How to Explain an AI Investment to Your Board, Partners, or Investors

Getting approval for an AI investment — from a board, a partner group, investors, or a parent company — requires a business case that speaks the language of the decision-maker, not the language of technology. Most AI proposals fail to secure approval not because the investment is poor, but because the proposal focuses on what the AI does technically rather than what it delivers financially and strategically.

This article provides the structure and language for an AI business case that resonates with non-technical stakeholders.

What Non-Technical Decision-Makers Actually Need to Know

Board members, investors, and senior partners evaluating an AI investment are asking four questions, even if they do not state them directly:

1. What problem does this solve, and how costly is that problem today?

2. What are we expecting in return, and how confident are we in that expectation?

3. What are the risks, and how are they managed?

4. Why now, and why this approach?

A business case that answers all four questions clearly and credibly will succeed in most approval conversations. One that goes deep on technology architecture and skips the financial case will not.

Structure the Business Case in Five Parts

Part 1: The Current State (The Cost of Not Acting)

Open with the problem in financial terms. Not "our AP process is inefficient" — that is an opinion. Instead: "Our accounts payable team currently spends 12 hours per week on manual invoice processing at a loaded cost of $37,440/year. At our current growth rate, that will require an additional 0.5 FTE within 18 months at a further cost of $45,000/year. This is a recurring, growing cost that we have identified an efficient way to address."

Quantify the problem. Numbers make it real. The McKinsey Global Institute has published benchmark data on the proportion of time knowledge workers spend on automatable tasks — for most Canadian SMBs, the figure is substantial. (McKinsey Global Institute, 2023)

Part 2: The Proposed Investment

Describe what you are proposing simply:

• What the AI application will do (in plain language, not technical terms)
• What it will cost (total first-year investment including all phases: discovery, build, training, and maintenance)
• When it will be available
• What internal resources are required (primarily: who will own it)

Do not lead with technology. Lead with the capability: "A custom application that processes incoming invoices automatically, reducing our AP team's manual work from 12 hours to 2 hours per week."

Part 3: The Financial Return

Present the ROI calculation in the format the decision-maker uses for other investments:

• Investment: total cost over the evaluation period (typically 3 years)
• Return: quantified benefits over the same period, with line items and assumptions stated
• Payback period: how long before cumulative returns exceed the investment
• Net present value or IRR: if your audience uses these metrics for capital allocation

State your assumptions clearly and conservatively. A business case built on optimistic assumptions that do not materialize creates credibility problems for future proposals.

Part 4: The Risks and Mitigations

Stakeholders who approve capital investments think about risk. Pre-empt the questions by stating the risks explicitly and explaining how they are managed:

• Technology risk: what if the AI does not perform as expected? (Answer: pilot before full build, with defined success criteria and go/no-go decision points)
• Implementation risk: what if the project exceeds budget or timeline? (Answer: milestone-based payments, discovery phase to validate scope before committing)
• Adoption risk: what if staff do not use the new system? (Answer: change management plan, named internal owner, management accountability)
• Data and privacy risk: what if the AI mishandles customer data? (Answer: Canadian data residency, PIPEDA-compliant vendor contracts, audit logging)

The existence of a mitigation for each risk is more reassuring than a claim that the risks are low.

Part 5: Why Now

Decision-makers will ask why this investment should be prioritized over other uses of capital. The answer typically combines urgency and opportunity:

Urgency: the problem cost is growing (more volume, more staff, more errors); competitive pressure from businesses that have already adopted similar capabilities; regulatory changes (Bill C-27, new PIPEDA obligations) that make data governance investment increasingly necessary.

Opportunity: the technology has matured to the point where the investment is now feasible and the ROI is compelling; government programs (CDAP) may offset a significant portion of the cost; the business is at a scale where the ROI calculation clearly works. (ISED CDAP)

The One-Page Summary

Busy decision-makers want the core case on one page:

• The problem: [specific description with cost quantification]
• The solution: [what it does, in plain language]
• The investment: [total cost, 3-year view]
• The return: [quantified benefits, 3-year view; payback period]
• The ask: [specific approval or funding requested]
• The next step: [what happens if approved]

Everything else is supporting material.

Sources

• McKinsey Global Institute. *The Economic Potential of Generative AI, 2023.* mckinsey.com
• Innovation, Science and Economic Development Canada. *Canada Digital Adoption Program.* ised-isde.canada.ca
• BDC. *Writing a Business Case for Technology Investment.* bdc.ca
• Statistics Canada. *Key Small Business Statistics, 2024.* statcan.gc.ca

Cloud Forces develops business cases for AI investments alongside technical proposals — helping Canadian SMBs present their AI investment to boards, partners, and investors in language that secures approval. Explore our AI Strategy services or book a consultation to develop your AI investment case.