Artificial Intelligence and Productivity: How Can You Achieve Real Gains?

Artificial intelligence can improve productivity by speeding up information searches, document preparation and data analysis. However, actual gains depend on your processes, data quality, team skills and how results are checked.

For an SME, the challenge is to identify uses that create measurable value, then integrate AI into everyday workflows.

Does AI Actually Improve Business Productivity?

Saving time on a task is not always enough

An employee may use AI to prepare a report more quickly. But if that report then requires extensive corrections or several additional reviews, the benefit to the business may be limited.

Productivity gains must therefore be assessed across the entire process. This includes the time spent preparing information, using the tool, checking results and handling exceptions.

Quality matters too. Producing more documents does not necessarily improve productivity if their content is less reliable or useful.

What Canadian data shows

An analysis published by Statistics Canada in April 2026 found an association between AI adoption and higher labour productivity. However, that association weakened when firms’ initial productivity and complementary capabilities were taken into account. The study therefore does not establish that AI alone accounts for the observed differences.

For Quebec SMEs, these findings suggest treating AI as one component of a broader improvement effort rather than a stand-alone solution.

Which AI Uses Can Improve an SME’s Productivity?

The most relevant opportunities address a specific problem: lengthy information searches, large volumes of documents or repetitive report preparation.

These are potential uses to assess, not guaranteed outcomes. Their suitability depends on the information available, how often the tasks occur and how easily the outputs can be verified.

A first project benefits from a limited scope. For example, testing response preparation for one category of customer inquiries allows you to compare results before expanding its use.

Why Are Productivity Gains Not Automatic?

Preparation, review and correction time

Generating content may be quick, but it is only part of the work. Employees may still need to gather documents, refine their requests, check facts and correct omissions.

When these steps take too long, they can absorb the time saved. A suitable use case should allow results to be verified with an effort proportionate to the expected benefit.

Scattered data and poorly connected systems

Outdated documents, duplicate files and inconsistent data make outputs harder to use. Employees then have to find the right information or manually repeat certain steps.

Before deploying a solution, you may need to organize and secure your data for AI, identify authoritative sources and improve connections between systems.

Inefficient processes that remain unchanged

Adding AI to a process with duplicate data entry, unnecessary approvals or unclear responsibilities does not necessarily solve the problem. It may make it worse.

Start by reviewing the steps:

• Which ones are necessary?

• Where do delays occur?

• Which information is entered more than once?

This analysis helps determine whether the business needs AI, conventional automation or a simpler way of working.

Insufficient training or unclear responsibilities

Teams need to know when to use AI, what information they can provide and how to verify its outputs.

Responsibilities for validation, monitoring and updating source information must also be assigned. Supporting and training your teams in AI helps establish consistent practices suited to their actual tasks.

AI or Conventional Automation: Which Should You Choose?

Not every repetitive task requires artificial intelligence. The right choice depends on the information being processed and the rules involved.

Conventional automation may be easier to control when the rules are known. AI becomes relevant when variable content needs to be interpreted, provided the results can be verified.

The goal is to choose the right approach to automating your business processes.

How Can You Measure AI’s Actual Productivity Gains?

Establish a baseline before starting the project

Before testing, measure current performance: average time per case, volume processed, error rate and correction effort.

This baseline helps you define a concrete objective. For example, you might aim to reduce report preparation time while maintaining its accuracy and usefulness for managers.

Measure the entire process, not just generation time

The comparison should include every step required to produce a usable result.

Consider a hypothetical example: preparing a report previously took 60 minutes. With AI, data preparation takes 10 minutes, generation takes 5 minutes and validation takes 20 minutes.

The total is now 35 minutes. The net saving is therefore 25 minutes per report, provided quality remains comparable.

Measuring only the 5 minutes spent generating the report would give an incomplete picture of the benefit.

Compare time, costs and quality

To assess a project, track a few complementary metrics:

• Time: Total processing time and turnaround time.

• Costs: Licences, integration, training, monitoring and validation.

• Quality: Errors, required corrections and usefulness of the output.

• Usage: Team adoption and difficulties encountered.

Time saved does not automatically translate into financial savings. It may allow employees to handle more cases, improve service or devote more effort to analysis. The business needs to determine how that additional capacity will be used.

Make AI a Driver of Productivity for Your Business

Achieving real gains starts with a well-chosen process, reliable data and measurable objectives. A gradual approach allows you to assess AI’s value before expanding its use.

Want to identify the processes where AI could create measurable value? Mallette’s experts can help you assess your opportunities and develop an AI roadmap tailored to your business.