What Business Task Should You Use AI for First?
Use this practical screening method to choose a first AI task based on frequency, business value, data risk, reviewability, and workflow fit.
Use AI first on a task that is repetitive, text- or data-heavy, easy for a person to review, and tied to a measurable business outcome.
For many small businesses, the best candidate is found inside routine work such as reporting, research, follow-up preparation, administrative processing, or internal handoffs. Wallai uses those categories in its live strategy-call process because they describe work an owner can inspect rather than an abstract promise about AI.[1]
The best first task is not automatically the one that takes the most time. Data sensitivity, error consequences, workflow fit, and the team's ability to review the output also matter.
What Canadian businesses are already using AI for
Statistics Canada's second-quarter 2025 survey provides a factual picture of reported business uses. Among Canadian businesses that had used AI to produce goods or deliver services, the most commonly reported applications included text analytics, data analytics, virtual agents or chatbots, natural-language processing, and marketing automation.[2]
| Reported AI application | Share of AI-using businesses reporting it |
|---|---|
| Text analytics | 35.7% |
| Data analytics | 26.4% |
| Virtual agents or chatbots | 24.8% |
| Natural-language processing | 23.1% |
| Marketing automation | 23.1% |
These figures describe adoption categories. They do not prove that every task in those categories is appropriate for every business.
A practical screening method
Score each candidate task against the questions below. This is Wallai's practical decision method, not a regulatory or industry standard.
1. How often does the task happen?
A daily or weekly task offers more opportunities to observe the effect of a new workflow than a rare event. Frequency also makes it easier to collect before-and-after evidence.
2. Is the task based on available source material?
Generative AI is easier to govern when the business can point to the documents, records, approved data, or instructions the output should use. If the source information is incomplete, contradictory, or inaccessible, the AI workflow inherits that problem.
NIST recommends mapping the context of an AI system, including its intended purpose, information, users, impacts, and risk before deployment.[3]
3. Can a person judge the output?
A reviewer should know what correct, useful, and acceptable work looks like. NIST identifies confabulation as a generative-AI risk in which a system confidently presents false or erroneous content.[4]
If no one can reliably review the answer, the task is a weak first pilot.
4. What happens when the output is wrong?
Separate low-consequence drafting or analysis support from decisions that could materially affect customers, employees, safety, legal rights, financial commitments, or regulatory obligations.
The Office of the Privacy Commissioner of Canada states that accountability for decisions rests with the organization, not the automated system used to support the decision.[5]
5. Is the data approved for this use?
Before testing the task, identify any personal, confidential, financial, employee, customer, health, or commercially sensitive information. Confirm the organization's legal authority and rules, the vendor's terms, and the product's administration and data controls.
6. Will the AI fit the existing workflow?
Statistics Canada found that developing new workflows was the most common organizational change reported by Canadian businesses that had used AI.[2] The AI-supported task should have a clear place in the process:
- who initiates it;
- where the source information comes from;
- where the output goes;
- who reviews it;
- what happens when the result is rejected.
7. Can success be measured?
Choose an operational measure before the pilot. Useful measures may include turnaround time, backlog, response time, rework, completion rate, or accepted outputs after review.
Good first-task patterns
The following are candidate patterns to assess, not promises that AI will improve them in every company:
- drafting a first version from approved source material;
- extracting or classifying information from a controlled document set;
- summarizing non-sensitive material for human review;
- preparing routine follow-up options for approval;
- organizing research collected from public or approved sources;
- producing a recurring internal report from governed data;
- routing information to the right person using defined criteria.
These patterns align with reported business uses such as text analytics, data analytics, natural-language processing, and marketing automation.[2]
Tasks that deserve more caution
A first pilot should not begin where the business cannot define the risk, review standard, or accountability. Extra care is warranted when the task involves:
- personal or sensitive information;
- employment, credit, health, housing, insurance, or other high-impact decisions;
- legal, financial, safety, or regulatory advice;
- direct customer communication with no review;
- actions that change business systems automatically;
- information the business is not authorized to use.
The privacy commissioners' generative-AI principles specifically call for heightened attention to vulnerable groups and high-impact contexts.[5]
A one-page task-selection worksheet
| Field | Your answer |
|---|---|
| Task being considered | |
| Current owner | |
| Frequency | |
| Current steps | |
| Source information | |
| Sensitive information involved | |
| Approved AI product | |
| Required human reviewer | |
| Consequence of a wrong output | |
| Baseline measure | |
| Pilot boundary | |
| Stop condition |
If several fields cannot be answered, the task needs more discovery before implementation.
Frequently asked questions
Should we start with the task that consumes the most hours?
Only if the data, risk, review process, and workflow are also manageable. A narrower task may be a stronger first pilot if it can produce trustworthy evidence with less exposure.
Is content writing always the easiest AI use case?
No. The difficulty depends on the source material, review standard, privacy requirements, brand risk, and intended use. A draft for internal review has different consequences from an unsupervised public claim.
Can AI take over the entire process?
Sometimes a mature system may automate more steps, but the appropriate level depends on the use case and risk. Start by defining where human review and accountability remain necessary.
Find the first task without turning it into a side project
Wallai's free 15-minute call focuses on one repeated piece of work and one practical next move. There is no requirement to prepare a technical brief before the call.[1]
Book the free 15-minute AI Strategy CallSources
- Wallai, “Free 15-Minute AI Strategy Call,” accessed July 17, 2026. wallai.ca/strategy-call
- Statistics Canada, “Analysis on artificial intelligence use by businesses in Canada, second quarter of 2025,” released June 16, 2025. statcan.gc.ca
- NIST, “AI Risk Management Framework,” released January 26, 2023. nist.gov
- NIST, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile,” NIST AI 600-1, July 2024. doi.org/10.6028/NIST.AI.600-1
- Office of the Privacy Commissioner of Canada, “Principles for responsible, trustworthy and privacy-protective generative AI technologies,” modified May 6, 2025. priv.gc.ca