AI for Finance and Operations Managers

A Practical AI Course for Managers Who Need Better Results, Not More Hype

Use AI to Improve Finance and Operations

Artificial intelligence can help finance and operations teams work faster, analyse information more effectively and make better-informed decisions. But introducing AI without understanding the process, the data or the risks can create expensive mistakes.

AI for Finance and Operations Managers gives you a practical management framework for identifying genuine opportunities, designing controlled human–AI workflows and testing them safely. This is not another generic ChatGPT or prompt-writing course. It is a comprehensive professional programme focused on measurable business improvement, responsible implementation and human accountability.

Turn Business Problems into Controlled AI Opportunities

The course includes approximately 20–24 hours of structured learning, with up to 30 hours of practical study and application. Every module includes a practical activity, an applied Northbridge Business Services case study and five knowledge-check questions with detailed explanations.

You will also receive the complete AI Manager’s Toolkit, containing 15 editable resources, including:

AI Readiness Self-Assessment
Process Discovery Canvas
AI Opportunity and Risk Scorecard
Human–AI Workflow Designer
AI Risk and Control Assessment
Controlled Pilot Charter
Supplier Due-Diligence Checklist
Employee Communication Planner
AI Roles and Accountability Matrix
90-Day AI Implementation Plan

These are practical tools you can adapt and use within your own finance, operations or customer-management environment.

Course curriculum

1

AI for Finance and Operations Managers
- Course Introduction: AI Without the Hype
- Why This Course Is Different Free Preview
- Process Before Platform
- What This Course Will and Will Not Cover
- Self-Assessment Activity
2

Module 1
- 1.0 What Finance and Operations Managers Need to Know About AI
- 1.1 What AI Actually Is
- 1.2 What AI Does Well
- 1.3 What AI Does Poorly
- 1.4 Automation, Augmentation and Decision Support
- 1.5 Capability Does Not Equal Authority
- Module 1 Activity: Automate, Assist or Retain?
- Module 1 Applied Case Study: Five AI Applications in Three Months
- Module 1 Knowledge Checker
- Module 1 Summary
3

Module 2
- 2.0 Process Discovery and AI Use-Case Selection
- 2.1 Start with the Business Problem
- 2.2 Understand the Current Process
- 2.3 Find the Root Cause
- 2.4 Characteristics of a Strong AI Use Case
- 2.5 The AI Opportunity and Risk Matrix
- 2.6 Knowing When Not to Use AI
- Module 2 Activity: Map Before You Automate
- Module 2 Applied Case Study: Selecting the Right AI Opportunity
- Module 2 Knowledge Checker
- Module 2 Summary
4

Module 3
- 3.0 Designing Effective Human–AI Workflows
- 3.1 From Prompts to Proper Work Instructions
- 3.2 Giving AI Sufficient Context
- 3.3 AI-Assisted Reporting and Analysis
- 3.4 AI-Assisted Communication
- 3.5 Verification and Human Review
- 3.6 Creating Reusable AI Workflows
- Module 3 Activity: Build an AI Work Instruction
- Module 3 Applied Case Study: The Revenue Explanation That Was Never Confirmed
- Module 3 Knowledge Checker
- Module 3 Summary
5

Module 4
- 4.0 Practical AI Applications in Finance
- 4.1 Management Reporting
- 4.2 Forecasting and Scenario Analysis
- 4.3 Accounts Payable and Procure to Pay
- 4.4 Accounts Receivable and Order to Cash
- 4.5 Credit and Risk
- 4.6 Reconciliations, Exceptions and Anomalies
- Module 4 Activity: Design a Finance Human–AI Workflow
- Module 4 Applied Case Study: Selecting Northbridge’s First Finance AI Opportunity
- Module 4 Knowledge Checker
- Module 4 Summary
6

Module 5
- 5.0 Practical AI Applications in Operations and Customer Management
- 5.1 Workflow Triage and Prioritisation
- 5.2 Customer Service and Contact Centres
- 5.3 Complaints and Escalations
- 5.4 Vulnerable Customers and Sensitive Situations
- 5.5 Operational Planning
- 5.6 Knowledge and Standard Operating Procedures
- Module 5 Activity: Redesign an Operational Workflow
- Module 5 Applied Case Study: AI-Assisted Complaint Handling at Northbridge
- Module 5 Knowledge Checker
- Module 5 Summary
7

Module 6
- 6.0: Data, Confidentiality, Risk and Accountability
- 6.1 Know What Information Is Being Used
- 6.2 Approved and Unapproved Tools
- 6.3 Hallucinations and Inaccurate Outputs
- 6.4 Bias, Fairness and Unequal Outcomes
- 6.5 Explainability and Auditability
- 6.6 Human Accountability
- 6.7 Supplier and Tool Due Diligence
- Module 6 Activity: Complete an AI Risk and Control Assessment
- Module 6 Applied Case Study: The Customer Ledger Uploaded to a Public AI Tool
- Module 6 Knowledge Checker
- Module 6 Summary
8

Module 7
- 7.0: Building and Managing a Controlled AI Pilot
- 7.1 Define the Pilot Hypothesis
- 7.2 Establish the Baseline
- 7.3 Limit the Scope
- 7.4 Test in Shadow or Parallel Mode
- 7.5 Define Success and Failure
- 7.6 Pilot Governance
- 7.7 Go, Change or Stop
- Module 7 Activity: Create an AI Pilot Charter
- Module 7 Applied Case Study: AI-Assisted Collections Email Triage
- Module 7 Knowledge Checker
- Module 7 Summary
9

Module 8
- 8.0: Leading People Through AI Adoption
- 8.1 Understanding Employee Anxiety
- 8.2 Communicating Honestly
- 8.3 Involving the People Who Perform the Process
- 8.4 Skills and Role Redesign
- 8.5 Psychological Safety
- 8.6 Creating Appropriate AI Ownership
- 8.7 Scaling Without Losing Control
- Module 8 Activity: Prepare a Team AI Conversation
- Module 8 Applied Case Study: “This Will Save Five Full-Time Roles”
- Module 8 Knowledge Checker
- Module 8 Summary
10

Module 9
- Module 9: Course Conclusion and 90-Day AI Action Plan
- 9.1 Revisiting the PROCESS Framework
- 9.2 The Manager’s AI Decision Checklist
- 9.3 Creating a 90-Day Roadmap
- Module 9: Final Module Activity: Personal AI Implementation Plan
- Module 9: Final Applied Case Study: Northbridge’s Board Recommendation
- Module 9: Final Knowledge Checker
- Module 9: Final Summary and Conclusion
11

TaylorMade Academy AI for Finance Managers Toolkit
- TaylorMade Academy AI for Finance Managers Toolkit

Move from an Interesting Idea to a Measurable Pilot

A successful AI initiative requires more than a promising demonstration. You will learn how to establish a baseline, define a pilot hypothesis, limit the test population and compare AI output with existing decisions before it affects live customers or financial outcomes.

Using the TaylorMade PROCESS Framework, you will build a controlled pilot with clear measures, governance, human-review requirements and stopping criteria. By the end of the course, you will have a practical 90-day action plan showing how to discover, design and test an AI opportunity within your own organisation.

Choose a pricing plan

AI for Finance and Operations Managers

£149.99