After a business chooses its first AI workflow, the next mistake is treating the pilot like a demo. A demo proves that a tool can produce an interesting output. A pilot should prove whether the workflow is worth funding, scaling, training around, and governing.
That difference matters for Canadian SMEs. AI adoption is moving from curiosity into operating practice, and funding or financing conversations increasingly need more than enthusiasm. A stronger case shows the workflow, the baseline, the measured result, the risks, the human review model, and the next implementation step.
This is the role of an AI pilot evidence pack. It gives leadership, staff, advisors, and funding reviewers a shared view of what happened before the project becomes larger and more expensive.
What is an AI pilot evidence pack?
An AI pilot evidence pack is a short working file that captures what a business learned from one carefully chosen AI workflow. It is not a vendor pitch, a generic strategy deck, or a technical build log. It is a decision record.
The pack should answer five practical questions:
- What workflow did we test, and why was it worth testing?
- What was the baseline before AI was introduced?
- What changed during the pilot in time, cost, quality, risk, or capacity?
- What controls were used for privacy, data access, review, and approval?
- What should happen next: stop, improve, scale, train, or seek funding support?
When the answer is clear, the business can move faster. When the answer is unclear, the pack prevents a small experiment from turning into a vague implementation project.
Start with one workflow and one baseline
The evidence pack starts before the pilot begins. Choose one workflow, then write down how that work happens today. A useful baseline is specific enough that someone could compare before and after results without guessing.
Examples of workable pilot workflows include quoting support, intake triage, internal knowledge search, proposal drafting, compliance document review, service ticket summarization, invoice exception handling, or production planning assistance. The right workflow is not necessarily the most exciting one. It is the one with enough repetition, enough pain, enough measurable signal, and a safe review path.
Capture the baseline in plain language:
- Volume: how many requests, documents, tickets, quotes, or cases happen each week?
- Cycle time: how long does the work take from start to finish?
- Labour time: how many staff hours are involved?
- Quality: what errors, rework, delays, or missed steps happen now?
- Risk: what personal information, confidential data, financial data, or regulated content is involved?
- Decision rights: who approves the final output today?
A baseline does not need to be perfect. It needs to be honest enough to support a decision.
Measure more than time saved
Time savings matter, but they are not the whole story. A pilot that saves time while increasing review burden, privacy risk, error rates, or staff confusion is not ready to scale. A better evidence pack uses a balanced scorecard.
1. Time and throughput
Track the before and after time required to complete the workflow. Also track whether the team can handle more volume without adding pressure. For example, a pilot may reduce first-draft preparation from two hours to thirty minutes, but the real value may be faster turnaround during peak periods.
2. Quality and rework
Record the number of corrections, missing fields, rejected drafts, customer clarifications, or internal review cycles. AI is useful when it improves the work, not just when it creates more of it.
3. Human review fit
Document who reviewed the AI-assisted output, what they checked, and what they changed. This is especially important for workflows that affect customers, employees, financial decisions, safety, or compliance. Human review should be visible, repeatable, and appropriate to the risk level.
4. Privacy and data boundaries
Write down what data was used, what data was excluded, and what rules staff followed. The Office of the Privacy Commissioner of Canada and provincial and territorial privacy authorities emphasize necessity, proportionality, validity, reliability, transparency, and safeguards for generative AI use. In practical terms, a pilot should avoid exposing personal or sensitive data unless it is clearly necessary and appropriately controlled.
5. Staff adoption and training
Measure whether staff can actually use the new workflow. Track who was trained, what instructions they received, where they hesitated, and what support they needed. A pilot that only works for one power user may still be useful, but it is not yet operationally mature.
6. Governance signals
Canada’s voluntary code for advanced generative AI highlights themes such as accountability, safety, transparency, human oversight and monitoring, and validity and robustness. SMEs do not need to turn a pilot into a compliance theatre exercise, but they should show that risks were named and managed before scale.
The simple evidence pack format
A practical evidence pack can be concise. For most SMEs, the first version can be ten to fifteen pages, or a working document with the following sections:
- Pilot summary: workflow, business owner, pilot period, participating team, and decision deadline.
- Baseline: current process, volume, time, cost, quality issues, risk profile, and approval model.
- AI-assisted process: what changed in the workflow, where AI supported the work, and where human review stayed in place.
- Measurement results: time, cost, quality, throughput, review effort, and staff feedback.
- Risk and controls: data boundary, privacy notes, permissions, approval gates, error handling, and escalation path.
- Training and adoption notes: who was trained, what changed in day-to-day work, and what support is still needed.
- Funding and scale fit: estimated implementation cost, expected benefit, evidence gaps, and the next recommended route.
This format keeps the pilot tied to business evidence. It also helps avoid two common traps: buying technology before proving the workflow, and applying for support before the project has a credible operating case.
How this supports funding conversations
Funding and financing programs change, so businesses should always check current program rules before making decisions. Still, the pattern is stable: stronger applications are easier to understand when they show a real business problem, a realistic implementation plan, measurable benefit, and readiness to manage risk.
For example, BDC’s LIFT initiative is positioned for Canadian businesses seeking to improve efficiency or competitiveness through AI or advanced technology. Ontario’s Digital Adoption Plan engagement materials point toward a digital adoption plan and implementation roadmap. Neither route replaces business judgment. A pilot evidence pack helps clarify whether the project is ready for a funding conversation, needs a smaller implementation sprint first, or should be redesigned.
The goal is not to make the pilot look bigger than it is. The goal is to make the next decision easier to trust.
A practical before and after example
Imagine a manufacturer wants to test AI support for incoming quote requests. Before the pilot, sales and operations staff manually review customer emails, extract requirements, check past orders, and draft an initial response. The process takes inconsistent effort, and missing details often create back-and-forth delays.
A focused pilot might test AI-assisted intake summarization and draft preparation, with a human reviewer approving every customer-facing response. The evidence pack would track quote volume, preparation time, missing information, review corrections, staff feedback, and whether sensitive customer data stayed inside the agreed boundary.
At the end, the team may learn that the AI support reduced first-draft time but exposed gaps in product data quality. That is still a valuable result. The right next step may be data cleanup and staff training before automation expands. In another case, the workflow may be ready for a larger implementation sprint or an AI and funding review.
Where Digid fits
Digid AI Pathfinder helps Canadian businesses choose the right AI workflow, check funding fit, and identify the safest route before tool selection. AI Onboarding turns that route into a practical pilot with staff training, review gates, and governance notes. The AI and funding review helps decide whether the evidence is strong enough for a funding conversation or whether the project needs a smaller next step first.
Funding can accelerate a good AI project. It should not be used to rescue a vague one. The evidence pack keeps the work grounded: one workflow, one baseline, one measured pilot, and one clear next decision.
Useful official references
- Office of the Privacy Commissioner of Canada: Principles for responsible, trustworthy and privacy-protective generative AI technologies
- ISED: Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems
- BDC: LIFT – Lead with Innovation and Focus on Technology
- Ontario Centre of Innovation: Digital Adoption Plan engagement overview