Building the AI Business Case

Learning Objectives

By the end of this lesson, learners will be able to identify and assess the key components that make an effective AI business case, understand how to quantify both tangible and intangible benefits, and develop a structured approach to presenting AI initiatives for approval and adoption within their organisation.

  • Identify Organisational Needs: Begin by clarifying the specific business problem, challenge, or opportunity AI can address.
  • Define Objectives and Scope: Establish clear goals, deliverables, and boundaries for the proposed AI project.
  • Assess Value Creation: Determine how AI can drive measurable outcomes (e.g., increased efficiency, cost savings, risk reduction, or new revenue streams).
  • Estimate Resources and Costs: Calculate the required investment including data acquisition, technology, talent, and ongoing maintenance.
  • Evaluate Risks and Challenges: Identify potential pitfalls such as data privacy issues, talent shortages, and change management barriers.
  • Develop Success Metrics: Define how success will be measured both quantitatively and qualitatively.
  • Align with Strategy: Ensure the AI initiative supports and advances broader organisational objectives.
  • Prepare Executive Summary: Summarise the business case in a clear narrative for key decision-makers.

Building the AI Business Case Overview

The rapid evolution of artificial intelligence is reshaping the way organisations operate, compete, and deliver value. To harness AI’s full potential, businesses must begin with a robust and well-founded business case that not only articulates the benefits, but also addresses the associated risks and required investments. Understanding how to construct and present such a business case is therefore a crucial skill for leaders.

This lesson will guide you through the key considerations, frameworks, and evidence required to build a persuasive AI business case. We will explore methods to evaluate value, estimate costs, align with company strategy, and anticipate resistance or challenges, ensuring you are equipped to lead AI-driven transformation with clarity and confidence.

Commonly Used Terms

Here are some key terms introduced in this lesson, explained in simple terms:

  • Business Case: A document or argument that explains why a particular project or investment (like AI) is worth pursuing, covering costs, benefits, and risks.
  • Return on Investment (ROI): A way to measure how much benefit (such as profit or savings) you get in return for the money you spend on the project.
  • Change Management: The processes and techniques used to prepare and support individuals, teams, and organisations in making organisational change, especially when introducing new technologies like AI.
  • Key Performance Indicators (KPIs): Specific and measurable values that help an organisation understand whether it is achieving its objectives.
  • AI-Driven Transformation: Significant changes to the way a business operates, brought about by the adoption and integration of artificial intelligence technologies.

Q&A

What should be included in an AI business case?

An AI business case should cover the problem or opportunity, business objectives, projected benefits, estimated costs, required resources, risks and mitigations, alignment to business strategy, and success metrics.

How can I estimate the return on investment for an AI project?

To estimate ROI, calculate the projected financial gains (like savings or new revenue) and compare these against the total investment costs, taking into consideration both direct and indirect benefits over a realistic time period.

What if my organisation does not have enough data or AI expertise?

You should highlight this as a risk within your business case, but also explore phased approaches, such as starting with small pilot projects, partnering with external experts, or investing in staff training to gradually build internal capabilities.

Case Study Example

Case Study: AI-Powered Demand Forecasting at a UK Retail Chain

A leading UK high-street retailer faced persistent challenges with overstocking and understocking products, resulting in lost sales and excess inventory costs. Its commercial director proposed leveraging AI-driven demand forecasting to improve stock replenishment and reduce wastage. The business case included detailed analysis of current inefficiencies, outlined projected benefits such as an estimated 15% reduction in unnecessary inventory, and mapped these to quantified cost savings and revenue uplifts.

The proposal clearly presented initial investment needs for technology and staff upskilling, as well as a risk review (e.g., data quality concerns and cultural adoption). By including short pilot phases with defined KPIs, the team demonstrated a realistic path to value. The retailer’s board approved the investment, leading to a successful rollout that improved both operational efficiency and customer satisfaction.

Key Takeaways

  • Building an AI business case requires aligning proposed initiatives with strategic organisational goals and priorities.
  • Quantifying both direct and indirect benefits is essential; consider long-term as well as short-term gains.
  • Thoroughly assess risks, including data quality, ethical considerations, and workforce impact.
  • Clear success metrics and KPIs are crucial for tracking the effectiveness of AI projects.
  • Engagement with stakeholders from different departments is vital for realistic planning and adoption.

Reflection Question

How might you convince key decision makers in your organisation of the value of an AI initiative, given potential concerns about cost and organisational change?

➡️ Module Navigator

Previous Module: Governance, Ethics and Risk Management

Next Module: Scaling from Pilots to Organisation-Wide Adoption