How to choose an AI solution for a BC business

Why this decision is harder than a software demo makes it look

AI adoption is growing, but adoption is not the same as value. Statistics Canada reported that 19.2% of Canadian firms used AI to produce goods or deliver services in 2026. That is a national figure, not a BC-specific rate, and it does not say that every deployment produced a positive return.

A persuasive demonstration can hide the work required to prepare data, define instructions, connect systems, review outputs, train people, and manage exceptions. A useful buying process makes those costs visible before the contract becomes difficult to reverse.

Step 1: Write the workflow before writing the requirements

Describe the current process in six lines:

1. What triggers the work?
2. Who performs it?
3. What information enters the process?
4. What decisions or outputs leave it?
5. How often does it happen?
6. What causes delay, rework, error, or lost opportunity?

“We need an AI assistant” is not a workflow. “Two coordinators spend six hours each week extracting action items from project documents, then a manager verifies them before assignment” is specific enough to evaluate.

Step 2: Define a result the business can observe

Choose one primary measure and one guardrail. A primary measure might be first-pass review time, response time, cases completed, quote follow-up rate, or time spent finding information. A guardrail might be error rate, customer complaints, privacy incidents, missed approvals, or human-review time.

Step 3: Classify the consequences of a wrong answer

Not every AI task deserves the same controls. Drafting an internal agenda is different from approving a payment, evaluating a job candidate, interpreting a legal obligation, or providing health advice.

Use a simple three-level screen:

• Lower consequence: reversible drafts, brainstorming, formatting, or public-information summaries.
• Moderate consequence: customer communication, internal analysis, recommendations, or documents requiring accountable review.
• Higher consequence: decisions affecting rights, safety, employment, finance, health, legal position, or sensitive personal information.

As consequence rises, require better evidence, access controls, testing, logging, human approval, and escalation.

Step 4: Compare the whole solution, not only the model

The model is one layer. The working solution may also need document storage, retrieval, integrations, permissions, monitoring, workflow logic, support, and training.

Step 5: Ask for evidence in the same shape as your use case

A case study from another industry may still be useful, but the underlying workflow must be comparable. Ask what the starting process was, what changed, how performance was measured, what human work remained, what failed, and how long adoption took.

When evidence is unavailable, treat the purchase as an experiment. Narrow the scope, shorten the term, reduce the data exposed, and define an exit condition.

Step 6: Run the smallest safe pilot

A good pilot is not a miniature indefinite implementation. It has a defined population, owner, start and end date, baseline, success measure, guardrail, and decision at the end.

1. Select one repeatable workflow.
2. Use a limited and approved data set.
3. Keep accountable human review.
4. Record corrections and exceptions.
5. Compare the result with the baseline.
6. Choose: expand, revise, stop, or replace.

Step 7: Buy for adoption, not shelfware

The person who approves the invoice is often not the person who has to use the system. Before expanding, confirm that the workflow owner understands the new process, employees know what information is permitted, managers know how quality is checked, and someone owns support after launch.

Training is part of implementation. If a vendor cannot explain the new operating process in plain language, the business is not ready to scale the tool.

A practical go-or-no-go rule

Proceed when the workflow is frequent enough to matter, the expected improvement is observable, the necessary information can be used appropriately, human responsibility remains clear, the integration burden is understood, and the pilot can be stopped safely.

Pause when the business cannot define the task, the vendor relies on unsupported performance claims, sensitive information would be exposed without clear controls, or the contract is much larger than the evidence.