA guidance and joint papers (e.g. with the Bank of England and ICO) for your firm
5. Take practical steps to ensure responsible AI use, including documentation, internal challenge, and risk assessment processes

Implementing AI in any firm requires more than just a strong technical plan – it demands clear communication, stakeholder engagement, and strategic alignment with your firm’s goals. This session invites you to present your AI implementation plan to your programme leaders and, if you desire, your line manager in a way that secures support, addresses concerns, and positions the initiative as a value-adding opportunity.
Following a template, your presentation will highlight the purpose, expected impact, and practical roadmap of your AI plan. After your presentation, programme leaders will provide immediate feedback on your presentation, ensuring that your strategy aligns with your firm’s priorities, such as innovation, efficiency, compliance, and client outcomes. In addition, your programme leaders will prepare you to respond to questions that you’ll likely be asked upon your return to the office regarding risks, resourcing, and ethical considerations.

Learning Objectives

1. Clearly articulate within 30 minutes the key components of your AI implementation plan, including objectives, data needs, solutions, and implementation steps.
2. Demonstrate the strategic value and feasibility of your AI project to peers and stakeholders.
3. Incorporate feedback to refine and strengthen your plans.
4. Develop confidence in communicating complex AI strategies in a clear, compelling manner.

Learning Outcomes

By the end of this session, you’ll receive direction and feedback from programme leaders to able you to:

1. Structure and deliver a clear AI implementation pitch tailored to internal decision-makers
2. Align their AI proposal with business goals, regulatory priorities, and risk appetite
3. Anticipate and respond to common objections (e.g. cost, risk, complexity, ethics)
4. Demonstrate value through metrics, pilots and milestones to build confidence and reduce resistance
5. Secure support across compliance, IT, and senior leadership