Module 1 - Foundational AI Literacy

For finance professionals, this AI Foundations module provides a practical primer on artificial intelligence – covering its evolution from early concepts to today’s deployed tools, the different types of AI, and how generative AI works along with its limitations and risks in regulated environments. You’ll learn to map everyday finance tasks and client workflows to concrete AI opportunities, from risk analytics and client onboarding to back‑office automation and reporting. The module is designed to help you understand what is technically feasible, what it means for business value, and how to discuss AI with stakeholders (without the hype).

The benefits for finance organisations are clear. You’ll gain the ability to speak confidently about AI with executives and regulators, translating capabilities into tangible business outcomes. The curriculum spans a broad range of AI applications beyond generative models – AI agents, predictive and prescriptive analytics for risk and customer insights, computer vision for identity verification, and intelligent automation to streamline processes – so you can advocate for scalable, compliant AI initiatives that improve efficiency and financial outcomes.

SPEAKER: Louise Compagnone, Director of Artificial Intelligence, Datacom

Module 2 - Effective Prompting 

The module introduces the components of generative AI prompt engineering, prompt enhancers, and practical ways to improve your prompting. You’ll start with the basics of what prompts are and how they shape AI behaviour, then move into crafting effective prompts, adjusting tone and structure, and applying advanced techniques to handle complex interactions. You’ll learn reusable prompt patterns that organise inputs and controls outputs, and culminate with a focus on responsible use, including recognising limitations and designing ethically sound prompts.

For finance professionals, this module translates these skills into practical, production-ready capabilities. You’ll be able to generate more reliable and consistent outputs from AI assistants in areas like risk analysis, regulatory reporting, client communications, and investment research, while ensuring outputs are appropriate for regulated environments. Focusing on governance, transparency, and ethical design helps you stay compliant and maintain audit-ready processes; while reducing risks like bias or errors so you can use AI more reliably across finance. By mastering prompt design, you’ll improve efficiency, accuracy, and decision support without sacrificing controls or governance.

SPEAKER: Alex Williams, Consulting Director-AI Strategy, Datacom

Module 3 - AI for Personal Productivity 

This module looks at how generative AI can be used at work, comparisons of different AI tools, and creating personal agents. We’ll help you spot tasks that will benefit most from AI by mapping the cognitive demands of your work, identifying opportunities at the right level of detail to design workflows that save time, improve accuracy, and accelerate decision-making in both work and everyday life. The module covers how to frame problems and opportunities practically, whether we’re researching, planning, or organising information for any AI project.

For finance professionals, the module still translates to work-ready benefits, but it’s not restricted to work tasks. Expect tangible improvements in areas like research, reporting, client communications, and decision support, while staying mindful of governance and ethical use. Beyond the office, you can apply the same prompts and workflows to personal finance, learning plans, travel planning, and hobby projects, anything that benefits from organising data, summarising content, generating ideas, and automating routine steps. A practical framework encourages responsible experimentation, pilot AI improvements in small, controlled ways before scaling up – so you can realise value across both professional responsibilities and everyday activities.

SPEAKER: Richard Kenyon, Associate Director – AI & Automation, Datacom

Module 4 - Data Quality

The Data Quality module explores what data quality is, why it matters for AI, and how to recognise its key characteristics. You’ll learn where data quality problems originate, how bad data can derail AI projects, and study real-world examples of data quality impacting AI outcomes. The module also includes practical steps you can apply in your own role to assess and improve data quality from day one.

The module delivers clear benefits for both stakeholders and your professional development. For clients and colleagues, you’ll gain a practical, non‑hype language for data quality, learn to identify and remediate risks early, evaluate data readiness before you use AI, and see how high‑quality data translates into more reliable AI results. For your development, you’ll build foundational data literacy, bridge gaps between technical and business teams, learn from documented failures, and design AI solutions with strong data foundations from the outset.

You’ll cover the seven key characteristics of data quality – accuracy, completeness, consistency, timeliness, reliability, representativeness, and relevancy – along with how these factors influence AI results. The module also delves into where bad data comes from and how problems can arise at every stage of the data lifecycle (entry, collection, integration, storage, preparation). Real-world impacts and case studies illustrate the consequences of poor data quality and the benefits of high-quality data, helping you identify risks, tell the data quality story to stakeholders, and avoid AI failures caused by flawed data.

SPEAKER: Dan Piper, Practice Lead – Managed Ops,  Datacom

Module 6 - Avoiding the 70% failure rate

AI is no longer a nice to have – it's becoming central to how businesses operate, compete and deliver value. The narrative that AI projects fail more often that traditional IT projects is pervasive but it's not a rule. The "70% failure rate" typically refers to the claim that a large portion of organisational change initiatives, particularly those involving AI, fail to achieve their intended goals.

This module looks at why AI projects often struggle to move from pilot to production, and how this differs from traditional IT initiatives. You’ll examine what the so‑called 70% failure rate really means, explore the unique risks AI projects face, and learn practical approaches to overcome them. Through real‑world case studies, you’ll see how projects fail and succeed, and you’ll gain a framework for moving ideas from concept to measurable, production‑level results.

The emphasis on governance, intentional design, realistic expectations, and auditable processes helps you translate AI ideas into safe, production‑ready solutions without sacrificing speed or innovation. The module encourages viewing failures as learning opportunities and provides frameworks to communicate decisions and trade‑offs to stakeholders. By the end, you’ll be better prepared to help your organisation implement responsible, working AI solutions that delivers demonstrable value in finance.

SPEAKER: Louise Compagnone, Director of Artificial Intelligence, Datacom