Five practical ways to use AI in your business this year

Short answer: the best first uses of AI are the boring ones: answering frequent questions, processing documents, sorting requests, moving data between systems and spotting patterns in data you already have. Start with one repetitive task, measure the result, then expand.

AI is not a strategy on its own. It is a set of tools that are very good at specific jobs. This guide covers five uses that consistently pay off, what each one needs to work, the mistakes we see most often, and how to choose and launch your first project without a big budget.

First, a few terms in plain language

Vendors use these words loosely, so it helps to know what they mean before you compare options.

• Automation: software that follows rules you define. “When an order arrives, send a confirmation and add it to the spreadsheet.” It does exactly what it is told, every time.
• Artificial intelligence (AI): software that handles inputs that vary, such as a customer email written in their own words or a scanned invoice with an unusual layout. It makes a best guess, which means it can be wrong.
• Generative AI: AI that writes text, summarizes documents or answers questions in natural language. Most modern chatbots use it.
• Machine learning model: AI trained on your own historical data to predict something, such as which leads are likely to buy or which invoices are likely to be paid late.
• Integration or API: the connection that lets two systems exchange data automatically. Most business tools offer one.
• Human in the loop: a person reviews or approves the AI’s output before it has a real effect. This is the single most useful safety habit in any AI project.

Most successful projects combine these: rules-based automation for the predictable steps, AI only where the input varies, and a person for the decisions that matter.

1. Answer frequent questions at any hour

What it is: a chatbot on your website or messaging channels that answers common questions, such as hours, prices, requirements or order status, and hands the rest to a person.

Why it works: most customer questions repeat. A chatbot trained on your own information answers them instantly, day or night.

What it needs: a clear list of your frequent questions and answers, and a simple handoff to a human for everything else.

In our group’s work with Banco Integral, AI chatbots became one part of a larger digital ecosystem, alongside a digital advisor, a mobile app and online pre-approvals.

How to set it up well

1. Collect the real questions. Export a few months of emails, chats and call notes, and group them. You will usually find that a small set of topics covers most of the volume.
2. Write the approved answers. The chatbot should answer from your own content, not from general internet knowledge.
3. Define the handoff. Decide which topics always go to a person (complaints, refunds, anything legal or medical) and how the customer reaches them.
4. Review conversations weekly for the first month and fix the answers that confused people.

Common mistake: launching a chatbot with no way to reach a human. Customers forgive a bot that says “let me connect you with someone”. They do not forgive one that loops.

2. Pre-qualify leads and applications

What it is: an online flow that collects the right information, scores it and tells the customer what happens next, whether that is a credit pre-approval, a quote request or a booking.

Why it works: your team spends its time on the people most likely to buy, and customers get an answer without waiting.

What it needs: clear criteria for what makes a good lead, and a connection to where your team works.

For CrediQ, our engineering partner built online pre-approvals and machine learning models that help prioritize applicants and guide collections, along with a mobile collections app.

A simple example

A renovation company receives many quote requests, and many are outside its service area or budget. A short online form asks for the postal code, project type, timeline and rough budget. Rules sort each request: in-area and ready to start goes straight to the estimator’s calendar, out-of-area gets a polite reply, and “just researching” receives a helpful guide by email. No AI model is needed yet. Once there is enough history, a model can learn which requests turn into jobs and rank them.

Common mistake: scoring leads on criteria nobody agreed on. Sit down with sales first and write down what a good lead looks like.

3. Read and process documents

What it is: AI that extracts information from invoices, forms, IDs or contracts and puts it where it belongs.

Why it works: manual data entry is slow and error-prone. Document AI reads and fills in the data, and a person only reviews exceptions.

What it needs: a consistent type of document, a destination for the data (your accounting system, CRM or database) and a review step.

What “review exceptions” means in practice

Good document tools report how confident they are about each field. You set a rule: if every field is read with high confidence and the totals add up, the record goes through. If not, it lands in a short review queue where a person checks only the flagged fields. Over time you learn which suppliers or forms cause trouble and can fix them at the source.

Common mistake: starting with the messiest documents. Begin with the type you receive most often and in the most consistent format, prove it works, then add harder ones.

4. Connect the tools you already use

What it is: automations that move information between your systems, such as a form submission that creates a CRM contact, sends a confirmation and notifies your team.

Why it works: it removes the copying and pasting that eats hours every week, and nothing falls through the cracks.

What it needs: tools with integrations or APIs, and a clear map of the steps that happen today.

The website we built pro bono for NotWorking to Networking is a simple example: it pulls events from Eventbrite and posts from Instagram automatically, so volunteers never update the site by hand. They keep publishing where they always have, and the site stays current on its own.

Map the process before you automate it

Write each step on its own line: who does it, in which tool, and what triggers the next step. For example: “Customer submits form on website. Office manager copies details into CRM. Office manager emails a confirmation. Office manager posts in the team chat.” Every line that says “copies” or “emails” is a candidate for automation. This map also reveals steps nobody needs any more, which you should remove rather than automate.

Common mistake: automating a broken process. If the manual version is confusing, the automated version will be confusing and faster.

5. Turn your data into decisions

What it is: dashboards and predictive models that show what is happening in your business and what is likely to happen next.

Why it works: most businesses already collect valuable data in spreadsheets and systems but never look at it together.

What it needs: data that is reasonably clean and in one place. Often the first project is simply bringing it together in the cloud. Our cloud migration guide explains how to approach that step.

Start with questions, not charts

Before building a dashboard, list the five questions you ask every week. “Which services brought in the most revenue this month?” “Which customers have not ordered in 90 days?” Build the dashboard to answer those questions and nothing else. Predictive models come later, once the basic numbers are trusted.

Common mistake: building a model on data nobody has checked. If two systems disagree on how many customers you have, fix that before asking AI to predict anything.

How to choose your first project

Score each candidate task from 1 (low) to 3 (high) on the first three questions, and from 1 (high risk) to 3 (low risk) on the last.

Question	What a high score looks like
How often does it happen?	Many times a day or week
Does it follow clear rules?	Two people would handle it the same way
How much time or money does it cost now?	Hours every week, or errors that cost money
What happens if it goes wrong?	Easy to spot and fix, no harm to customers

The task with the highest total is usually your best first project. Tasks that score low on the last question, such as anything affecting credit, hiring, health or legal rights, can still be automated, but they need a person approving every decision.

How to start without a big budget

1. Pick one process that is repetitive, rule-based and frequent.
2. Measure it today: how many hours a week, how many errors, how long customers wait.
3. Automate the simplest version first, with a person reviewing the output.
4. Measure again after a few weeks.
5. Expand to the next process only when the first one is working.

Connect, buy or build?

• Connect tools you already have when the steps are predictable and both systems offer integrations. This is usually the fastest and least expensive option.
• Buy a ready-made AI product when your need is common, such as a help-desk chatbot or invoice reading, and the product fits your process.
• Build custom software or models when the process is specific to your business, the data is your advantage, or off-the-shelf tools would force you to change how you work. Our guide to choosing a software development company covers what to look for.

Mistakes that sink AI projects

• Starting with the most ambitious idea instead of the most repetitive task.
• No owner. Every automation needs one person responsible for checking it works and updating it when the business changes.
• No baseline. Without “before” numbers, nobody can say whether the project paid off.
• Removing people too early. Keep a review step until the results are consistently right, and keep it forever for high-impact decisions.
• Ignoring the edge cases. Decide in advance what happens when the AI is unsure, a system is down or a customer asks for a person.
• Pasting sensitive data into public tools. Staff often do this with good intentions. Give them an approved tool and a short written policy instead.

Privacy and the rules in Canada

If AI touches customer information, choose providers with clear data policies, limit what each tool can access and keep people in the loop for important decisions. In Canada, the handling of personal information by businesses is governed by PIPEDA.

The federal, provincial and territorial privacy commissioners have also published principles for responsible, trustworthy and privacy-protective generative AI. They include having a legal basis and valid consent, collecting only what you need, being open with people about how their data is used, keeping information accurate and protecting it against new threats such as prompt injection attacks. They are a practical checklist for any AI project that handles personal information.

If you use AI in hiring in Ontario, there is a specific rule. Since January 1, 2026, employers with 25 or more employees must include a statement in publicly advertised job postings disclosing whether they use artificial intelligence to screen, assess or select applicants. The Ontario government’s guide explains the details.

Security matters as much as privacy: every new integration is another account and another connection to protect. Our cybersecurity basics guide covers the essentials.

Questions to ask an AI or automation provider

• Which of my tasks would you automate first, and why that one?
• Where will my data be stored and processed, and is it used to train anyone else’s models?
• What happens when the AI is unsure or wrong? Who reviews it?
• How will we measure whether it worked?
• Who owns the automations, prompts and code once the project ends?
• What does ongoing maintenance involve when my tools or processes change?

A good provider answers these in plain language and is happy to start small.

Where we can help

We help businesses find the one or two automations that will pay off first, then build them, with a person in the loop and clear before and after measurements. See our AI and automation service or tell us which task eats most of your week.

Frequently asked questions

What is the easiest way to start using AI in a business?

Start with one repetitive task that follows clear rules and happens often, such as answering the same customer questions, sorting incoming requests or copying data between systems. Automate it, measure the time saved, then move to the next one.

Is AI automation expensive?

It does not have to be. Many automations connect tools you already pay for. Custom AI models cost more and make sense when the problem is specific to your business and the value is clear.

Will an AI chatbot replace my customer service team?

A good chatbot handles frequent, simple questions at any hour and hands complex cases to a person. It frees your team for the conversations that need judgment, rather than replacing them.

Is it safe to use AI with customer data?

It can be, with the right setup: choose providers with clear data policies, limit what data the AI can access, keep a person in the loop for important decisions and follow Canadian privacy law (PIPEDA).

What is the difference between automation and AI?

Automation follows fixed rules you define, such as "when a form is submitted, create a contact". AI handles tasks where the input varies, such as reading a free-text email or an invoice with a different layout, and it needs a review step because it can make mistakes.

Do I need to tell job applicants if I use AI to screen them in Ontario?

Yes, if you have 25 or more employees. Since January 1, 2026, Ontario employers of that size must state in publicly advertised job postings whether they use artificial intelligence to screen, assess or select applicants.

How do I know if an automation is working?

Measure the task before you automate it (hours per week, errors, response time) and measure the same things a few weeks after launch. If the numbers have not moved, fix or drop the automation before building the next one.