Our study entitled A Finance Team in Full Transformation: Challenges and Skills for 2030 revealed that 78% of business leaders consider AI implementation in finance important, but only 53% believe their team is mature enough to adopt it.
There’s a lot of excitement around artificial intelligence within organizations, but its real value must be measured using tangible results. How can AI be used to boost performance and support teams and decision-making without losing sight of employees?
In these articles, our experts will share clear benchmarks that allow you to understand the potential of AI, choose relevant use cases and take a pragmatic approach to integrating it into your key functions (finance, human resources and management). These insights can help you turn AI into a sustainable and impactful tool.
Artificial Intelligence: Numerous Benefits for Businesses
AI offers organizations tangible opportunities to boost their productivity, decision-making and operations. However, leaders must understand where AI can truly make a difference and how to leverage it using their business data.
Why Should Your Organization use AI?
AI is no longer exclusively used by large organizations or for complex projects. When it’s integrated correctly, it becomes an accessible tool that allows you to boost efficiency, support your teams and make better decisions.
Results-driven AI: How to Implement It Successfully
In order to generate value, AI must be part of a clear vision and meet well-defined needs. This article proposes a pragmatic approach to integrating AI one step at a time and avoiding initiatives that don’t deliver a tangible impact.
Making AI a Performance Tool: the Role of the CFO
The finance function offers a strategic opportunity to transform AI investments into measurable results. CFOs play a key role in determining the AI integration process, prioritizing its use and ensuring sustainable value creation.
How can you use AI to Enhance Your HR Processes?
AI can simplify HR processes, reduce the administrative burden and free up time for what really matters: a human approach. However, pinpointing relevant use cases and integrating the technology in a responsible and progressive manner is important.
Your questions on AI in business settings and our straight answers
You don’t need a large budget or complex technological infrastructure to take the first steps. The most pertinent question is what causes you the most concern on a daily basis. This could be a repetitive task, a time-consuming process or difficulty making a decision for lack of data. This is where AI can quickly make a real difference. Start out by properly defining a problem, measuring the results and expanding the scope. AI isn’t a silver bullet, but you can start using available generative and agentic AI tools on the first day.
The good news is that you don’t need to have perfectly organized data to take the first step. You can access generative AI immediately and use it for tangible purposes such as document management, chatbots that can answer FAQs from your employees and clients, drafting and summarizing content and even analyzing bids in the construction sector, for example. These applications create value within a few weeks.
The good news is that there are solutions for every budget. Licences for tools such as Microsoft Copilot cost approximately $500 per user per year, which is a very accessible entry point. Targeted chatbot projects generally cost between $30,000 and $100,000. More complex predictive AI integration generally requires an investment of between $120,000 and $400,000. If you’re seeking financing, bear in mind that certain programs in Quebec can cover a portion of the costs. These include Investissement Québec’s Essor program, Infor training programs and the federal Regional Artificial Intelligence Initiative ($38.2M for Quebec and open until 2029).
There are risks, and being aware of them is the best approach. In Quebec, the use of personal data in AI systems must comply with Law 25. This is non-negotiable. When it comes to security, AI can become a vector for data leaks where access has not been properly defined. On a legal level, contracts with your clients and suppliers should be updated to reflect the data use. You should also be aware of algorithmic bias. If your algorithm training data is not representative, your results will not be representative. These risks can be managed, as long as you anticipate them rather than discovering them down the line.
A total of 52% of Quebecers use AI every week (Université Laval, 2025). Your employees are probably already using it, with or without your consent. There’s no point in prohibiting it. However, a framework can be effective. Define which tools have been approved, for what use and under which guidelines. In practical terms, don’t upload your confidential information to publicly accessible tools, opt for corporate versions with contractual guarantees of confidentiality and train your teams on distinguishing between what data can and cannot be shared.
Operations teams. Always. AI adoption is not a technology project. It’s a business project. Approximately 80% of AI projects fail and, in most cases, this is not due to technology issues. The root cause is a poorly defined project, unclear governance or underestimating change management. Your IT teams play a key role with regard to infrastructure, but your management and operations teams must define the priorities and expected value and take ownership of the process.
No, at least not in the way that you might imagine. AI frees up time previously spent on repetitive tasks so your teams can concentrate on what adds tangible value. An inspector who automates their visual detection can reinvest time in analyzing and resolving issues. That said, roles will evolve and we must plan for that. Be transparent with your teams, train them and manage the change process. In order to successfully complete their AI shift, organizations must work with their employees and not against them.
AI is not simply another tool. It’s a new way of working. In the long term, it will redefine roles, shift processes, improve decision-making and even open up new markets. However, this shift involves more than one large project. It requires several stages such as targeting certain initiatives, measuring results and progressively integrating it into your work culture. The organizations that will derive the most value are not those that invest the most money, but those that weave it into their daily work practices.