Small Business Canada

While talk around AI and automation have been going on for some time now, it’s clear there remains a widespread lack of clarity about how SMEs can actually implement these new technologies. The principal concern is not a matter of how, or even where to find the skills required – it is more commonly a matter of exactly where.

But this is no easier a question to answer. Where does an SME start in trying to identify where AI or automation can make the most difference?

For many businesses, we find the greatest benefits are gained from identifying what processes drain the most resource time, or are most repetitive or formulaic – and probably demoralize staff the most! You will probably find these are comparatively simpler matters of automation rather than the more adventurous deep insight projects.

Here is a guide to how SMEs can take a more informed and practical approach to smoothen the adoption of AI and automation.

Understand where the value lies

a. Investigate which processes are most suitable for automation, have the greatest potential value and have the least risk to the business.

Maturity assessment

a. Ensure you have sufficient foundations to make the most of your data to automate a business process. While AI and automation are independent data processing tasks, they still require the appropriate technical architecture to launch from and the skills to maintain it before they can be rolled out.


a. Once you have ensured your business is suitable data mature to embark on the project, identify the objectives, obstacles, how they will be overcome, and what results are expected. Look wider than the project itself and include how it can integrate with or even impair wider initiatives and plan accordingly.

Solution architecture

a. How is it going to work? Consider the practicalities of the project, the technical processes and requirements, the responsibilities and most importantly, the privacy of any data subjects whose data may be used. Privacy by Design should be a prerequisite for any project that uses data – not just because legislation such as GDPR demands it, but because it is the ethical way to treat data.

Ensure all the relevant stakeholders are informed

a. This is one of the most delicate parts of the process. It is where reality hits and where any concerns are aired. Stakeholders need to know what they are signing up for, what the consequences to other initiatives may be, the costs and ROI, the likelihood of success and how the project fits within the wider strategic initiatives.

Try a prototype or soft launch

a. Get testing! This is the ideal time to finesse what you will measure to prove eventual success/failure and conduct any final checks of data sources and their suitability, and test the reliability and accuracy of any integrations.

Integration and review

a. Once you’re happy with how it’s running and that the potential benefits remain likely, set the process live and ensure you are monitoring it and anything related to it closely in the first few weeks.

b. Data is being collected, and the algorithms are learning from outcomes and performance is improving every day. But has the project done what it set out to do? Is it delivering the cost and efficiency savings planned or the insights expected? Simply, is it successful? Review this regularly and to ensure you are meeting the ROI set out in your initial proposals.

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