How to Write an AI Brief That Actually Gets You the Right Solution

The brief is the document that bridges the gap between what you need and what a development partner builds. A well-written AI brief produces proposals that are aligned, comparable, and actionable. A poorly written brief produces proposals that are misaligned, incomparable, and often expensive to course-correct later.

Most SMB owners approach an AI brief by describing the technology they think they need: "We want an AI chatbot" or "We want to automate our invoicing." This describes a solution rather than a problem — and solutions written into the brief constrain the development partner's ability to recommend a better one.

Here is the structure and content of an AI brief that consistently produces better outcomes.

Section 1: Business Context (1–2 paragraphs)

Describe your business briefly: what you do, who your customers are, how big the team is, and what your primary business model is. Development partners who understand your business context can make better architecture and technology decisions.

Include: industry, company size, primary revenue model, geographic scope (Canadian only vs. international), and any regulatory context that applies to your business (healthcare, financial services, legal, etc.).

Section 2: The Problem (the most important section)

Describe the specific problem, not the desired solution. Use quantitative terms where possible.

Effective problem description:

"Our accounts payable team currently spends approximately 12 hours per week processing incoming invoices manually — extracting vendor name, invoice number, amounts, due dates, and GL codes from PDF invoices and entering them into QuickBooks Online. We receive approximately 200 invoices per month from 40 active vendors. The error rate is approximately 4%, resulting in 8 invoices per month requiring correction. We want to reduce the manual processing time to less than 2 hours per week and the error rate to less than 1%."

Ineffective problem description:

"We want to automate our accounts payable process with AI."

The effective version gives the development partner enough information to propose a specific solution with a specific ROI model. The ineffective version invites guessing.

Section 3: Current State Workflow

Describe the current workflow step by step, including:

• Who does each step
• What systems are involved at each step
• What inputs are required and what outputs are produced
• Where errors typically occur
• What happens in exception cases (what if an invoice does not match a purchase order? what if the vendor is unknown?)

This section is where the development partner discovers the complexity that is not visible from the problem description. It is also where you may discover inefficiencies in the current workflow that can be improved independently of AI.

Section 4: System Inventory

List all software systems relevant to the workflow:

• System name and version
• What data it holds that is relevant to the use case
• Whether API access is available (and at what subscription tier)
• Whether any legacy or custom systems are involved

Include authentication methods if known, and flag any systems that have restrictions on third-party integration (some enterprise ERP systems restrict API access by default).

Section 5: Constraints

Be explicit about what cannot change:

• Budget range: a specific range ("$30,000–$60,000 CAD for the initial build") produces better proposals than no guidance or "within budget"
• Timeline: what is the desired launch date, and why? Are there seasonal factors, contract renewals, or business events that make timing important?
• Data residency: specify if data must remain in Canada (for PIPEDA compliance or regulatory reasons)
• Existing system dependencies: what systems must be retained (even if they are part of the problem)?
• Regulatory requirements: any industry-specific regulations that affect how the AI can be used

Section 6: Success Criteria

Define what success looks like, specifically and measurably. These criteria will become the acceptance tests for the delivered application.

Example: "Success is defined as: (a) 90% or more of incoming invoices are processed automatically without human intervention; (b) the AP team's weekly time on invoice processing is reduced from 12 hours to 2 hours or less; (c) the error rate on automatically processed invoices is 1% or less; (d) the system can be fully operational within 60 days of project start."

Section 7: What You Have Already Considered

If you have already evaluated specific tools, platforms, or approaches, include them. This tells the development partner what ground does not need to be re-covered and surfaces any constraints or preferences that are relevant to the design.

Also include: any approaches you have ruled out and why. "We evaluated Rossum and found it did not integrate with our version of Sage" tells the development partner not to recommend Rossum.

Sources

• BDC. *Hiring Technology Consultants.* bdc.ca
• Innovation, Science and Economic Development Canada. *Canada Digital Adoption Program.* ised-isde.canada.ca
• McKinsey Global Institute. *The Economic Potential of Generative AI, 2023.* mckinsey.com
• Statistics Canada. *Survey on Digital Technology and Internet Use, 2023.* statcan.gc.ca

Cloud Forces helps Canadian SMBs write effective AI briefs and evaluate the proposals they receive — ensuring the investment goes toward the right solution for the right problem. Explore our AI Strategy services or book a free brief review session to get feedback on your brief before you send it to development partners.