Self-Service AI in Action
The mini-course
What AI agents actually are, why the hype around them is real but the vendor shortcuts aren't, and what it takes to build an agent your finance team can actually trust.
What you'll learn
- What an AI agent actually is — and why it's different from a chatbot or simple automation
- Four reasons agents matter specifically for finance and business teams
- The three challenges that stop off-the-shelf AI from working in real business processes
- How to structure an agent: role, skills, tools, and business context
- The five steps to building your first agent on a foundation that lasts
Course video
Free with LDI membership
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What this course covers
Most people working in finance have heard about AI agents. Fewer understand what they actually do — or why the gap between what vendors promise and what businesses actually get is so wide.
This mini-course cuts through the noise. In 12 minutes you'll get a clear picture of what agents are, why the standard shortcuts fail, and what the right architecture looks like — using the Self-Service AI framework built and taught at LDI.
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01
~2 min
Are agents overhyped?
A precise definition of what an AI agent actually does: perceive inputs, reason, act, and remember. And an honest answer to whether the capabilities match the hype.
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02
~2 min
Why agents matter for finance teams
Four specific reasons agents create value in finance: deeper analysis across structured and unstructured data, faster adaptation, time savings, and better data governance.
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03
~3 min
The three challenges with Gen AI for business
Why personal-productivity AI tools don't transfer cleanly to business process automation: scattered data, non-determinism, and business context that can't be handed over through prompts.
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04
~3 min
The Self-Service AI framework
How to build an agent like hiring a team: a job role (markdown instruction file), skills (markdown knowledge files), tools (MCP servers), and the business data and context that makes it reliable.
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05
~2 min
Five things to take away — and how to start
The five-step implementation path: data fundamentals, semantic models, agentic workflow design, building with human oversight, and testing in parallel with your existing process.
The five principles you'll leave with
- 01 Agents are powerful — but they don't work because of clever prompts alone. They work when they're built on the right structure and architecture.
- 02 Gen AI by itself is not enough for business processes. Business needs consistency, accountability, and control.
- 03 The best way to think about agents is to think about them like a team. Define the role, give them the right skills, connect them to the right tools.
- 04 Data is the foundation for everything. If the data is messy or fragmented, the agent's outputs will be too.
- 05 Start small — but start properly. Test in parallel. Trust is not built by assumption. It is built through results, consistency and time.
Your instructor
Rishi Sapra
Chartered Accountant · Microsoft MVP · Founder, Learn Data Insights
Rishi spent over a decade in finance and professional services — at Deloitte, HSBC, Barclays, and KPMG — before moving fully into data and AI education. He is a Microsoft Most Valuable Professional, an ICAEW Chartered Accountant, and a regular speaker at FabCon Europe, SQLBits, Accountex, and the ICAEW. LDI is his attempt to close the gap between what the technology can do and what finance teams are actually equipped to build.
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